Statistical modeling of days alive out-of-hospital: An illustration using the SSU trial

Authors

• Xin Wu
• David A Berger
• Jason J Bischof
• Douglas Char
• Deborah Diercks
• Erik P Hess
• Alan E Jones
• Kathleen A Lane
• Phillip Levy
• Xiaochun Li
• Joseph B. Miller, Henry Ford HealthFollow
• Simon A Mahler
• Susan J Pressler
• Arvind Venkat
• Peter S Pang
• Sean Collins
• Bryan S Blette

Recommended Citation

Wu X, Berger DA, Bischof JJ, Char D, Diercks D, Hess EP, Jones AE, Lane KA, Levy P, Li X, Miller JB, Mahler SA, Pressler SJ, Venkat A, Pang PS, Collins S, Blette BS. Statistical Modeling of Days Alive out-of-Hospital: An Illustration Using the Ssu Trial. J Clin Transl Sci. 2026;10(1):e129.

Document Type

Article

Publication Date

7-7-2026

Publication Title

J Clin Transl Sci

Abstract

BACKGROUND: Days alive out-of-hospital (DAOOH) is a well-accepted outcome in clinical research, as it summarizes both hospitalization and mortality. However, its distribution is often skewed, making accurate modeling challenging in practice. We utilized data from a randomized trial to discuss estimands and statistical methods of interest for DAOOH. METHODS: We examined several estimands and statistical models targeting these estimands, including linear regression, gamma regression, ordinal regression, and the probabilistic index model. Comparison was based on interpretability, precision, and model fit. We adopted G-computation to assess causal effects leveraging different regression models to target a simple unified estimand. RESULTS: For unadjusted analyses, the Mann-Whitney U-test is a simple and assumption-light approach, which can be paired with a probabilistic index to enhance interpretation. For adjusted analyses, while the mean difference and mean ratio are intuitive, the methods targeting them (linear and gamma regression) showed sub-optimal model fit. Conversely, ordinal regression showed a superior fit, but its odds ratio estimand is difficult to interpret. Pairing gamma or ordinal regression with g-computation facilitates estimation of a marginal causal mean difference estimand under certain assumptions, allowing for both a good model fit and interpretability. CONCLUSIONS: Our findings illustrate trade-offs of estimands and model choices for DAOOH, providing guidance for method selection. Based on data from this randomized trial, the Mann-Whitney U-test is recommended for unadjusted analyses. Ordinal regression paired with g-computation provides the best balance of precision, model fit, and interpretability to report an adjusted effect.

PubMed ID

42534386

Volume

10

Issue

1

First Page

129

Last Page

129