Liberal or Restrictive Transfusion Strategy in Patients with Traumatic Brain Injury.

BACKGROUND: The effect of a liberal transfusion strategy as compared with a restrictive strategy on outcomes in critically ill patients with traumatic brain injury is unclear. METHODS: We randomly assigned adults with moderate or severe traumatic brain injury and anemia to receive transfusion of red cells according to a liberal strategy (transfusions initiated at a hemoglobin level of ≤10 g per deciliter) or a restrictive strategy (transfusions initiated at ≤7 g per deciliter). The primary outcome was an unfavorable outcome as assessed by the score on the Glasgow Outcome Scale-Extended at 6 months, which we categorized with the use of a sliding dichotomy that was based on the prognosis of each patient at baseline. Secondary outcomes included mortality, functional independence, quality of life, and depression at 6 months. RESULTS: A total of 742 patients underwent randomization, with 371 assigned to each group. The analysis of the primary outcome included 722 patients. The median hemoglobin level in the intensive care unit was 10.8 g per deciliter in the group assigned to the liberal strategy and 8.8 g per deciliter in the group assigned to the restrictive strategy. An unfavorable outcome occurred in 249 of 364 patients (68.4%) in the liberal-strategy group and in 263 of 358 (73.5%) in the restrictive-strategy group (adjusted absolute difference, restrictive strategy vs. liberal strategy, 5.4 percentage points; 95% confidence interval, -2.9 to 13.7). Among survivors, a liberal strategy was associated with higher scores on some but not all the scales assessing functional independence and quality of life. No association was observed between the transfusion strategy and mortality or depression. Venous thromboembolic events occurred in 8.4% of the patients in each group, and acute respiratory distress syndrome occurred in 3.3% and 0.8% of patients in the liberal-strategy and restrictive-strategy groups, respectively. CONCLUSIONS: In critically ill patients with traumatic brain injury and anemia, a liberal transfusion strategy did not reduce the risk of an unfavorable neurologic outcome at 6 months. (Funded by the Canadian Institutes of Health Research and others; HEMOTION ClinicalTrials.gov number, NCT03260478.).

Trial of an Intervention to Improve Acute Heart Failure Outcomes.

BACKGROUND: Patients with acute heart failure are frequently or systematically hospitalized, often because the risk of adverse events is uncertain and the options for rapid follow-up are inadequate. Whether the use of a strategy to support clinicians in making decisions about discharging or admitting patients, coupled with rapid follow-up in an outpatient clinic, would affect outcomes remains uncertain. METHODS: In a stepped-wedge, cluster-randomized trial conducted in Ontario, Canada, we randomly assigned 10 hospitals to staggered start dates for one-way crossover from the control phase (usual care) to the intervention phase, which involved the use of a point-of-care algorithm to stratify patients with acute heart failure according to the risk of death. During the intervention phase, low-risk patients were discharged early (in ≤3 days) and received standardized outpatient care, and high-risk patients were admitted to the hospital. The coprimary outcomes were a composite of death from any cause or hospitalization for cardiovascular causes within 30 days after presentation and the composite outcome within 20 months. RESULTS: A total of 5452 patients were enrolled in the trial (2972 during the control phase and 2480 during the intervention phase). Within 30 days, death from any cause or hospitalization for cardiovascular causes occurred in 301 patients (12.1%) who were enrolled during the intervention phase and in 430 patients (14.5%) who were enrolled during the control phase (adjusted hazard ratio, 0.88; 95% confidence interval [CI], 0.78 to 0.99; P = 0.04). Within 20 months, the cumulative incidence of primary-outcome events was 54.4% (95% CI, 48.6 to 59.9) among patients who were enrolled during the intervention phase and 56.2% (95% CI, 54.2 to 58.1) among patients who were enrolled during the control phase (adjusted hazard ratio, 0.95; 95% CI, 0.92 to 0.99). Fewer than six deaths or hospitalizations for any cause occurred in low- or intermediate-risk patients before the first outpatient visit within 30 days after discharge. CONCLUSIONS: Among patients with acute heart failure who were seeking emergency care, the use of a hospital-based strategy to support clinical decision making and rapid follow-up led to a lower risk of the composite of death from any cause or hospitalization for cardiovascular causes within 30 days than usual care. (Funded by the Ontario SPOR Support Unit and others; COACH ClinicalTrials.gov number, NCT02674438.).

