Relative efficiencies of alternative preference-based designs for randomised trials

Walter, Stephen D.; Bian, Mengjie

doi:10.1177/0962280220941874

Cited by 7 publications

(9 citation statements)

References 27 publications

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“…Further, treatment assignment and partial randomization occurred prior to baseline data collection which limited our ability to characterize participants who withdrew from the study and leaves the possibility that randomization may have affected the baseline level of outcomes. Finally, the partially randomized structure of intervention assignment means there were limited cases in which a participant received an intervention that they specifically did not initially prefer, thus we could not explicitly measure the effect of preference on study outcomes, only a selection effect [78].…”

Section: Discussionmentioning

confidence: 99%

The Selah trial: A preference-based partially randomized waitlist control study of three stress management interventions

Proeschold‐Bell

Eagle

Tice

et al. 2023

Preprint

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Objective: Chronic stress can undermine psychological and physiological health. We sought to evaluate three stress management interventions among clergy, accounting for intervention preferences. Methods: United Methodist clergy in North Carolina enrolled in a partially randomized, preference-based waitlist control trial. The interventions were: mindfulness-based stress reduction (MBSR), Daily Examen prayer practice, and Stress Proofing (stress inoculation plus breathing skills). The intervention period spanned 12 weeks with a 12-week follow-up. Daily text message data were collected to assess practice across the 24 weeks. Co-primary outcomes were symptoms of stress using the Calgary Symptoms of Stress Inventory and 48-hour ambulatory heart rate variability (HRV) at 12-weeks post-intervention compared to waitlist control. Survey data were collected at 0, 12 and 24 weeks, with HRV collected at 0 and 12 weeks. Results: 255 participants (mean age=54 years old; 91% white; 48% female) were randomized and initiated an intervention (n=184) or waitlist control (n=71). Compared to waitlist control, lower stress symptoms were found for MBSR participants [Mean Difference (MD)=-0.30, 95% CI:-0.41,-0.20; p<.001] and Stress Proofing (MD=-0.27, 95% CI:-0.40,-0.14; p<.001) at 12 weeks, and Daily Examen participants not until 24 weeks (MD=-0.24, 95% CI:-0.41,-0.08). Only MBSR participants demonstrated improvement in HRV at 12 weeks (MD=+3.31 millisecond; 95% CI:0.20,6.43; p=.037). Conclusions: MBSR demonstrated robust improvement in self-reported and objective physical correlates of stress whereas Stress Proofing and Daily Examen resulted in improvements in self-reported correlates of stress only. These brief practices were sustainable and beneficial for an occupational sample during the COVID pandemic. Registration: ClinicalTrials.gov identifier:NCT04625777(https://clinicaltrials.gov/ct2/show/NCT04625777)

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Section: Discussionmentioning

confidence: 99%

The Selah trial: A preference-based partially randomized waitlist control study of three stress management interventions

Proeschold‐Bell

Eagle

Tice

et al. 2023

Preprint

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“…Finally, as well as envisaging the effects of preferences on outcomes, we note that there may also be effects on the standard error of the estimated treatment effect. 1,[9][10][11]14 With a suitable design and analysis, we can get a valid (unbiased) estimate of the treatment effect, taking account of patient preferences and selection effects, wherever that is possible. 1,[9][10][11]14 The usual primary analyses would not allow for an interaction between the treatment effect and treatment preferences, so a change in the concordance rate would then not affect the estimated treatment benefit.…”

Section: Discussionmentioning

confidence: 99%

“…1,[9][10][11]14 With a suitable design and analysis, we can get a valid (unbiased) estimate of the treatment effect, taking account of patient preferences and selection effects, wherever that is possible. 1,[9][10][11]14 The usual primary analyses would not allow for an interaction between the treatment effect and treatment preferences, so a change in the concordance rate would then not affect the estimated treatment benefit. On the other hand, if there is such an interaction, it could only be estimated in situations where individual patient preferences are known.…”

Section: Discussionmentioning

confidence: 99%

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

Walter

Bláha

Esserman

2023

Stat Methods Med Res

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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.

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“…This is a strong assumption that needs to be satisfied and possible deviation from this assumption can lead to bias in the current PSS-PRPD formulation. 30 We plan to include extensions to allow for undecided participants in the first stage, possibly along the lines of Walter et al in the context of two-stage designs. 31,32 Second, as with other PS methods developed in the context of observational studies, the validity of the PSS-PRPD depends on (i) the untestable, conditional exchangeability assumption (i.e., no unmeasured confounders at the first stage) and (ii) a correct PS model specification.…”

Section: Discussionmentioning

confidence: 99%

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

Wang

Bláha

et al. 2022

Stat Methods Med Res

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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.

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Relative efficiencies of alternative preference-based designs for randomised trials

Cited by 7 publications

References 27 publications

The Selah trial: A preference-based partially randomized waitlist control study of three stress management interventions

The Selah trial: A preference-based partially randomized waitlist control study of three stress management interventions

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

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

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