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Trials. 2019 Sep 5;20(1):552. doi: 10.1186/s13063-019-3577-z.

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

Author information

1
Department of Population Medicine, Harvard Medical School & Harvard Pilgrim Health Care Institute, Boston, USA. jyoung@hsph.harvard.edu.
2
Pathways M.D. Program, Harvard Medical School, Boston, USA.
3
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, USA.
4
Department of Epidemiology, Boston University School of Public Health, Boston, USA.
5
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, USA.
6
Harvard-MIT Division of Health Sciences and Technology, Boston, USA.

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 concretely illustrate the potential magnitude of bias due to infrequent measurement of time-varying covariates, we simulated data from a very large trial with a survival outcome and time-varying confounding affected by past treatment. We generated the data such that the true underlying per-protocol effect is null and under varying degrees of confounding (strong, moderate, weak). In the simulated data, we estimated per-protocol survival curves and associated contrasts using inverse probability weighting under monthly measurement of the time-varying covariates (which constituted complete measurement in our simulation), yearly measurement, as well as 3- and 6-month intervals.

RESULTS:

Using inverse probability weighting, we were able to recover the true null under the complete measurement scenario no matter the strength of confounding. Under yearly measurement intervals, the estimate of the per-protocol effect diverged from the null; inverse probability weighted estimates of the per-protocol 5-year risk ratio based on yearly measurement were 1.19, 1.12, and 1.03 under strong, moderate, and weak confounding, respectively. Bias decreased with measurement interval length. Under all scenarios, inverse probability weighted estimators were considerably less biased than a naive estimator that ignored time-varying confounding completely.

CONCLUSIONS:

Bias that arises from interval measurement designs highlights the need for planning in the design of randomized trials for collection of time-varying covariate data. This may come from more frequent in-person measurement or external sources (e.g., electronic medical record data). Such planning will provide improved estimates of the per-protocol effect through the use of methods that appropriately adjust for time-varying confounders.

KEYWORDS:

Interval cohorts; Inverse probability weighting; Per-protocol effect; Simulation

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