Optimising the two-stage randomised trial design when some participants are indifferent in their treatment preferences

Group sequential two-stage preference designs

The two-stage preference design (TSPD) enables inference for treatment efficacy while allowing for incorporation of patient preference to treatment. It can provide unbiased estimates for selection and preference effects, where a selection effect occurs when patients who prefer one treatment respond differently than those who prefer another, and a preference effect is the difference in response caused by an interaction between the patient's preference and the actual treatment they receive. One potential barrier to adopting TSPD in practice, however, is the relatively large sample size required to estimate selection and preference effects with sufficient power. To address this concern, we propose a group sequential two-stage preference design (GS-TSPD), which combines TSPD with sequential monitoring for early stopping. In the GS-TSPD, pre-planned sequential monitoring allows investigators to conduct repeated hypothesis tests on accumulated data prior to full enrollment to assess study eligibility for early trial termination without inflating type I error rates. Thus, the procedure allows investigators to terminate the study when there is sufficient evidence of treatment, selection, or preference effects during an interim analysis, thereby reducing the design resource in expectation. To formalize such a procedure, we verify the independent increments assumption for testing the selection and preference effects and apply group sequential stopping boundaries from the approximate sequential density functions. Simulations are then conducted to investigate the operating characteristics of our proposed GS-TSPD compared to the traditional TSPD. We demonstrate the applicability of the design using a study of Hepatitis C treatment modality.

Factors associated with choice of behavioural weight loss program by adults with obesity

We assessed the preference for two behavioural weight loss programs, Diabetes Prevention Program (DPP) and Healthy Weight for Living (HWL) in adults with obesity. A cross-sectional survey was fielded on the Amazon Mechanical Turk. Eligibility criteria included reporting BMI ≥30 and at least two chronic health conditions. Participants read about the programs, selected their preferred program, and answered follow-up questions. The estimated probability of choosing either program was not significantly different from .5 (N = 1005, 50.8% DPP and 49.2% HWL, p = .61). Participants' expectations about adherence, weight loss magnitude, and dropout likelihood were associated with their choice (p < .0001). Non-White participants (p = .040) and those with monthly income greater than $4999 (p = .002) were less likely to choose DPP. Participants who had postgraduate education (p = .007), did not report high serum cholesterol (p = .028), and reported not having tried losing weight before (p = .025) were more likely to choose DPP. Those who chose HWL were marginally more likely to report that being offered two different programs rather than one would likely affect their decision to enrol in one of the two (p = .052). The enrolment into DPP and HWL was balanced, but race, educational attainment, income, previous attempt to lose weight, and serum cholesterol levels had significant associations with the choice of weight loss program.

Ensuring patient centeredness in upper extremity lymphedema research: Identifying patient-prioritized agenda and preferences

PURPOSE

To elicit a patient-prioritized agenda and preferences for upper extremity lymphedema (LE) research.

METHODS

Focus group sessions (FGs) were conducted with English-speaking, adult women (18 years and older) with breast cancer-related LE (BCRL) seeking conservative or surgical care at two tertiary cancer centers in Ontario, Canada. An interview guide was used; women were asked to describe health-related quality of life (HRQL) outcomes that mattered the most to them, followed by their preferences for research study design and for providing patient-reported outcomes measure (PROM) data. Inductive content analysis was used to identify themes and subthemes.

RESULTS

A total of 16 women participated in 4 FG sessions (55 ± 9.5 years) and described the impact of LE on their appearance, physical, psychosocial, and sexual well-being. Women emphasized that psychosocial well-being was often not discussed in clinical care and that they were poorly informed of LE risk and care options. Most women said that they would not be willing to be randomized to surgical versus conservative management of LE. They also expressed a preference to complete PROM data electronically. All women emphasized the value of having an open text option alongside PROMs to expand on their concerns.

CONCLUSION

Patient centeredness is key to generating meaningful data and ensuring ongoing engagement in clinical research. In LE, comprehensive PROMs that measure a range of HRQL concerns, especially psychosocial well-being, should be considered. Women with BCRL are reluctant to be randomized to conservative care when a surgical option is available, resulting in implications for planning trial sample size and recruitment.

Taking a chance: How likely am I to receive my preferred treatment in a clinical trial?

