Group-randomized Trials Research Articles

High-Flow Nasal Cannula Compared With Noninvasive Positive Pressure Ventilation in Acute Hypoxic Respiratory Failure: A Systematic Review and Meta-Analysis.

We performed a comprehensive search of MEDLINE, Embase, CINAHL, the Cochrane library, and the international Health Technology Assessment database from inception to September 14, 2022. We included randomized control studies that compared HFNC to NIPPV in adult patients with AHRF. For clinical outcomes, we included only parallel group and crossover randomized control trials (RCTs). For economic outcomes, we included any study design that evaluated cost-effectiveness, cost-utility, or cost benefit analyses. Clinical outcomes of interest included intubation, mortality, ICU and hospital length of stay (LOS), and patient-reported dyspnea. Economic outcomes of interest included costs, cost-effectiveness, and cost-utility. We included nine RCTs (n = 1,539 patients) and one cost-effectiveness study. Compared with NIPPV, HFNC may have no effect on the need for intubation (relative risk [RR], 0.93; 95% CI, 0.69-1.27; low certainty) and an uncertain effect on mortality (RR, 0.84; 95% CI, 0.59-1.21; very low certainty). In subgroup analysis, NIPPV delivered through the helmet interface-as opposed to the facemask interface-may reduce intubation compared with HFNC (p = 0.006; moderate credibility of subgroup effect). There was no difference in ICU or hospital LOS (both low certainty) and an uncertain effect on patient-reported dyspnea (very low certainty). We could make no conclusions regarding the cost-effectiveness of HFNC compared with NIPPV. HFNC and NIPPV may be similarly effective at reducing the need for intubation with an uncertain effect on mortality in hospitalized patients with hypoxemic respiratory failure. More research evaluating different interfaces in varying clinical contexts is needed to improve generalizability and precision of findings.

Occupational health coaching for job stress management among technical college teachers: Implications for educational administrators.

The need for stress management strategies has been empirically investigated and supported considering demands in workplaces. However, some people in public offices do not seem to have been exposed to occupational health strategies that could reduce the adverse impacts of stress on job productivity and quality of life. Consequently, they become susceptible to mental health disturbances requiring the attention of occupational therapists. Given this, we studied the impact of occupational health coaching for job stress management among technical college teachers. Using a randomized control design study, 90 technical college teachers were screened and ready to participate. The eligible teachers were included and assigned to intervention and control groups. An occupational stress index was given to the participants before, immediately after, and 2 months after the delivery of occupational coaching program by career counselors while the comparison group received no intervention. Data collected were analyzed using multivariate analysis of variance analysis. The results showed a significant improvement in the management of job stress after receiving rational emotive occupational health coaching. According to the multivariate analysis of variance analysis, there were between-group differences immediately after the intervention and 3 months later. As a result, the study suggested that career counselors and school management systems should incorporate rational-emotive behavioral therapy into workforce and workplace programs.

Analysis of multiple-period group randomized trials: random coefficients model or repeated measures ANOVA?

BackgroundMultiple-period parallel group randomized trials (GRTs) analyzed with linear mixed models can represent time in mean models as continuous or categorical. If time is continuous, random effects are traditionally group- and member-level deviations from condition-specific slopes and intercepts and are referred to as random coefficients (RC) analytic models. If time is categorical, random effects are traditionally group- and member-level deviations from time-specific condition means and are referred to as repeated measures ANOVA (RM-ANOVA) analytic models. Longstanding guidance recommends the use of RC over RM-ANOVA for parallel GRTs with more than two periods because RC exhibited nominal type I error rates for both time parameterizations while RM-ANOVA exhibited inflated type I error rates when applied to data generated using the RC model. However, this recommendation was developed assuming a variance components covariance matrix for the RM-ANOVA, using only cross-sectional data, and explicitly modeling time × group variation. Left unanswered were how well RM-ANOVA with an unstructured covariance would perform on data generated according to the RC mechanism, if similar patterns would be observed in cohort data, and the impact of not modeling time × group variation if such variation was present in the data-generating model.MethodsContinuous outcomes for cohort and cross-sectional parallel GRT data were simulated according to RM-ANOVA and RC mechanisms at five total time periods. All simulations assumed time × group variation. We varied the number of groups, group size, and intra-cluster correlation. Analytic models using RC, RM-ANOVA, RM-ANOVA with unstructured covariance, and a Saturated random effects structure were applied to the data. All analytic models specified time × group random effects. The analytic models were then reapplied without specifying random effects for time × group.ResultsResults indicated the RC and saturated analytic models maintained the nominal type I error rate in all data sets, RM-ANOVA with an unstructured covariance did not avoid type I error rate inflation when applied to cohort RC data, and analytic models omitting time-varying group random effects when such variation exists in the data were prone to substantial type I error inflation unless the residual error variance is high relative to the time × group variance.ConclusionThe time × group RC and saturated analytic models are recommended as the default for multiple period parallel GRTs.

