scholarly journals Permutation tests for stepped-wedge cluster-randomized trials

2019 ◽  
Vol 19 (4) ◽  
pp. 803-819 ◽  
Author(s):  
Jennifer Thompson ◽  
Calum Davey ◽  
Richard Hayes ◽  
James Hargreaves ◽  
Katherine Fielding
Keyword(s):  
Statistical Tests ◽  
Permutation Tests ◽  
Weighted Average ◽  
Stepped Wedge ◽  
Intervention Effect ◽  
Trial Testing ◽  
Stepped Wedge Trials ◽  
Using Data ◽  
Cluster Randomized ◽  
The Impact

Permutation tests are useful in stepped-wedge trials to provide robust statistical tests of intervention-effect estimates. However, the permute command does not produce valid tests in this setting because individual observations are not exchangeable. We introduce the swpermute command, which permutes clusters to sequences to maintain exchangeability. The command provides additional functionality for performing analyses of stepped-wedge trials. In particular, we include the withinperiod option, which performs the specified analysis separately in each period of the study with the resulting period-specific intervention-effect estimates combined as a weighted average. We also include functionality to test nonzero null hypotheses to aid in the construction of confidence intervals. Examples of the application of swpermute are given using data from a trial testing the impact of a new tuberculosis diagnostic test on bacterial confirmation of a tuberculosis diagnosis.

Sample size determination for stepped wedge cluster randomized trials in pragmatic settings

2021 ◽  
pp. 096228022110223
Author(s):  
Jijia Wang ◽  
Jing Cao ◽  
Song Zhang ◽  
Chul Ahn
Keyword(s):  
Sample Size ◽  
Closed Form ◽  
Sample Size Calculation ◽  
Repeated Measurements ◽  
Sample Size Determination ◽  
Stepped Wedge ◽  
Pragmatic Trials ◽  
Stepped Wedge Trials ◽  
Cluster Randomized ◽  
The Impact

The stepped-wedge cluster randomized design has been increasingly employed by pragmatic trials in health services research. In this study, based on the GEE approach, we present closed-form sample size calculation that is applicable to both closed-cohort and cross-sectional stepped wedge trials. Importantly, the proposed method is flexible to accommodate design issues routinely encountered in pragmatic trials, such as different within- and between-subject correlation structures, irregular crossover schedules for the switch to intervention, and missing data due to repeated measurements over prolonged follow-up. The closed-form formulas allow researchers to analytically assess the impact of different design factors on sample size requirement. We also recognize the potential issue of limited numbers of clusters in pragmatic stepped wedge trials and present an adjustment approach for underestimated variance of the treatment effect. We conduct extensive simulation to assess the performance of the proposed sample size method. An application example to a real clinical trial is presented.

scholarly journals Point-of-care molecular testing and antiviral treatment of influenza in residents of homeless shelters in Seattle, WA: study protocol for a stepped-wedge cluster-randomized controlled trial

Trials ◽  
2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Kira L. Newman ◽  
◽  
Julia H. Rogers ◽  
Denise McCulloch ◽  
Naomi Wilcox ◽  
...  
Keyword(s):  
Point Of Care ◽  
Antiviral Treatment ◽  
Homeless Shelters ◽  
Molecular Testing ◽  
Symptom Duration ◽  
Homeless Shelter ◽  
Stepped Wedge ◽  
Site Testing ◽  
Cluster Randomized ◽  
The Impact

Abstract Introduction Influenza is an important public health problem, but data on the impact of influenza among homeless shelter residents are limited. The primary aim of this study is to evaluate whether on-site testing and antiviral treatment of influenza in residents of homeless shelters reduces influenza spread in these settings. Methods and analysis This study is a stepped-wedge cluster-randomized trial of on-site testing and antiviral treatment for influenza in nine homeless shelter sites within the Seattle metropolitan area. Participants with acute respiratory illness (ARI), defined as two or more respiratory symptoms or new or worsening cough with onset in the prior 7 days, are eligible to enroll. Approximately 3200 individuals are estimated to participate from October to May across two influenza seasons. All sites will start enrollment in the control arm at the beginning of each season, with routine surveillance for ARI. Sites will be randomized at different timepoints to enter the intervention arm, with implementation of a test-and-treat strategy for individuals with two or fewer days of symptoms. Eligible individuals will be tested on-site with a point-of-care influenza test. If the influenza test is positive and symptom onset is within 48 h, participants will be administered antiviral treatment with baloxavir or oseltamivir depending upon age and comorbidities. Participants will complete a questionnaire on demographics and symptom duration and severity. The primary endpoint is the incidence of influenza in the intervention period compared to the control period, after adjusting for time trends. Trial registration ClinicalTrials.gov NCT04141917. Registered 28 October 2019. Trial sponsor: University of Washington.

scholarly journals Atmiyata, a community led intervention to address common mental disorders: Study protocol for a stepped wedge cluster randomized controlled trial in rural Gujarat, India

