ArticlePharmacoepidemiology and drug safety2022
Bias of time-varying exposure effects due to time-varying covariate measurement strategies.
Article in Pharmacoepidemiology and drug safety, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Causal Inference Approaches to Estimating the Effect of Ibrutinib Dose Modifications on Progression-Free Survival in Chronic Lymphocytic Leukemia.Clinical pharmacology and therapeutics · 2026Article
- When Cumulative Frailty Includes Future CVD Events: Time-Order Concerns in CKM Syndrome Research.Geriatrics & gerontology international · 2026Article
- Prediction of myelosuppression risk in postoperative cervical cancer patients undergoing concurrent chemoradiotherapy using machine learning models.Frontiers in oncology · 2026Article
- Ursodeoxycholic acid and severe COVID-19 outcomes in a cohort study using the OpenSAFELY platform.Communications medicine · 2024Article
- Article
- Bias of time-varying exposure effects due to time-varying covariate measurement strategies.Pharmacoepidemiology and drug safety · 2022Article
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2 authors.
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Abstract
purposeIn studies of effects of time-varying drug exposures, adequate adjustment for time-varying covariates is often necessary to properly control for confounding. However, the granularity of the available covariate data may not be sufficiently fine, for example when covariates are measured for participants only when their exposure levels change.
methodsTo illustrate the impact of choices regarding the frequency of measuring time-varying covariates, we simulated data for a large target trial and for large observational studies, varying in covariate measurement design. Covariates were measured never, on a fixed-interval basis, or each time the exposure level switched. For the analysis, it was assumed that covariates remain constant in periods of no measurement. Cumulative survival probabilities for continuous exposure and non-exposure were estimated using inverse probability weighting to adjust for time-varying confounding, with special emphasis on the difference between 5-year event risks.
resultsWith monthly covariate measurements, estimates based on observational data coincided with trial-based estimates, with 5-year risk differences being zero. Without measurement of baseline or post-baseline covariates, this risk difference was estimated to be 49% based on the available observational data. With measurements on a fixed-interval basis only, 5-year risk differences deviated from the null, to 29% for 6-monthly measurements, and with magnitude increasing up to 35% as the interval length increased. Risk difference estimates diverged from the null to as low as -18% when covariates were measured depending on exposure level switching.
conclusionOur simulations highlight the need for careful consideration of time-varying covariates in designing studies on time-varying exposures. We caution against implementing designs with long intervals between measurements. The maximum length required will depend on the rates at which treatments and covariates change, with higher rates requiring shorter measurement intervals.
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