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(Methodologische Analyse – Evidenzklasse IIa) Art: Narrativer Methoden-Review zu Surrogatendpunkten. Begründung: Auf PubMed als „Review“ gelistet; keine Primärdaten; daher Level 5 (methodischer Überblick).

“Reliance on surrogate end points may lead to false conclusions about the effects of treatment on the true clinical outcome.”

“Surrogates must be validated to ensure that treatment effects on the surrogate accurately predict effects on the clinical end point.”

Kommentar:
Diese Arbeit zeigt, dass Zwischenmarker (z. B. Blutdruck, Cholesterin) kein Beweis für tatsächlichen Gesundheitsnutzen sind. Sie unterstreicht, warum Ernährungsstudien harte Endpunkte brauchen.

Motivation für Surrogat-Endpunkte

Erkenntnis: Surrogat-Endpunkte werden genutzt, um Kosten und Dauer klinischer Studien zu reduzieren.

Textstelle:

“There has recently been great interest in the development of alternative outcomes, or surrogate end points, to reduce the cost and shorten the duration of phase 3 trials.”

Erklärung: Da klinische Endpunkte oft teuer und langwierig sind, sucht man nach Ersatzmessgrössen, die schneller und günstiger zu erheben sind.

Definition von Surrogat-Endpunkten

Erkenntnis: Surrogat-Endpunkte sind Laborwerte oder körperliche Zeichen, die stellvertretend für klinisch bedeutsame Endpunkte stehen.

Textstelle:

“A surrogate endpoint of a clinical trial is a laboratory measurement or a physical sign used as a substitute for a clinically meaningful endpoint that measures directly how a patient feels, functions or survives. changes induced by a therapy on a surrogate endpoint are expected to reflect changes in a clinically meaningful endpoint.”

Erklärung: Sie sollen anzeigen, ob eine Therapie wirkt – etwa durch Veränderungen im Blutbild oder Tumorgrösse – ohne direkt das Überleben oder Wohlbefinden zu messen.

Surrogat ist nicht gleich Ersatz

Erkenntnis: Eine Korrelation mit dem klinischen Endpunkt reicht nicht aus – der Surrogat-Endpunkt muss die Wirkung der Therapie auf den echten Endpunkt vorhersagen.

Textstelle:

A correlate does not a surrogate make. It is a common misconception that if an outcome is a cor relate (that is, correlated with the true clinical out come) it can be used as a valid surrogate end point (that is, a replacement for the true clinical outcome).

Erklärung: Nur weil ein Surrogat mit dem klinischen Ergebnis zusammenhängt, heisst das nicht, dass es als Ersatz taugt. Es muss die Wirkung der Behandlung auf das echte Ergebnis abbilden.

Idealszenario für Surrogat-Endpunkte

Erkenntnis: Surrogat-Endpunkte sind am zuverlässigsten, wenn sie den einzigen kausalen Pfad zur klinischen Wirkung darstellen.

Textstelle:

“The surrogate is in the only causal pathway of the disease process, and the intervention's entire effect on the true clinical outcome is mediated through its effect on the surrogate.”

Erklärung: Wenn die Therapie ausschliesslich über das Surrogat wirkt, kann dieses den echten Endpunkt gut vorhersagen – aber selbst dann sind Messfehler oder kurzfristige Effekte problematisch.

Risiko von Fehleinschätzungen

Erkenntnis: Surrogat-Endpunkte können die Wirkung einer Therapie über- oder unterschätzen.

Textstelle:

“The intervention's effect on the true clinical end point could be underestimated if there is considerable noise in the measurement of effects on the surro gate end point. The effect on the true end point could be overestimated if the effect on the surro gate, although statistically significant, is not of suf ficient size or duration to meaningfully alter the true clinical outcome. This overestimation could readily arise, for example, in the ongoing evaluation of protease inhibitors in HIV-infected patients, in which effects on the surrogate end point (viral RNA levels in the peripheral blood) are substantial but of only short duration."

Erklärung: Ein signifikanter Effekt auf das Surrogat bedeutet nicht automatisch einen relevanten klinischen Nutzen – etwa bei HIV-Therapien mit kurzfristiger Senkung der Viruslast.

Surrogat-Endpunkte versagen oft bei der Vorhersage klinischer Effekte

Erkenntnis: Surrogat-Endpunkte spiegeln häufig nicht die tatsächlichen klinischen Auswirkungen einer Behandlung wider. Die plausibelste Erklärung für das Versagen von Surrogaten sind unbeabsichtigte Wirkungen der Therapie, die unabhängig vom Krankheitsprozess auftreten.

Textstelle:

“Effects on surrogate end points often do not predict the true clinical effects of interventions. Al though there are many explanations for this failure, such as the existence of causal pathways of the disease process that are not mediated through the surrogate end point and that might be influenced differently by the intervention, the most plausible explanation is usually that the intervention has unintended mechanisms of action that are inde pendent of the disease process. These unintended mechanisms can readily cause the effect on the true clinical outcome to be inconsistent with what would have been expected solely on the basis of evaluation of surrogate end points. These mechanisms are in sidious because they are often unanticipated and unrecognized.”

Erklärung: Auch wenn ein Surrogat positiv beeinflusst wird, heisst das nicht, dass die Therapie dem Patienten tatsächlich hilft – etwa durch längeres Überleben oder bessere Lebensqualität. Diese Nebenwirkungen können das klinische Ergebnis negativ beeinflussen – selbst wenn das Surrogat eine Verbesserung zeigt.

Validierung erfordert grosse Studien und tiefes Verständnis

Erkenntnis: Die Validierung von Surrogaten ist komplex, erfordert grosse Stichproben und ein tiefes Verständnis der Krankheitsmechanismen.

Textstelle:

“Proper validation of surrogates also requires an in-depth understanding of the causal pathways of the disease process as well as the intervention's intended and unintended mechanisms of action.”

Erklärung: Solche Erkenntnisse sind selten verfügbar – was die zuverlässige Nutzung von Surrogaten stark einschränkt.

Surrogat-Endpunkte sind am besten in Phase-2-Studien aufgehoben

Erkenntnis: Surrogat-Endpunkte sollten vor allem in frühen Studienphasen eingesetzt werden, um vielversprechende Therapien zu identifizieren.

Textstelle:

“Surrogate end points should be used where they perform best—in screening for promising new therapies through evaluation of biological activity in preliminary phase 2 trials.”

Erklärung: In Phase-2-Studien können Surrogaten helfen, Kandidaten für grössere Studien auszuwählen – aber in Phase-3-Studien sollten echte klinische Endpunkte im Vordergrund stehen.

DOI: 10.7326/0003-4819-125-7-199610010-00011

Thema:
Ein weit umfassenderer methodischer Überblick über statistische Prinzipien in klinischen Studien – Randomisierung, Endpunktwahl, Zwischenanalysen, Multiplikitätsprobleme, Datenmonitoring usw. Er geht dabei auch auf die Rolle von Surrogaten ein, allerdings nur als ein Unterkapitel im breiteren Kontext biostatistischer Planung.

Kernpassagen:

“The primary objective of a clinical trial should be to provide reliable evidence about the effects of an intervention on clinically meaningful outcomes.”

“When surrogate endpoints are used, their validity must be established through rigorous statistical and biological justification.”

Einfluss der Endpunktwahl auf Studienqualität

“The selection of the primary ‘endpoint’ or ‘outcome measure’ has considerable influence on the reliability and interpretability of clinical trials intended to evaluate the benefit-to-risk profile of an intervention.”

Erklärung: Die Wahl des primären Endpunkts beeinflusst massgeblich, wie aussagekräftig und vertrauenswürdig eine klinische Studie ist. Ein schlecht gewählter Endpunkt kann zu Fehlinterpretationen führen.

Klinisch relevante Endpunkte

“The most important characteristic in guiding the selection of the primary endpoint in definitive trials is that effects on such an endpoint should provide reliable evidence about whether the intervention provides clinically meaningful benefit.”