Inconsistencies in Adolescent Self-Reported Sexual Behavior: Experience from Four Randomized Controlled Trials.

To examine the degree, correlates, and implications of inconsistent self-report data on sexual risk behaviors of adolescents. We analyzed data from four longitudinal group-randomized controlled trials of evidence-based HIV/STI/pregnancy prevention programs in Texas and California from 2000 to 2010. Across- and within-time logical inconsistencies in sexual behavior survey responses were analyzed using multilevel logistic regression. Rates of any inconsistencies ranged from 12 to 18% across the four trials. In all trials, rates were higher in males than in females. Age, normative beliefs, and race/ethnicity were most strongly associated with inconsistencies. We found substantial rates of inconsistencies in adolescents' self-reports of their sexual behavior, which did not occur at random. Studies should routinely report observed rates of inconsistencies and methods used to adjust for them so that any biases in the population to which the study generalizes are understood by public health practitioners and policy-makers looking to adopt programs for their particular population.

Practical considerations in utilizing cluster randomized controlled trials conducted in biopharmaceutical industry.

Cluster randomized controlled trials (cluster RCTs), also known as parallel-arm group-randomized trials, are trials in which the randomized units are groups of participants, as opposed to individual participants. These trials have largely been implemented to address broad public health issues, but with the growing interest in use of real-world data in the regulatory setting, this design may be increasingly considered for industry trials. The key difference between cluster RCTs and traditional RCTs is the intraclass correlation coefficient (ICC) that needs to be considered in cluster RCTs. In this article, we discuss some key practical considerations that are related to ICC in the design, conduct, analysis, and report stages of a cluster RCT. These key considerations related to ICC can lead to improvement in how we translate research findings from cluster RCTs into practices in the biopharmaceutical industry.

Influential methods reports for group-randomized trials and related designs.

This article identifies the most influential methods reports for group-randomized trials and related designs published through 2020. Many interventions are delivered to participants in real or virtual groups or in groups defined by a shared interventionist so that there is an expectation for positive correlation among observations taken on participants in the same group. These interventions are typically evaluated using a group- or cluster-randomized trial, an individually randomized group treatment trial, or a stepped wedge group- or cluster-randomized trial. These trials face methodological issues beyond those encountered in the more familiar individually randomized controlled trial. PubMed was searched to identify candidate methods reports; that search was supplemented by reports known to the author. Candidate reports were reviewed by the author to include only those focused on the designs of interest. Citation counts and the relative citation ratio, a new bibliometric tool developed at the National Institutes of Health, were used to identify influential reports. The relative citation ratio measures influence at the article level by comparing the citation rate of the reference article to the citation rates of the articles cited by other articles that also cite the reference article. In total, 1043 reports were identified that were published through 2020. However, 55 were deemed to be the most influential based on their relative citation ratio or their citation count using criteria specific to each of the three designs, with 32 group-randomized trial reports, 7 individually randomized group treatment trial reports, and 16 stepped wedge group-randomized trial reports. Many of the influential reports were early publications that drew attention to the issues that distinguish these designs from the more familiar individually randomized controlled trial. Others were textbooks that covered a wide range of issues for these designs. Others were "first reports" on analytic methods appropriate for a specific type of data (e.g. binary data, ordinal data), for features commonly encountered in these studies (e.g. unequal cluster size, attrition), or for important variations in study design (e.g. repeated measures, cohort versus cross-section). Many presented methods for sample size calculations. Others described how these designs could be applied to a new area (e.g. dissemination and implementation research). Among the reports with the highest relative citation ratios were the CONSORT statements for each design. Collectively, the influential reports address topics of great interest to investigators who might consider using one of these designs and need guidance on selecting the most appropriate design for their research question and on the best methods for design, analysis, and sample size.

