Good adherence to anti-retroviral therapy (ART) is widely accepted as a key factor for sustained viral suppression of human immunodeficiency virus (HIV), and is often considered a prerequisite for maintenance on a prescribed drug regimen and optimal patient outcomes. However, the extent to which adherence to a given choice of therapy (relative to another therapy), contributes to virologic failure is complex and stll poorly understood, and is a pressing mediation question in HIV research. Understanding this issue is particularly important in resource poor settings where ART regimen options are limited and adherence to lifelong multi- drug daily dosing is challenging but necessary. In such settings, quantifying to what degree differential rates of virologic failure are due to difference in adherence rates between therapies would inform the extent to which failure rates could be improved by programs that increase adherence rates for certain ARTs, rather than improving ART regimens themselves. Such adherence interventions have been very successful in the treatment of tuberculosis and are considered important in the treatment of HIV. Unfortunately, traditional approaches to mediation analysis assume static settings with no unobserved confounding, and are thus not suited for analysis of complex longitudinal data typically encountered in observational studies of HIV. Our plan is to remedy this deficiency using recent developments in causal inference based on potential outcomes and graphical models, and modern semi-parametric theory.
Patient outcome of a treatment regimen is often governed not only by the quality of the treatment itself, but also by patient adherence to the regimen. We plan to develop and apply modern techniques of mediation analysis to determine how much of the regimen's success is due to treatment quality, and how much to patient adherence to the regimen. We plan to apply our analysis to improving outcomes of HIV patients in Nigeria.
| Marden, Jessica R; Wang, Linbo; Tchetgen, Eric J Tchetgen et al. (2018) Implementation of Instrumental Variable Bounds for Data Missing Not at Random. Epidemiology 29:364-368 |
| Wang, Linbo; Tchetgen Tchetgen, Eric (2018) Bounded, efficient and multiply robust estimation of average treatment effects using instrumental variables. J R Stat Soc Series B Stat Methodol 80:531-550 |
| Nabi, Razieh; Shpitser, Ilya (2018) Fair Inference on Outcomes. Proc Conf AAAI Artif Intell 2018:1931-1940 |
| Fulcher, Isabel R; Tchetgen Tchetgen, Eric J; Williams, Paige L (2017) Mediation Analysis for Censored Survival Data Under an Accelerated Failure Time Model. Epidemiology 28:660-666 |
| VanderWeele, Tyler J; Tchetgen Tchetgen, Eric J (2017) Mediation analysis with time varying exposures and mediators. J R Stat Soc Series B Stat Methodol 79:917-938 |
| Tchetgen Tchetgen, Eric J; Phiri, Kelesitse (2017) Evaluation of Medication-mediated Effects in Pharmacoepidemiology. Epidemiology 28:439-445 |
| Marden, Jessica R; Tchetgen Tchetgen, Eric J; Kawachi, Ichiro et al. (2017) Contribution of Socioeconomic Status at 3 Life-Course Periods to Late-Life Memory Function and Decline: Early and Late Predictors of Dementia Risk. Am J Epidemiol 186:805-814 |
| Miao, Wang; Tchetgen Tchetgen, Eric (2017) Invited Commentary: Bias Attenuation and Identification of Causal Effects With Multiple Negative Controls. Am J Epidemiol 185:950-953 |
| Gilsanz, Paola; Kubzansky, Laura D; Tchetgen Tchetgen, Eric J et al. (2017) Changes in Depressive Symptoms and Subsequent Risk of Stroke in the Cardiovascular Health Study. Stroke 48:43-48 |
| Tchetgen Tchetgen, Eric J; Wirth, Kathleen E (2017) A general instrumental variable framework for regression analysis with outcome missing not at random. Biometrics 73:1123-1131 |
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