A preclinical systematic review and meta-analysis assessing the effect of biological sex in lipopolysaccharide-induced acute lung injury.

It is unclear what effect biological sex has on outcomes of acute lung injury (ALI). Clinical studies are confounded by their observational design. We addressed this knowledge gap with a preclinical systematic review of ALI animal studies. We searched MEDLINE and Embase for studies of intratracheal/intranasal/aerosolized lipopolysaccharide administration (the most common ALI model) that reported sex-stratified data. Screening and data extraction were conducted in duplicate. Our primary outcome was histological tissue injury and secondary outcomes included alveolar-capillary barrier alterations and inflammatory markers. We used a random-effects inverse variance meta-analysis, expressing data as standardized mean difference (SMD) with 95% confidence intervals (CIs). Risk of bias was assessed using the Systematic Review Centre for Laboratory Animal Experimentation (SYRCLE) tool. We identified six studies involving 132 animals across 11 independent experiments. A total of 41 outcomes were extracted, with the direction of effect suggesting greater severity in males than females in 26/41 outcomes (63%). One study reported on lung histology and found that male mice exhibited greater injury than females (SMD: 1.61, 95% CI: 0.53-2.69). Meta-analysis demonstrated significantly elevated albumin levels (SMD: 2.17, 95% CI: 0.63-3.70) and total cell counts (SMD: 0.80, 95% CI: 0.27-1.33) in bronchoalveolar lavage fluid from male mice compared with female mice. Most studies had an "unclear risk of bias." Our findings suggest sex-related differences in ALI severity. However, these conclusions are drawn from a small number of animals and studies. Further research is required to address the fundamental issue of biological sex differences in LPS-induced ALI.

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.

A Population-Based Matched Cohort Study of Digestive System Cancer Incidence and Mortality in Individuals With and Without Inflammatory Bowel Disease.

INTRODUCTION: To study digestive system cancer risks in inflammatory bowel diseases (IBD) in the biologic era. METHODS: We used population-level administrative and cancer registry data from Ontario, Canada (1994 - 2020) to compare people with IBD to matched controls (1:10 by sex and birth year) on trends in age-sex standardized cancer incidence and risk ratios of incident cancers and cancer-related deaths. RESULTS: Among 110,919 IBD and 1,109,190 controls, colorectal cancer (CRC) incidence (per 100,000 person-years) declined similarly in people with ulcerative colitis (average annual percentage change (AAPC) -1.81; 95% CI, -2.48, -1.156) and controls (AAPC -2.79; 95% CI, -3.44, -2.14), while small bowel cancer incidence rose faster in those with Crohn's disease (AAPC 9.68; 95% CI, 2.51, 17.3) than controls (AAPC 3.64; 95% CI, 1.52, 5.80). Extra-intestinal digestive cancer incidence rose faster in people with IBD (AAPC 3.27; 95% CI, 1.83, 4.73) than controls (AAPC -1.87; 95% CI, -2.33, -1.42), particularly for liver (IBD AAPC 8.48; 95% CI, 4.11, 13.1) and bile duct (IBD AAPC 7.22; 95 % CI, 3.74, 10.8) cancers. Beyond 2010, the incidences (and respective mortality rates) of colorectal (1.60; 95% CI, 1.46, 1.75), small bowel (4.10; 95% CI 3.37, 4.99), bile duct (2.33; 95% CI 1.96, 2.77) and pancreatic (1.19; 95% CI, 1.00, 1.40) cancers, were higher in people with IBD. DISCUSSION: Cancer incidence is declining for CRC and rising for other digestive cancers in people with IBD. Incidence and mortality remain higher in IBD than controls for colorectal, small bowel, bile duct and pancreatic cancers.

