Extending inferences from a randomized trial to a new target population
| Autor: | Issa J Dahabreh, Sarah E. Robertson, Jon A. Steingrimsson, Elizabeth A. Stuart, Miguel A. Hernán |
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| Rok vydání: | 2020 |
| Předmět: |
Statistics and Probability
medicine.medical_specialty Epidemiology Average treatment effect Population Sample (statistics) 01 natural sciences law.invention Coronary artery disease 010104 statistics & probability 03 medical and health sciences 0302 clinical medicine Randomized controlled trial law Covariate medicine Humans 030212 general & internal medicine Cardiac Surgical Procedures 0101 mathematics education Probability Randomized Controlled Trials as Topic education.field_of_study business.industry medicine.disease Causality Causal inference Cohort Physical therapy business |
| Zdroj: | Statistics in Medicine. 39:1999-2014 |
| ISSN: | 1097-0258 0277-6715 |
| Popis: | When treatment effect modifiers influence the decision to participate in a randomized trial, the average treatment effect in the population represented by the randomized individuals will differ from the effect in other populations. In this tutorial, we consider methods for extending causal inferences about time-fixed treatments from a trial to a new target population of nonparticipants, using data from a completed randomized trial and baseline covariate data from a sample from the target population. We examine methods based on modeling the expectation of the outcome, the probability of participation, or both (doubly robust). We compare the methods in a simulation study and show how they can be implemented in software. We apply the methods to a randomized trial nested within a cohort of trial-eligible patients to compare coronary artery surgery plus medical therapy versus medical therapy alone for patients with chronic coronary artery disease. We conclude by discussing issues that arise when using the methods in applied analyses. |
| Databáze: | OpenAIRE |
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