Analyzing clustered continuous response variables with ordinal regression models.
Biometrics
; 79(4): 3764-3777, 2023 12.
Article
en En
| MEDLINE
| ID: mdl-37459181
ABSTRACT
Continuous response data are regularly transformed to meet regression modeling assumptions. However, approaches taken to identify the appropriate transformation can be ad hoc and can increase model uncertainty. Further, the resulting transformations often vary across studies leading to difficulties with synthesizing and interpreting results. When a continuous response variable is measured repeatedly within individuals or when continuous responses arise from clusters, analyses have the additional challenge caused by within-individual or within-cluster correlations. We extend a widely used ordinal regression model, the cumulative probability model (CPM), to fit clustered, continuous response data using generalized estimating equations for ordinal responses. With the proposed approach, estimates of marginal model parameters, cumulative distribution functions , expectations, and quantiles conditional on covariates can be obtained without pretransformation of the response data. While computational challenges arise with large numbers of distinct values of the continuous response variable, we propose feasible and computationally efficient approaches to fit CPMs under commonly used working correlation structures. We study finite sample operating characteristics of the estimators via simulation and illustrate their implementation with two data examples. One studies predictors of CD4CD8 ratios in a cohort living with HIV, and the other investigates the association of a single nucleotide polymorphism and lung function decline in a cohort with early chronic obstructive pulmonary disease.
Palabras clave
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Modelos Estadísticos
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Biometrics
Año:
2023
Tipo del documento:
Article
País de afiliación:
Estados Unidos