Simulation and minimization: technical advances for factorial experiments designed to optimize clinical interventions

When should factorial designs be used for late-phase randomised controlled trials?

BACKGROUND

A 2×2 factorial design evaluates two interventions (A versus control and B versus control) by randomising to control, A-only, B-only or both A and B together. Extended factorial designs are also possible (e.g. 3×3 or 2×2×2). Factorial designs often require fewer resources and participants than alternative randomised controlled trials, but they are not widely used. We identified several issues that investigators considering this design need to address, before they use it in a late-phase setting.

METHODS

We surveyed journal articles published in 2000-2022 relating to designing factorial randomised controlled trials. We identified issues to consider based on these and our personal experiences.

RESULTS

We identified clinical, practical, statistical and external issues that make factorial randomised controlled trials more desirable. Clinical issues are (1) interventions can be easily co-administered; (2) risk of safety issues from co-administration above individual risks of the separate interventions is low; (3) safety or efficacy data are wanted on the combination intervention; (4) potential for interaction (e.g. effect of A differing when B administered) is low; (5) it is important to compare interventions with other interventions balanced, rather than allowing randomised interventions to affect the choice of other interventions; (6) eligibility criteria for different interventions are similar. Practical issues are (7) recruitment is not harmed by testing many interventions; (8) each intervention and associated toxicities is unlikely to reduce either adherence to the other intervention or overall follow-up; (9) blinding is easy to implement or not required. Statistical issues are (10) a suitable scale of analysis can be identified; (11) adjustment for multiplicity is not required; (12) early stopping for efficacy or lack of benefit can be done effectively. External issues are (13) adequate funding is available and (14) the trial is not intended for licensing purposes. An overarching issue (15) is that factorial design should give a lower sample size requirement than alternative designs. Across designs with varying non-adherence, retention, intervention effects and interaction effects, 2×2 factorial designs require lower sample size than a three-arm alternative when one intervention effect is reduced by no more than 24%-48% in the presence of the other intervention compared with in the absence of the other intervention.

CONCLUSIONS

Factorial designs are not widely used and should be considered more often using our issues to consider. Low potential for at most small to modest interaction is key, for example, where the interventions have different mechanisms of action or target different aspects of the disease being studied.

Optimization of an Information Leaflet to Influence Medication Beliefs in Women With Breast Cancer: A Randomized Factorial Experiment

BACKGROUND

Adherence to adjuvant endocrine therapy (AET) is low in women with breast cancer. Negative beliefs about the necessity of AET and high concerns are barriers to adherence.

PURPOSE

To use the multiphase optimization strategy to optimize the content of an information leaflet intervention, to change AET beliefs.

METHODS

We conducted an online screening experiment using a 25 factorial design to optimize the leaflet. The leaflet had five components, each with two levels: (i) diagrams about AET mechanisms (on/off); (ii) infographics displaying AET benefits (enhanced/basic); (iii) AET side effects (enhanced/basic); (iv) answers to AET concerns (on/off); (v) breast cancer survivor (patient) input: quotes and photographs (on/off). Healthy adult women (n = 1,604), recruited via a market research company, were randomized to 1 of 32 experimental conditions, which determined the levels of components received. Participants completed the Beliefs about Medicines Questionnaire before and after viewing the leaflet.

RESULTS

There was a significant main effect of patient input on beliefs about medication (β = 0.063, p < .001). There was one significant synergistic two-way interaction between diagrams and benefits (β = 0.047, p = .006), and one antagonistic two-way interaction between diagrams and side effects (β = -0.029, p = .093). There was a synergistic three-way interaction between diagrams, concerns, and patient input (β = 0.029, p = .085), and an antagonistic four-way interaction between diagrams, benefits, side effects, and concerns (β = -0.038, p = .024). In a stepped approach, we screened in four components and screened out the side effects component.

CONCLUSIONS

The optimized leaflet did not contain enhanced AET side effect information. Factorial experiments are efficient and effective for refining the content of information leaflet interventions.

Impact of minimal sufficient balance, minimization, and stratified permuted blocks on bias and power in the estimation of treatment effect in sequential clinical trials with a binary endpoint

Minimization is among the most common methods for controlling baseline covariate imbalance at the randomization phase of clinical trials. Previous studies have found that minimization does not preserve allocation randomness as well as other methods, such as minimal sufficient balance, making it more vulnerable to allocation predictability and selection bias. Additionally, minimization has been shown in simulation studies to inadequately control serious covariate imbalances when modest biased coin probabilities (≤0.65) are used. This current study extends the investigation of randomization methods to the analysis phase, comparing the impact of treatment allocation methods on power and bias in estimating treatment effects on a binary outcome using logistic regression. Power and bias in the estimation of treatment effect was found to be comparable across complete randomization, minimization, and minimal sufficient balance in unadjusted analyses. Further, minimal sufficient balance was found to have the most modest impact on power and the least bias in covariate-adjusted analyses. The minimal sufficient balance method is recommended for use in clinical trials as an alternative to minimization when covariate-adaptive subject randomization takes place.

Statistical properties of minimal sufficient balance and minimization as methods for controlling baseline covariate imbalance at the design stage of sequential clinical trials

When the number of baseline covariates whose imbalance needs to be controlled in a sequential randomized controlled trial is large, minimization is the most commonly used method for randomizing treatment assignments. The lack of allocation randomness associated with the minimization method has been the source of controversy, and the need to reduce even minor imbalances inherent in the minimization method has been challenged. The minimal sufficient balance (MSB) method is an alternative to the minimization method. It prevents serious imbalance from a large number of covariates while maintaining a high level of allocation randomness. In this study, the two treatment allocation methods are compared with regards to the effectiveness of balancing covariates across treatment arms and allocation randomness in equal allocation clinical trials. The MSB method proves to be equal or superior in both respects. In addition, type I error rate is preserved in analyses for both balancing methods, when using a binary endpoint.