“Thus, the primary outcome measure in definitive trials should be ‘a clinical event relevant to the patient’ [8], or an endpoint that ‘measures directly how a patient feels, functions or survives’ [9]…”

Erklärung: Der primäre Endpunkt sollte direkt zeigen, ob eine Behandlung dem Patienten wirklich hilft – etwa durch Verbesserung von Lebensqualität, Funktion oder Überleben.

Surrogat-Endpunkte und deren Validierung

“A surrogate endpoint is an outcome measure ‘used as a substitute for a clinically meaningful endpoint...changes induced by a therapy on a surrogate endpoint are expected to reflect changes in a clinically meaningful endpoint’ [9].”

Erklärung: Surrogat-Endpunkte sind Ersatzmessgrössen, die stellvertretend für echte klinische Ergebnisse verwendet werden. Ihre Aussagekraft muss jedoch sorgfältig validiert werden.

Definition von Biomarkern

“They will be called biomarkers, and ‘include physiological measurements, blood tests and other chemical analyses of tissue or bodily fluids, genetic or metabolic data, and measurements from images’ [10].”

Erklärung: Biomarker sind objektive Messgrössen biologischer Prozesse – etwa Blutwerte, genetische Daten oder Bildgebung – und dienen oft als Grundlage für Surrogat-Endpunkte.

Korrelation ≠ Kausalität

“However, such evidence about correlations does not allow one to understand the true nature of causality. Was the longer survival duration in responders causally induced by the antitumor effects of the intervention, or did the treatment-induced tumor response simply allow identification of the immunologically or inherently stronger patients who both responded and lived longer because of their inherently better status?”

Erklärung: Nur weil ein Biomarker mit einem klinischen Ergebnis korreliert, heisst das nicht, dass er kausal dafür verantwortlich ist – ein häufiger Trugschluss in der Forschung.

Korrelation kann irreführend sein

“Although the effect of an intervention on a biomarker does provide direct evidence regarding biological activity, such evidence could be unreliable regarding effects on true clinical efficacy measures even when the biomarker is strongly correlated with these clinical efficacy measures in natural history observations.”

Erklärung: Auch starke Korrelationen zwischen Biomarkern und klinischen Endpunkten können täuschen, wenn der Biomarker nicht Teil des Krankheitsmechanismus ist.

Biomarker können auch ohne kausalen Zusammenhang nützlich sein

“For some of these roles, the biomarker can be used to effectively achieve the intended objective even if it is not on a pathway through which the disease process causally induces risk of symptoms or mortality.”

Erklärung: Für Diagnose oder Prognose reicht oft eine Korrelation – ein kausaler Zusammenhang ist nicht zwingend notwendig.

Zwei besonders anspruchsvolle Einsatzbereiche für Biomarker

“The greatest clinical utility of biomarkers might be in the two clinical settings where it can be most challenging to justify their validity and reliability. These two settings are the use as surrogate endpoints in place of clinical efficacy measures in definitive trials, or the use to achieve enrichment when one expects greater effects with interventions in specific groups of subjects (i.e., effect modification).”

Erklärung: Besonders heikel ist der Einsatz von Biomarkern als Surrogat-Endpunkte oder zur Identifikation von Patientengruppen mit erhöhtem Therapieerfolg.

Risiken bei Zulassung basierend auf Surrogat-Endpunkten

“It should not be surprising, then, that agents receiving regulatory approval using efficacy assessments based on surrogate endpoints are more vulnerable to having clinically unacceptable safety issues discovered during the post-marketing period.”

Erklärung: Medikamente, die auf Basis von Surrogaten zugelassen werden, bergen ein erhöhtes Risiko für spätere Sicherheitsprobleme.

Motivation für Biomarker-Nutzung: Zeit und Effizienz

“Using biomarkers as surrogate endpoints often is motivated by interests to reduce the size and duration of definitive clinical trials, with the hope that this will allow more timely evaluation of the benefit-to-risk profile of experimental interventions…”

Erklärung: Biomarker werden oft gewählt, um Studien schneller und kostengünstiger durchzuführen – was jedoch Risiken birgt.

Wichtigkeit evidenzbasierter Rechtfertigung

“However, a rigorous evidence-based justification should be provided in any setting where use of biomarkers as surrogate endpoints is proposed because the scientific evaluation of benefit and risk needs to be not only timely but also valid and reliable.”

Erklärung: Der Einsatz von Biomarkern als Surrogat muss immer durch solide wissenschaftliche Daten gestützt sein – sonst drohen Fehleinschätzungen.

DOI: 10.1002/sim.5403

(Methodenhandbuch – Evidenzklasse Ia)  Art: Methodenhandbuch, kein Studien-Design. Begründung: Offizielles Cochrane-Manual (Wiley/Cochrane); Referenzwerk zur SR/MAs; „keine Evidenzklasse (Methodenreferenz)“.

“A systematic review attempts to identify, appraise and synthesize all the empirical evidence that meets pre-specified eligibility criteria to answer a specific research question.”

“Meta-analysis is the use of statistical methods to summarize the results of these studies.”

AG: A systematic review attempts to collate all the empirical evidence that fits pre-specified eligibility criteria in order to answer a specific research question. It uses explicit, systematic methods that are selected with a view to minimizing bias, thus providing more reliable findings from which conclusions can be drawn and decisions made.

Kommentar:
Das Cochrane-Handbuch legt die Standards für systematische Reviews fest – die höchste Evidenzstufe. Es zeigt, wie mehrere Studien zuverlässig zusammengefasst werden und liefert die methodische Basis für Euren Evidenz-Begriff.

DOI: 10.1002/9781119536604

(Editorial / Grundsatzartikel – Evidenzklasse V) Art: Grundsatz-/Konzeptpapier ohne Primärdaten („Evidence based medicine: what it is and what it isn’t“). Begründung: BMJ-Seite und PMC zeigen einen kurzen Definitionsartikel („Education & debate“), keine Datenerhebung; daher Level 5 (Meinungs-/Konzeptpapier). 

Sackett und Kolleg:innen definierten erstmals klar, was „Evidence Based Medicine“ bedeutet.

“Evidence based medicine is the conscientious, explicit and judicious use of current best evidence in making decisions about the care of individual patients. The practice of evidence based medicine means integrating individual clinical expertise with the best available external clinical evidence from systematic research.”

“Good doctors use both individual clinical expertise and the best available external evidence, and neither alone is enough.”

Kommentar:
Dieser kurze Text begründete den Begriff der evidenzbasierten Medizin. Er betont, dass Daten nur im Zusammenspiel mit Erfahrung und Werten nützlich sind – eine Grundidee, die sich durch den gesamten Beitrag zieht.

Weitere Aussagen:

EBM ist die bewusste, explizite und umsichtige Nutzung der besten verfügbaren Evidenz für Entscheidungen in der Patientenversorgung.

“Evidence based medicine is the conscientious, explicit, and judicious use of current best evidence in making decisions about the care of individual patients.”

EBM bedeutet nicht, klinische Erfahrung zu ignorieren – sie wird mit wissenschaftlicher Evidenz verbunden.

“Good doctors use both individual clinical expertise and the best available external evidence, and neither alone is enough.”

EBM ist kein starres Schema, sondern ein individueller, patientenzentrierter Prozess.

“Evidence based medicine is not ‘cookbook’ medicine. Because it requires a bottom-up approach that integrates the best external evidence with individual clinical expertise and patients’ choice, it cannot result in slavish, cookbook approaches to individual patient care.”

Auch andere Studiendesigns und klinische Beobachtungen können wertvolle Evidenz liefern.

Evidence based medicine is not restricted to randomised trials and meta-analyses. It involves tracking down the best external evidence with which to answer our clinical questions. To find out about the accuracy ofa diagnostic test, we need to find proper cross sectional studies of patients clinically suspected of harbouring the relevant disorder, not a rando mised trial. For a question about prognosis, we need proper follow up studies of patients assembled at a uniform, early point in the clinical course oftheir disease. And sometimes the evidence we need will come from the basic sciences such as genetics or immunology. It is when asking questions about therapy that we should try to avoid the non-experimental approaches, since these routinely lead to false positive conclusions about efficacy. Because the randomised trial, and especially the systematic review of several randomised trials, is so much more likely to inform us and so much less likely to mislead us, it has become the "gold standard" for judging whether a treatment does more good than harm. However, some questions about therapy do not require randomised trials (successful interventions for otherwise fatal conditions) or cannot wait for the trials to be conducted.

