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.     

A Bayesian optimal interval design for dose optimization with a randomization scheme based on pharmacokinetics outcomes in oncology

The primary objective of an oncology dose-finding trial for novel therapies, such as molecularly targeted agents and immune-oncology therapies, is to identify the optimal dose (OD) that is tolerable and therapeutically beneficial for subjects in subsequent clinical trials. Pharmacokinetic (PK) information is considered an appropriate indicator for evaluating the level of drug intervention in humans from a pharmacological perspective. Several novel anticancer agents have been shown to have significant exposure-efficacy relationships, and some PK information has been considered an important predictor of efficacy. This paper proposes a Bayesian optimal interval design for dose optimization with a randomization scheme based on PK outcomes in oncology. A simulation study shows that the proposed design has advantages compared to the other designs in the percentage of correct OD selection and the average number of patients allocated to OD in various realistic settings.     

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.     

How to avoid concerns with the interpretation of two primary endpoints if significant superiority in one is sufficient for formal proof of efficacy

Formal proof of efficacy of a drug requires that in a prospective experiment, superiority over placebo, or either superiority or at least non-inferiority to an established standard, is demonstrated. Traditionally one primary endpoint is specified, but various diseases exist where treatment success needs to be based on the assessment of two primary endpoints. With co-primary endpoints, both need to be “significant” as a prerequisite to claim study success. Here, no adjustment of the study-wise type-1-error is needed, but sample size is often increased to maintain the pre-defined power. Studies that use an at-least-one concept have been proposed where study success is claimed if superiority for at least one of the endpoints is demonstrated. This is sometimes also called the dual primary endpoint concept, and an appropriate adjustment of the study-wise type-1-error is required. This concept is not covered in the European Guideline on multiplicity because study success can be claimed if one endpoint shows significant superiority, despite a possible deterioration in the other. In line with Röhmel's strategy, we discuss an alternative approach including non-inferiority hypotheses testing that avoids obvious contradictions to proper decision-making. This approach leads back to the co-primary endpoint assessment, and has the advantage that minimum requirements for endpoints can be modeled flexibly for several practical needs. Our simulations show that, if planning assumptions are correct, the proposed additional requirements improve interpretation with only a limited impact on power, that is, on sample size.     

“Super-covariates”: Using predicted control group outcome as a covariate in randomized clinical trials

The power of randomized controlled clinical trials to demonstrate the efficacy of a drug compared with a control group depends not just on how efficacious the drug is, but also on the variation in patients' outcomes. Adjusting for prognostic covariates during trial analysis can reduce this variation. For this reason, the primary statistical analysis of a clinical trial is often based on regression models that besides terms for treatment and some further terms (e.g., stratification factors used in the randomization scheme of the trial) also includes a baseline (pre-treatment) assessment of the primary outcome. We suggest to include a “super-covariate”—that is, a patient-specific prediction of the control group outcome—as a further covariate (but not as an offset). We train a prognostic model or ensembles of such models on the individual patient (or aggregate) data of other studies in similar patients, but not the new trial under analysis. This has the potential to use historical data to increase the power of clinical trials and avoids the concern of type I error inflation with Bayesian approaches, but in contrast to them has a greater benefit for larger sample sizes. It is important for prognostic models behind “super-covariates” to generalize well across different patient populations in order to similarly reduce unexplained variability whether the trial(s) to develop the model are identical to the new trial or not. In an example in neovascular age-related macular degeneration we saw efficiency gains from the use of a “super-covariate”.     

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.     

Just say no to data listings!

Sponsor companies often create voluminous static listings for Clinical Study Reports (CSRs) and regulatory submissions, and possibly for internal use to review participant-level data. This is likely due to the perception that they are required and/or lack of knowledge of various alternatives. However, there are other ways of viewing clinical study data that can provide an improved user experience, and are made possible by standard data structures such as the Study Data Tabulation Model (SDTM). The purpose of this paper is to explore some alternatives to providing a complete set of static listings and make a case for sponsors to begin considering these alternatives. We will discuss the recommendations from the PHUSE white paper, “Data Listings in Clinical Study Reports.”     

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.     

The development of a reliable change index and cutoff score for the SCORE-15

The Systemic Clinical Outcome and Routine Evaluation version 15 (SCORE-15) is a measure used to assess family-level change in family therapy. The SCORE-15 has been demonstrated to be a reliable and valid measure, with high clinical utility; however, the SCORE-15 lacks the ability to determine whether the change in family functioning during the course of therapy is clinically significant. This study aimed to establish a reliable change index (RCI) and clinical cutoff score so that researchers and clinicians can determine clinically significant change in family therapy. US samples of 71 clinical participants and 244 community participants completed the SCORE-15. Results indicated a cutoff score of 40.37 and an RCI of 9.52. Consequently, family members who improve their SCORE-15 score during the course of therapy by at least 9 points and who cross the threshold of 40 during the course of therapy are considered to have experienced clinically significant change.