Using cluster-robust standard errors when analyzing group-randomized trials with few clusters.

Accounting for dependent observations in cluster-randomized trials (CRTs) using nested data is necessary in order to avoid misestimated standard errors resulting in questionable inferential statistics. Cluster-robust standard errors (CRSEs) are often used to address this issue. However, CRSEs are still well-known to underestimate standard errors for group-level variables when the number of clusters is low (e.g., < 50) and with CRTs, a small number of clusters, due to logistical or financial considerations, is the norm rather than the exception. Using a simulation with various conditions, we investigate the use of a small sample correction (i.e., CR2 estimator) proposed by Bell and McCaffrey (2002) together with empirically derived degrees of freedom estimates (dofBM). Findings indicate that even with as few as 10 clusters, the CR2 estimator used with dofBM yields generally unbiased results with acceptable type I error and coverage rates. Results show that coverage and type I error rates can be largely influenced by the choice of dof, not just the standard error adjustments. An applied example is provided together with R syntax to conduct the analysis. To facilitate the use of different CRSEs, a free graphical, menu-driven SPSS add-on to compute the various cluster-robust variance estimates can be downloaded from https://github.com/flh3/CR2/tree/master/SPSS .

Evaluation of Chemical Composition and Lipid Components in Maize Straw based Complete Feed Diets Supplemented by Conden Tannin and Myristic Acid

Although maize straw as an agricultural by products could be used as an alternative feed ingredient for ruminants, its low nutritional value and palatability require an early processing. Supplementation of feed additive that functions as a methane inhibitor. The study was to examine the chemical composition in maize straw based complete feed diets supplemented by conden tannin and myristic acid. The experimental method used a group-randomized trials by ANOVA with 4 dietary treatments and 3 replications. The treatments are T1: Maize Straw Based Complete Rations (Control Diet), T2: Control diet +Mimosa powder (MP) 1,5%/kgDM + Myristic acid (MA)2%/kgDM, T3: Control diet + MP 1,5%/kg DM + MA 3%/kg DM, and T4: Control diet + MP 1,5%/kg DM + MA 4%/kg DM. Proximate and van soest analysis was analysed in the Feed and Nutrition Laboratory, animal Science faculty, Brawijaya University, Malang. The result showed that the maize straw complete feed without supplemented has not different with a chemical compositions Maize straw with 3 level of Conden tannin and myristic acid supplementation. Besides higher EE components contained on T4 which supplemented by CT 1,5 %/kg DM and MA 40 g/kg DM. It can be concluded that CT and MA in Maize Straw based complete feed diets can be used as rumen modifier without effected on chemical component.

Essential Ingredients and Innovations in the Design and Analysis of Group-Randomized Trials.

This article reviews the essential ingredients and innovations in the design and analysis of group-randomized trials. The methods literature for these trials has grown steadily since they were introduced to the biomedical research community in the late 1970s, and we summarize those developments. We review, in addition to the group-randomized trial, methods for two closely related designs, the individually randomized group treatment trial and the stepped-wedge group-randomized trial. After describing the essential ingredients for these designs, we review the most important developments in the evolution of their methods using a new bibliometric tool developed at the National Institutes of Health. We then discuss the questions to be considered when selecting from among these designs or selecting the traditional randomized controlled trial. We close with a review of current methods for the analysis of data from these designs, a case study to illustrate each design, and a brief summary.

Researching the Effectiveness of Tree of Life: an Imbeleko Approach to Counseling Refugee Youth.

