Generalizing Experimental Findings

Pearl, Judea

doi:10.21236/ada621962

Cited by 15 publications

(27 citation statements)

References 12 publications

(16 reference statements)

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“…The methods we propose rest on two identifiability conditions beyond those supported by randomization: mean generalizability from randomized to nonrandomized individuals and positivity of trial participation . Directed acyclic graphs can facilitate reasoning about the mean generalizability condition (Pearl and Bareinboim, ; Pearl, ). Arguably, the identifiability conditions are most plausible in studies explicitly designed to collect information on all trial‐eligible individuals, including those who are not randomized.…”

Section: Discussionmentioning

confidence: 99%

Generalizing Causal Inferences from Individuals in Randomized Trials to All Trial-Eligible Individuals

et al. 2018

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We consider methods for causal inference in randomized trials nested within cohorts of trial‐eligible individuals, including those who are not randomized. We show how baseline covariate data from the entire cohort, and treatment and outcome data only from randomized individuals, can be used to identify potential (counterfactual) outcome means and average treatment effects in the target population of all eligible individuals. We review identifiability conditions, propose estimators, and assess the estimators' finite‐sample performance in simulation studies. As an illustration, we apply the estimators in a trial nested within a cohort of trial‐eligible individuals to compare coronary artery bypass grafting surgery plus medical therapy vs. medical therapy alone for chronic coronary artery disease.

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Section: Discussionmentioning

confidence: 99%

Generalizing Causal Inferences from Individuals in Randomized Trials to All Trial-Eligible Individuals

et al. 2018

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“…It is a methodological challenge to produce research with clear external validity, and this challenge might be met by new methods for generalizability and transportability of estimates from studies across various populations. [23][24][25][26][27][28][29][30] Is academic epidemiology removed from public health impact? Developing methods and validating them is not of public health impact per se, but it does empower the field.…”

Section: Axioms Of Epidemiologymentioning

confidence: 99%

Epidemiology at a time for unity

Lau

Duggal

Ehrhardt

2018

International Journal of Epidemiology

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“…Clinical trials are designed to answer the question, ‘is the tested treatment better than the control?’ and to establish a causal link between receiving the tested treatment and the difference in outcome rates. The estimate of the size of this difference provides a quantitative estimate of how much better the treatment is for a group of patients or, even, a given patient [1, 2].…”

Section: Introductionmentioning

confidence: 99%

From Clinical Trial Efficacy to Real-Life Effectiveness: Why Conventional Metrics do not Work

Boissel¹,

Cogny²,

Marko

et al. 2019

Drugs - Real World Outcomes

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Background Randomised, double-blind, clinical trial methodology minimises bias in the measurement of treatment efficacy. However, most phase III trials in non-orphan diseases do not include individuals from the population to whom efficacy findings will be applied in the real world. Thus, a translation process must be used to infer effectiveness for these populations. Current conventional translation processes are not formalised and do not have a clear theoretical or practical base. There is a growing need for accurate translation, both for public health considerations and for supporting the shift towards personalised medicine. Objective Our objective was to assess the results of translation of efficacy data to population efficacy from two simulated clinical trials for two drugs in three populations, using conventional methods. Methods We simulated three populations, two drugs with different efficacies and two trials with different sampling protocols. Results With few exceptions, current translation methods do not result in accurate population effectiveness predictions. The reason for this failure is the non-linearity of the translation method. One of the consequences of this inaccuracy is that pharmacoeconomic and postmarketing surveillance studies based on direct use of clinical trial efficacy metrics are flawed. Conclusion There is a clear need to develop and validate functional and relevant translation approaches for the translation of clinical trial efficacy to the real-world setting. Electronic supplementary material The online version of this article (10.1007/s40801-019-0159-z) contains supplementary material, which is available to authorized users.

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Generalizing Experimental Findings

Cited by 15 publications

References 12 publications

Generalizing Causal Inferences from Individuals in Randomized Trials to All Trial-Eligible Individuals

Generalizing Causal Inferences from Individuals in Randomized Trials to All Trial-Eligible Individuals

Epidemiology at a time for unity

From Clinical Trial Efficacy to Real-Life Effectiveness: Why Conventional Metrics do not Work

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