DOI: 10.1136/bmj.312.7023.71

(Methodenartikel – Evidenzklasse IIa)  Art: Methodentheorie zur Selektionsverzerrung. Begründung: Peer-reviewter Methodenartikel; keine Patientendaten; struktureller Ansatz (DAGs) zur Klassifikation/Adjustierung; Level 5 (methodische Theorie).

“Selection bias arises when the association between exposure and outcome differs for those selected for study compared with those eligible but not selected.”

“Even when analyses adjust for measured covariates, unmeasured factors related to selection may still bias the effect estimates.”

Kommentar:
Diese Arbeit begründete den modernen Bias-Begriff in der Epidemiologie. Sie zeigt, warum Auswahl und Teilnahmemuster Ergebnisse verzerren können – wichtig für die Interpretation von Ernährungs-Kohortenstudien.

Definition und Wirkung von Selection Bias

“The common consequence of selection bias is that the association between exposure and outcome among those selected for analysis differs from the association among those eligible.”

Diese Aussage zeigt, dass Studienergebnisse verzerrt sein können, wenn die untersuchte Gruppe nicht repräsentativ für die Gesamtpopulation ist – etwa weil gesundheitsbewusste Menschen sich selbst selektieren.

Volunteer Bias / Selbstselektion

“Figures 6a–d can also represent a study in which C is agreement to participate (yes = 1, no = 0), E is cigarette smoking, D is coronary heart disease, U is family history of heart disease, and U* is healthy lifestyle.” “Bias will be present if the study is restricted to those who volunteered or elected to participate (C = 1).”

Hier wird explizit beschrieben, dass ein „healthy lifestyle“ (U*) sowohl die Teilnahme an der Studie als auch das Verhalten beeinflusst – was zu einer Verzerrung führt, wenn nur Freiwillige untersucht werden.

Healthy Worker Bias

“Figures 6a–d can also describe a bias that could arise when estimating the effect of a chemical E [...] The underlying unmeasured true health status U is a determinant of both death (D) and of being at work (C).”

Auch hier wird gezeigt, dass gesündere Menschen eher Teil der untersuchten Gruppe sind – was zu einer systematischen Verzerrung führt.

DOI: 10.1097/01.ede.0000135174.63482.43

(Übersichtsartikel – Evidenzklasse III) Art: Narrative Review/Primer. Begründung: PubMed listet „Review“; fasst „healthy user/healthy adherer“-Bias in Beobachtungsstudien zusammen; keine Primärdaten; Level 5.

“Healthy users are more likely to engage in other health-seeking behaviors that reduce their risk of adverse outcomes, leading to spurious associations between preventive interventions and improved health.”

Kommentar:
Gesundheitsbewusste Teilnehmende leben in vielen Bereichen anders – nicht die Intervention, sondern der Lebensstil erklärt oft den Effekt. Ein Schlüsseltext zum „Healthy-User-Bias“.

Healthy User effect

The healthy user effect is best described as the propensity for patients who receive one preventive therapy to also seek other preventive services or partake in other healthy behaviors.18 Patients who choose to receive preventive therapy may exercise more, eat a healthier diet, wear a seatbelt when they drive, and avoid tobacco. As a result, an observational study evaluating the effect of a preventive therapy (e.g., statin therapy) on a related outcome (e.g., myocardial infarction) without adjusting for other related preventive behaviors (e.g., healthy diet or exercise) will tend to overstate the effect of the preventive therapy under study.

The healthy user effect has been widely cited as a likely source of bias in observational studies of HRT. Studies indicate that women who took HRT were more likely to engage in healthy behaviors such as regular exercise, a healthy diet, abstinence from alcohol, and maintenance of a healthy weight as compared to non-users.2 The apparent protective effect of HRT on cardiovascular disease likely reflects these unmeasured differences in patient characteristics.

Kernaussage: Menschen, die eine präventive Massnahme ergreifen (z. B. Statine einnehmen), leben oft generell gesünder – sie treiben Sport, essen besser, rauchen weniger. Studien überschätzen dadurch den Nutzen der Massnahme, weil sie andere gesunde Verhaltensweisen nicht mitberücksichtigen.

Das Beispiel Hormontherapie HRT illustriert dies: Beobachtungsstudien zeigten, dass HRT das Risiko für Herzkrankheiten senkt. Spätere RCTs (z. B. Women’s Health Initiative) widerlegten das – der vermeintliche Nutzen war auf gesündere Lebensweise der HRT-Nutzerinnen zurückzuführen.

Healthy-Adherer-Bias

„Patients who adhere to one chronic medication are more likely to adhere to other therapies and more likely to receive recommended cancer screening tests and immunizations.“

Kernaussage: Menschen, die Medikamente regelmässig einnehmen, sind auch sonst gesundheitsbewusster. Das führt zu einer Verzerrung, wenn Studien den Nutzen von Medikamenten anhand der Adhärenz bewerten.

Funktionelle Einschränkungen als Confounder

„Observational studies that do not account for functional status or cognitive impairment will overstate the effect of a preventive therapy if sicker patients disproportionately do not receive preventive therapies.“

Kernaussage: Kranke oder eingeschränkte Menschen nehmen seltener an Präventionsmassnahmen teil. Studien, die das nicht berücksichtigen, überschätzen den Nutzen der Massnahme.

Selektive Verschreibung

„Physicians frequently decide not to prescribe preventive therapy to patients who are frail or who have terminal or acute illness…“

Kernaussage: Ärzte verschreiben Präventionsmassnahmen eher gesunden Patienten. Das führt zu einer systematischen Verzerrung in Studien, die den Nutzen solcher Massnahmen untersuchen.

Methoden zur Bias-Korrektur

„New user designs, active comparators, improved statistical adjustment, and sensitivity analyses…“

Empfohlene Strategien:

• New-User-Designs: Nur neue Nutzer vs. Nicht-Nutzer vergleichen.

• Aktive Vergleichsgruppen: Vergleich mit anderen Präventionsmassnahmen.

• Statistische Adjustierung: Für gesundheitsbezogene Verhaltensweisen.

• Sensitivitätsanalysen: Negative Kontrollgruppen, Instrumentvariablen, Subgruppenanalysen.

DOI: 10.1007/s11606-010-1609-1

(Systematisches Review – Evidenzklasse Ia) Art: Systematisches Review empirischer Kohorten zu Publikations-/Outcome-Reporting-Bias. Begründung: Formales SR über Inzeptions-Kohorten von RCTs; daher Level 1 (systematisches Review methodischer Studien; für Aussagen zu Bias sehr hoch einzuordnen)

“Studies with statistically significant results are more likely to be published and statistically significant outcomes are more likely to be fully reported.”

Kommentar:
Dieses systematische Review zeigt, dass positive Resultate häufiger veröffentlicht werden. Es erklärt den Publikations-Bias und warum gesamte Forschungslagen dadurch verzerrt werden.

Beleg für Publikations-Bias

“Empirical research consistently suggests that published work is more likely to be positive or statistically significant (P<0.05) than unpublished research.”

“Study publication bias will lead to overestimation of treatment effects; it has been recognised as a threat to the validity of meta-analysis and can make the readily available evidence unreliable for decision making.”

“Twelve of the included empirical studies demonstrate consistent evidence of an association between positive or statistically significant results and publication. They suggest that studies reporting positive/statistically significant results are more likely to be published and that statistically significant outcomes have higher odds of being fully reported.”

Five of the empirical studies [3], [4], [7], [23], [24] that examined the association between publication and statistical significance found that studies with statistically significant results were more likely to be published than those with non-significant results.

One empirical study [19] found that studies with statistically significant results were more likely to be submitted for publication than those with non-significant results.

“Ioannidis et al. found that positive trials were submitted for publication more rapidly after completion than negative trials (median 1 vs 1.6 years, p <0.001) and were published more rapidly after submission (median 0.8 vs 1.1 years, p <0.04).”

