Opportunistic Treatment of Hepatitis C Infection Among Hospitalized People Who Inject Drugs (OPPORTUNI-C): A Stepped Wedge Cluster Randomized Trial.

BACKGROUND: We aimed to evaluate the efficacy of opportunistic treatment of hepatitis C virus (HCV) infection among hospitalized people who inject drugs (PWID). METHODS: We performed a pragmatic, stepped wedge cluster randomized trial recruiting HCV RNA positive individuals admitted for inpatient care in departments of internal medicine, addiction medicine, and psychiatry at three hospitals in Oslo, Norway. Seven departments were sequentially randomized to change from control conditions (standard of care referral to outpatient care) to intervention conditions (immediate treatment initiation). The primary outcome was treatment completion, defined as dispensing the final package of the prescribed treatment within six months after enrolment. RESULTS: A total of 200 HCV RNA positive individuals were enrolled between 1 October 2019 and 31 December 2021 (mean age 47.4 years, 72.5% male, 60.5% injected past 3 months, 20.4% cirrhosis). Treatment completion was accomplished by 67 of 98 (68.4% [95% confidence interval {CI}: 58.2-77.4]) during intervention conditions and by 36 of 102 (35.3% [95% CI: 26.1-45.4]) during control conditions (risk difference 33.1% [95% CI: 20.0-46.2]; risk ratio 1.9 [95% CI: 1.4-2.6]). The intervention was superior in terms of treatment completion (adjusted odds ratio [aOR] 4.8 [95% CI: 1.8-12.8]; P = .002) and time to treatment initiation (adjusted hazard ratio [aHR] 4.0 [95% CI: 2.5-6.3]; P < .001). Sustained virologic response was documented in 60 of 98 (61.2% [95% CI: 50.8-70.9]) during intervention and in 66 of 102 (64.7% [95% CI: 54.6-73.9]) during control conditions. CONCLUSIONS: An opportunistic test-and-treat approach to HCV infection was superior to standard of care among hospitalized PWID. The model of care should be considered for broader implementation. Clinical Trials Registration. NCT04220645.

The Ottawa Statement implementation guidance document for cluster randomized trials in the hemodialysis setting.

Research teams are increasingly interested in using cluster randomized trial (CRT) designs to generate practice-guiding evidence for in-center maintenance hemodialysis. However, CRTs raise complex ethical issues. The Ottawa Statement on the Ethical Design and Conduct of Cluster Randomized Trials, published in 2012, provides 15 recommendations to address ethical issues arising within 7 domains: justifying the CRT design, research ethics committee review, identifying research participants, obtaining informed consent, gatekeepers, assessing benefits and harms, and protecting vulnerable participants. But applying the Ottawa Statement recommendations to CRTs in the hemodialysis setting is complicated by the unique features of the setting and population. Here, with the help of content experts and patient partners, we co-developed this implementation guidance document to provide research teams, research ethics committees, and other stakeholders with detailed guidance on how to apply the Ottawa Statement recommendations to CRTs in the hemodialysis setting, the result of a 4-year research project. Thus, our work demonstrates how the voices of patients, caregivers, and all stakeholders may be included in the development of research ethics guidance.

Evaluating tests for cluster-randomized trials with few clusters under generalized linear mixed models with covariate adjustment: A simulation study.

Generalized linear mixed models (GLMM) are commonly used to analyze clustered data, but when the number of clusters is small to moderate, standard statistical tests may produce elevated type I error rates. Small-sample corrections have been proposed for continuous or binary outcomes without covariate adjustment. However, appropriate tests to use for count outcomes or under covariate-adjusted models remains unknown. An important setting in which this issue arises is in cluster-randomized trials (CRTs). Because many CRTs have just a few clusters (eg, clinics or health systems), covariate adjustment is particularly critical to address potential chance imbalance and/or low power (eg, adjustment following stratified randomization or for the baseline value of the outcome). We conducted simulations to evaluate GLMM-based tests of the treatment effect that account for the small (10) or moderate (20) number of clusters under a parallel-group CRT setting across scenarios of covariate adjustment (including adjustment for one or more person-level or cluster-level covariates) for both binary and count outcomes. We find that when the intraclass correlation is non-negligible ( ≥ $$ \ge $$ 0.01) and the number of covariates is small ( ≤ $$ \le $$ 2), likelihood ratio tests with a between-within denominator degree of freedom have type I error rates close to the nominal level. When the number of covariates is moderate ( ≥ $$ \ge $$ 5), across our simulation scenarios, the relative performance of the tests varied considerably and no method performed uniformly well. Therefore, we recommend adjusting for no more than a few covariates and using likelihood ratio tests with a between-within denominator degree of freedom.

