# Statistical Methods for Ordinal Variables in HIV/AIDS Studies

> **NIH NIH R01** · VANDERBILT UNIVERSITY MEDICAL CENTER · 2020 · $453,264

## Abstract

ABSTRACT
Many important variables in biomedical studies of HIV/AIDS are orderable, and some statistical methods for
ordered categorical data can be applied to continuous data, providing robust analysis approaches that make
fewer assumptions than standard approaches. In the first cycle of our grant, we developed a new residual for
orderable outcomes, we showed that Spearman's partial correlation can be computed using this new
residual, and we demonstrated the use of cumulative probability models (CPMs; also known as cumulative
link models) with continuous response variables. In this renewal application, we focus on novel and exciting
extensions of these methods that could have a large impact on the analysis of HIV/AIDS and other
biomedical data. First, the analysis of continuous responses with models typically reserved for ordered
categorical data is innovative and permits very flexible modeling – particularly of data that require some sort
of transformation and/or have detection limits (e.g., HIV viral load). We propose to investigate the asymptotic
properties of these techniques, extend them to repeated measures data using generalized estimating
equations approaches, and develop them for settings with multiple detection limits. Second, our extension of
Spearman's rank correlation to remove the effect of, or to condition on, covariates fills an important gap in the
statistical literature and will be commonly employed in practice given the ubiquity of Spearman's correlation in
biomedical studies. We propose to extend our approach to estimate the rank correlation, both covariate-
adjusted and unadjusted, between bivariate survival data and to longitudinal or clustered data. Finally, in this
era of big data, there is a need to be able to perform these techniques in a computationally efficient manner.
We propose to study divide-and-combine and other techniques for fitting CPMs and covariate-adjusted
Spearman's correlations in large datasets. We will package our methods in freely available software and
apply our analyses to important studies of HIV/AIDS.

## Key facts

- **NIH application ID:** 9838140
- **Project number:** 5R01AI093234-08
- **Recipient organization:** VANDERBILT UNIVERSITY MEDICAL CENTER
- **Principal Investigator:** Chun Li
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $453,264
- **Award type:** 5
- **Project period:** 2011-05-18 → 2022-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9838140

## Citation

> US National Institutes of Health, RePORTER application 9838140, Statistical Methods for Ordinal Variables in HIV/AIDS Studies (5R01AI093234-08). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9838140. Licensed CC0.

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