Researchers should ideally conduct clinical trials under a presumption of clinical equipoise, but in fact trial patients will often prefer one or other of the treatments being compared. Receiving an unblinded preferred treatment may affect the study outcome, possibly beneficially, but receiving a non-preferred treatment may induce 'reluctant acquiescence', and poorer outcomes. Even in blinded trials, patients' primary motivation to enrol may be the chance of potentially receiving a desirable experimental treatment, which is otherwise unavailable. Study designs with a higher probability of receiving a preferred treatment (denoted as 'concordance') will be attractive to potential participants, and investigators, because they may improve recruitment and hence enhance study efficiency. Therefore, it is useful to consider the concordance rates associated with various study designs. We consider this question with a focus on comparing the standard, randomised, two-arm, parallel group design with the two-stage randomised patient preference design and Zelen designs; we also mention the fully randomised and partially randomised patient preference designs. For each of these designs, we evaluate the concordance rate as a function of the proportions randomised to the alternative treatments, the distribution of preferences over treatments, and (for the Zelen designs) the proportion of patients who consent to receive their assigned treatment. We also examine the equity of each design, which we define as the similarity between the concordance rates for participants with different treatment preferences. Finally, we contrast each of the alternative designs with the standard design in terms of gain in concordance and change in equity.

Design and analysis of partially randomized preference trials with propensity score stratification

While the two-stage randomized design allows us to unbiasedly evaluate the impact of patients' treatment preference on the outcome of interest, it may not always be practical to implement in clinical practice; patients with a strong preference may not be willing to be randomized. The more pragmatic, partially randomized preference design (PRPD) allows patients who are unwilling to be randomized, but willing to state their preference, to receive their preferred treatment in lieu of the first-stage randomization in the two-stage design, at the cost of potentially introducing bias in estimating the effects of interest. In this article, we consider the application of propensity score stratification (PSS) in a PRPD to recreate a conditional first-stage randomization based on observed covariates, enabling the estimation and inference of the overall treatment, selection and preference effects with minimum bias. We additionally derive a set of closed-form sample size formulas for detecting all three effects of interest in a PSS-PRPD. Simulation studies demonstrate the bias reduction properties of the PSS-PRPD, and validate the accuracy of the proposed sample size formulas. Our results show that 5 to 10 propensity score strata may be needed to correct for biases in effect estimates, and the exact number of strata needed to achieve the best match between the empirical power and formula prediction may depend on the degree of effect heterogeneity. Finally, we demonstrate our proposed formulas by estimating the required sample sizes to detect treatment, selection and preference effects in the context of the Harapan Study.

Two-stage randomized trial design for testing treatment, preference, and self-selection effects for count outcomes

While the traditional clinical trial design lays emphasis on testing the treatment effect between randomly assigned groups, it ignores the role of patient preference for a particular treatment in the trial. Yet, for healthcare providers who seek to optimize the patient-centered treatment strategy, the evaluation of a patient's psychology toward each treatment could be a key consideration. The two-stage randomized trial design allows researchers to test patient's preference and selection effects, in addition to the treatment effect. The current methodology for the two-stage design is limited to continuous and binary outcomes; this article extends the model to include count outcomes. The test statistics for preference, selection, and treatment effects are derived. Closed-form sample size formulae are presented for each effect. Simulations are presented to demonstrate the properties of the unstratified and stratified designs. Finally, we apply methods to the use of antimicrobials at the end of life to demonstrate the applicability of the methods.

Relative efficiencies of alternative preference-based designs for randomised trials

Recent work has shown that outcomes in clinical trials can be affected by which treatment the trial participants would select if they were allowed to do so, and if they do or do not actually receive that treatment. These influences are known as selection and preference effects, respectively. Unfortunately, they cannot be evaluated in conventional, parallel group trials because patient preferences remain unknown. However, several alternative designs have been proposed, to measure and take account of patient preferences. In this paper, we discuss three preference-based designs (the two-stage, fully randomised, and partially randomised designs). In conventional trials, only the treatment effect is estimable, while the preference-based designs have the potential to estimate some or all of the selection and preference effects. The relative efficiency of these designs is affected by several factors, including the proportion of participants who are undecided about treatments, or who are unable or unwilling to state a preference; the relative preference rate between the treatments being compared, among patients who do have a preference; and the ratio of patients randomised to each treatment. We also discuss the advantages and disadvantages of these designs under different scenarios.