# Improved analysis of experiments and observational studies in HIV

> **NIH NIH R01** · UNIV OF NORTH CAROLINA CHAPEL HILL · 2022 · $721,030

## Abstract

ABSTRACT
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.

## Key facts

- **NIH application ID:** 10460562
- **Project number:** 5R01AI157758-03
- **Recipient organization:** UNIV OF NORTH CAROLINA CHAPEL HILL
- **Principal Investigator:** Stephen R Cole
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $721,030
- **Award type:** 5
- **Project period:** 2020-09-22 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10460562

## Citation

> US National Institutes of Health, RePORTER application 10460562, Improved analysis of experiments and observational studies in HIV (5R01AI157758-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10460562. Licensed CC0.

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