Pseudo-value regression of clustered multistate current status data with informative cluster sizes.
Stat Methods Med Res
; 32(8): 1494-1510, 2023 08.
Article
em En
| MEDLINE
| ID: mdl-37323013
ABSTRACT
Multistate current status data presents a more severe form of censoring due to the single observation of study participants transitioning through a sequence of well-defined disease states at random inspection times. Moreover, these data may be clustered within specified groups, and informativeness of the cluster sizes may arise due to the existing latent relationship between the transition outcomes and the cluster sizes. Failure to adjust for this informativeness may lead to a biased inference. Motivated by a clinical study of periodontal disease, we propose an extension of the pseudo-value approach to estimate covariate effects on the state occupation probabilities for these clustered multistate current status data with informative cluster or intra-cluster group sizes. In our approach, the proposed pseudo-value technique initially computes marginal estimators of the state occupation probabilities utilizing nonparametric regression. Next, the estimating equations based on the corresponding pseudo-values are reweighted by functions of the cluster sizes to adjust for informativeness. We perform a variety of simulation studies to study the properties of our pseudo-value regression based on the nonparametric marginal estimators under different scenarios of informativeness. For illustration, the method is applied to the motivating periodontal disease dataset, which encapsulates the complex data-generation mechanism.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Doenças Periodontais
/
Modelos Estatísticos
Tipo de estudo:
Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
Stat Methods Med Res
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
Estados Unidos