Comparison of statistical methods used to meta-analyse results from interrupted time series studies: an empirical study.

BACKGROUND: The Interrupted Time Series (ITS) is a robust design for evaluating public health and policy interventions or exposures when randomisation may be infeasible. Several statistical methods are available for the analysis and meta-analysis of ITS studies. We sought to empirically compare available methods when applied to real-world ITS data. METHODS: We sourced ITS data from published meta-analyses to create an online data repository. Each dataset was re-analysed using two ITS estimation methods. The level- and slope-change effect estimates (and standard errors) were calculated and combined using fixed-effect and four random-effects meta-analysis methods. We examined differences in meta-analytic level- and slope-change estimates, their 95% confidence intervals, p-values, and estimates of heterogeneity across the statistical methods. RESULTS: Of 40 eligible meta-analyses, data from 17 meta-analyses including 282 ITS studies were obtained (predominantly investigating the effects of public health interruptions (88%)) and analysed. We found that on average, the meta-analytic effect estimates, their standard errors and between-study variances were not sensitive to meta-analysis method choice, irrespective of the ITS analysis method. However, across ITS analysis methods, for any given meta-analysis, there could be small to moderate differences in meta-analytic effect estimates, and important differences in the meta-analytic standard errors. Furthermore, the confidence interval widths and p-values for the meta-analytic effect estimates varied depending on the choice of confidence interval method and ITS analysis method. CONCLUSIONS: Our empirical study showed that meta-analysis effect estimates, their standard errors, confidence interval widths and p-values can be affected by statistical method choice. These differences may importantly impact interpretations and conclusions of a meta-analysis and suggest that the statistical methods are not interchangeable in practice.

Describing the content of trial recruitment interventions using the TIDieR reporting checklist: a systematic methodology review.

BACKGROUND: Recruiting participants to clinical trials is an ongoing challenge, and relatively little is known about what recruitment strategies lead to better recruitment. Recruitment interventions can be considered complex interventions, often involving multiple components, targeting a variety of groups, and tailoring to different groups. We used the Template for Intervention Description and Replication (TIDieR) reporting checklist (which comprises 12 items recommended for reporting complex interventions) to guide the assessment of how recruitment interventions are described. We aimed to (1) examine to what extent we could identify information about each TIDieR item within recruitment intervention studies, and (2) observe additional detail for each item to describe useful variation among these studies. METHODS: We identified randomized, nested recruitment intervention studies providing recruitment or willingness to participate rates from two sources: a Cochrane review of trials evaluating strategies to improve recruitment to randomized trials, and the Online Resource for Research in Clinical triAls database. First, we assessed to what extent authors reported information about each TIDieR item. Second, we developed descriptive categorical variables for 7 TIDieR items and extracting relevant quotes for the other 5 items. RESULTS: We assessed 122 recruitment intervention studies. We were able to extract information relevant to most TIDieR items (e.g., brief rationale, materials, procedure) with the exception of a few items that were only rarely reported (e.g., tailoring, modifications, planned/actual fidelity). The descriptive variables provided a useful overview of study characteristics, with most studies using various forms of informational interventions (55%) delivered at a single time point (90%), often by a member of the research team (59%) in a clinical care setting (41%). CONCLUSIONS: Our TIDieR-based variables provide a useful description of the core elements of complex trial recruitment interventions. Recruitment intervention studies report core elements of complex interventions variably; some process elements (e.g., mode of delivery, location) are almost always described, while others (e.g., duration, fidelity) are reported infrequently, with little indication of a reason for their absence. Future research should explore whether these TIDieR-based variables can form the basis of an approach to better reporting of elements of successful recruitment interventions.

Adherence to key recommendations for design and analysis of stepped-wedge cluster randomized trials: A review of trials published 2016-2022.

