Case Study: Longitudinal HIV Cohort Data

Abstract

In this chapter, we introduce a case study based on the treatment of HIV infection. A series of scientific questions concerning how best to detect and manage antiretroviral treatment failure in resource-limited settings are used to illustrate the general road map for targeted learning. We emphasize the translation of background knowledge into a formal causal and statistical model and the translation of scientific questions into target causal parameters and corresponding statistical parameters of the distribution of the observed data. Readers may be interested in first reading the longitudinal sections of Appendix A for a rigorous treatment of longitudinal TMLE and related topics.