Impact of covariate omission and categorization from the Fine–Gray model in randomized-controlled trials

• Published in: Japanese journal of statistics and data science Vol. 4; no. 2; pp. 983 - 997
• Main Authors: Bakoyannis, Giorgos, Chu, Fang-I., Babiker, Abdel G. A., Touloumi, Giota
• Format: Journal Article
• Language: English
• Published: Singapore Springer Singapore 01-12-2021
• Subjects: Chemistry and Earth Sciences; Computer Science; Economics; Finance; Health Sciences; Humanities; Insurance; Law; Management; Mathematics and Statistics; Medicine; Original Paper; Physics; Statistical Theory and Methods; Statistics; Statistics and Computing/Statistics Programs; Statistics for Business; Statistics for Engineering; Statistics for Life Sciences; Statistics for Social Sciences; Clinical trial; Competing risks; Fine–Gray model; Covariate omission
• ISSN: 2520-8756; 2520-8764
• DOI: 10.1007/s42081-021-00111-5

In this paper, we study the statistical issues related to the omission and categorization of important covariates in the context of the Fine–Gray model in randomized-controlled trials with competing risks. We show that the omission of an important covariate from the Fine–Gray model leads to attenuated estimates for treatment effect and loss of proportionality in general. Our simulation studies reveal substantial attenuation in the estimate for treatment effect and the loss of statistical power, while dichotomizing a continuous covariate leads to similar but less pronounced impact. Our results are illustrated using data from a randomized clinical trial of HIV-infected individuals. The relative merits of conducting an adjusted versus an unadjusted analysis of treatment effect in light of both statistical and practical considerations are discussed.