BACKGROUND/AIMS: The stepped-wedge cluster randomized trial (SW-CRT), in which clusters are randomized to a time at which they will transition to the intervention condition - rather than a trial arm - is a relatively new design. SW-CRTs have additional design and analytical considerations compared to conventional parallel arm trials. To inform future methodological development, including guidance for trialists and the selection of parameters for statistical simulation studies, we conducted a review of recently published SW-CRTs. Specific objectives were to describe (1) the types of designs used in practice, (2) adherence to key requirements for statistical analysis, and (3) practices around covariate adjustment. We also examined changes in adherence over time and by journal impact factor. METHODS: We used electronic searches to identify primary reports of SW-CRTs published 2016-2022. Two reviewers extracted information from each trial report and its protocol, if available, and resolved disagreements through discussion. RESULTS: We identified 160 eligible trials, randomizing a median (Q1-Q3) of 11 (8-18) clusters to 5 (4-7) sequences. The majority (122, 76%) were cross-sectional (almost all with continuous recruitment), 23 (14%) were closed cohorts and 15 (9%) open cohorts. Many trials had complex design features such as multiple or multivariate primary outcomes (50, 31%) or time-dependent repeated measures (27, 22%). The most common type of primary outcome was binary (51%); continuous outcomes were less common (26%). The most frequently used method of analysis was a generalized linear mixed model (112, 70%); generalized estimating equations were used less frequently (12, 8%). Among 142 trials with fewer than 40 clusters, only 9 (6%) reported using methods appropriate for a small number of clusters. Statistical analyses clearly adjusted for time effects in 119 (74%), for within-cluster correlations in 132 (83%), and for distinct between-period correlations in 13 (8%). Covariates were included in the primary analysis of the primary outcome in 82 (51%) and were most often individual-level covariates; however, clear and complete pre-specification of covariates was uncommon. Adherence to some key methodological requirements (adjusting for time effects, accounting for within-period correlation) was higher among trials published in higher versus lower impact factor journals. Substantial improvements over time were not observed although a slight improvement was observed in the proportion accounting for a distinct between-period correlation. CONCLUSIONS: Future methods development should prioritize methods for SW-CRTs with binary or time-to-event outcomes, small numbers of clusters, continuous recruitment designs, multivariate outcomes, or time-dependent repeated measures. Trialists, journal editors, and peer reviewers should be aware that SW-CRTs have additional methodological requirements over parallel arm designs including the need to account for period effects as well as complex intracluster correlations.

Assessing the quality and value of metabolic chart data for capturing core outcomes for pediatric medium-chain acyl-CoA dehydrogenase (MCAD) deficiency.

BACKGROUND: Generating rigorous evidence to inform care for rare diseases requires reliable, sustainable, and longitudinal measurement of priority outcomes. Having developed a core outcome set for pediatric medium-chain acyl-CoA dehydrogenase (MCAD) deficiency, we aimed to assess the feasibility of prospective measurement of these core outcomes during routine metabolic clinic visits. METHODS: We used existing cohort data abstracted from charts of 124 children diagnosed with MCAD deficiency who participated in a Canadian study which collected data from birth to a maximum of 11 years of age to investigate the frequency of clinic visits and quality of metabolic chart data for selected outcomes. We recorded all opportunities to collect outcomes from the medical chart as a function of visit rate to the metabolic clinic, by treatment centre and by child age. We applied a data quality framework to evaluate data based on completeness, conformance, and plausibility for four core MCAD outcomes: emergency department use, fasting time, metabolic decompensation, and death. RESULTS: The frequency of metabolic clinic visits decreased with increasing age, from a rate of 2.8 visits per child per year (95% confidence interval, 2.3-3.3) among infants 2 to 6 months, to 1.0 visit per child per year (95% confidence interval, 0.9-1.2) among those ≥ 5 years of age. Rates of emergency department visits followed anticipated trends by child age. Supplemental findings suggested that some emergency visits occur outside of the metabolic care treatment centre but are not captured. Recommended fasting times were updated relatively infrequently in patients' metabolic charts. Episodes of metabolic decompensation were identifiable but required an operational definition based on acute manifestations most commonly recorded in the metabolic chart. Deaths occurred rarely in these patients and quality of mortality data was not evaluated. CONCLUSIONS: Opportunities to record core outcomes at the metabolic clinic occur at least annually for children with MCAD deficiency. Methods to comprehensively capture emergency care received at outside institutions are needed. To reduce substantial heterogeneous recording of core outcome across treatment centres, improved documentation standards are required for recording of recommended fasting times and a consensus definition for metabolic decompensations needs to be developed and implemented.