Diese Textstellen zeigen klar, dass Studien mit positiven Ergebnissen bevorzugt veröffentlicht werden, was zu einer verzerrten Wahrnehmung des Nutzens führen kann. Wenn du magst, kann ich dir auch eine kurze Zusammenfassung oder ein Zitat für eine Präsentation oder ein Paper formulieren.

DOI: 10.1371/journal.pone.0066844

(Methodischer Kommentar – Evidenzklasse III) Art: Narrative Review zu Recall-Bias. Begründung: Überblick ohne neue Primärdaten; definiert/erläutert Rückerinnerungsverzerrung; Level 5

“Recall bias occurs when there are systematic differences in the accuracy or completeness of recall to memory of past events or experiences.”

Kommentar:
Menschen mit Krankheiten erinnern sich an ihre Vergangenheit anders als Gesunde – ein Problem für rückblickende Ernährungsstudien und Fragebögen.

It is generally accepted that comparative studies which attempt to retrospectievely ascertain exposure through interviewing techniques may be subject to bias due to differential recall, and case-control studies which do not validate interview data are frequently criticized on this basis. 

Recall bias may be thought of as a form of differential misclassification bias and the risk estimate may be biased away from or towards the null. 

Past exposures may be more vivid or meaningful to cases, possibly becasue of their awarness of potential risk factorws for their condition or because of repeated interviewing by physicians.

DOI: 10.1016/0895-4356(90)90060-3

Confounding ist kein „Fehler“, den man einfach rausrechnen kann, sondern ein Hinweis darauf, wie komplex kausale Zusammenhänge sind. Confounding verschwindet nicht einfach, weil es ein strukturelles Problem in der Datenanalyse ist, kein technisches. Es entsteht, wenn ein Drittmerkmal sowohl mit der Exposition als auch mit dem Ergebnis assoziiert ist und dadurch die wahre Beziehung zwischen beiden verzerrt.

Measurement error in explanatory variables and unmeasured confounders can cause considerable problems in epidemiologic studies. It is well recognized that under certain conditions, nondifferential measurement error in the exposure variable produces bias towards the null. Measurement error in confounders will lead to residual con founding, but this is not a straightforward issue, and it is not clear in which direction the bias will point. Unmeasured confounders further complicate matters. There has been discussion about the amount of bias in exposure effect estimates that can plausibly occur due to residual or unmeasured confounding.

This study highlights the need to perform sensitivity ana lyses to assess whether unmeasured and residual confound ing are likely problems. We have shown that unmeasured confounders have a cumulative effect on the bias of expo sure effect estimates. The possibility of the presence of several unmeasured confounders should be taken into ac count when performing sensitivity analyses. It may not be enough to state that a single unmeasured confounder would need an implausibly large odds ratio to remove the observed effect. Several unmeasured confounders with small or mod erate effects may be able to produce the same effects. Sen sitivity analysis methods for assessing the possible effects of selection bias, misclassification of covariates, and unmea sured confounding have been proposed and illustrated by Greenland (41) and Lash and Fink (42).

The effect of measurement error on exposure effect esti mates should be explored, either by adjusting the estimates based on knowledge of the likely measurement error or by performing sensitivity analyses. Of course, the ideal circum stance is that the variables are measured without error, but this is unlikely to occur in reality. While efforts should be made to minimize measurement error, the measurement er ror that has occurred should be quantified and used in the f inal effect estimate.

DOI: 10.1093/aje/kwm165

(RCT-Analyse – Evidenzklasse Ib–IIa) Art: Meta-Forschungs-Querschnittsstudie von RCT-Berichten (Spin bei nicht-signifikanten Primärendpunkten). Begründung: Systematische Auswahl und standardisierte Bewertung von RCT-Publikationen (Dez. 2006); empirische Auswertung von Berichten, nicht von Patienten-Outcomes; daher Beobachtungs-/Querschnitts-Metaforschung = Level 3 (nicht-interventionelle Evidenz zu Berichtsqualität).

“More than 40% of the abstracts highlighted statistically significant secondary outcomes or subgroup analyses, despite nonsignificant primary results.”

Kommentar:
Diese Arbeit zeigt, dass selbst bei nicht signifikanten Hauptergebnissen oft positive Nebenresultate betont werden – ein Beispiel für selektive Kommunikation in der Wissenschaft.

Spin can be defined as specific reporting that could distort the inter pretation of results and mislead read ers.3,5,6 The use of spin in scientific writ ing can result from ignorance of the scientific issue, unconscious bias, or willful intent to deceive.3

Such dis torted presentation and interpretation of trial results in published articles has been highlighted in letters to editors criticizing the interpretation of re sults7 and in methodological reviews evaluating misleading claims in pub lished reports of RCTs8,9 or systematic reviews.10

More than 40% of the reports had spin in at least 2 of these sections in the main text.

DOI: 10.1001/jama.2010.651

(Systematisches Review – Evidenzklasse Ia) Art: Systematisches Review/Metaanalyse zu Sponsoreneinfluss. Begründung: Cochrane-Methodology-Review (MR000033) zeigt konsistent günstigere Ergebnisse/Schlussfolgerungen bei Industriesponsoring; daher Level 1. 

“Industry-sponsored studies more often have favorable efficacy results and conclusions than non-industry sponsored studies.”

Kommentar:
Diese Cochrane-Analyse zeigt, dass Industriefinanzierung die Resultate systematisch in Richtung positiver Schlussfolgerungen verschiebt – eine Schlüsselquelle zum Thema Interessenkonflikte.

„Industry sponsored studies more often had favorable efficacy results, RR: 1.27 (95% CI: 1.17 to 1.37) […] and more often favorable conclusions RR: 1.34 (95% CI: 1.19 to 1.51) compared with non‐industry sponsored studies.“

Diese Zahlen zeigen, dass industriefinanzierte Studien signifikant häufiger positive Ergebnisse und Schlussfolgerungen zugunsten des Sponsors berichten.

„Sponsorship of drug and device studies by the manufacturing company leads to more favorable efficacy results and conclusions than sponsorship by other sources. Our analyses suggest the existence of an industry bias that cannot be explained by standard 'Risk of bias' assessments.“

Die Autoren betonen hier, dass der sogenannte „Industry Bias“ über die üblichen methodischen Verzerrungen hinausgeht und systematisch wirkt.

„Previous research has found that pharmaceutical industry sponsored studies tend to favor the sponsors’ drugs more than studies with any other sources of sponsorship. This suggests that industry sponsored studies are biased in favor of the sponsor’s products.“

Diese vereinfachte Zusammenfassung bestätigt, dass die Tendenz zu positiven Ergebnissen bei industriefinanzierten Studien ein wiederholt beobachtetes Muster ist.

„In industry sponsored studies, there was less agreement between the results and the conclusions than in non‐industry sponsored studies, RR: 0.83 (95% CI: 0.70 to 0.98).“

Das bedeutet: Die Schlussfolgerungen in industriefinanzierten Studien stimmen seltener mit den tatsächlichen Ergebnissen überein – ein Hinweis auf „Spin“ oder selektive Interpretation.

DOI: 10.1002/14651858.MR000033.pub3

(Qualitatives Review – Evidenzklasse Ia–Ib) Art: Qualitatives systematisches Review. Begründung: PubMed/Journal weisen explizit „Systematic Review“ aus, qualitativ; keine Primärdaten; für die Frage „Sponsoring ↔ Ergebnisse“ als SR → Level 1 (qualitatives SR der Methoden-/Metaforschung).

“Pharmaceutical companies shape the literature in which their products are evaluated by sponsoring research, ghostwriting papers, and planning publication strategies.”

Kommentar:
Sismondo zeigt, dass Industrien nicht nur finanzieren, sondern auch Publikationen steuern. Er beschreibt das Phänomen des „Ghostwriting“ und verdeutlicht den Einfluss kommerzieller Akteure auf die Forschung.