Multiply robust generalized estimating equations for cluster randomized trials with missing outcomes.

Generalized estimating equations (GEEs) provide a useful framework for estimating marginal regression parameters based on data from cluster randomized trials (CRTs), but they can result in inaccurate parameter estimates when some outcomes are informatively missing. Existing techniques to handle missing outcomes in CRTs rely on correct specification of a propensity score model, a covariate-conditional mean outcome model, or require at least one of these two models to be correct, which can be challenging in practice. In this article, we develop new weighted GEEs to simultaneously estimate the marginal mean, scale, and correlation parameters in CRTs with missing outcomes, allowing for multiple propensity score models and multiple covariate-conditional mean models to be specified. The resulting estimators are consistent provided that any one of these models is correct. An iterative algorithm is provided for implementing this more robust estimator and practical considerations for specifying multiple models are discussed. We evaluate the performance of the proposed method through Monte Carlo simulations and apply the proposed multiply robust estimator to analyze the Botswana Combination Prevention Project, a large HIV prevention CRT designed to evaluate whether a combination of HIV-prevention measures can reduce HIV incidence.

Advancing randomized controlled trial methodologies: The place of innovative trial design in eating disorders research.

Randomized controlled trials can be used to generate evidence on the efficacy and safety of new treatments in eating disorders research. Many of the trials previously conducted in this area have been deemed to be of low quality, in part due to a number of practical constraints. This article provides an overview of established and more innovative clinical trial designs, accompanied by pertinent examples, to highlight how design choices can enhance flexibility and improve efficiency of both resource allocation and participant involvement. Trial designs include individually randomized, cluster randomized, and designs with randomizations at multiple time points and/or addressing several research questions (master protocol studies). Design features include the use of adaptations and considerations for pragmatic or registry-based trials. The appropriate choice of trial design, together with rigorous trial conduct, reporting and analysis, can establish high-quality evidence to advance knowledge in the field. It is anticipated that this article will provide a broad and contemporary introduction to trial designs and will help researchers make informed trial design choices for improved testing of new interventions in eating disorders. PUBLIC SIGNIFICANCE: There is a paucity of high quality randomized controlled trials that have been conducted in eating disorders, highlighting the need to identify where efficiency gains in trial design may be possible to advance the eating disorder research field. We provide an overview of some key trial designs and features which may offer solutions to practical constraints and increase trial efficiency.

Reconsidering stepped wedge cluster randomized trial designs with implementation periods: Fewer sequences or the parallel-group design with baseline and implementation periods are potentially more efficient.

BACKGROUND/AIMS: When designing a cluster randomized trial, advantages and disadvantages of tentative designs must be weighed. The stepped wedge design is popular for multiple reasons, including its potential to increase power via improved efficiency relative to a parallel-group design. In many realistic settings, it will take time for clusters to fully implement the intervention. When designing the HEALing (Helping to End Addiction Long-termSM) Communities Study, implementation time was a major consideration, and we examined the efficiency and practicality of three designs. Specifically, a three-sequence stepped wedge design with implementation periods, a corresponding two-sequence modified design that is created by removing the middle sequence, and a parallel-group design with baseline and implementation periods. In this article, we study the relative efficiencies of these specific designs. More generally, we study the relative efficiencies of modified designs when the stepped wedge design with implementation periods has three or more sequences. We also consider different correlation structures. METHODS: We compare efficiencies of stepped wedge designs with implementation periods consisting of three to nine sequences with a variety of corresponding designs. The three-sequence design is compared to the two-sequence modified design and to the parallel-group design with baseline and implementation periods analysed via analysis of covariance. Stepped wedge designs with implementation periods consisting of four or more sequences are compared to modified designs that remove all or a subset of 'middle' sequences. Efficiencies are based on the use of linear mixed effects models. RESULTS: In the studied settings, the modified design is more efficient than the three-sequence stepped wedge design with implementation periods. The parallel-group design with baseline and implementation periods with analysis of covariance-based analysis is often more efficient than the three-sequence design. With respect to stepped wedge designs with implementation periods that are comprised of more sequences, there are often corresponding modified designs that improve efficiency. However, use of only the first and last sequences has the potential to be either relatively efficient or inefficient. Relative efficiency is impacted by the strength of the statistical correlation among outcomes from the same cluster; for example, the relative efficiencies of modified designs tend to be greater for smaller cluster auto-correlation values. CONCLUSION: If a three-sequence stepped wedge design with implementation periods is being considered for a future cluster randomized trial, then a corresponding modified design using only the first and last sequences should be considered if sole focus is on efficiency. However, a parallel-group design with baseline and implementation periods and analysis of covariance-based analysis can be a practical, efficient alternative. For stepped wedge designs with implementation periods and a larger number of sequences, modified versions that remove 'middle' sequences should be considered. Due to the potential sensitivity of design efficiencies, statistical correlation should be carefully considered.

