Interval-cohort designs and bias in the estimation of per-protocol effects: a simulation study.

ABSTRACT:

Background

Randomized trials are considered the gold standard for making inferences about the causal effects of treatments. However, when protocol deviations occur, the baseline randomization of the trial is no longer sufficient to ensure unbiased estimation of the per-protocol effect: post-randomization, time-varying confounders must be sufficiently measured and adjusted for in the analysis. Given the historical emphasis on intention-to-treat effects in randomized trials, measurement of post-randomization confounders is typically infrequent. This may induce bias in estimates of the per-protocol effect, even using methods such as inverse probability weighting, which appropriately account for time-varying confounders affected by past treatment.

Methods/design

In order to conc

SUBMITTER: Young JG 

PROVIDER: S-EPMC6729016 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature