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Stepped wedge cluster randomized trials (SW-CRTs) are increasingly being used to evaluate interventions in medical, public health, educational, and social science contexts. With the longitudinal and crossover natures of an SW-CRT, complex analysis techniques are often needed, which makes appropriately powering SW-CRTs challenging. In this article, we introduce a newly developed SW-CRT power calculator embedded within the powercommand in Stata. The power calculator assumes a marginal model (that is, generalized estimating equations) for the primary analysis of SW-CRTs, for which other currently available SW-CRT power calculators may not be suitable. The command accommodates complete cross-sectional and closed-cohort designs and includes multilevel correlation structures appropriate for such designs. We discuss the methods and formulas underlying our SW-CRT calculator and provide illustrative examples of the use of power swgee. We provide suggestions about the choice of parameters in power swgeeand conclude by discussing areas of future research that may improve the command.

Maternal depression has a recurring course that can influence offspring outcomes. Evidence on how to treat maternal depression to improve longer-term maternal outcomes and reduce intergenerational transmission of psychopathology is scarce, particularly for task-shifted, low-intensity, and scalable psychosocial interventions. We evaluated the effects of a peer-delivered, psychosocial intervention on maternal depression and child development at 3 years postnatal.

Cluster randomized trials, where clusters (for example, schools or clinics) are randomized to comparison arms but measurements are taken on individuals, are commonly used to evaluate interventions in public health, education, and the social sciences. Analysis is often conducted on individual-level outcomes, and such analysis methods must consider that outcomes for members of the same cluster tend to be more similar than outcomes for members of other clusters. A popular individual-level analysis technique is generalized estimating equations (GEE). However, it is common to randomize a small number of clusters (for example, 30 or fewer), and in this case, the GEE standard errors obtained from the sandwich variance estimator will be biased, leading to inflated type I errors. Some bias-corrected standard errors have been proposed and studied to account for this finite-sample bias, but none has yet been implemented in Stata. In this article, we describe several popular bias corrections to the robust sandwich variance. We then introduce our newly created command, xtgeebcv, which will allow Stata users to easily apply finite-sample corrections to standard errors obtained from GEE models. We then provide examples to demonstrate the use of xtgeebcv. Finally, we discuss suggestions about which finite-sample corrections to use in which situations and consider areas of future research that may improve xtgeebcv.

Weight-loss maintenance is essential to sustain the health benefits of weight loss. Studies with lower intensity intervention supports under real-world conditions are lacking. This study examined changes in weight and cardiometabolic biomarkers among Spirited Life participants following initial 12-month weight loss at 12-24 months and 24-42 months. A total of 719 clergy received a wellness intervention, including a 10-week online weight-loss program in the first 12 months and monthly health coaching throughout 24 months. Mean changes in weight, blood pressure, high-density lipoproteins, and triglycerides were estimated using random effects linear models, accounting for repeated measures. Weight was additionally analyzed in subsamples stratified by body mass index (BMI). At baseline, 17.1% of participants had BMI < 25 kg/m2 and 11.8% had BMI ≥ 40 kg/m2. Mean 12-month weight loss was -2.4 kg (95% CI: -2.8 kg, -2.1 kg). On average, at 42 months, participants regained weight but did not exceed baseline (-0.5 kg, 95% CI: -1.2 kg, 0.2 kg), improvements in triglycerides were completely sustained (-13.9 mg/dL, 95% CI: -18.6 mg/dL, -9.2 mg/dL), and systolic blood pressure improvements remained significant (-1.9 mmHg, 95% CI: -3.0 mmHg, -0.9 mmHg). Participants with a BMI ≥ 40 kg/m2 lost significantly more weight that was sustained at 42 months (-5.8 kg, 95% CI: -8.9 kg, -2.7 kg). The Spirited Life wellness intervention produced weight loss and, for participants with higher levels of obesity, sustained weight-loss maintenance. The intervention was effective for long-term prevention of weight gain among participants with BMI of 25 to ≤40 kg/m2, through 42 months. Wellness interventions such as Spirited Life should be considered for adoption.