Study protocol of the OrkA project: orofacial and communicative activation in old age- a cluster randomized prevention study in long-term care facilities in Lower Saxony, Germany.

BACKGROUND: The process of aging involves numerous changes in the body, influencing physical, mental, and emotional well-being. Age-related changes and degradation can impact various functions of the swallowing process and lead to delayed word retrieval. Individuals with limited linguistic stimulation may experience a more rapid decline in cognitive performance. Thus, this project explores a preventive training program targeting swallowing and linguistic-communicative skills, aimed at preserving the social participation of older individuals residing in nursing homes. METHODS: A preventive intervention program, combining orofaciopharyngeal and linguistic-communicative components, will be offered twice weekly over 12 weeks in long-term care facilities in the greater Hanover area. The program will aim at: (a) activating sensitive and motor skills in the orofaciopharyngeal area to counter age-related swallowing disorders, and (b) enhancing communicative abilities through semantic-lexical activation. A cluster randomized controlled trial will be conducted to investigate whether the intervention program improves swallowing skills in older adults. Additionally, a secondary analysis will explore the impact on language skills and social participation, as well as program acceptance. DISCUSSION: The results will provide valuable insight into the effectiveness of preventive measures addressing swallowing and speech issues in older individuals. TRIAL REGISTRATION: The trial was registered with DRKS (German register for clinical trials) in June 2023 (study ID: DRKS00031594) and the WHO International Clinical Trail Registry Platform (secondary register).

Integrated pulmonary index during procedural sedation and analgesia: A cluster-randomized trial.

AIM: To evaluate the effectiveness of utilizing the integrated pulmonary index for capnography implementation during sedation administered by nurses. DESIGN: Cluster-randomized trial. METHODS: Participants were enrolled from the interventional radiology department at an academic hospital in Canada. Nurses were randomized to either enable or disable the Integrated Pulmonary Index feature of the capnography monitor. Procedures were observed by a research assistant to collect information about alarm performance characteristics. The primary outcome was the number of seconds in an alert condition state without an intervention being applied. RESULTS: The number of seconds in an alarm state without intervention was higher in the group that enabled the integrated pulmonary index compared to the group that disabled this feature, but this difference did not reach statistical significance. Likewise, the difference between groups for the total alarm duration, total number of alarms and the total number of appropriate alarms was not statistically significant. The number of inappropriate alarms was higher in the group that enabled the Integrated Pulmonary Index, but this estimate was highly imprecise. There was no difference in the odds of an adverse event (measured by the Tracking and Reporting Outcomes of Procedural Sedation tool) occurring between groups. Desaturation events were uncommon and brief in both groups but the area under the SpO2 90% desaturation curve scores were lower for the group that enabled the integrated pulmonary index. CONCLUSION: Enabling the integrated pulmonary index during nurse-administered procedural sedation did not reduce nurses' response times to alarms. Therefore, integrating multiple physiological parameters related to respiratory assessment into a single index did not lower the threshold for intervention by nurses. IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: The time it takes to respond to capnography monitor alarms will not be reduced if the integrated pulmonary Iindex feature of capnography monitors is enabled during nurse-administered procedural sedation. IMPACT: Results do not support the routine enabling of the integrated pulmonary index when nurses use capnography to monitor patients during procedural sedation as a strategy to reduce the time it takes to initiate responses to alarms. REPORTING METHOD: CONSORT. PATIENT OR PUBLIC CONTRIBUTION: There was no patient or public contribution. TRIAL REGISTRATION: This study was prospectively registered at ClinicalTrials.gov (ID: NCT05068700).