"The causes of this bias are complicated, ranging from ghost-management of the literature by pharmaceutical companies to subtle actions provoked by relationships between companies and researchers. Such actions have been demonstrated to create publication biases through over-publication of positive results and under-publication of negative ones, to create design biases, to duplicate known positive results, to affect the interpretation of data, and may also prompt more serious cases of scientific misconduct. It can easily be seen that these causal connections between funding and outcomes are relatively unaffected by such commonly proposed solutions as: stronger disclosure requirements, rigorous trial reporting standards, and trial registries. Because the bias is not the result of simple methodological problems, radical solutions are called for, that divorce the pharmaceutical industry from published research. In the meantime, the fact that pharmaceutical company funding has such strong effects deserves to be more widely known, and measures to address it more widely explored."

DOI: 10.1016/j.cct.2007.08.001

Die Studie zeigt, dass die Verwendung von tierischem Eiweiss als Vergleichssubstanz in industriefinanzierten Studien, deren Evidenzqualität als sehr niedrig bis niedrig eingestuft wurde, den kardiovaskulären Nutzen einer Reduzierung des Verzehrs von rotem Fleisch unterschätzen könnte. Die meisten Studien ohne Interessenkonflikte mit der Fleischindustrie deuteten auf einen ungünstigen Effekt des Verzehrs von unverarbeitetem rotem Fleisch auf Risikofaktoren für Herz-Kreislauf-Erkrankungen hin.

A total of 44 studies were included, of which 66% had a link to the red meat industry. All independent studies reported either unfavorable (73.3%) or neutral (26.7%) cardiovascular outcomes when consuming unprocessed red meat. Conversely, all studies related to the red meat industry reported either favorable (20.7%) or neutral (79.3%) cardiovascular outcomes for red meat intake. A total of 69.6% of trials (16 of 23) showed a neutral effect of unprocessed red meat compared with other animal proteins, whereas 70% (7 of 10) reported an unfavorable effect compared with plant proteins. Studies with conflicts of interest were nearly 4 times more likely to report “Favorable/Neutral” outcomes compared with independent studies (odds ratio 3.75, 95% confidence interval: 1.62, 8.67).

DOI: 10.1016/j.ajcnut.2025.02.030

In den Medien dominieren industrienahe Stimmen. Kritische Fachleute mit belastbarer Evidenz erhalten wenig Raum, während PR-Botschaften der Branche direkt in Nachrichten einfliessen.

Eine australische Analyse von Brooks et al. (2024) belegte, dass Nachrichtenportale in 80 von 86 Beiträgen über Fastfood-Ketten diese in einem positiven Bild darstellen. 

The vast majority (93%; n=80) of news media items had a slant that was favourable to a brand. These included items with favourable headlines, such as “Macca’s menu item we’ve been waiting for” and “Have an easy dinner and help with flood relief efforts thanks to Domino’s”, as well as favourable descriptions of new food products like “…there's no trick to this Halloween treat, just delicious thickshake goodness” and “The deluxe burger features… a tasty rasher of bacon”, among others. All news media items that had a slant that was unfavourable to a brand (7%; n=6) were coded as such because they quoted social media users’ criticisms of brands’ products. For example, one news media item stated that “Complaints of a lacklustre amount of toppings or – as one Caramilk fan so eloquently put it – tasting like “crap in a cup” flooded social media.”.

DOI: 10.1016/j.anzjph.2024.100150

(Übersichtsarbeit – Evidenzklasse III–IV) Art: Narrative Review/Positionspapier über Desinformation. Begründung: Journal of Nutrition-Artikel als Überblick, ohne Primärdaten; Level 5.

“Misinformation and disinformation in food science are widespread and may be amplified by economic interests and selective communication.”

Kommentar:
Diese Arbeit belegt, dass Fehlinformation und gezielte Desinformation in der Ernährungswissenschaft systemisch verbreitet sind – ein zentrales Thema Eures Kapitels über Desinformation.

Food and nutrition are popular topics in the media and on social media. The ubiquity of social media has created new opportunities for qualified or credentialed experts in the scientific community to connect with clients and the public. It has also created challenges. Health and wellness gurus, or self-proclaimed experts, utilize social media platforms to garner attention through compelling narratives, build audience followings, and influence public opinion by sharing (often) misleading information about food and nutrition. 

With the abundance of misinformation and disinformation being shared on social media, credentialed nutrition scientists and providers play a vital role in the assessment of the quality and accuracy of information against the body of evidence. 

DOI: 10.1016/j.tjnut.2022.10.001

(Essay – Evidenzklasse V) Art: Essay/Kommentar zur OA-Landschaft. Begründung: Zeitschriftenseite weist eine diskursive Analyse ohne Primärdaten aus; Level 5. 

“While open access aims to democratize knowledge, current models often shift the financial burden from readers to authors, creating new inequities.”

Kommentar:
Ein kritischer Beitrag zum Open-Access-System: Der freie Zugang zu Wissen kann neue Ungleichheiten schaffen, wenn Autoren statt Leser bezahlen müssen.

Unfaire finanzielle Belastung durch APCs (Article Processing Charges).

Viele Open-Access-Journale verlangen hohe Publikationsgebühren, die nicht alle Forschenden tragen können.

„The most direct downside of OA is the unfair economic burden of APC for researchers with no external grant funding that pays APCs.“ „Most research institutions have no funds of their own to pay APC charged by OA journals, when researchers have a paper accepted after peer review.“

„Unfortunately, the APC approach hurts peripheral scholars who otherwise have greatly benefited from the growth of OA publishing. These financial (and other) barriers to publishing create a demand for ‘sub-standard journals.’ Publishing in those, to a large extent, diminishes the reputation of work carried out.“

Zunahme von „Predatory Journals“

Das Open-Access-Modell hat zur Verbreitung unseriöser Zeitschriften geführt, die kaum Peer-Review betreiben und primär auf Profit aus sind.

„A more subtle consequence of OA's ascendancy, in the eyes of some, is the global proliferation of ‘predatory’ journals.“ „These lie on a spectrum from the outright fraudulent (so called ‘fake journals’) through to those whose business models are based on enticing as many potential authors as possible into parting with APCs.“

„OA carries with it the perverse incentives of any piece-work payment system: a potential reduction in output quality, as production volume increases, motivated purely by financial rewards.“

Qualitätsverlust durch Masse statt Klasse

Die wirtschaftlichen Anreize im OA-Modell fördern eine hohe Publikationsrate, oft auf Kosten der Qualität. Peer-Review wird teilweise vernachlässigt, da abgelehnte Artikel keine Einnahmen bringen. Dies erschwert systematische Reviews, da die Zahl minderwertiger Studien steigt und die Effizienz wissenschaftlicher Synthese sinkt.

„OA has led to a ‘race to the bottom’ in terms of the scientific quality of peer-reviewed scientific publications in recent years.“

„Indeed, a widely respected journal indexing database, Scopus, has recently had to undertake a thorough ‘cleanout’ to remove hundreds of journals deemed, on careful review, to be unworthy of inclusion due to inadequate peer-review.“

Der Versuch, OA durch Förderbedingungen zu erzwingen (z. B. Plan S), stösst auf Widerstand:

„Scholarly resistance is based on the concern that forcing everyone to use OA will: likely lock in place high APCs, may prevent scholars from publishing in their preferred journals, may place severe limitations on journals published by professional societies, and may cause a loss of income for professional societies.“

DOI: 10.1016/j.socscimed.2022.115592

(Rechtswissenschaftliche Analyse – Evidenzklasse V)

“The right to research should be recognized as a fundamental component of the right to science.”

Kommentar:
Geiger fordert ein Recht auf Forschung als Bestandteil des Menschenrechts auf Wissenschaft – eine juristische Ergänzung zum Thema Open Science.

Unsicherheit durch restriktive Verlagsverträge

“The necessity to publish with prestigious publishers effectively requires researchers to agree to terms that restrict their ability to control the dissemination of their work.” 

“Authors transaction their right to control publication away.” 

Diese Aussagen zeigen, dass Forschende oft gezwungen sind, ihre Rechte an der Verlagsversion abzugeben – was Unsicherheit schafft, besonders im Hinblick auf Open Access.

Bedeutung eines „Rechts auf Forschung“ und Open Science

“A right to research creates new imperatives that can be instrumentalized to shift the normative focus of copyright law from a restrictive right to an enabling legal framework.”

“The conflict between the proprietary interests of rightholders and the societal interests in unhindered purpose-bound research should, in case of doubt, be decided in favour of research and open science.”

