Latent class joint model of ovarian function suppression and DFS for premenopausal breast cancer patients

A framework for prospectively defining progression rules for internal pilot studies monitoring recruitment

Just over half of publicly funded trials recruit their target sample size within the planned study duration. When recruitment targets are missed, the funder of a trial is faced with the decision of either committing further resources to the study or risk that a worthwhile treatment effect may be missed by an underpowered final analysis. To avoid this challenging situation, when there is insufficient prior evidence to support predicted recruitment rates, funders now require feasibility assessments to be performed in the early stages of trials. Progression criteria are usually specified and agreed with the funder ahead of time. To date, however, the progression rules used are typically ad hoc. In addition, rules routinely permit adaptations to recruitment strategies but do not stipulate criteria for evaluating their effectiveness. In this paper, we develop a framework for planning and designing internal pilot studies which permit a trial to be stopped early if recruitment is disappointing or to continue to full recruitment if enrolment during the feasibility phase is adequate. This framework enables a progression rule to be pre-specified and agreed upon prior to starting a trial. The novel two-stage designs stipulate that if neither of these situations arises, adaptations to recruitment should be made and subsequently evaluated to establish whether they have been successful. We derive optimal progression rules for internal pilot studies which minimise the expected trial overrun and maintain a high probability of completing the study when the recruitment rate is adequate. The advantages of this procedure are illustrated using a real trial example.

Bayesian modeling and prediction of accrual in multi-regional clinical trials

In multi-regional trials, the underlying overall and region-specific accrual rates often do not hold constant over time and different regions could have different start-up times, which combined with initial jump in accrual within each region often leads to a discontinuous overall accrual rate, and these issues associated with multi-regional trials have not been adequately investigated. In this paper, we clarify the implication of the multi-regional nature on modeling and prediction of accrual in clinical trials and investigate a Bayesian approach for accrual modeling and prediction, which models region-specific accrual using a nonhomogeneous Poisson process and allows the underlying Poisson rate in each region to vary over time. The proposed approach can accommodate staggered start-up times and different initial accrual rates across regions/centers. Our numerical studies show that the proposed method improves accuracy and precision of accrual prediction compared to existing methods including the nonhomogeneous Poisson process model that does not model region-specific accrual.

A Bayesian growth mixture model to examine maternal hypertension and birth outcomes

Maternal hypertension is a major contributor to adverse pregnancy outcomes, including preterm birth (PTB) and low birth weight (LBW). Although several studies have explored the relationship between maternal hypertension and fetal health, few have examined how the longitudinal trajectory of blood pressure, considered over the course of pregnancy, affects birth outcomes. In this paper, we propose a Bayesian growth mixture model to jointly examine the associations between longitudinal blood pressure measurements, PTB, and LBW. The model partitions women into distinct classes characterized by a mean arterial pressure (MAP) curve and joint probabilities of PTB and LBW. Each class contains a unique mixed effects model for MAP with class-specific regression coefficients and random effect covariances. To account for the strong correlation between PTB and LBW, we introduce a bivariate probit model within each class to capture residual within-class dependence between PTB and LBW. The model permits the association between PTB and LBW to vary by class, so that for some classes, PTB and LBW may be positively correlated, whereas for others, they may be uncorrelated or negatively correlated. We also allow maternal covariates to influence the class probabilities via a multinomial logit model. For posterior computation, we propose an efficient MCMC algorithm that combines full-conditional Gibbs and Metropolis steps. We apply our model to a sample of 1027 women enrolled in the Healthy Pregnancy, Healthy Baby Study, a prospective cohort study of host, social, and environmental contributors to disparities in pregnancy outcomes.