Cluster Randomized Trials with a Pretest and Posttest: Equivalence of Three-, Two- and One-Level Analyses, and Sample Size Calculation.

In a cluster randomized trial clusters of persons, for instance, schools or health centers, are assigned to treatments, and all persons in the same cluster get the same treatment. Although less powerful than individual randomization, cluster randomization is a good alternative if individual randomization is impossible or leads to severe treatment contamination (carry-over). Focusing on cluster randomized trials with a pretest and post-test of a quantitative outcome, this paper shows the equivalence of four methods of analysis: a three-level mixed (multilevel) regression for repeated measures with as levels cluster, person, and time, and allowing for unstructured between-cluster and within-cluster covariance matrices; a two-level mixed regression with as levels cluster and person, using change from baseline as outcome; a two-level mixed regression with as levels cluster and time, using cluster means as data; a one-level analysis of cluster means of change from baseline. Subsequently, similar equivalences are shown between a constrained mixed model and methods using the pretest as covariate. All methods are also compared on a cluster randomized trial on mental health in children. From these equivalences follows a simple method to calculate the sample size for a cluster randomized trial with baseline measurement, which is demonstrated step-by-step.

Sample Size Calculation for an Individual Stepped-Wedge Randomized Trial.

In the individual stepped-wedge randomized trial (ISW-RT), subjects are allocated to sequences, each sequence being defined by a control period followed by an experimental period. The total follow-up time is the same for all sequences, but the duration of the control and experimental periods varies among sequences. To our knowledge, there is no validated sample size calculation formula for ISW-RTs unlike stepped-wedge cluster randomized trials (SW-CRTs). The objective of this study was to adapt the formula used for SW-CRTs to the case of individual randomization and to validate this adaptation using a Monte Carlo simulation study. The proposed sample size calculation formula for an ISW-RT design yielded satisfactory empirical power for most scenarios except scenarios with operating characteristic values near the boundary (i.e., smallest possible number of periods, very high or very low autocorrelation coefficient). Overall, the results provide useful insights into the sample size calculation for ISW-RTs.

Routine vs. On-Demand Discharge Planning Strategy in Intermediate-Risk Patients for Complex Discharge: a Cluster-Randomized, Multiple Crossover Trial.

BACKGROUND: Early hospital discharge planning can help to reduce the length of stay and unplanned readmission in high-risk patients. Therefore, it is important to select patients who can benefit from a personalized discharge planning based on validated tools. The modified Blaylock Risk Assessment Screening Score (BRASS) is routinely used in the Molinette Hospital (Turin, Italy) to screen patients at high risk for discharge, but the effectiveness of the discharge planning is uncertain in intermediate-risk patients. OBJECTIVE: To evaluate the best strategy for discharge planning by the Continuity of Care Hospital Unit (CCHU) in intermediate-risk patients according to modified BRASS. DESIGN: Cluster-randomized, multiple crossover trial. PARTICIPANTS: Adult patients admitted in the Medicine and Neurology departments of the Molinette Hospital in Turin, Italy, between June 2018 and May 2019 with a BRASS intermediate risk. INTERVENTIONS: A routine discharge planning strategy (RDP, Routine Discharge Plan), which involved the management of all intermediate-risk patients, was compared to an on-demand discharge planning strategy (DDP, on-Demand Discharge Planning), which involved only selected patients referred to the CCHU by ward staff. MAIN MEASURES: The primary outcome was the 90-day hospital readmission for any cause (HR90). Secondary outcomes included the prolonged length of stay (pLOS). KEY RESULTS: Eight hundred two patients (median age 79 years) were included (414 RDP and 388 DDP). Comparing RDP vs. DDP periods, HR90 was 27.6% and 27.3% (OR 1.01, 90%CI 0.76-1.33, p = 0.485); and pLOS was 47 (11.4%) and 40 (10.3%) (OR 1.24, 95%CI 0.72-2.13, p = 0.447), respectively. CONCLUSIONS: This is one of the largest randomized study conducted to compare the effectiveness of two different hospital discharge planning strategies. In patients with intermediate risk of hospital discharge, a RDP offers no advantage over a DDP and results in an unnecessary increase in staff workload. TRIAL REGISTRATION: Clinicaltrials.gov: NCT03436940.