Temporal Trends in Gestational Diabetes Mellitus and Associated Risk Factors in Ontario, Canada, 2012 to 2020: A Population-Based Cohort Study.

OBJECTIVE: The prevalence of gestational diabetes mellitus (GDM) has been increasing globally over recent decades; however, underlying reasons for the increase remain unclear. We analyzed trends in GDM rates and evaluated risk factors associated with the observed trends in Ontario, Canada. METHODS: We conducted a retrospective population-based cohort study using the Better Outcomes Registry and Network (BORN) Ontario, linked with the Canadian Institute for Health Information Discharge Abstract Database (CIHI-DAD). All pregnant individuals who had a singleton hospital delivery from 1 April 2012 to 31 March 2020 were included. We calculated rates and 95% confidence intervals (CIs) for GDM by year of delivery and contrasted fiscal year 2019/20 with 2012/13. Temporal trends in GDM were quantified using crude and adjusted risk ratios (aRRs) by modified Poisson regression. We further quantified the temporal increase attributable to changes in maternal characteristics by decomposition analysis. RESULTS: Among 1 044 258 pregnant individuals, 82 896 (7.9%) were diagnosed with GDM over the 8 years. GDM rate rose from 6.1 to 10.4 per 100 deliveries between fiscal years 2012/13 and 2019/20. The risk of GDM in 2019/20 was 1.53 times (95% CI 1.50-1.56) higher compared with 2012/13. 27% of the increase in GDM was due to changes in maternal age, pre-pregnancy BMI, and Asian ethnicity. CONCLUSION: The GDM rate has been consistently increasing in Ontario, Canada. The contribution of increasing maternal age, pre-pregnancy obesity, and Asian ethnicity to the recent increase in GDM is notable. Further investigation is required to better understand the contributors to increasing GDM.

Effectiveness and cost-effectiveness against malaria of three types of dual-active-ingredient long-lasting insecticidal nets (LLINs) compared with pyrethroid-only LLINs in Tanzania: a four-arm, cluster-randomised trial.