Diese Passagen stützen die Forderung nach einem Urheberrecht, das Forschung und Open Science aktiv ermöglicht – genau wie du es in deiner Zusammenfassung beschrieben hast.

Sekundärveröffentlichungsrecht als Lösung

"A right to simultaneously (re-)publish the results of scientific research would help to make science more open and accessible to academic peers and researchers around the globe."

“Such rights have already been introduced at national level, for example Paragraph 38(4) of the German Copyright Act […] Similarly, France has introduced Article L 533-4 of the Research Code […] The introduction of such right has recently been explicitly welcomed by the EU Council and should be urgently put on the copyright reform agenda in the near future.” 

Diese Stellen zeigen, dass Geiger und Jütte konkrete rechtliche Reformen vorschlagen, um die Unsicherheit zu beseitigen und Open Access zu fördern.

DOI: -

(Quantitative Analyse – Evidenzklasse IIa–III)

“Sharing research data and code is associated with a citation advantage of approximately 20%.”

Kommentar:
Preprint und die Offenlegeung von Forschungsdaten führen zu einer 20 % bzw. 4,3 % häufigeren Zitation. Es gibt kein Vorteil für das Offenlegen von Code (skrips).

We show that Open Science practices are adopted to different degrees across scientific disciplines. We find that the early release of a publication as a preprint correlates with a significant positive citation advantage of about 20.2% (±.7) on average. We also find that sharing data in an online repository correlates with a smaller yet still positive citation advantage of 4.3% (±.8) on average. However, we do not find a significant citation advantage for sharing code. Further research is needed on additional or alternative measures of impact beyond citations. Our results are likely to be of interest to researchers, as well as publishers, research funders, and policymakers.

DOI: 10.1371/journal.pone.0311493

(Politikwissenschaftliche Studie – Evidenzklasse IV–V)

“Open Science 2.0 must integrate transparency, data availability, and policy coordination to ensure equitable access to knowledge.”

Kommentar:
Dieser Artikel zeigt, dass offene Wissenschaft politische Steuerung braucht – sonst entstehen erneut Ungleichheiten.

Zur Dominanz westlicher Verlage und Paywalls:

“Publishing technology has undergone remarkable transformations, and scientists can now instantaneously share nearly all aspects of their scholarship with a worldwide audience. However, the academic research community continues to treat journal articles as the principal way of sharing research and efforts for change generally remain tied to this journal-centric system.”

“One unfortunate legacy of the print era—when publishing was expensive and limited in length and structure—is that publications often serve as an advertisement of research rather than a complete record of the research process and outcomes.”

Diese Stellen kritisieren die fortbestehende Abhängigkeit vom traditionellen Publikationssystem, das oft durch Paywalls und selektive Veröffentlichung geprägt ist.

Zur Forderung nach politischer Steuerung und öffentlicher Finanzierung:

“We argue for concerted and persistent efforts, supported by sustained public funding mechanisms, that treat open science as a milepost toward a more effective research ecosystem.”

“To date, support for open science infrastructure and training has relied in good part on volunteers and philanthropic funding... As a scientific community, we do not rely on volunteers and philanthropists as the primary means to support research; we should not rely on them as the primary means to ensure research is open, rigorous, and collaborative.”

Diese Passagen zeigen deutlich die Forderung nach politischer Verantwortung und öffentlicher Finanzierung zur Sicherstellung von Open Science.

Zur Betonung von Transparenz und Datenverfügbarkeit:

“Conversations about open science have reached the mainstream, yet many open science practices such as data sharing remain uncommon. Our efforts towards openness therefore need to increase in scale and aim for a more ambitious target.”

“We need an ecosystem not only where research outputs are openly shared but also in which transparency permeates the research process from the start and lends itself to more rigorous and collaborative research.”

DOI: 10.1371/journal.pbio.3002362

(Kommentar – Evidenzklasse V)

“Predatory publishers are exploiting the author-pays model of open access for their own profit.”

Kommentar:
Warnung vor unseriösen „Raubverlagen“, die gegen Gebühr veröffentlichen, ohne Qualitätssicherung – ein Schlüsselbeispiel für Quellenkritik.

Early experiments with open-access publishing, such as the Journal of Medical Internet Research and BioMed Central, were very promising. Set up more than a decade ago, they helped to inspire a social movement that has changed academic publishing for the better, lowered costs and expanded worldwide access to the latest research.

Then came predatory publishers, which publish counterfeit journals to exploit the open-access model in which the author pays. These predatory publishers are dishonest and lack transparency. They aim to dupe researchers, especially those inexperienced in scholarly communication. They set up websites that closely resemble those of legitimate online publishers, and publish journals of questionable and downright low quality. Many purport to be headquartered in the United States, United Kingdom, Canada or Australia but really hail from Pakistan, India or Nigeria.

Some predatory publishers spam researchers, soliciting manuscripts but failing to mention the required author fee. Later, after the paper is accepted and published, the authors are invoiced for the fees, typically US$1,800. Because the scientists are often asked to sign over their copyright to the work as part of the submission process (against the spirit of open access) they feel unable to withdraw the paper and send it elsewhere.

I also get e-mails from the predators’ victims. Some have been named as members of editorial boards without their knowledge or permission. Others have had an article partially or completely plagiarized in a predatory journal. 

Now there is a journal willing to accept almost every article, as long as the author is willing to pay the fee. Authors, rather than libraries, are the customers of open-access publishers, so a powerful incentive to maintain quality has been removed.

Perhaps nowhere are these abuses more acute than in India, where new predatory publishers or journals emerge each week. They are appearing because of the market need — hundreds of thousands of scientists in India and its neighbouring countries need to get published to earn tenure and promotion.

Here, the problem is not just with the publishers. Scientists themselves are also to blame. Many are taking unethical shortcuts and paying for the publication of plagiarized or self-plagiarized work.

DOI: 10.1038/489179a

(Editorial / Grundsatzartikel – Evidenzklasse V) Art: Essay/Theoriepapier, keine neuen Daten. Begründung: PLOS kennzeichnet den Beitrag als „Essay“; es wird ein theoretisches Modell zur Positiven-Prädiktiv-Wahrscheinlichkeit vorgestellt, keine empirische Studie; daher Level 5. PLOS+1

John Ioannidis zeigte mit einem einfachen mathematischen Modell, warum viele veröffentlichte Studienergebnisse wahrscheinlich falsch sind.

Langform

Published research findings are sometimes refuted by subsequent evidence, with ensuing confusion and disappointment. Refutation and controversy is seen across the range of research designs, from clinical trials and traditional epidemiological studies [1–3] to the most modern molecular research [4,5]. There is increasing concern that in modern research, false findings may be the majority or even the vast majority of published research claims [6–8]. However, this should not be surprising. It can be proven that most claimed research findings are false. Here I will examine the key factors that influence this problem and some corollaries thereof.

Kurzform

There is increasing concern that in modern research, false findings may be the majority or even the vast majority of published research claims. It can be proven that most claimed research findings are false. Here I will examine the key factors that influence this problem and some corollaries thereof.

Er weist darauf hin, dass kleine Stichproben, flexible Studiendesigns und Publikationsbias die Zahl falscher „positiver“ Resultate stark erhöhen.

Kommentar:
Dieser Essay ist der meistgelesene methodische Beitrag in PLoS Medicine. Er mahnt, dass auch wissenschaftliche Resultate kritisch geprüft werden müssen – ein Grundgedanke der gesamten „Evidenz-für-Studien“-Einführung.

Die Grundthese: Die meisten veröffentlichten Forschungsergebnisse sind falsch

“It can be proven that most claimed research findings are false.”

Diese Aussage bildet die Grundlage des gesamten Artikels. Ioannidis argumentiert, dass methodische Schwächen, Bias und geringe statistische Power dazu führen, dass viele Studienergebnisse nicht zutreffen.

Einfluss von Studiendesign und Bias

“The probability that a research finding is indeed true depends on the prior probability of it being true, the statistical power of the study, and the level of bias.”

Er zeigt, dass selbst bei statistisch signifikanten Ergebnissen die Wahrscheinlichkeit, dass sie wahr sind, oft gering ist – besonders bei niedriger Power und hohem Bias.