With the growing worldwide refugee crisis, there is a need for evidence-based interventions that specifically deal with the consequences of cumulative trauma-exposure in refugee youth. Refugee children have unique service needs and differ from their non-refugee peers in terms of sociocultural trauma, language, culture, and educational barriers. This article explains the complexities associated with understanding refugee youth and presents a proposal for studying the possible benefits of Tree of Life therapy. At the present time, Tree of Life therapy has no evidence-base because the published studies of Tree of Life with refugee and/or immigrant youth have sample sizes of one, six, eight, and twenty-nine. As a culturally-grounded, strength-based group counseling approach, Tree of Life therapy addresses traumatic experiences, recognizes participants' cultural differences, highlights individual skills, and aids in instilling both confidence and hope for the future. Ncazelo Ncube, the co-founder and main developer of Tree of Life, describes this therapy as a collective narrative practice that considers cultural beliefs and values (2006, 2010, 2018, 2019). The proposed research design is to study the effectiveness of the Tree of Life in Canada, the United States, the United Kingdom, and South Africa. The research plan is to use pragmatic, group-randomized controlled trials in the "real world" settings of schools and agencies in each of the four countries. In addition, the article describes the development of the Roots and Wings Questionnaire for Children and Youth, a culturally relevant, child-friendly questionnaire. The Tree of Life is a readily available therapy with great potential for helping traumatized refugee youth as well as other trauma-impacted young people worldwide.

Use of a Geographic Information System to create treatment groups for group-randomized community trials: The Minnesota Heart Health Program

BackgroundGroup-randomized trials of communities often rely on the convenience of pre-existing administrative divisions, such as school district boundaries or census entities, to divide the study area into intervention and control sites. However, these boundaries may include substantial heterogeneity between regions, introducing unmeasured confounding variables. This challenge can be addressed by the creation of exchangeable intervention and control territories that are equally weighted by pertinent socio-demographic characteristics. The present study used territory design software as a novel approach to partitioning study areas for The Minnesota Heart Health Program’s “Ask about Aspirin” Initiative.MethodsTwenty-four territories were created to be similar in terms of age, sex, and educational attainment, as factors known to modify aspirin use. To promote ease of intervention administration, the shape and spread of the territories were controlled. Means of the variables used in balancing the territories were assessed as well as other factors that were not used in the balancing process.ResultsThe analysis demonstrated that demographic characteristics did not differ significantly between the intervention and control territories created by the territory design software.ConclusionsThe creation of exchangeable territories diminishes geographically based impact on outcomes following community interventions in group-randomized trials. The method used to identify comparable geographical units may be applied to a wide range of population-based health intervention trials.Trial registrationNational Institutes of Health (Clinical Trials.gov), Identifier: NCT02607917. Registered on 16 November 2015.

The Impact of a Healthy Weight Intervention Embedded in a Home-Visiting Program on Children's Weight and Mothers’ Feeding Practices

ObjectiveTo examine whether a healthy weight intervention embedded in the Parents as Teachers (PAT) home visiting program, which was previously found to improve mothers’ body mass index (BMI) and obesity-related behaviors, changed the BMI of preschool children or maternal feeding practices. MethodsThis stratified randomized trial included preschool-aged children at risk for overweight whose mothers were overweight or had obesity (n = 179). The Healthy Eating and Active Living Taught at Home (HEALTH) intervention was based on the Diabetes Prevention Program. Differences were examined using repeated-measures mixed-ANOVA models. ResultsCompared with PAT usual care, the HEALTH intervention had no effect on children's BMI or maternal feeding practices. However, combined analyses showed that children's BMI percentile decreased (P = .007), BMI z-scores were maintained (P = .19), and 3 of 8 feeding practices improved over time (P < .05). Conclusions and ImplicationsAdditional research is needed to assess the effectiveness of PAT to prevent preschool-age obesity using rigorous designs (eg, group-randomized trials) and to identify its active components. HEALTH is ready to be scaled up to prevent maternal weight gain through embedding within the national PAT program.

Between-School Variation in Students' Achievement, Motivation, Affect, and Learning Strategies: Results from 81 Countries for Planning Group-Randomized Trials in Education

ABSTRACTTo plan group-randomized trials where treatment conditions are assigned to schools, researchers need design parameters that provide information about between-school differences in outcomes as well as the amount of variance that can be explained by covariates at the student (L1) and school (L2) levels. Most previous research has offered these parameters for U.S. samples and for achievement as the outcome. This paper and the online supplementary materials provide design parameters for 81 countries in three broad outcome categories (achievement, affect and motivation, and learning strategies) for domain-general and domain-specific (mathematics, reading, and science) measures. Sociodemographic characteristics were used as covariates. Data from representative samples of 15-year-old students stemmed from five cycles of the Programme for International Student Assessment (PISA; total number of students/schools: 1,905,147/70,098). Between-school differences as well as the amount of variance explained at L1 and L2 varied widely across countries and educational outcomes, demonstrating the limited generalizability of design parameters across these dimensions. The use of the design parameters to plan group-randomized trials is illustrated.