Sample size considerations for stepped wedge designs with subclusters.

The stepped wedge cluster randomized trial (SW-CRT) is an increasingly popular design for evaluating health service delivery or policy interventions. An essential consideration of this design is the need to account for both within-period and between-period correlations in sample size calculations. Especially when embedded in health care delivery systems, many SW-CRTs may have subclusters nested in clusters, within which outcomes are collected longitudinally. However, existing sample size methods that account for between-period correlations have not allowed for multiple levels of clustering. We present computationally efficient sample size procedures that properly differentiate within-period and between-period intracluster correlation coefficients in SW-CRTs in the presence of subclusters. We introduce an extended block exchangeable correlation matrix to characterize the complex dependencies of outcomes within clusters. For Gaussian outcomes, we derive a closed-form sample size expression that depends on the correlation structure only through two eigenvalues of the extended block exchangeable correlation structure. For non-Gaussian outcomes, we present a generic sample size algorithm based on linearization and elucidate simplifications under canonical link functions. For example, we show that the approximate sample size formula under a logistic linear mixed model depends on three eigenvalues of the extended block exchangeable correlation matrix. We provide an extension to accommodate unequal cluster sizes and validate the proposed methods via simulations. Finally, we illustrate our methods in two real SW-CRTs with subclusters.

Implementation research of a cluster randomized trial evaluating the implementation and effectiveness of intermittent preventive treatment for malaria using dihydroartemisinin-piperaquine on reducing malaria burden in school-aged children in Tanzania: methodology, challenges, and mitigation.

BACKGROUND: It has been more than 20 years since the malaria epidemiologic shift to school-aged children was noted. In the meantime, school-aged children (5-15 years) have become increasingly more vulnerable with asymptomatic malaria prevalence reaching up to 70%, making them reservoirs for subsequent transmission of malaria in the endemic communities. Intermittent Preventive Treatment of malaria in schoolchildren (IPTsc) has proven to be an effective tool to shrink this reservoir. As of 3rd June 2022, the World Health Organization recommends IPTsc in moderate and high endemic areas. Even so, for decision-makers, the adoption of scientific research recommendations has been stifled by real-world implementation challenges. This study presents methodology, challenges faced, and mitigations used in the evaluation of the implementation of IPTsc using dihydroartemisinin-piperaquine (DP) in three councils (Handeni District Council (DC), Handeni Town Council (TC) and Kilindi DC) of Tanga Region, Tanzania so as to understand the operational feasibility and effectiveness of IPTsc on malaria parasitaemia and clinical malaria incidence. METHODS: The study deployed an effectiveness-implementation hybrid design to assess feasibility and effectiveness of IPTsc using DP, the interventional drug, against standard of care (control). Wards in the three study councils were the randomization unit (clusters). Each ward was randomized to implement IPTsc or not (control). In all wards in the IPTsc arm, DP was given to schoolchildren three times a year in four-month intervals. In each council, 24 randomly selected wards (12 per study arm, one school per ward) were chosen as representatives for intervention impact evaluation. Mixed design methods were used to assess the feasibility and acceptability of implementing IPTsc as part of a more comprehensive health package for schoolchildren. The study reimagined an existing school health programme for Neglected Tropical Diseases (NTD) control include IPTsc implementation. RESULTS: The study shows IPTsc can feasibly be implemented by integrating it into existing school health and education systems, paving the way for sustainable programme adoption in a cost-effective manner. CONCLUSIONS: Through this article other interested countries may realise a feasible plan for IPTsc implementation. Mitigation to any challenge can be customized based on local circumstances without jeopardising the gains expected from an IPTsc programme. Trial registration clinicaltrials.gov, NCT04245033. Registered 28 January 2020, https://clinicaltrials.gov/ct2/show/NCT04245033.

Permutation-based multiple testing corrections for P $$ P $$ -values and confidence intervals for cluster randomized trials.