Probleme bei kleinen Studien und vielen Hypothesen

“Small sample size, small effect size, and flexibility in designs, definitions, outcomes, and analytical modes increase the proportion of false findings.”

Diese Aussage kritisiert die Praxis, viele Hypothesen zu testen oder Daten mehrfach zu analysieren, was die Wahrscheinlichkeit von Zufallsbefunden erhöht.

Interessenkonflikte und finanzielle Einflüsse

“The greater the financial and other interests and prejudices in a scientific field, the less likely the research findings are to be true.”

Ioannidis warnt davor, dass wirtschaftliche Interessen die Objektivität der Forschung gefährden.

Replikation ist selten – und oft negativ

“Most research findings are not only false, but they are also relatively rarely replicated or directly tested.”

Er betont, dass viele Studien nie überprüft werden – und wenn doch, oft nicht bestätigt werden können.

Wissenschaftliche Publikation als Spiel mit Wahrscheinlichkeiten

“Claimed research findings may often be simply accurate measures of the prevailing bias.”

Das bedeutet: Ein „signifikantes“ Ergebnis kann oft eher ein Spiegel der systematischen Verzerrung sein als ein echter Effekt.

DOI: 10.1371/journal.pmed.0020124

Wissenschaftliche Rezeption: Zitationszahlen gelten seit Garfield (1955) als Indikator dafür, wie stark eine Studie in der Fachliteratur Beachtung findet.^{Quelle:Garfield E. Citation indexes for science; a new dimension in documentation through association of ideas. Science. 1955;122(3159):108–11. doi:10.1126/science.122.3159.108.} Eugene Garfield verfasste nicht nur den Klassiker zur Entwicklung der Science Citation Index-Methode.^{Quelle:The mystery of the transposed journal list – wherein Bradford’s law of scattering is generalized according to Garfield’s law of concentration. In: E. Garfield: Essays of an Information Scientist. Vol 1. ISI Press, Philadelphia 1977, S. 222–223.}

(Methodische Innovation – Evidenzklasse V)

“Citation indexes permit an association of ideas that may be nearly as valid as the association of subjects through traditional indexing.”

Kommentar:
Garfield erfand die Zitationsanalyse – Grundlage für heutige Impact-Messungen und wissenschaftliche Rezeption.

In this paper I propose a bibliographic system for science literature that can eliminate the uncritical citation of fraudulent, incomplete, or obsolete data by making it possible for the conscientious scholar to be aware of criticisms of earlier papers. It is too much to expect a research worker to spend an inordinate amount of time searching for the bibliographic descendants of antecedent papers. It would not be excessive to demand that the thorough scholar check all papers that have cited or criticized such papers. if they could be located quickly. The citation index makes this check practicable. Even if there were no other use for a citation index than that of minimizing the citation of poor data, the index would be well worth the effort required to compile it.

Citation indexes depend on a simple system of coding entries, one that re quires minimum space and facilitates the gathering together of a great volume of material. However, a code is not absolutely necessary if one chooses to compiler a systematic listing of individual cases or reports, with a complete bibliographic history of each of them.

DOI: 10.1126/science.122.3159.108

(Positionspapier – Evidenzklasse V)

“The value of research lies not only in citations but in its broader influence – on policy, education, and public engagement.”

Kommentar:
Dieses Manifest führte den Begriff „Altmetrics“ ein – alternative Kennzahlen jenseits der Zitationen.

Because altmetrics are themselves diverse, they’re great for measuring impact in this diverse scholarly ecosystem. In fact, altmetrics will be essential to sift these new forms, since they’re outside the scope of traditional filters. This di versity can also help in measuring the aggregate impact of the research enter prise itself.

Altmetrics are fast, using public APIs to gather data in days or weeks. They’re open–not just the data, but the scripts and algorithms that collect and inter pret it.

Altmetrics look beyond counting and emphasize semantic content like usernames, timestamps, and tags. Altmetrics aren’t citations, nor are they webometrics; although these latter approaches are related to altmetrics, they are relatively slow, unstructured, and closed.

Altmetrics are in their early stages; many questions are unanswered. But given the crisis facing existing filters and the rapid evolution of scholarly communi cation, the speed, richness, and breadth of altmetrics make them worth invest ing in.

(Übersichtsartikel – Evidenzklasse III)

“Altmetrics capture attention rather than scientific quality and should be interpreted with caution.”

Kommentar:
Hohe Social-Media-Präsenz bedeutet nicht automatisch hohe wissenschaftliche Qualität – ein wichtiger Hinweis zur Quellenbewertung.

It goes without saying that altmetrics have disadvantages as well as advantages. They share this characteristic with traditional metrics. Not everything that is cited has been read, and the relevant publications are not always cited in the correct place in a manuscript.

According to Priem et al. 2010, in order to be able to answer the question of whether altmetrics measure impact (“or just empty buzz”), it should be compared with expert evaluations. Do altmetric counts correlate with the evaluations by experts of the societal impact of a paper?

It is much easier to manipulate altmetrics than bibliometrics.

The lack of evidence of altmetrics relates to the scarcity of sophisticated empirical studies on altmetrics.

As not everyone (in a city, a country, etc.) uses social media platforms, a measurement of impact always relates to a specific sample of people who have mentioned a paper more or less frequently. It is assumed that this sample has a systematic bias towards younger or more fad-embracing people or towards those with a professional interest in research. As there are no accurate user statistics or sample descriptions for individual social media platforms, this bias cannot be quantified.

Altmetric counts are frequently made available as counts of all relevant mentions on a platform. However, more information about user groups who have had to do with a scientific paper is essential for a valid measurement of societal impact; has impact been measured in government documents or on social media comment sites?  This more specific description of the impact achieved is usually lacking nowadays.

DOI: 10.1016/j.joi.2014.09.005

Keine signifikanten Unterschiede im Gewichtsverlust zwischen Low-Fat und Low-Carb

Beschreibung: Die Studie verglich zwei Diätformen über 12 Monate und fand keinen statistisch signifikanten Unterschied im durchschnittlichen Gewichtsverlust.

Originalzitat:

“Weight change at 12 months was −5.3 kg for the HLF diet vs −6.0 kg for the HLC diet (mean between-group difference, 0.7 kg [95% CI, −0.2 to 1.6 kg]).”

Genotyp hatte keinen Einfluss auf den Diäterfolg

Beschreibung: Die genetische Veranlagung (Low-Fat- oder Low-Carb-Genotyp) beeinflusste nicht, wie viel Gewicht die Teilnehmer verloren.

Originalzitat:

“There was no significant diet-genotype pattern interaction (P = .20) … This indicates that there was no significant difference in weight change among participants matched vs mismatched to their diet assignment based on their 3-SNP genotype pattern.”

Insulinsekretion hatte keinen Einfluss auf den Diäterfolg

Beschreibung: Auch die Insulinantwort nach Glukosegabe (INS-30) zeigte keinen Zusammenhang mit dem Erfolg der jeweiligen Diät.

Originalzitat:

“Similarly, the test for interaction among diet, baseline insulin secretion (INS-30), and the 12-month time point was not statistically significant.”

Grosse individuelle Unterschiede im Gewichtsverlust

Beschreibung: Unabhängig von der Diätform variierten die individuellen Ergebnisse stark – manche nahmen über 30 kg ab, andere nahmen sogar zu.

Originalzitat:

“There was a similar range for weight change of approximately 40 kg within each group (−30 kg to 10 kg).”

Beide Diäten führten zu Verbesserungen der Gesundheit

Beschreibung: Beide Gruppen zeigten Verbesserungen bei Blutdruck, Blutzucker und Lipidwerten – mit unterschiedlichen Vorteilen je nach Diät.

Originalzitat:

“At 12 months relative to baseline, both diets improved lipid profiles and lowered blood pressure, insulin, and glucose levels, with the exception of low-density lipoprotein cholesterol concentrations, which increased for participants in the healthy low-carbohydrate group”

Die Hypothesen zur personalisierten Diätwahl wurden nicht bestätigt

Beschreibung: Die Studie konnte nicht zeigen, dass genetische oder metabolische Marker helfen, die „richtige“ Diät für eine Person vorherzusagen.

