Modeling of cumulative effects of time-varying drug exposures on within-subject changes in a continuous outcome

Abstract

An accurate assessment of the safety or effectiveness of drugs in pharmaco-epidemiological studies requires defining an etiologically correct time-varying exposure model, which specifies how previous drug use affects the outcome of interest. To address this issue, we develop, and validate in simulations, a new approach for flexible modeling of the cumulative effects of time-varying exposures on repeated measures of a continuous response variable, such as a quantitative surrogate outcome, or a biomarker. Specifically, we extend the linear mixed effects modeling to estimate how past and recent drug exposure affects the way individual values of the outcome change throughout the follow-up. To account for the dosage, duration and timing of past exposures, we rely on a flexible weighted cumulative exposure methodology to model the cumulative effects of past drug use, as the weighted sum of past doses. Weights, modeled with unpenalized cubic regression B-splines, reflect the relative importance of doses taken at different times in the past. In simulations, we evaluate the performance of the model under different assumptions concerning (i) the shape of the weight function, (ii) the sample size, (iii) the number of the longitudinal observations and (iv) the intra-individual variance. Results demonstrate the accuracy of our estimates of the weight function and of the between- and within-patients variances, and good correlation between the observed and predicted longitudinal changes in the outcome. We then apply the proposed method to re-assess the association between time-varying glucocorticoid exposure and weight gain in people living with rheumatoid arthritis.

Acute cardiovascular health effects in a panel study of personal exposure to traffic-related air pollutants and noise in Toronto, Canada

“…SDNN and RMSSD) in response to 30-min exposures. This method models the cumulative effects of past exposures as their weighted sum 67 . Weights, modeled with unpenalized cubic regression B-splines, reflect the relative importance of exposures occurring at different times in the past.…”

Time-Varying Associations Between an Exposure History and a Subsequent Health Outcome: A landmark Approach to Identify Critical Windows.

“…In addition, they had only considered time-to-event outcomes while we wanted to also consider repeated outcomes that are frequently encountered in longitudinal studies. Danieli et al [19] have just proposed a WCIE methodology for repeated outcome data.…”

“…However, most often, it has to be estimated directly from the data. As others previously [19,24,25], we chose to estimate the weight function by regression using a basis of splines [13], which are flexible enough to capture a variety of clinically plausible shapes [26].…”

“…Following Sylvestre et al [25] and Danieli et al [19], we directly estimated the timevarying coefficients without constraining the weights by setting the coefficients γ I and γ S to 1. Therefore, the time-varying effects directly correspond to the weights and Equation ( 4) becomes:…”

“…Finally, all these methodologies have been developed for survival or binary outcomes. To date, we are aware of only one pharmacoepidemiological study applying the WCIE in association with a longitudinal outcome [19]. Nonetheless, it considered a complete error-free exposure history measured concomitantly with the outcome, and it only assessed its association with the level of the longitudinal outcome while in etiological studies, the interest lies in the change over time of the longitudinal outcome.…”