In this article, we derive and compare methods to derive P-values and sets of confidence intervals with strong control of the family-wise error rates and coverage for estimates of treatment effects in cluster randomized trials with multiple outcomes. There are few methods for P-value corrections and deriving confidence intervals, limiting their application in this setting. We discuss the methods of Bonferroni, Holm, and Romano and Wolf and adapt them to cluster randomized trial inference using permutation-based methods with different test statistics. We develop a novel search procedure for confidence set limits using permutation tests to produce a set of confidence intervals under each method of correction. We conduct a simulation-based study to compare family-wise error rates, coverage of confidence sets, and the efficiency of each procedure in comparison to no correction using both model-based standard errors and permutation tests. We show that the Romano-Wolf type procedure has nominal error rates and coverage under non-independent correlation structures and is more efficient than the other methods in a simulation-based study. We also compare results from the analysis of a real-world trial.

Maximin optimal cluster randomized designs for assessing treatment effect heterogeneity.

Cluster randomized trials (CRTs) are studies where treatment is randomized at the cluster level but outcomes are typically collected at the individual level. When CRTs are employed in pragmatic settings, baseline population characteristics may moderate treatment effects, leading to what is known as heterogeneous treatment effects (HTEs). Pre-specified, hypothesis-driven HTE analyses in CRTs can enable an understanding of how interventions may impact subpopulation outcomes. While closed-form sample size formulas have recently been proposed, assuming known intracluster correlation coefficients (ICCs) for both the covariate and outcome, guidance on optimal cluster randomized designs to ensure maximum power with pre-specified HTE analyses has not yet been developed. We derive new design formulas to determine the cluster size and number of clusters to achieve the locally optimal design (LOD) that minimizes variance for estimating the HTE parameter given a budget constraint. Given the LODs are based on covariate and outcome-ICC values that are usually unknown, we further develop the maximin design for assessing HTE, identifying the combination of design resources that maximize the relative efficiency of the HTE analysis in the worst case scenario. In addition, given the analysis of the average treatment effect is often of primary interest, we also establish optimal designs to accommodate multiple objectives by combining considerations for studying both the average and heterogeneous treatment effects. We illustrate our methods using the context of the Kerala Diabetes Prevention Program CRT, and provide an R Shiny app to facilitate calculation of optimal designs under a wide range of design parameters.

Power analyses for stepped wedge designs with multivariate continuous outcomes.

Multivariate outcomes are common in pragmatic cluster randomized trials. While sample size calculation procedures for multivariate outcomes exist under parallel assignment, none have been developed for a stepped wedge design. In this article, we present computationally efficient power and sample size procedures for stepped wedge cluster randomized trials (SW-CRTs) with multivariate outcomes that differentiate the within-period and between-period intracluster correlation coefficients (ICCs). Under a multivariate linear mixed model, we derive the joint distribution of the intervention test statistics which can be used for determining power under different hypotheses and provide an example using the commonly utilized intersection-union test for co-primary outcomes. Simplifications under a common treatment effect and common ICCs across endpoints and an extension to closed-cohort designs are also provided. Finally, under the common ICC across endpoints assumption, we formally prove that the multivariate linear mixed model leads to a more efficient treatment effect estimator compared to the univariate linear mixed model, providing a rigorous justification on the use of the former with multivariate outcomes. We illustrate application of the proposed methods using data from an existing SW-CRT and present extensive simulations to validate the methods.

Sample size considerations for assessing treatment effect heterogeneity in randomized trials with heterogeneous intracluster correlations and variances.

An important consideration in the design and analysis of randomized trials is the need to account for outcome observations being positively correlated within groups or clusters. Two notable types of designs with this consideration are individually randomized group treatment trials and cluster randomized trials. While sample size methods for testing the average treatment effect are available for both types of designs, methods for detecting treatment effect modification are relatively limited. In this article, we present new sample size formulas for testing treatment effect modification based on either a univariate or multivariate effect modifier in both individually randomized group treatment and cluster randomized trials with a continuous outcome but any types of effect modifier, while accounting for differences across study arms in the outcome variance, outcome intracluster correlation coefficient (ICC) and the cluster size. We consider cases where the effect modifier can be measured at either the individual level or cluster level, and with a univariate effect modifier, our closed-form sample size expressions provide insights into the optimal allocation of groups or clusters to maximize design efficiency. Overall, our results show that the required sample size for testing treatment effect heterogeneity with an individual-level effect modifier can be affected by unequal ICCs and variances between arms, and accounting for such between-arm heterogeneity can lead to more accurate sample size determination. We use simulations to validate our sample size formulas and illustrate their application in the context of two real trials: an individually randomized group treatment trial (the AWARE study) and a cluster randomized trial (the K-DPP study).