An evaluation of constrained randomization for the design and analysis of group-randomized trials with binary outcomes.

Group-randomized trials are randomized studies that allocate intact groups of individuals to different comparison arms. A frequent practical limitation to adopting such research designs is that only a limited number of groups may be available, and therefore, simple randomization is unable to adequately balance multiple group-level covariates between arms. Therefore, covariate-based constrained randomization was proposed as an allocation technique to achieve balance. Constrained randomization involves generating a large number of possible allocation schemes, calculating a balance score that assesses covariate imbalance, limiting the randomization space to a prespecified percentage of candidate allocations, and randomly selecting one scheme to implement. When the outcome is binary, a number of statistical issues arise regarding the potential advantages of such designs in making inference. In particular, properties found for continuous outcomes may not directly apply, and additional variations on statistical tests are available. Motivated by two recent trials, we conduct a series of Monte Carlo simulations to evaluate the statistical properties of model-based and randomization-based tests under both simple and constrained randomization designs, with varying degrees of analysis-based covariate adjustment. Our results indicate that constrained randomization improves the power of the linearization F-test, the KC-corrected GEE t-test (Kauermann and Carroll, 2001, Journal of the American Statistical Association 96, 1387-1396), and two permutation tests when the prognostic group-level variables are controlled for in the analysis and the size of randomization space is reasonably small. We also demonstrate that constrained randomization reduces power loss from redundant analysis-based adjustment for non-prognostic covariates. Design considerations such as the choice of the balance metric and the size of randomization space are discussed.

25 Years of Complex Intervention Trials: Reflections on Lived and Scientific Experiences.

For the past 25 years, I have led multiple group-randomized trials, each focused on a specific underserved population of youth and each one evaluated health effects of complex interventions designed to prevent high-risk behaviors. I share my reflections on issues of intervention and research design, as well as how research results fostered my evolution toward addressing fundamental social determinants of health and well-being. Reflections related to intervention design emphasize the importance of careful consideration of theory of causes and theory of change, theoretical comprehensiveness versus fundamental determinants of population health, how high to reach, and health in all policies. Flowing from these intervention design issues are reflections on implications for research design, including the importance of matching the unit of intervention to the unit of assignment, the emerging field of public health law research, and consideration of design options and design elements beyond and in combination with random assignment.

Review of Recent Methodological Developments in Group-Randomized Trials: Part 2-Analysis.

In 2004, Murray et al. reviewed methodological developments in the design and analysis of group-randomized trials (GRTs). We have updated that review with developments in analysis of the past 13 years, with a companion article to focus on developments in design. We discuss developments in the topics of the earlier review (e.g., methods for parallel-arm GRTs, individually randomized group-treatment trials, and missing data) and in new topics, including methods to account for multiple-level clustering and alternative estimation methods (e.g., augmented generalized estimating equations, targeted maximum likelihood, and quadratic inference functions). In addition, we describe developments in analysis of alternative group designs (including stepped-wedge GRTs, network-randomized trials, and pseudocluster randomized trials), which require clustering to be accounted for in their design and analysis.

Review of Recent Methodological Developments in Group-Randomized Trials: Part 1-Design.

In 2004, Murray et al. reviewed methodological developments in the design and analysis of group-randomized trials (GRTs). We have highlighted the developments of the past 13 years in design with a companion article to focus on developments in analysis. As a pair, these articles update the 2004 review. We have discussed developments in the topics of the earlier review (e.g., clustering, matching, and individually randomized group-treatment trials) and in new topics, including constrained randomization and a range of randomized designs that are alternatives to the standard parallel-arm GRT. These include the stepped-wedge GRT, the pseudocluster randomized trial, and the network-randomized GRT, which, like the parallel-arm GRT, require clustering to be accounted for in both their design and analysis.