BACKGROUND: Long-lasting insecticidal nets (LLINs) have successfully reduced malaria in sub-Saharan Africa, but their effectiveness is now partly compromised by widespread resistance to insecticides among vectors. We evaluated new classes of LLINs with two active ingredients with differing modes of action against resistant malaria vectors. METHODS: We did a four-arm, cluster-randomised trial in Misungwi, Tanzania. Clusters were villages, or groups of hamlets, with at least 119 households containing children aged 6 months to 14 years living in the cluster's core area. Constrained randomisation was used to allocate clusters (1:1:1:1) to receive one of four types of LLIN treated with the following: α-cypermethrin only (pyrethroid-only [reference] group); pyriproxyfen and α-cypermethrin (pyriproxyfen group); chlorfenapyr and α-cypermethrin (chlorfenapyr group); or the synergist piperonyl butoxide and permethrin (piperonyl butoxide group). At least one LLIN was distributed for every two people. Community members and the field team were masked to group allocation. Malaria prevalence data were collected through cross-sectional surveys of randomly selected households from each cluster, in which children aged 6 months to 14 years were assessed for Plasmodium falciparum malaria infection by rapid diagnostic tests. The primary outcome was malaria infection prevalence at 24 months after LLIN distribution, comparing each of the dual-active-ingredient LLINs to the standard pyrethroid-only LLINs in the intention-to-treat population. The primary economic outcome was cost-effectiveness of dual-active-ingredient LLINs, based on incremental cost per disability-adjusted life-year (DALY) averted compared with pyrethroid-only LLINs, modelled over a 2-year period; we included costs of net procurement and malaria diagnosis and treatment, and estimated DALYs in all age groups. This study is registered with ClinicalTrials.gov (NCT03554616), and is ongoing but no longer recruiting. FINDINGS: 84 clusters comprising 39 307 households were included in the study between May 11 and July 2, 2018. 147 230 LLINs were distributed among households between Jan 26 and Jan 28, 2019. Use of study LLINs was reported in 3155 (72·1%) of 4378 participants surveyed at 3 months post-distribution and decreased to 8694 (40·9%) of 21 246 at 24 months, with varying rates of decline between groups. Malaria infection prevalence at 24 months was 549 (45·8%) of 1199 children in the pyrethroid-only reference group, 472 (37·5%) of 1258 in the pyriproxyfen group (adjusted odds ratio 0·79 [95% CI 0·54-1·17], p=0·2354), 512 (40·7%) of 1259 in the piperonyl butoxide group (0·99 [0·67-1·45], p=0·9607), and 326 [25·6%] of 1272 in the chlorfenapyr group (0·45 [0·30-0·67], p=0·0001). Skin irritation or paraesthesia was the most commonly reported side-effect in all groups. Chlorfenapyr LLINs were the most cost-effective LLINs, costing only US$19 (95% uncertainty interval 1-105) more to public providers or $28 (11-120) more to donors per DALY averted over a 2-year period compared with pyrethroid-only LLINs, and saving costs from societal and household perspectives. INTERPRETATION: After 2 years, chlorfenapyr LLINs provided significantly better protection than pyrethroid-only LLINs against malaria in an area with pyrethroid-resistant mosquitoes, and the additional cost of these nets would be considerably below plausible cost-effectiveness thresholds ($292-393 per DALY averted). Before scale-up of chlorfenapyr LLINs, resistance management strategies are needed to preserve their effectiveness. Poor textile and active ingredient durability in the piperonyl butoxide and pyriproxyfen LLINs might have contributed to their relative lack of effectiveness compared with standard LLINs. FUNDING: Joint Global Health Trials scheme (UK Foreign, Commonwealth and Development Office; UK Medical Research Council; Wellcome; UK Department of Health and Social Care), US Agency for International Development, President's Malaria Initiative.

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.

Power analysis for cluster randomized trials with continuous coprimary endpoints.

Pragmatic trials evaluating health care interventions often adopt cluster randomization due to scientific or logistical considerations. Systematic reviews have shown that coprimary endpoints are not uncommon in pragmatic trials but are seldom recognized in sample size or power calculations. While methods for power analysis based on K ( K ≥ 2 $K\ge 2$ ) binary coprimary endpoints are available for cluster randomized trials (CRTs), to our knowledge, methods for continuous coprimary endpoints are not yet available. Assuming a multivariate linear mixed model (MLMM) that accounts for multiple types of intraclass correlation coefficients among the observations in each cluster, we derive the closed-form joint distribution of K treatment effect estimators to facilitate sample size and power determination with different types of null hypotheses under equal cluster sizes. We characterize the relationship between the power of each test and different types of correlation parameters. We further relax the equal cluster size assumption and approximate the joint distribution of the K treatment effect estimators through the mean and coefficient of variation of cluster sizes. Our simulation studies with a finite number of clusters indicate that the predicted power by our method agrees well with the empirical power, when the parameters in the MLMM are estimated via the expectation-maximization algorithm. An application to a real CRT is presented to illustrate the proposed method.

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).

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.

Clustering of Health Behaviors in Canadians: A Multiple Behavior Analysis of Data from the Canadian Longitudinal Study on Aging.

