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.

Public Health Relevance

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.

Agency
National Institute of Health (NIH)
Institute
National Institute of Allergy and Infectious Diseases (NIAID)
Type
Research Project (R01)
Project #
5R01AI104459-02
Application #
8666718
Study Section
AIDS Clinical Studies and Epidemiology Study Section (ACE)
Program Officer
Mckaig, Rosemary G
Project Start
2013-06-01
Project End
2018-05-31
Budget Start
2014-06-01
Budget End
2015-05-31
Support Year
2
Fiscal Year
2014
Total Cost
$403,750
Indirect Cost
$153,750
Name
Harvard University
Department
Public Health & Prev Medicine
Type
Schools of Public Health
DUNS #
149617367
City
Boston
State
MA
Country
United States
Zip Code
02115
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
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
Lin, Sheng-Hsuan; Young, Jessica; Logan, Roger et al. (2017) Parametric Mediational g-Formula Approach to Mediation Analysis with Time-varying Exposures, Mediators, and Confounders. Epidemiology 28:266-274
Tchetgen Tchetgen, Eric J; Phiri, Kelesitse (2017) Evaluation of Medication-mediated Effects in Pharmacoepidemiology. Epidemiology 28:439-445
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
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
Ferguson, Kelly K; Chen, Yin-Hsiu; VanderWeele, Tyler J et al. (2017) Mediation of the Relationship between Maternal Phthalate Exposure and Preterm Birth by Oxidative Stress with Repeated Measurements across Pregnancy. Environ Health Perspect 125:488-494
Martinussen, Torben; Vansteelandt, Stijn; Tchetgen Tchetgen, Eric J et al. (2017) Instrumental variables estimation of exposure effects on a time-to-event endpoint using structural cumulative survival models. Biometrics 73:1140-1149
Sofer, Tamar; Cornelis, Marilyn C; Kraft, Peter et al. (2017) CONTROL FUNCTION ASSISTED IPW ESTIMATION WITH A SECONDARY OUTCOME IN CASE-CONTROL STUDIES. Stat Sin 27:785-804
Marden, Jessica R; Mayeda, Elizabeth R; Tchetgen Tchetgen, Eric J et al. (2017) High Hemoglobin A1c and Diabetes Predict Memory Decline in the Health and Retirement Study. Alzheimer Dis Assoc Disord 31:48-54

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