The Cost-Effectiveness of Financial Incentives to Achieve Heroin Abstinence in Individuals With Heroin Use Disorder Starting New Treatment Episodes: A Cluster Randomized Trial-Based Economic Evaluation.

OBJECTIVES: Cost-effectiveness analysis of two 12-week contingency management (CM) schedules targeting heroin abstinence or attendance at weekly keyworker appointments for opioid agonist treatment compared with treatment as usual (TAU). METHODS: A cost-effectiveness analysis was conducted alongside a cluster randomized trial of 552 patients from 34 clusters (drug treatment clinics) randomly allocated 1:1:1 to opioid agonist treatment plus weekly keyworker appointments with (1) CM targeted at heroin abstinence (CM abstinence), (2) CM targeted at on-time attendance at weekly appointments (CM attendance), or (3) no CM (TAU). The primary cost-effectiveness analysis at 24 weeks after randomization took a societal cost perspective with effects measured in heroin-negative urine samples. RESULTS: At 24 weeks, mean differences in weekly heroin-negative urine results compared with TAU were 0.252 (95% confidence interval [CI] -0.397 to 0.901) for CM abstinence and 0.089 (95% CI -0.223 to 0.402) for CM attendance. Mean differences in costs were £2562 (95% CI £32-£5092) for CM abstinence and £317 (95% CI -£882 to £1518) for CM attendance. Incremental cost-effectiveness ratios were £10 167 per additional heroin-free urine for CM abstinence and £3562 for CM attendance with low probabilities of cost-effectiveness of 3.5% and 36%, respectively. Results were sensitive to timing of follow-up for CM attendance, which dominated TAU (better outcomes, lower costs) at 12 weeks, with an 88.4% probability of being cost-effective. Probability of cost-effectiveness remained low for CM abstinence (8.6%). CONCLUSIONS: Financial incentives targeted toward heroin abstinence and treatment attendance were not cost-effective over the 24-week follow-up. Nevertheless, CM attendance was cost-effective over the treatment period (12 weeks), when participants were receiving keyworker appointments and incentives.

Accounting for expected attrition in the planning of cluster randomized trials for assessing treatment effect heterogeneity.

BACKGROUND: Detecting treatment effect heterogeneity is an important objective in cluster randomized trials and implementation research. While sample size procedures for testing the average treatment effect accounting for participant attrition assuming missing completely at random or missing at random have been previously developed, the impact of attrition on the power for detecting heterogeneous treatment effects in cluster randomized trials remains unknown. METHODS: We provide a sample size formula for testing for a heterogeneous treatment effect assuming the outcome is missing completely at random. We also propose an efficient Monte Carlo sample size procedure for assessing heterogeneous treatment effect assuming covariate-dependent outcome missingness (missing at random). We compare our sample size methods with the direct inflation method that divides the estimated sample size by the mean follow-up rate. We also evaluate our methods through simulation studies and illustrate them with a real-world example. RESULTS: Simulation results show that our proposed sample size methods under both missing completely at random and missing at random provide sufficient power for assessing heterogeneous treatment effect. The proposed sample size methods lead to more accurate sample size estimates than the direct inflation method when the missingness rate is high (e.g., ≥ 30%). Moreover, sample size estimation under both missing completely at random and missing at random is sensitive to the missingness rate, but not sensitive to the intracluster correlation coefficient among the missingness indicators. CONCLUSION: Our new sample size methods can assist in planning cluster randomized trials that plan to assess a heterogeneous treatment effect and participant attrition is expected to occur.

Only one cluster per condition is an invalid design for a cluster randomized trial and should be re-labeled a quasi-experimental study. Response to: "Effect of behavioral activation on time and frequency domain heart rate variability in older adults with subthreshold depression: a cluster randomized controlled trial in Thailand".

Ayudhaya et al. examined the effect of Behavioral Activation on daily step count and heart rate variability among older adults with depression in a study labeled a cluster randomized controlled trial (cRCT). However, only one cluster was assigned to either of the study conditions. Such a design would have zero degrees of freedom for inferential testing, because the variation due to cluster membership cannot be estimated apart from the variation due to treatment assignment. Thus, the intervention effect is completely confounded with the cluster effect. The study should be labeled a quasi-experimental study, not a cRCT. Accordingly, the numerical results should be interpreted as associations but not evidence for causal relationships.