Originalzitat:

“In the context of these 2 common weight loss diet approaches, neither of the 2 hypothesized predisposing factors was helpful in identifying which diet was better for whom.”

DOI: 10.1001/jama.2018.0245

The Prospective Urban Rural Epidemiology (PURE) study is a large, epidemiological cohort study of individuals aged 35–70 years (enrolled between Jan 1, 2003, and March 31, 2013) in 18 countries with a median follow-up of 7·4 years (IQR 5·3–9·3). Dietary intake of 135 335 individuals was recorded using validated food frequency questionnaires. The primary outcomes were total mortality and major cardiovascular events (fatal cardiovascular disease, non-fatal myocardial infarction, stroke, and heart failure). Secondary outcomes were all myocardial infarctions, stroke, cardiovascular disease mortality, and non-cardiovascular disease mortality. Participants were categorised into quintiles of nutrient intake (carbohydrate, fats, and protein) based on percentage of energy provided by nutrients. We assessed the associations between consumption of carbohydrate, total fat, and each type of fat with cardiovascular disease and total mortality. We calculated hazard ratios (HRs) using a multivariable Cox frailty model with random intercepts to account for centre clustering.

During follow-up, we documented 5796 deaths and 4784 major cardiovascular disease events. Higher carbohydrate intake was associated with an increased risk of total mortality (highest [quintile 5] vs lowest quintile [quintile 1] category, HR 1·28 [95% CI 1·12–1·46], p_trend=0·0001) but not with the risk of cardiovascular disease or cardiovascular disease mortality. Intake of total fat and each type of fat was associated with lower risk of total mortality (quintile 5 vs quintile 1, total fat: HR 0·77 [95% CI 0·67–0·87], p_trend<0·0001; saturated fat, HR 0·86 [0·76–0·99], p_trend=0·0088; monounsaturated fat: HR 0·81 [0·71–0·92], p_trend<0·0001; and polyunsaturated fat: HR 0·80 [0·71–0·89], p_trend<0·0001). Higher saturated fat intake was associated with lower risk of stroke (quintile 5 vs quintile 1, HR 0·79 [95% CI 0·64–0·98], p_trend=0·0498). Total fat and saturated and unsaturated fats were not significantly associated with risk of myocardial infarction or cardiovascular disease mortality.

High carbohydrate intake was associated with higher risk of total mortality, whereas total fat and individual types of fat were related to lower total mortality. Total fat and types of fat were not associated with cardiovascular disease, myocardial infarction, or cardiovascular disease mortality, whereas saturated fat had an inverse association with stroke. Global dietary guidelines should be reconsidered in light of these findings.

In this large prospective cohort study from 18 countries in five continents, we found that high carbohydrate intake (more than about 60% of energy) was associated with an adverse impact on total mortality and non cardiovascular disease mortality. By contrast, higher fat intake was associated with lower risk of total mortality, non-cardiovascular disease mortality, and stroke. Furthermore, higher intakes of individual types of fat were associated with lower total mortality, non cardiovascular disease mortality, and stroke risk and were not associated with risk of major cardiovascular disease events, myocardial infarction, or cardiovascular disease mortality. Our findings do not support the current recommendation to limit total fat intake to less than 30% of energy and saturated fat intake to less than 10% of energy. Individuals with high carbohydrate intake might benefit from a reduction in carbohydrate intake and increase in the consumption of fats.

Moreover, in our study most participants from low-income and middle-income countries consumed a very high carbohydrate diet (at least 60% of energy), especially from refined sources (such as white rice and white bread), which have been shown to be associated with increased risk of total mortality and cardiovascular events.42 Therefore, recommending lowering carbohydrate might be particularly applicable to such settings if replacement foods from fats and protein are available and affordable.

DOI: 10.1016/S0140-6736(17)32252-3

In a multicenter trial in Spain, we randomly assigned participants who were at high cardiovascular risk, but with no cardiovascular disease at enrollment, to one of three diets: a Mediterranean diet supplemented with extra-virgin olive oil, a Mediterranean diet supplemented with mixed nuts, or a control diet (advice to reduce dietary fat). Participants received quarterly individual and group educational sessions and, depending on group assignment, free provision of extra-virgin olive oil, mixed nuts, or small nonfood gifts. The primary end point was the rate of major cardiovascular events (myocardial infarction, stroke, or death from cardiovascular causes). On the basis of the results of an interim analysis, the trial was stopped after a median follow-up of 4.8 years.

A total of 7447 persons were enrolled (age range, 55 to 80 years); 57% were women. The two Mediterranean-diet groups had good adherence to the intervention, according to self-reported intake and biomarker analyses. A primary end-point event occurred in 288 participants.

DOI: 10.1056/NEJMoa1200303

In a multicenter trial in Spain, we assigned 7447 participants (55 to 80 years of age, 57% women) who were at high cardiovascular risk, but with no cardiovascular disease at enrollment, to one of three diets: a Mediterranean diet supplemented with extra-virgin olive oil, a Mediterranean diet supplemented with mixed nuts, or a control diet (advice to reduce dietary fat). 

DOI: 10.1056/NEJMoa1800389

Werte Table 2

Vegan 23,6,

Lacto-ovo vegetarian 25,7

Pesco-vegetarian 26,3

Semi-vegetarian 27,3

Nonvegetarian 28,8

The prevalence of type 2 diabetes increased incrementally among vegans, lacto-ovo vegetarians, pesco-vegetarians, semi-vegetarians, and nonvegetarians 

DOI: 10.2337/dc08-1886

Those following vegan diets were younger than omnivores (mean: 36 vs. 44 years) and more likely to be females (68% vs. 52%). Vegans also had slightly lower body mass index (BMI) than omnivores (23.7 vs. 26.9 kg/m²) and were more likely to have a university education compared to omnivores (75% vs. 52%). Most vegans (97%) and omnivores (72%) reported taking one or more food supplements.

DOI: 10.1038/s41598-025-03193-3

The plant-based diet group reported a higher rate of physical activity than the omnivorous group (p=0.01). The mean BMI was significantly lower in the plant-based diet group than in the omnivorous group and the prevalence of overweight and obesity was significantly higher in the omnivorous than in the plant-based group (p=0.001).

DOI: 10.1136/bmjnph-2023-000629

When including only obese individuals (BMI ≥ 30 kg/m², n = 153) in the analysis, we observed lower concentrations of GGT and ferritin in vegetarians than in omnivores, regardless of gender and menstrual blood loss (p = 0.0395). Our data showed that for both vegetarians and omnivores, the higher the BMI, the worse the metabolic parameters. However, regarding obesity, vegetarians showed better antioxidant status (lower GGT elevation) and lower inflammatory status (lower ferritin elevation), which may provide them with potential protection in the development of morbidities associated with overweight.

DOI: 10.3390/nu14112204

Zur Kritik an schematischer Checklisten-Medizin:

“Inexperienced clinicians may (partly through fear of litigation) engage mechanically and defensively with decision support technologies, stifling the development of a more nuanced clinical expertise that embraces accumulated practical experience, tolerance of uncertainty, and the ability to apply practical and ethical judgment in a unique case.”

“Templates and point of care prompts also contribute to the creeping managerialism and politicisation of clinical practice.”

“As the language of EBM becomes ever more embedded in medical practice, and as bureaucratic rules become the accepted way to implement ‘the best’ evidence, its requirements for evidence are quietly attenuated in favour of an emphasis on rules.”

Zur Ignoranz gegenüber realer Komplexität:

“Evidence based guidelines often map poorly to complex multimorbidity.”

“Multimorbidity (a single condition only in name) affects every person differently and seems to defy efforts to produce or apply objective scores, metrics, interventions, or guidelines.”

“As serious illness is lived, evidence based guidelines may become irrelevant, absurd, or even harmful (most obviously, in terminal illness).”

But others argued that evidence based medicine, if practised knowledgably and compassionately, could accommodate basic scientific principles, the subtleties of clinical judgment, and the patient’s clinical and personal idiosyncrasies.1

DOI: 10.1136/bmj.g3725