BACKGROUND: Health behaviors such as physical inactivity, unhealthy eating, smoking tobacco, and alcohol use are each leading risk factors for non-communicable chronic disease. Better understanding which behaviors tend to co-occur (i.e., cluster together) and co-vary (i.e., are correlated) may provide novel opportunities to develop more comprehensive interventions to promote multiple health behavior change. However, whether co-occurrence or co-variation-based approaches are better suited for this task remains relatively unknown. PURPOSE: To compare the utility of co-occurrence vs. co-variation-based approaches for understanding the interconnectedness between multiple health-impacting behaviors. METHODS: Using baseline and follow-up data (N = 40,268) from the Canadian Longitudinal Study of Aging, we examined the co-occurrence and co-variation of health behaviors. We used cluster analysis to group individuals based on their behavioral tendencies across multiple behaviors and to examine how these clusters are associated with demographic characteristics and health indicators. We compared outputs from cluster analysis to behavioral correlations and compared regression analyses of clusters and individual behaviors predicting future health outcomes. RESULTS: Seven clusters were identified, with clusters differentiated by six of the seven health behaviors included in the analysis. Sociodemographic characteristics varied across several clusters. Correlations between behaviors were generally small. In regression analyses individual behaviors accounted for more variance in health outcomes than clusters. CONCLUSIONS: Co-occurrence-based approaches may be more suitable for identifying sub-groups for intervention targeting while co-variation approaches are more suitable for building an understanding of the relationships between health behaviors.

Health behaviors such as physical inactivity, unhealthy eating, smoking tobacco, and alcohol use are each leading risk factors for non-communicable chronic disease. A better understanding of which behavioral combinations people engage in, and which behaviors are associated with each other, may provide new insights to support the development of interventions to promote multiple health behavior change. Using data with two time points (N = 40,268) from the Canadian Longitudinal Study of Aging, we grouped people into clusters based on their health behaviors and examined how these clusters are associated with demographic characteristics and health indicators. Seven clusters were identified with sociodemographic patterns evident across several clusters. Correlations between behaviors were generally small. We compared whether individual health behaviors, or groupings of people based on their health behaviors, were better predictors of future health outcomes. Individual behaviors were slightly better predictors of future health outcomes than clusters.

Practice Facilitation to Support Family Physicians in Encouraging COVID-19 Vaccine Uptake: A Multimethod Process Evaluation.

PURPOSE: We offered a practice facilitation intervention to family physicians in Ontario, Canada, known to have large numbers of patients not yet vaccinated against coronavirus disease 2019 (COVID-19). METHODS: We conducted a multimethod process evaluation embedded within a randomized controlled trial (clinical trial #NCT05099497). We collected descriptive statistics regarding engagement and qualitative interview data from family physicians and practice facilitators, as well as data from facilitator field notes. We analyzed and triangulated the data using thematic analysis and mapped barriers to and enablers for implementation to structural, organizational, physician, and patient factors. RESULTS: Of the 300 approached, 90 family physicians (30%) accepted facilitation. Of these, 57% received technical support to identify unvaccinated patients, 29% used trained medical student volunteers to contact patients on their behalf, and 30% used automated calling to reach patients. Key factors affecting engagement with the intervention were staff shortages owing to COVID-19 (structural), clinic characteristics such as technical issues and gatekeeping by staff, which prevented facilitators from talking with physicians (organizational), burnout (physician), and specialized populations that required targeted resources (patient). The facilitator's ability to address technical issues and connect family physicians with medical students helped with engagement. CONCLUSIONS: Strategies to help underresourced family physicians serving high-needs populations for issues of public health importance, such as vaccine promotion, must acknowledge the scarcity of physicians' time and provide new resources. To successfully engage family physicians, practice facilitators should seek to build trust and relationships over time, including with front-office staff.

Accounting for complex intracluster correlations in longitudinal cluster randomized trials: a case study in malaria vector control.

