Bias of time‐varying exposure effects due to time‐varying covariate measurement strategies

Abstract

Purpose: In 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.Methods: To 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.Results: With 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 À18% when covariates were measured depending on exposure level switching. Conclusion:Our simulations highlight the need for careful consideration of timevarying 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.

A 40-year study of child maltreatment over the early life course predicting psychiatric morbidity, using linked birth cohort and administrative health data: protocol for the Childhood Adversity and Lifetime Morbidity (CALM) study

“…There may also be bias owing to follow-ups occurring too infrequently to capture changes in time-varying confounders or not all confounders being measured and available for inclusion in analyses. [39][40][41] These biases may limit the capacity for causal inferences.…”

Tell me what you want, what you really really want: Estimands in observational pharmacoepidemiologic comparative effectiveness and safety studies

“…Another limitation is that we did not explicate how an estimand can inform the choice of study design and analytical approach. We refer to existing papers on this topic, 29,33,41,[45][46][47] particularly to explanations about aligning estimands to estimators in randomized trials. 38,48 An increasingly popular methodological approach is the target trial emulation framework.…”

Handling time‐varying treatments in observational studies: A scoping review and recommendations

“…4,5 Time-varying treatment is an important while challenging methodological issue in observational studies. 3,[6][7][8] One of the main challenges is the adjustment for time-varying confounders, which can be influenced by prior treatment and affect both subsequent treatment and outcome. These confounders also act as mediators, necessitating the use of specific statistical models for their consideration.…”

“…Time‐varying treatment is an important while challenging methodological issue in observational studies 3,6–8 . One of the main challenges is the adjustment for time‐varying confounders, which can be influenced by prior treatment and affect both subsequent treatment and outcome.…”