Statistical Power for Causally Defined Indirect Effects in Group-Randomized Trials With Individual-Level Mediators

Designs that facilitate inferences concerning both the total and indirect effects of a treatment potentially offer a more holistic description of interventions because they can complement “what works” questions with the comprehensive study of the causal connections implied by substantive theories. Mapping the sensitivity of designs to detect these effects is of critical importance because it directly governs the types of evidence researchers can bring to bear on theories of action under realistic sample sizes. In this study, we develop closed-form expressions to estimate the variance of and the power to detect causally defined indirect effects in two-level group-randomized studies examining individual-level mediators (i.e., 2-1-1 mediation). We formulate our approach within the purview of typical multilevel mediation models and anchor their interpretation in the potential outcomes framework. The results provide power analysis formulas that reduce calculations to simple functions of the primary path coefficients (e.g., treatment–mediator and mediator–outcome relationships) and common summary statistics (e.g., intraclass correlation coefficients). Probing these formulas suggests that group-randomized designs can be well powered to detect indirect effects when carefully planned. The power formulas are implemented in the PowerUp software ( causalevaluation.org ).

Statistical lessons learned for designing cluster randomized pragmatic clinical trials from the NIH Health Care Systems Collaboratory Biostatistics and Design Core.

Pragmatic clinical trials embedded within health care systems provide an important opportunity to evaluate new interventions and treatments. Networks have recently been developed to support practical and efficient studies. Pragmatic trials will lead to improvements in how we deliver health care and promise to more rapidly translate research findings into practice. The National Institutes of Health (NIH) Health Care Systems Collaboratory was formed to conduct pragmatic clinical trials and to cultivate collaboration across research areas and disciplines to develop best practices for future studies. Through a two-stage grant process including a pilot phase (UH2) and a main trial phase (UH3), investigators across the Collaboratory had the opportunity to work together to improve all aspects of these trials before they were launched and to address new issues that arose during implementation. Seven Cores were created to address the various considerations, including Electronic Health Records; Phenotypes, Data Standards, and Data Quality; Biostatistics and Design Core; Patient-Reported Outcomes; Health Care Systems Interactions; Regulatory/Ethics; and Stakeholder Engagement. The goal of this article is to summarize the Biostatistics and Design Core's lessons learned during the initial pilot phase with seven pragmatic clinical trials conducted between 2012 and 2014. Methodological issues arose from the five cluster-randomized trials, also called group-randomized trials, including consideration of crossover and stepped wedge designs. We outlined general themes and challenges and proposed solutions from the pilot phase including topics such as study design, unit of randomization, sample size, and statistical analysis. Our findings are applicable to other pragmatic clinical trials conducted within health care systems. Pragmatic clinical trials using the UH2/UH3 funding mechanism provide an opportunity to ensure that all relevant design issues have been fully considered in order to reliably and efficiently evaluate new interventions and treatments. The integrity and generalizability of trial results can only be ensured if rigorous designs and appropriate analysis choices are an essential part of their research protocols.

An evaluation of constrained randomization for the design and analysis of group-randomized trials.

In group-randomized trials, a frequent practical limitation to adopting rigorous research designs is that only a small number of groups may be available, and therefore, simple randomization cannot be relied upon to balance key group-level prognostic factors across the comparison arms. Constrained randomization is an allocation technique proposed for ensuring balance and can be used together with a permutation test for randomization-based inference. However, several statistical issues have not been thoroughly studied when constrained randomization is considered. Therefore, we used simulations to evaluate key issues including the following: the impact of the choice of the candidate set size and the balance metric used to guide randomization; the choice of adjusted versus unadjusted analysis; and the use of model-based versus randomization-based tests. We conducted a simulation study to compare the type I error and power of the F-test and the permutation test in the presence of group-level potential confounders. Our results indicate that the adjusted F-test and the permutation test perform similarly and slightly better for constrained randomization relative to simple randomization in terms of power, and the candidate set size does not substantially affect their power. Under constrained randomization, however, the unadjusted F-test is conservative, while the unadjusted permutation test carries the desired type I error rate as long as the candidate set size is not too small; the unadjusted permutation test is consistently more powerful than the unadjusted F-test and gains power as candidate set size changes. Finally, we caution against the inappropriate specification of permutation distribution under constrained randomization. An ongoing group-randomized trial is used as an illustrative example for the constrained randomization design.