Invited Commentary: Comparing the Independent Segments Procedure with Group Sequential Designs

Psychological science would become more efficient if researchers implemented sequential designs where feasible. Miller and Ulrich (2020) propose an independent segments procedure where data can be analyzed at a prespecified number of equally spaced looks while controlling the Type 1 error rate. Such procedures already exist in the sequential analysis literature, and in this commentary I reflect on whether psychologist should choose to adopt these existing procedure instead. I believe limitations in the independent segments procedure make it relatively unattractive. Being forced to stop for futility based on a bound not chosen to control Type 2 errors, or reject a smallest effect size of interest in an equivalence test, limit the inferences one can make. Having to use a prespecified number of equally spaced looks is logistically inconvenient. And not having the flexibility to choose α and β spending functions limit the possibility to design efficient studies based on the goal and limitations of the researcher. Recent software packages such as rpact (Wassmer & Pahlke, 2019) make sequential designs equally easy to perform as the independent segments procedure. While learning new statistical methods always takes time, I believe psychological scientists should start on a path that will not limit them in the flexibility and inferences their statistical procedure provides.

Randomized and non-randomized designs for causal inference with longitudinal data in rare disorders

In the United States, approximately 7000 rare diseases affect 30 million patients, and only 10% of these diseases have existing therapies. Sound study design and causal inference methods are essential to demonstrate the therapeutic efficacy, safety, and effectiveness of new therapies. In the rare diseases setting, several factors challenge the use of typical parallel control designs: the small patient population size, genotypic and phenotypic diversity, and the complexity and incomplete understanding of the disorder’s progression. Repeated measures, when spaced appropriately relative to disease progression and exploited in design and analysis, can increase study power and reduce variability in treatment effect estimation. This paper reviews these longitudinal designs and draws the parallel between some new and existing randomized studies in rare diseases and their less well-known controlled observational study designs. We show that self-controlled randomized crossover and N-of-1 designs have similar considerations as the observational case series and case-crossover designs. Also, randomized sequential designs have similar considerations to longitudinal cohort studies using sequential matching or weighting to control confounding. We discuss design and analysis considerations for valid causal inference and illustrate them with examples of analyses in multiple rare disorders, including urea cycle disorder and cystic fibrosis.

Mapping the Policy Regulatory Environment of Transnational Education (TNE) in the Ghanaian Tertiary Education System

Given the critical role of public policy in TNE arrangements of countries, and the fact that TNE partnerships are growing steadily in the Ghanaian tertiary education sector, a robust and resilient public policy environment is imperative. However, the public policy environment of TNE partnerships in the tertiary education sector in Ghana is unexamined by any scientific study to guide decision on TNE partnerships in Ghanaian tertiary education institutions (TEIs). Against this backdrop, this chapter examines the level of influence of public policy frameworks on TNE partnerships in TEIs in Ghana to ignite a national discourse on TNE regulation. A multiphase mixed-method research design, informed by exploratory and explanatory sequential designs was adopted for the study. The findings reveal that TNE partnerships are an emerging concept in the Ghanaian tertiary education system with less than 20% of the over 200 TEIs engaged in TNE partnerships. More importantly, the findings indicate that the policy environment of TNE partnerships of TEIs in Ghana is not sufficiently robust because no tailor-made policy regulatory frameworks exist to regulate TNE partnerships in TEIs. To this end, the study concludes that the existing policy regulatory frameworks for the Ghanaian tertiary education system are incapable of helping the country maximise the full benefits of TNE partnerships by ensuring win-win situations for TEIs engaged in TNE partnerships. In view of this, the study recommends that the government should develop a tailor-made policy framework for regulating TNE partnerships in Ghanaian TEIs.

Cost-effective clinical trial design: Application of a Bayesian sequential model to the ProFHER pragmatic trial

Background/Aims: There is growing interest in the use of adaptive designs to improve the efficiency of clinical trials. We apply a Bayesian decision-theoretic model of a sequential experiment using cost and outcome data from the ProFHER pragmatic trial. We assess the model’s potential for delivering value-based research. Methods: Using parameter values estimated from the ProFHER pragmatic trial, including the costs of carrying out the trial, we establish when the trial could have stopped, had the model’s value-based stopping rule been used. We use a bootstrap analysis and simulation study to assess a range of operating characteristics, which we compare with a fixed sample size design which does not allow for early stopping. Results: We estimate that application of the model could have stopped the ProFHER trial early, reducing the sample size by about 14%, saving about 5% of the research budget and resulting in a technology recommendation which was the same as that of the trial. The bootstrap analysis suggests that the expected sample size would have been 38% lower, saving around 13% of the research budget, with a probability of 0.92 of making the same technology recommendation decision. It also shows a large degree of variability in the trial’s sample size. Conclusions: Benefits to trial cost stewardship may be achieved by monitoring trial data as they accumulate and using a stopping rule which balances the benefit of obtaining more information through continued recruitment with the cost of obtaining that information. We present recommendations for further research investigating the application of value-based sequential designs.