# miR 92/19 cluster in the ERK context

> **NIH NIH P01** · YALE UNIVERSITY · 2022 · $520,083

## Abstract

The Tsepamo birth outcomes surveillance study has accrued many thousands of women living with HIV from
Botswana. The Tsepamo Plus study (Project 1) will continue to collect ARV exposures in pregnancy which are
rarely studied with high precision. With such large samples, Tsepamo can detect differences in rare pregnancy
outcomes such as neural tube defects, and can provide precise estimates of effects for common outcomes such
as prematurity. Because Tsepamo can provide high precision when there is much uncertainty about safety in
pregnancy and Botswana typically rolls out new ARVs before other African countries, Tsepamo may be the first
to report on novel safety signals. This in turn makes Tsepamo highly influential in the understanding of adverse
ARV effects in pregnancy. A natural question is if first and subsequent analyses should apply group sequential
statistical methodology to plan for “when to look” and to enhance the understanding of uncertainty. Further,
guidance is sorely needed for understanding uncertainty and interpreting safety signals for unplanned analyses.
Moreover, surveillance data alone describes circumstances as they are and are not directly set up to inform
personal, clinical, or societal decision-making. For example, while a surveillance database may help detect an
increase in neural tube defects, the data alone do not inform the optimal decision about what’s to be done to
reduce neural tube defects in future pregnancies. Ideally, such decisions would be informed by randomized trials,
but trials are often too costly, unethical, or not timely enough. Instead, we can only empirically inform decisions
by emulating target trials using observational data. The principle behind target trial emulation is simple: lacking
a randomized trial for a given research question, we describe in detail the protocol of the randomized trial we
would like to conduct, and then combine subject matter expertise and appropriate statistical analyses to emulate
that hypothetical trial using observational data. By embracing this framework, many of the common pitfalls of
current observational research practices can be avoided; however, target trial emulation requires
multidisciplinary collaboration and often novel analytic approaches.
The objectives of this proposal are to evaluate methodologies and provide guidance on methods for making best
use of Tsepamo data and similar studies. Specifically, to evaluate the use of group sequential methodology for
surveillance systems in studies of pregnancy and to create guidance on statistical adjustments for unplanned
analyses we have created the following aims:
Aim 1: To create guidance on when to publicly report on safety signals based on unplanned analyses.
We will compare the statistical properties of group sequential methods from Aim 1 to fixed sample methods
which were used in the NTD example, and provide recommendations on the timing of public release of unplanned
analyses. Considerations will include the pro...

## Key facts

- **NIH application ID:** 10433819
- **Project number:** 5P01HL107205-10
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Yajaira Suarez
- **Activity code:** P01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $520,083
- **Award type:** 5
- **Project period:** 2012-02-10 → 2024-10-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10433819

## Citation

> US National Institutes of Health, RePORTER application 10433819, miR 92/19 cluster in the ERK context (5P01HL107205-10). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10433819. Licensed CC0.

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