There have been many advances in statistical methodology for the analysis of recurrent event data in recent years. Multiplicative semiparametric rate-based models are widely used in clinical trials, as are more general partially conditional rate-based models involving event-based stratification. The partially conditional model provides protection against extra-Poisson variation as well as event-dependent censoring, but conditioning on outcomes post-randomization can induce confounding and compromise causal inference. The purpose of this article is to examine the consequences of model misspecification in semiparametric marginal and partially conditional rate-based analysis through omission of prognostic variables. We do so using estimating function theory and empirical studies.
Our case study focused on the adoptive identity development of two female Chinese adoptees over the course of five years (from when they were 7 and 9 until they were 12 and 14 years old, respectively). The study investigated the adoptive parent’s and family identities through six interviews with the adoptive mother, adoptees’ behavioral adjustment reported by the mother, two unstructured observations, and exploration of adoptees’ narratives. The study was guided by a narrative-based framework situated with the cultural socialization approach. Results highlight four central themes: 1) becoming Chinese-Americans; 2) meaning related to adoption is both spoken and unspoken; 3) a we-ness identity, and 4) social-cultural contexts of identity work. Findings demonstrate the incorporation of adoption and the adoptees’ race and culture into the adoptive parent’s and family identities. Findings further illuminate that one’s identity is developed within personal, familial, and social-cultural contexts. 相似文献