Appropriate implementation and rigorous reporting of randomization procedures is vital in factorial trials for ensuring the efficiency and validity of the results.The multiphase optimization strategy (MOST) is an increasingly popular framework to prepare, optimize, and evaluate multicomponent behavioral health interventions. Within this framework, it is common to use a factorial trial to assemble an optimized multicomponent intervention by simultaneously testing several intervention components. With the possibility of a large number of conditions (unique combinations of components) and a goal to balance conditions on both sample size (for statistical efficiency) and baseline covariates (for internal validity), such trials face additional randomization challenges compared to the standard two-arm trial. The purpose of the current paper is to compare and contrast potential randomization methods for factorial trials in the context of MOST and to provide guidance for the reporting of those methods. We describe the principles, advantages, and disadvantages of several randomization methods in the context of factorial trials. We then provide examples to examine current practice in the MOST-related literature and provide recommendations for reporting of randomization. We identify two key randomization decisions for MOST-related factorial trials: (i) whether to randomize to components or conditions and (ii) whether to use restricted randomization techniques, such as stratification, permuted blocks, and minimization. We also provide a checklist to assist researchers in ensuring complete reporting of randomization methods used. As more investigators use factorial trials within the MOST framework for assembling optimized multicomponent behavioral interventions, appropriate implementation and rigorous reporting of randomization procedures will be essential for ensuring the efficiency and validity of the results.

In cluster randomized trials, individuals from the same cluster tend to have more similar outcomes than individuals from different clusters. This correlation must be taken into account in the analysis of every cluster trial to avoid incorrect inferences. In this paper, we describe the principles guiding the analysis of cluster trials including how to correctly account for intra-cluster correlations as well as how to analyze more advanced designs such as stepped-wedge and cluster cross-over trials. We then describe how to handle specific issues such as small sample sizes and missing data.

We study optimal allocation of clusters for a fixed number of periods in cohort stepped wedge cluster randomized trials. The optimal design turns more clusters into treatment during the second and final periods, and depends on values of correlation parameters. [ABSTRACT FROM AUTHOR]

Cluster randomized trials (CRTs), where clusters (for example, schools or clinics) are randomized to comparison arms but measurements are taken on individuals, are commonly used to evaluate interventions in public health, education, and the social sciences. Because CRTs typically involve a small number of clusters (for example, fewer than 20), simple randomization frequently leads to baseline imbalance of cluster characteristics across study arms, threatening the internal validity of the trial. In CRTs with a small number of clusters, classic approaches to balancing baseline characteristics—such as matching and stratification—have several drawbacks, especially when the number of baseline characteristics the researcher desires to balance is large (Ivers et al., 2012, Trials13: 120). An alternative design approach is covariate-constrained randomization, whereby a randomization scheme is randomly selected from a subset of all possible randomization schemes based on the value of a balancing criterion (Raab and Butcher, 2001, Statistics in Medicine20: 351–365). Subsequently, a clustered permutation test can be used in the analysis, which provides increased power under constrained randomization compared with simple randomization (Li et al., 2016, Statistics in Medicine35: 1565–1579). In this article, we describe covariate-constrained randomization and the permutation test for the design and analysis of CRTs and provide an example to demonstrate the use of our new commands cvcrandand cptestto implement constrained randomization and the permutation test.

There is growing interest in the relationship between socioeconomic status (SES), poverty, and mental health in low and middle-income countries (LMIC). However, it is not clear whether a gradient approach focused on a wider SES distribution or a binary poverty approach is more salient for mental health in LMIC. Yet this distinction has implications for interventions aimed at improving population health. We contribute to the literature by examining how multiple indicators of socioeconomic status, including gradient SES and binary poverty indicators, contribute to prenatal depression symptoms in a LMIC context. Prenatal depression is an important public health concern with negative sequela for the mother and her children. We use data on assets, education, food insecurity, debt, and depression symptoms from a sample of 1154 pregnant women residing in rural Pakistan. Women who screened positive for depression participated in a cluster randomized controlled trial of a perinatal depression intervention; all women were interviewed October 2015-February 2016, prior to the start of the intervention. Cluster-specific sampling weights were used to approximate a random sample of pregnant women in the area. Findings indicate that fewer assets, experiencing food insecurity, and having household debt are independently associated with worse depression symptoms. The association with assets is linear with no evidence of a threshold effect, supporting the idea of a gradient in the association between levels of SES and depression symptoms. A gradient was also initially observed with woman’s educational attainment, but this association was attenuated once other SES variables were included in the model. Together, the asset, food insecurity, and debt indicators explain 14% of the variance in depression symptoms, more than has been reported in high income country studies. These findings support the use of multiple SES indicators to better elucidate the complex relationship between socioeconomic status and mental health in LMIC.