BACKGROUND: The effectiveness of malaria vector control interventions is often evaluated using cluster randomized trials (CRT) with outcomes assessed using repeated cross-sectional surveys. A key requirement for appropriate design and analysis of longitudinal CRTs is accounting for the intra-cluster correlation coefficient (ICC). In addition to exchangeable correlation (constant ICC over time), correlation structures proposed for longitudinal CRT are block exchangeable (allows a different within- and between-period ICC) and exponential decay (allows between-period ICC to decay exponentially). More flexible correlation structures are available in statistical software packages and, although not formally proposed for longitudinal CRTs, may offer some advantages. Our objectives were to empirically explore the impact of these correlation structures on treatment effect inferences, identify gaps in the methodological literature, and make practical recommendations. METHODS: We obtained data from a parallel-arm CRT conducted in Tanzania to compare four different types of insecticide-treated bed-nets. Malaria prevalence was assessed in cross-sectional surveys of 45 households in each of 84 villages at baseline, 12-, 18- and 24-months post-randomization. We re-analyzed the data using mixed-effects logistic regression according to a prespecified analysis plan but under five different correlation structures as well as a robust variance estimator under exchangeable correlation and compared the estimated correlations and treatment effects. A proof-of-concept simulation was conducted to explore general conclusions. RESULTS: The estimated correlation structures varied substantially across different models. The unstructured model was the best-fitting model based on information criteria. Although point estimates and confidence intervals for the treatment effect were similar, allowing for more flexible correlation structures led to different conclusions based on statistical significance. Use of robust variance estimators generally led to wider confidence intervals. Simulation results showed that under-specification can lead to coverage probabilities much lower than nominal levels, but over-specification is more likely to maintain nominal coverage. CONCLUSION: More flexible correlation structures should not be ruled out in longitudinal CRTs. This may be particularly important in malaria trials where outcomes may fluctuate over time. In the absence of robust methods for selecting the best-fitting correlation structure, researchers should examine sensitivity of results to different assumptions about the ICC and consider robust variance estimators.

Interpersonal Psychotherapy for Depression in Parkinson's Disease: A Feasibility Study.

Individuals living with Parkinson's disease (PD) experience interpersonal stressors that contribute to depressive risk. Interpersonal psychotherapy (IPT) emphasizes the bidirectional relationship between interpersonal stressors and mood may therefore be a suitable treatment for PD-depression. The primary aim of this study was to evaluate the feasibility of delivering 12 sessions of IPT to depressed PD patients and explore the need for modifications. A secondary aim was to obtain descriptive information about efficacy outcomes. The study used a pre-post design without a comparison group. Participants were 12 PD patients with a major depressive disorder. IPT was well accepted and tolerated by patients and required minimal modifications. Compliance with session attendance and completion of study questionnaires were excellent and treatment satisfaction was high. Depression scores declined from baseline to endpoint, with 7 patients meeting criteria for remission at endpoint. Findings are encouraging and a larger randomized controlled trial is currently underway to ascertain if IPT is an efficacious treatment for PD-depression.

A re-analysis of 150 women's health trials to investigate how the Bayesian approach may offer a solution to the misinterpretation of statistical findings.

OBJECTIVE: To investigate whether a Bayesian interpretation might help prevent misinterpretation of statistical findings and support authors to differentiate evidence of no effect from statistical uncertainty. DESIGN: A Bayesian re-analysis to determine posterior probabilities of clinically important effects (e.g., a large effect is set at a 4 percentage point difference and a trivial effect to be within a 0.5 percentage point difference). Posterior probabilities greater than 95% are considered as strong statistical evidence, and less than 95% as inconclusive. SAMPLE: 150 major women's health trials with binary outcomes. MAIN OUTCOME MEASURES: Posterior probabilities of large, moderate, small and trivial effects. RESULTS: Under frequentist methods, 48 (32%) were statistically significant (p-value ≤ 0.05) and 102 (68%) statistically non-significant. The frequentist and Bayesian point estimates and confidence intervals showed strong concordance. Of the statistically non-significant trials (n = 102), the Bayesian approach classified the majority (94, 92%) as inconclusive, neither able to confirm or refute effectiveness. A small number of statistically non-significant findings (8, 8%) were classified as having strong statistical evidence of an effect. CONCLUSIONS: Whilst almost all trials report confidence intervals, in practice most statistical findings are interpreted on the basis of statistical significance, mostly concluding evidence of no effect. Findings here suggest the majority are likely uncertain. A Bayesian approach could help differentiate evidence of no effect from statistical uncertainty.