1,721,097 research outputs found
Pseudo-likelihood methods and generalized estimating equations: efficient estimation techniques for the analysis of correlated multivariate data
No full textnot availabl
Pseudo-likelihood methods and generalized estimating equations: efficient estimation techniques for the analysis of correlated multivariate data
No full textnot availabl
Mixed Outcomes
No full textMeasurements of both continuous and discrete outcomes are encountered in many statistical problems. A common example is a developmental toxicity study, which typically involves exposing timepregnant animals (rats, mice) to an environmental agent during major organogenesis. The dams are sacrificed prior to normal delivery, at which time the uterus is removed and thoroughly examined. Among viable fetuses, the incidence of any malformation (binary data) and reductions in fetal weight (continuous data) are typically of primary concern, since both have been found to be sensitive indicators of a toxic effect. This motivates the formulation of a joint distribution with mixed continuous and discrete outcomes
Marginalized models for right-truncated and interval-censored time-to-event data
No full textAnalysis of clustered data is often performed using random effects regression models. In such conditional models, a cluster-specific random effect is often introduced into the linear predictor function. Parameter interpretation of the covariate effects is then conditioned on the random effects, leading to a subject-specific interpretation of the regression parameters. Recently, Marginalized Multilevel Models (MMM) and the Bridge distribution models have been proposed as a unified approach, which allows one to capture the within-cluster correlations by specifying random effects while still allowing for marginal parameter interpretation. In this paper, we investigate these two approaches, and the conditional Generalized Linear Mixed Model (GLMM), in the context of right-truncated, interval-censored time-to-event data, further characterized by clustering and additional overdispersion. While these models have been applied in literature to model the mean, here we extend their application to modeling the hazard function for the survival endpoints. The models are applied to analyze data from the HET-CAM(VT) experiment which was designed to assess the potential of a compound to cause injection site reaction. Results show that the MMM and Bridge distribution approaches are useful when interest is in the marginal interpretation of the covariate effects
Challenges in the methodology for the validation of surrogate endpoints in randomized trials
No full textThe validation of surrogate endpoints has been studied by Prentice (1989). He presented a definition as well as a set of criteria that are equivalent if the surrogate and true endpoints are binary. Freedman (1992) supplemented these
criteria with the so-called proportion explained. Buyse and Molenberghs (1998) proposed to replace the proportion explained by two quantities: (1) the relative effect linking the effect of treatment on both endpoints and (2) the adjusted association, an individual-level measure of agreement between both endpoints. In this paper, we argue that a meta-analytic approach should be adopted because
it overcomes difficulties which necessarily surround validation efforts based on a single trial
Challenges in the methodology for the validation of surrogate endpoints in randomized trials
No full textThe validation of surrogate endpoints has been studied by Prentice (1989). He presented a definition as well as a set of criteria that are equivalent if the surrogate and true endpoints are binary. Freedman (1992) supplemented these
criteria with the so-called proportion explained. Buyse and Molenberghs (1998) proposed to replace the proportion explained by two quantities: (1) the relative effect linking the effect of treatment on both endpoints and (2) the adjusted association, an individual-level measure of agreement between both endpoints. In this paper, we argue that a meta-analytic approach should be adopted because
it overcomes difficulties which necessarily surround validation efforts based on a single trial
- …
