Investigation of Active Ingredients Within Internet-Delivered Cognitive Behavioral Therapy for Depression: A Randomized Optimization Trial

Distributive randomization: a pragmatic fractional factorial design to screen or evaluate multiple simultaneous interventions in a clinical trial

BACKGROUND

In some medical indications, numerous interventions have a weak presumption of efficacy, but a good track record or presumption of safety. This makes it feasible to evaluate them simultaneously. This study evaluates a pragmatic fractional factorial trial design that randomly allocates a pre-specified number of interventions to each participant, and statistically tests main intervention effects. We compare it to factorial trials, parallel-arm trials and multiple head-to-head trials, and derive some good practices for its design and analysis.

METHODS

We simulated various scenarios involving 4 to 20 candidate interventions among which 2 to 8 could be simultaneously allocated. A binary outcome was assumed. One or two interventions were assumed effective, with various interactions (positive, negative, none). Efficient combinatorics algorithms were created. Sample sizes and power were obtained by simulations in which the statistical test was either difference of proportions or multivariate logistic regression Wald test with or without interaction terms for adjustment, with Bonferroni multiplicity-adjusted alpha risk for both. Native R code is provided without need for compiling or packages.

RESULTS

Distributive trials reduce sample sizes 2- to sevenfold compared to parallel arm trials, and increase them 1- to twofold compared to factorial trials, mostly when fewer allocations than for the factorial design are possible. An unexpectedly effective intervention causes small decreases in power (< 10%) if its effect is additive, but large decreases (possibly down to 0) if not, as for factorial designs. These large decreases are prevented by using interaction terms to adjust the analysis, but these additional estimands have a sample size cost and are better pre-specified. The issue can also be managed by adding a true control arm without any intervention.

CONCLUSION

Distributive randomization is a viable design for mass parallel evaluation of interventions in constrained trial populations. It should be introduced first in clinical settings where many undercharacterized interventions are potentially available, such as disease prevention strategies, digital behavioral interventions, dietary supplements for chronic conditions, or emerging diseases. Pre-trial simulations are recommended, for which tools are provided.

Using the 'Leapfrog' Design as a Simple Form of Adaptive Platform Trial to Develop, Test, and Implement Treatment Personalization Methods in Routine Practice

The route for the development, evaluation and dissemination of personalized psychological therapies is complex and challenging. In particular, the large sample sizes needed to provide adequately powered trials of newly-developed personalization approaches means that the traditional treatment development route is extremely inefficient. This paper outlines the promise of adaptive platform trials (APT) embedded within routine practice as a method to streamline development and testing of personalized psychological therapies, and close the gap to implementation in real-world settings. It focuses in particular on a recently-developed simplified APT design, the 'leapfrog' trial, illustrating via simulation how such a trial may proceed and the advantages it can bring, for example in terms of reduced sample sizes. Finally it discusses models of how such trials could be implemented in routine practice, including potential challenges and caveats, alongside a longer-term perspective on the development of personalized psychological treatments.

Beyond mediators: A critical review and methodological path forward for studying mechanisms in alcohol use treatment research

Understanding how treatments for alcohol use disorder (AUD) facilitate behavior change has long been recognized as an important area of research for advancing clinical care. However, despite decades of research, the specific mechanisms of change for most AUD treatments remain largely unknown because most prior work in the field has focused only on statistical mediation. Statistical mediation is a necessary but not sufficient condition to establish evidence for a mechanism of change. Mediators are intermediate variables that account statistically for the relationship between independent and dependent variables, whereas mechanisms provide more detailed explanations of how an intervention leads to a desired outcome. Thus, mediators and mechanisms are not equivalent. To advance mechanisms of behavior change research, in this critical review we provide an overview of methodological shortfalls of existing AUD treatment mechanism research and introduce an etiologically informed precision medicine approach that facilitates the testing of mechanisms of behavior change rather than treatment mediators. We propose a framework for studying mechanisms in alcohol treatment research that promises to facilitate our understanding of behavior change and precision medicine (i.e., for whom a given mechanism of behavior change operates and under what conditions). The framework presented in this review has several overarching goals, one of which is to provide a methodological roadmap for testing AUD recovery mechanisms. We provide two examples of our framework, one pharmacological and one behavioral, to facilitate future efforts to implement this methodological approach to mechanism research. The framework proposed in this critical review facilitates the alignment of AUD treatment mechanism research with current theories of etiologic mechanisms, precision medicine efforts, and cross-disciplinary approaches to testing mechanisms. Although no framework can address all the challenges related to mechanisms research, our goal is to help facilitate a shift toward more rigorous and falsifiable behavior change research.

The latest developments with internet-based psychological treatments for depression

INTRODUCTION

Internet-based psychological treatments for depression have been around for more than 20 years. There has been a continuous line of research with new research questions being asked and studies conducted.

AREAS COVERED

In this paper, the author reviews studies with a focus on papers published from 2020 and onwards based on a Medline and Scopus search. Internet-based cognitive behavior therapy (ICBT) programs have been developed and tested for adolescents, older adults, immigrant groups and to handle a societal crisis (e.g. COVID-19). ICBT works in regular clinical settings and long-term effects can be obtained. Studies on different treatment orientations and approaches such as acceptance commitment therapy, unified protocol, and tailored treatments have been conducted. Effects on quality-of-life measures, knowledge acquisition and ecological momentary assessment as a research tool have been reported. Factorial design trials and individual patient data meta-analysis are increasingly used in association with internet intervention research. Finally, prediction studies and recent advances in artificial intelligence are mentioned.

EXPERT OPINION

Internet-delivered treatments are effective, in particular if therapist guidance is provided. More target groups have been covered but there are many remaining challenges including how new tools like artificial intelligence will be used when treating depression.