Examining the influence of treatment preferences on attrition, adherence and outcomes: a protocol for a two-stage partially randomized trial

Abstract

BackgroundEmpirical evidence pertaining to the influence of treatment preferences on attrition, adherence and outcomes in intervention evaluation trials is inconsistent. The inconsistency can be explained by the method used for allocating treatment and measuring preferences. The current methodological study is designed to address these factors by implementing the two-stage partially randomized or preference trial design, and administering a validated measure to assess participants’ preferences for the treatments under evaluation. It aims to compare attrition, adherence and outcomes for participants allocated randomly or by preference to treatment. The study is in its final stages of data collection; its protocol is presented in this paper.Methods/DesignA partially randomized clinical or preference trial is used. Eligible participants are randomized to two trial arms. First is the random arm involving random assignment to treatments, and second is the preference arm involving allocation to the chosen treatment. Participants with chronic insomnia are targeted. Two behavioral treatments are offered, stimulus control therapy and sleep restriction therapy, in the same format (small group) and dose (two sessions given over a 4-week period). A participant log is used to collect data on attrition. Adherence is evaluated in terms of exposure and enactment of treatment. Sleep-related outcomes (sleep parameters and perceived insomnia severity) are measured at pretest, posttest, 6 and 12 month follow-up. Treatment preferences, adherence and outcomes are assessed with reliable and valid measures.DiscussionThe advantages and limitations of the preference trial design are highlighted. The challenges in implementing the trial are discussed relative to the distribution of participants in the groups defined by treatment received and method of treatment allocation.Trial registrationClinicalTrials.gov Registry NCT02513017

Preference option randomized design (PORD) for comparative effectiveness research: Statistical power for testing comparative effect, preference effect, selection effect, intent-to-treat effect, and overall effect

“…For the same specifications used for Table 1 and Figure 2, the magnitudes of empirical power (presented in gray lines in Figure 2) based on K ¼ 10,000 simulations for each specification are virtually identical to those of the theoretical power. In addition, under unbalanced sample sizes and various differential preference rates, Table 2 shows that both theoretical and empirical power, under normal distributions are again virtually identical to each other in view of maxj' À' j in equations (5) and (6). Table 3 shows that simulation results even under binomial distributions are also very close to each other in view of maxj' À' j, even if this quantity is in general slightly greater than that under the normal distributions.…”

“…In addition, variations of the two-stage designs such as fully or partially randomized preference design have also been adopted. 20,27 To the best of our knowledge, however, there is no design which can collectively encompass all desirable features that we believe are pertinent to the CER paradigm that: (1) minimize forced assignment and influenced switch; (2) maintain and compare randomized groups; (3) administer single randomization; (4) accommodate preference; (5) and thus maximize retention, adherence or compliance. To this end, building upon existing preference designs, 20,28 we propose a novel simpler study design, referred to herein as the ''preference option randomized design'' (PORD), which allows participants to switch to their preferred interventions only if they actively express preference after randomization.…”

“…However, it may be difficult to carry out such rigorous controlled designs in real-world settings, where treatments are often unmasked and participants have strong preferences. 4,5 The traditional parallel randomized design forces a randomly assigned intervention regardless of participants’ preference, and anticipates that the participants will comply with the assigned intervention. Therefore, this design may impose impractical limitations when planning CER trials where such compliance may not be realistic.…”

Nurses’ Perception of a Tailored Web-Based Intervention for the Self-Management of Pain After Cardiac Surgery

“…Last, enhancing patient engagement could be reached by making the intervention more relevant to specific subgroups and according to their preferences. Treatment preferences are determined by sociodemographic factors and they can influence adherence and clinical outcomes ( Mills et al., 2006 ; Sidani, Epstein, & Miranda, 2006 ; Sidani, Fox, & Epstein, 2017 ; Sidani et al., 2015 ; Sidani, Miranda, Epstein, & Fox, 2009 ). For instance, the acceptability study with patients underlined that women felt that the most important aspect of the intervention was the awareness-raising content, while men focused on the convenience of the support provided ( Martorella et al., 2014 ).…”

A Cross-Sectional Study Assessing Treatment Preference of People With Chronic Low Back Pain

“…11,23 Eliciting attribute ratings prior to assessing treatment preference allows the patient to make an informed decision about his preference. 11,18,24 Patients will typically rate the attributes of the preferred treatment higher than those of the non-preferred treatment. 23,24…”