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The exploratory nature of phase II trials makes it quite common to include heterogeneous patient subgroups with different prognoses in the same trial. Incorporating such patient heterogeneity or stratification into statistical calculation for sample size can improve efficiency and reduce sample sizes in single-arm phase II trials with binary outcomes. However, such consideration is lacking in randomized phase II trials. In this paper, we propose methods that can utilize some natural order constraints that may exist in stratified population to gain statistical efficiency for randomized phase II designs. For thoroughness and simplicity, we focus on the randomized phase II selection designs in this paper, although our method can be easily generalized to the randomized phase II screening designs. We consider both binary and time-to-event outcomes in our development. Compared with methods that do not use order constraints, our method is shown to improve the probabilities of correct selection or reduce sample size in our simulation and real examples. [ABSTRACT FROM AUTHOR]

Summary: We develop a joint analysis approach for recurrent and nonrecurrent event processes subject to case I interval censorship, which are also known in literature as current count and current status data, respectively. We use a shared frailty to link the recurrent and nonrecurrent event processes, while leaving the distribution of the frailty fully unspecified. Conditional on the frailty, the recurrent event is assumed to follow a nonhomogeneous Poisson process, and the mean function of the recurrent event and the survival function of the nonrecurrent event are assumed to follow some general form of semiparametric transformation models. Estimation of the models is based on the pseudo‐likelihood and the conditional score techniques. The resulting estimators for the regression parameters and the unspecified baseline functions are shown to be consistent with rates of square and cubic roots of the sample size, respectively. Asymptotic normality with closed‐form asymptotic variance is derived for the estimator of the regression parameters. We apply the proposed method to a fracture‐osteoporosis survey data to identify risk factors jointly for fracture and osteoporosis in elders, while accounting for association between the two events within a subject. [ABSTRACT FROM AUTHOR]

Summary: We propose a model selection criterion for semiparametric marginal mean regression based on generalized estimating equations. The work is motivated by a longitudinal study on the physical frailty outcome in the elderly, where the cluster size, that is, the number of the observed outcomes in each subject, is “informative” in the sense that it is related to the frailty outcome itself. The new proposal, called Resampling Cluster Information Criterion (RCIC), is based on the resampling idea utilized in the within‐cluster resampling method (Hoffman, Sen, and Weinberg, 2001, Biometrika 88, 1121–1134) and accommodates informative cluster size. The implementation of RCIC, however, is free of performing actual resampling of the data and hence is computationally convenient. Compared with the existing model selection methods for marginal mean regression, the RCIC method incorporates an additional component accounting for variability of the model over within‐cluster subsampling, and leads to remarkable improvements in selecting the correct model, regardless of whether the cluster size is informative or not. Applying the RCIC method to the longitudinal frailty study, we identify being female, old age, low income and life satisfaction, and chronic health conditions as significant risk factors for physical frailty in the elderly. [ABSTRACT FROM AUTHOR]

This work, motivated by an osteoporosis survey study, considers regression analysis with incompletely observed current status data. Here the current status data, including an examination time and an indicator for whether or not the event of interest has occurred by the examination time, is not observed for all subjects. Instead, a surrogate outcome subject to misclassification of the current status is available for all subjects. We focus on semiparametric regression under transformation models, including the proportional hazards and proportional odds models as special cases. Under the missing at random mechanism where the missingness of the current status outcome can depend only on the observed surrogate outcome and covariates, we propose an approach of validation likelihood based on the likelihood from the validation subsample where the data are fully observed, with adjustments of the probability of observing the current status outcome, as well as the distribution of the surrogate outcome in the validation subsample. We propose an efficient computation algorithm for implementation, and derive consistency and asymptotic normality for inference with the proposed estimator. The application to the osteoporosis survey data and simulations reveal that the validation likelihood performs well; it removes the bias from the 'complete case' analysis discarding subjects with missing data, and achieves higher efficiency than the inverse probability weighting analysis. [ABSTRACT FROM AUTHOR]

The generalized estimating equation (GEE) has been a popular tool for marginal regression analysis with longitudinal data, and its extension, the weighted GEE approach, can further accommodate data that are missing at random (MAR). Model selection methodologies for GEE, however, have not been systematically developed to allow for missing data. We propose the missing longitudinal information criterion (MLIC) for selection of the mean model, and the MLIC for correlation (MLICC) for selection of the correlation structure in GEE when the outcome data are subject to dropout/monotone missingness and are MAR. Our simulation results reveal that the MLIC and MLICC are effective for variable selection in the mean model and selecting the correlation structure, respectively. We also demonstrate the remarkable drawbacks of naively treating incomplete data as if they were complete and applying the existing GEE model selection method. The utility of proposed method is further illustrated by two real applications involving missing longitudinal outcome data. [ABSTRACT FROM AUTHOR]

Nested case-control sampling is a popular design for large epidemiological cohort studies due to its cost effectiveness. A number of methods have been developed for the estimation of the proportional hazards model with nested case-control data; however, the evaluation of modeling assumption is less attended. In this article, we propose a class of goodness-of-fit test statistics for testing the proportional hazards assumption based on nested case-control data. The test statistics are constructed based on asymptotically mean-zero processes derived from Samuelsen's maximum pseudo-likelihood estimation method. In addition, we develop an innovative resampling scheme to approximate the asymptotic distribution of the test statistics while accounting for the dependent sampling scheme of nested case-control design. Numerical studies are conducted to evaluate the performance of our proposed approach, and an application to the Wilms' Tumor Study is given to illustrate the methodology. [ABSTRACT FROM AUTHOR]