More robust and accurate health knowledge is a cornerstone of better health policy and action. There are tough questions in HIV that can be addressed better with new quantitative tools. Results from experimental and observational HIV studies can be made better and more policy-relevant through development and use of new methods at the interface of statistics, epidemiology, causal inference, and artificial intelligence. An innovative combination of semiparametric statistical theory, causal models, and ensemble machine learning provides a unique opportunity for better results from HIV studies. In this work, we propose new estimators of the risk (or survival) function. These new estimators improve accuracy, accommodate competing events, allow effects to be generalized to specific populations of interest, incorporate machine learning of nuisance functions to relax assumptions about model form, and allow sensitivity analyses to quantify the impact of uncontrolled biases.
The specific aims are vehicles to develop, test, and disseminate these new estimators.
These aims are to 1) estimate the long-term treated history of all-cause and cause-specific mortality in this large US cohort of women with HIV; 2) estimate the observational analog of the per-protocol parameter using a treatment decision design to compare composite endpoints under an integrase-inhibitor-based treatment compared to an efavirenz-based treatment in the North American AIDS Cohort Collaboration on Research and Design; 3) estimate the per-protocol parameter for TDF-FTC versus ABC-3TC arms; and 4) estimate the per- protocol parameter for 17 alpha-hydroxyprogesterone caproate versus masked placebo on risk of preterm birth in Zambian HIV+ pregnant women. The assembled team features field-leading expertise in epidemiology, statistics, and HIV medicine. Scientific products will include publications and workshop presentations describing new methodological approaches and new substantive findings that emerge after applying the proposed methods to each of the problems identified in the specific aims. We will also produce publicly available R packages and SAS macros to implement the proposed estimators.
More robust and accurate health knowledge is a cornerstone of better health policy and action. Results from experimental and observational HIV studies can be made better and more policy-relevant through development and use of new methods at the interface of statistics, epidemiology, causal inference, and artificial intelligence. In this work, we propose new and improved estimators of the risk (or survival) function.
