While statistical methods for analyzing cluster randomized trials with continuous and binary outcomes have been extensively studied and compared, little comparative evidence has been provided for analyzing cluster randomized trials with survival outcomes in the presence of competing risks. Motivated by the Strategies to Reduce Injuries and Develop Confidence in Elders trial, we carried out a simulation study to compare the operating characteristics of several existing population-averaged survival models, including the marginal Cox, marginal Fine and Gray, and marginal multi-state models. For each model, we found that adjusting for the intraclass correlations through the sandwich variance estimator effectively maintained the type I error rate when the number of clusters is large. With no more than 30 clusters, however, the sandwich variance estimator can exhibit notable negative bias, and a permutation test provides better control of type I error inflation. Under the alternative, the power for each model is differentially affected by two types of intraclass correlations—the within-individual and between-individual correlations. Furthermore, the marginal Fine and Gray model occasionally leads to higher power than the marginal Cox model or the marginal multi-state model, especially when the competing event rate is high. Finally, we provide an illustrative analysis of Strategies to Reduce Injuries and Develop Confidence in Elders trial using each analytical strategy considered.

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Motivated by a suicide prevention trial with hierarchical treatment allocation (cluster‐level and individual‐level treatments), we address the sample size requirements for testing the treatment effects as well as their interaction. We assume a linear mixed model, within which two types of treatment effect estimands (controlled effect and marginal effect) are defined. For each null hypothesis corresponding to an estimand, we derive sample size formulas based on large‐sample z‐approximation, and provide finite‐sample modifications based on a t‐approximation. We relax the equal cluster size assumption and express the sample size formulas as functions of the mean and coefficient of variation of cluster sizes. We show that the sample size requirement for testing the controlled effect of the cluster‐level treatment is more sensitive to cluster size variability than that for testing the controlled effect of the individual‐level treatment; the same observation holds for testing the marginal effects. In addition, we show that the sample size for testing the interaction effect is proportional to that for testing the controlled or the marginal effect of the individual‐level treatment. We conduct extensive simulations to validate the proposed sample size formulas, and find the empirical power agrees well with the predicted power for each test. Furthermore, the t‐approximations often provide better control of type I error rate with a small number of clusters. Finally, we illustrate our sample size formulas to design the motivating suicide prevention factorial trial. The proposed methods are implemented in the R package H2x2Factorial.

We propose an information borrowing strategy for the design and monitoring of phase II basket trials based on the local multisource exchangeability assumption between baskets (disease types). In our proposed local‐MEM framework, information borrowing is only allowed to occur locally, that is, among baskets with similar response rate and the amount of information borrowing is determined by the level of similarity in response rate, whereas baskets not considered similar are not allowed to share information. We construct a two‐stage design for phase II basket trials using the proposed strategy. The proposed method is compared to competing Bayesian methods and Simon's two‐stage design in a variety of simulation scenarios. We demonstrate the proposed method is able to maintain the family‐wise type I error rate at a reasonable level and has desirable basket‐wise power compared to Simon's two‐stage design. In addition, our method is computationally efficient compared to existing Bayesian methods in that the posterior profiles of interest can be derived explicitly without the need for sampling algorithms. R scripts to implement the proposed method are available at https://github.com/yilinyl/Bayesian-localMEM.

A primary focus of current methods for cluster randomized trials (CRTs) has been for continuous, binary, and count outcomes, with relatively less attention given to right‐censored, time‐to‐event outcomes. In this article, we detail considerations for sample size requirement and statistical inference in CRTs with time‐to‐event outcomes when the intervention effect parameter is specified through the additive hazards mixed model (AHMM), which includes a frailty term to explicitly account for the dependency between the failure times. First, we discuss improved inference for the treatment effect parameter via bias‐corrected sandwich variance estimators and randomization‐based test under AHMM, addressing potential small‐sample biases in CRTs. Next, we derive a new sample size formula for AHMM analysis of CRTs accommodating both equal and unequal cluster sizes. When the cluster sizes vary, our sample size formula depends on the mean and coefficient of variation of cluster sizes, based on which we articulate the impact of cluster size variation in CRTs with time‐to‐event outcomes. Furthermore, we obtain the insight that the classical variance inflation factor for CRTs with a non‐censored outcome can in fact apply to CRTs with a time‐to‐event outcome, providing that an appropriate definition of the intraclass correlation coefficient is considered under AHMM. Simulation studies are carried out to illustrate key design and analysis considerations in CRTs with a small to moderate number of clusters. The proposed sample size procedure and analytical methods are further illustrated using the context of the STrategies to Reduce Injuries and Develop Confidence in Elders CRT.

Background There is insufficient knowledge about how personal access to handheld ultrasound devices (HUDs) improves trainee learning with point-of-care ultrasound (POCUS). Objective To assess whether HUDs, alongside a yearlong lecture series, improved trainee POCUS usage and ability to acquire images. Methods Internal medicine intern physicians (n = 47) at a single institution from 2017 to 2018 were randomized 1:1 to receive personal HUDs (n = 24) for patient care/self-directed learning vs no-HUDs (n = 23). All interns received a repeated lecture series on cardiac, thoracic, and abdominal POCUS. Main outcome measures included self-reported HUD usage rates and post-intervention assessment scores using the Rapid Assessment of Competency in Echocardiography (RACE) scale between HUD and no-HUD groups. Results HUD interns reported performing POCUS assessments on patients a mean 6.8 (SD 2.2) times per week vs 6.4 (SD 2.9) times per week in non-HUD arm (P = .66). There was no relationship between the number of self-reported examinations per week and a trainee's post-intervention RACE score (rho = 0.022, P = .95). HUD interns did not have significantly higher post-intervention RACE scores (median HUD score 17.0 vs no-HUD score 17.8; P = .72). Trainee confidence with cardiac POCUS did not correlate with RACE scores. Conclusions Personal HUDs without direct supervision did not increase the amount of POCUS usage or improve interns' acquisition abilities. Interns who reported performing more examinations per week did not have higher RACE scores. Improved HUD access and lectures without additional feedback may not improve POCUS mastery.

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BackgroundHypnosis decreases perioperative pain and has opioid-sparing potential but has not been rigorously studied in knee arthroplasty. This trial investigates the impact of perioperative hypnosis on inpatient opioid use following total knee arthroplasty.MethodsThis prospective randomized controlled trial was conducted at a single academic medical center. The hypnosis arm underwent a scripted 10 min hypnosis session prior to surgery and had access to the recorded script. The control arm received hypnosis education only. The primary outcome was opioid use in milligram oral morphine equivalents per 24 hours during hospital admission. A secondary analysis was performed for patients taking opioids preoperatively.Results64 primary knee arthroplasty patients were randomized 1:1 to hypnosis (n=31) versus control (n=33) and included in the intent-to-treat analysis. The mean (SD) postoperative opioid use in oral morphine equivalents per 24 hours was 70.5 (48.4) in the hypnosis versus 90.7 (74.4) in the control arm, a difference that was not statistically significant (difference −20.1; 95% CI −51.8 to 11.4; p=0.20). In the subgroup analysis of the opioid-experienced patients, there was a 54% daily reduction in opioid use in the hypnosis group (82.4 (56.2) vs 179.1 (74.5) difference of −96.7; 95% CI -164.4 to –29.0; p=<0.01), equivalent to sparing 65 mg of oxycodone per day.ConclusionPerioperative hypnosis significantly reduced inpatient opioid use among opioid-experienced patients only. A larger study examining these findings is warranted.Trial registration numberNCT03308071.