Properties of inverse probability of adherence weighted estimator of the per-protocol effect for sustained treatment strategies under different data-generating mechanisms and adherence patterns

Authors

  • Lucy Mosquera Department of Statistics, University of British Columbia, 3182 Earth Sciences Building, 2207 Main Mall, Vancouver, BC Canada V6T 1Z4, Canada
  • Mohammad Ehsanul Karim School of Population and Public Health, University of British Columbia, 2206 East Mall, Vancouver, BC, V6T 1Z3, Canada; Centre for Health Evaluation and Outcome Sciences, 588 - 1081 Burrard Street; St. Paul’s Hospital, Vancouver, BC, V6Z 1Y6, Canada
  • Md Belal Hossain School of Population and Public Health, University of British Columbia, 2206 East Mall, Vancouver, BC, V6T 1Z3, Canada

DOI:

https://doi.org/10.3329/jsr.v56i2.67467

Keywords:

Non-adherence, causal inference, pragmatic trials, per-protocol

Abstract

Inverse Probability (of Adherence) Weighted per-protocol (IPW-PP) estimators are get- ting popular in addressing medication non-adherence while analyzing pragmatic trial data. However, their finite sample properties under different data generating mechanisms (DGMs) have not been investigated comprehensively. In the current work, we investigated the finite sample performances of such estimators in the context of a pragmatic random- ized controlled trial. We compared the performances of IPW-PP estimators with commonly used naive and baseline-adjusted per-protocol estimators, under different DGMs emulating pragmatic trials, comparing two sustained treatment strategies, possibly with a non-null effect. DGMs include (i) different roles of a baseline variable; whether future time-varying prognostic factors are impacted by past adherence; and whether the baseline variable is measured, (ii) whether adherence patterns observed in two arms are differential, and when we have access to measurements of adherence and confounders that are recorded infre- quently (sparsely). When baseline confounders are adjusted, we generally obtain unbiased estimates, but if some necessary variables are not measured, the IPW-PP estimator may still be preferable. High non-adherence patterns might negatively impact IPW-PP effect estimators, particularly when DGMs include confounding that may be influenced by previ- ous adherence history. We used the above estimators to analyze a case study from the Lipid Research Clinics Coronary Primary Prevention Trial data in the presence of non-adherence.

Journal of Statistical Research 2022, Vol. 56, No. 2, pp.134-154

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Published

2023-07-09

How to Cite

Mosquera, L. ., Karim, M. E. ., & Hossain, M. B. . (2023). Properties of inverse probability of adherence weighted estimator of the per-protocol effect for sustained treatment strategies under different data-generating mechanisms and adherence patterns. Journal of Statistical Research, 56(2), 133–154. https://doi.org/10.3329/jsr.v56i2.67467

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Articles