Differences in activity limitations between stroke survivors and people with other chronic conditions and how their levels of activity limitation vary by sociodemographic characteristics have not been well quantified.

In 2011, a US expert panel recommended universal cholesterol screening for children ages 9 to 11 years. Controversy exists over this recommendation, especially because the most recent systematic review on universal childhood screening was inconclusive.

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AbstractIntroductionLarge earthquakes can cause population displacement, critical sanitation infrastructure damage, and increased threats to water resources, potentially predisposing populations to waterborne disease epidemics such as cholera.ProblemThe risk of cholera outbreaks after earthquake disasters remains uncertain. A cross-country analysis of World Health Organization (WHO) cholera data that would contribute to this discussion has yet to be published.MethodsA cross-country longitudinal analysis was conducted among 63 low- and middle-income countries from 1995-2009. The association between earthquake disasters of various effect sizes and a relative spike in cholera rates for a given country was assessed utilizing fixed-effects logistic regression and adjusting for gross domestic product per capita, water and sanitation level, flooding events, percent urbanization, and under-five child mortality. Also, the association between large earthquakes and cholera rate increases of various degrees was assessed.ResultsForty-eight of the 63 countries had at least one year with reported cholera infections during the 15-year study period. Thirty-six of these 48 countries had at least one earthquake disaster. In adjusted analyses, country-years with ≥10,000 persons affected by an earthquake had 2.26 times increased odds (95 CI, 0.89-5.72, P= .08) of having a greater than average cholera rate that year compared to country-years having <10,000 individuals affected by an earthquake. The association between large earthquake disasters and cholera infections appeared to weaken as higher levels of cholera rate increases were tested.ConclusionA trend of increased risk of greater than average cholera rates when more people were affected by an earthquake in a country-year was noted. However these findings did not reach statistical significance at traditional levels and may be due to chance. Frequent large-scale cholera outbreaks after earthquake disasters appeared to be relatively uncommon.SumnerS, TurnerE, ThielmanN. Association between earthquake events and cholera outbreaks: a cross-country 15-year longitudinal analysis. Prehosp Disaster Med. 2013;28(6):1-6.

Cluster randomized trials (CRTs) usually randomize groups of individuals to interventions, and outcomes are typically measured at the individual level. Marginal intervention effects are frequently of interest in CRTs due to their population-averaged interpretations. Such effects are estimated using generalized estimating equations (GEE), or a recent alternative called the quadratic inference function (QIF). However, the performance of QIF relative to GEE have not been extensively evaluated in the CRT context, especially when the marginal mean model includes additional covariates. Motivated by the HALI trial, we conduct simulation studies to compare the finite-sample operating characteristics of QIF and GEE. We demonstrate that QIF and GEE are equivalent under some conditions. When the marginal mean model includes individual-level covariates, QIF shows an efficiency improvement over GEE with overall larger power, but its test size may be more liberal than GEE and GEE achieves better coverage than QIF. The test size inflation may not by fully addressed from using finite-sample bias corrections. The estimates of QIF tend to be closer to GEE in the HALI data, although the former presents a small standard error. Overall, we confirm that the QIF approach generally has potentially better efficiency than GEE in our simulation studies but might be more cautiously used as a viable approach for the analysis of CRTs. More research is needed, however, to address the finite-sample bias in the variance estimation of the QIF to better control its test size.