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

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-randomi...

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Autores principales: Young, Jessica G., Vatsa, Rajet, Murray, Eleanor J., Hernán, Miguel A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6729016/
https://www.ncbi.nlm.nih.gov/pubmed/31488202
http://dx.doi.org/10.1186/s13063-019-3577-z
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author Young, Jessica G.
Vatsa, Rajet
Murray, Eleanor J.
Hernán, Miguel A.
author_facet Young, Jessica G.
Vatsa, Rajet
Murray, Eleanor J.
Hernán, Miguel A.
author_sort Young, Jessica G.
collection PubMed
description 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13063-019-3577-z) contains supplementary material, which is available to authorized users.
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spelling pubmed-67290162019-09-12 Interval-cohort designs and bias in the estimation of per-protocol effects: a simulation study Young, Jessica G. Vatsa, Rajet Murray, Eleanor J. Hernán, Miguel A. Trials Methodology 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13063-019-3577-z) contains supplementary material, which is available to authorized users. BioMed Central 2019-09-05 /pmc/articles/PMC6729016/ /pubmed/31488202 http://dx.doi.org/10.1186/s13063-019-3577-z Text en © The Author(s) 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Methodology
Young, Jessica G.
Vatsa, Rajet
Murray, Eleanor J.
Hernán, Miguel A.
Interval-cohort designs and bias in the estimation of per-protocol effects: a simulation study
title Interval-cohort designs and bias in the estimation of per-protocol effects: a simulation study
title_full Interval-cohort designs and bias in the estimation of per-protocol effects: a simulation study
title_fullStr Interval-cohort designs and bias in the estimation of per-protocol effects: a simulation study
title_full_unstemmed Interval-cohort designs and bias in the estimation of per-protocol effects: a simulation study
title_short Interval-cohort designs and bias in the estimation of per-protocol effects: a simulation study
title_sort interval-cohort designs and bias in the estimation of per-protocol effects: a simulation study
topic Methodology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6729016/
https://www.ncbi.nlm.nih.gov/pubmed/31488202
http://dx.doi.org/10.1186/s13063-019-3577-z
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