Transporting attachment and biobehavioral catch-up to Norwegian child welfare services: A feasibility study

Transportation of evidence-based programs (EBPs) to a new cultural setting is often preferred over the comprehensive process of developing a new program. Intervention fidelity has been suggested as a predictor of successful transportation. The present study examined whether fidelity and parent behavior improved when implementing the U.S.-developed Attachment and Biobehavioral Catch-up (ABC) intervention in Norwegian child welfare services (CWS). 11 child welfare workers received training and supervision to become ABC parent coaches. Fidelity was assessed through video-recordings of parent coaches' in vivo feedback at each home-visit session. Parent sensitive behavior was assessed using video-recordings of parent–child interactions, recorded before each ABC session. Mixed effects modeling showed that ABC fidelity increased over the course of training and supervision. Furthermore, parent behavior improved over the course of families' exposure to the intervention. These demonstrate that an EBP transported to a novel cultural setting can obtain promising levels of fidelity and intervention outcomes.     

Vaccine clinical trials with dynamic borrowing of historical controls: Two retrospective studies

Traditional vaccine efficacy trials usually use fixed designs with fairly large sample sizes. Recruiting a large number of subjects requires longer time and higher costs. Furthermore, vaccine developers are more than ever facing the need to accelerate vaccine development to fulfill the public's medical needs. A possible approach to accelerate development is to use the method of dynamic borrowing of historical controls in clinical trials. In this paper, we evaluate the feasibility and the performance of this approach in vaccine development by retrospectively analyzing two real vaccine studies: a relatively small immunological trial (typical early phase study) and a large vaccine efficacy trial (typical Phase 3 study) assessing prophylactic human papillomavirus vaccine. Results are promising, particularly for early development immunological studies, where the adaptive design is feasible, and control of type I error is less relevant.     

Penalized likelihood ratio test for a biomarker threshold effect in clinical trials based on generalized linear models

In a clinical trial, the responses to the new treatment may vary among patient subsets with different characteristics in a biomarker. It is often necessary to examine whether there is a cutpoint for the biomarker that divides the patients into two subsets of those with more favourable and less favourable responses. More generally, we approach this problem as a test of homogeneity in the effects of a set of covariates in generalized linear regression models. The unknown cutpoint results in a model with nonidentifiability and a nonsmooth likelihood function to which the ordinary likelihood methods do not apply. We first use a smooth continuous function to approximate the indicator function defining the patient subsets. We then propose a penalized likelihood ratio test to overcome the model irregularities. Under the null hypothesis, we prove that the asymptotic distribution of the proposed test statistic is a mixture of chi-squared distributions. Our method is based on established asymptotic theory, is simple to use, and works in a general framework that includes logistic, Poisson, and linear regression models. In extensive simulation studies, we find that the proposed test works well in terms of size and power. We further demonstrate the use of the proposed method by applying it to clinical trial data from the Digitalis Investigation Group (DIG) on heart failure.     

Construction and validation of Rejection Sensitive Expectation,Perception, and Reaction Questionnaire—Partner (RSEPR-P)

The Rejection Sensitive Expectation, Perception, and Reaction Questionnaire—Partner (RSEPR-P) was developed to assess rejection expectation, perception of rejection, and reactions to perceived rejection among intimate partners. This article details the construction and validation procedures, including item pool generation, factor analysis, and the estimation of reliability and validity. The researchers examined the psychometric properties of RSEPR-P in a community sample of 151 participants. Confirmatory factor analysis (CFA) with the final 24 items suggested four-factor loading with each subscale loading on a separate factor. The subscales had good to excellent internal consistency. In support of the convergent validity, RSEPR-P exhibited robust correlations with rejection sensitivity-personal, mindfulness, marital adjustment, and self-esteem measures. A 6-month test–retest reliability was established. RSEPR-P is proposed to have potential therapeutic and research utility.     

Alone,together: On the benefits of Bayesian borrowing in a meta-analytic setting

It is common practice to use hierarchical Bayesian model for the informing of a pediatric randomized controlled trial (RCT) by adult data, using a prespecified borrowing fraction parameter (BFP). This implicitly assumes that the BFP is intuitive and corresponds to the degree of similarity between the populations. Generalizing this model to any $K\ge 1$ historical studies, naturally leads to empirical Bayes meta-analysis. In this paper we calculate the Bayesian BFPs and study the factors that drive them. We prove that simultaneous mean squared error reduction relative to an uninformed model is always achievable through application of this model. Power and sample size calculations for a future RCT, designed to be informed by multiple external RCTs, are also provided. Potential applications include inference on treatment efficacy from independent trials involving either heterogeneous patient populations or different therapies from a common class.     

Sample size re-estimation in Phase 2 dose-finding: Conditional power versus Bayesian predictive power

Unblinded sample size re-estimation (SSR) is often planned in a clinical trial when there is large uncertainty about the true treatment effect. For Proof-of Concept (PoC) in a Phase II dose finding study, contrast test can be adopted to leverage information from all treatment groups. In this article, we propose two-stage SSR designs using frequentist conditional power (CP) and Bayesian predictive power (PP) for both single and multiple contrast tests. The Bayesian SSR can be implemented under a wide range of prior settings to incorporate different prior knowledge. Taking the adaptivity into account, all type I errors of final analysis in this paper are rigorously protected. Simulation studies are carried out to demonstrate the advantages of unblinded SSR in multi-arm trials.