2019 ◽  
Author(s):  
Kaustubh Joag ◽  
Jasmine Kalha ◽  
Deepa Pandit ◽  
Susmita Chatterjee ◽  
Sadhvi Krishnamoorthy ◽  
...  
Keyword(s):  
Mental Health ◽  
Common Mental Disorders ◽  
Controlled Trial ◽  
Mixed Effects ◽  
Stepped Wedge ◽  
Linear Mixed Effects ◽  
Lay Health Worker ◽  
Randomized Controlled ◽  
Cluster Randomized ◽  
The Impact

Abstract Background: While lay-health worker models for mental health care have proven to be effective in controlled trials, there is limited evidence on the effectiveness and scalability of these models in rural communities in low- and middle-income countries (LMICs). Atmiyata is a rural community-led intervention using local community volunteers, called Champions, to identify and provide evidence-based counselling for persons with common mental disorders (CMD) as part of a package of community-based interventions for mental health. Methods: The impact of the Atmiyata intervention is evaluated through a stepped wedge cluster randomized controlled trial (SW-CRCT) with a nested economic evaluation. The trial spans across 10 sub-blocks (645 villages) in Mehsana district with 1.52 million rural adult population. There are 56Primary Health Centers (PHCs) in Mehsana district and villages covered under these PHCs are equally divided into four groups of clusters of 14 PHCs each, and the intervention is rolled out in a staggered manner in these groups of villages at an interval of 5 months. The primary outcome is symptomatic improvement measured through the GHQ-12 at 3-month follow-up. Secondary outcomes include: quality of life using the EURO-QoL (EQ- 5D), symptom improvement measured by the Self-Reporting Questionnaire-20 (SRQ-20), functioning using the WHO Disability Assessment Scale (WHO-DAS-12), depression symptoms using the Patient Health Questionnaire, (PHQ-9), anxiety symptoms using Generalized Anxiety Disorder Questionnaire, (GAD-7) and social participation using the Social Participation Scale (SPS). Generalized linear mixed effects model are employed for binary outcomes and linear mixed effects models for continuous outcomes. A Return on investment (ROI) analysis of the intervention will be conducted to understand whether the intervention generates any return on financial investments made into the project. Discussion: Stepped wedge designs are progressively being used to evaluate real-life effectiveness of interventions. To the best of our knowledge, this is the first SW-CRCT in a LMIC evaluating the impact of implementation of a psychosocial mental health intervention. The results of this study will contribute to the evidence on scaling-up lay health worker models for mental health interventions and contribute to the SW-CRCT literature in LMICs.

scholarly journals Impact of Introducing Hepatitis B Birth Dose Vaccines into the Infant Immunization Program in Burkina Faso: Study Protocol for a Stepped Wedge Cluster Randomized Trial (NéoVac Study)

Vaccines ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 583
Author(s):  
Haoua Tall ◽  
Pierrick Adam ◽  
Abdoul Salam Eric Tiendrebeogo ◽  
Jeanne Perpétue Vincent ◽  
Laura Schaeffer ◽  
...  
Keyword(s):  
Hepatitis B ◽  
Burkina Faso ◽  
Health Center ◽  
Sub Saharan Africa ◽  
Stepped Wedge ◽  
Immunization Program ◽  
Birth Dose ◽  
Sub Saharan ◽  
Cluster Randomized ◽  
The Impact

To achieve global hepatitis elimination by 2030, it is critical to prevent the mother-to-child transmission (MTCT) of hepatitis B virus (HBV). Since 2009, the WHO has recommended administering hepatitis B vaccine to all neonates within 24 h of birth to prevent MTCT. However, many countries in sub-Saharan Africa only provide hepatitis B immunization at the age of 6, 10, and 14 weeks or 8, 12, and 16 weeks using a combined vaccine. To accelerate the introduction of the hepatitis B birth dose vaccine (HepB-BD) into sub-Saharan Africa, it is critical to establish to what extent the addition of HepB-BD can further reduce HBV transmission in areas where three-dose infant vaccination has been implemented. We therefore designed a study to evaluate the impact, acceptability, and cost-effectiveness of incorporating the HepB-BD into the routine immunization program in a real-life field condition in Burkina Faso, where the hepatitis B vaccination is currently scheduled at 8-12-16 weeks. Through a multidisciplinary approach combining epidemiology, anthropology, and health economics, the Neonatal Vaccination against Hepatitis B in Africa (NéoVac) study conducts a pragmatic stepped wedge cluster randomized controlled trial in rural areas of the Hauts-Bassins Region. The study was registered in ClinicalTrials.gov (identifier: NCT04029454). A health center is designated as a cluster, and the introduction of HepB-BD will be rolled out sequentially in 24 centers. Following an initial period in which no health center administers HepB-BD, one center will be randomly allocated to incorporate HepB-BD. Then, at a regular interval, another center will be randomized to cross from the control to the intervention period, until all 24 centers integrate HepB-BD. Pregnant women attending antenatal care will be systematically invited to participate. Infants born during the control period will follow the conventional immunization schedule (8-12-16 weeks), while those born in the interventional period will receive HepB-BD in addition to the routine vaccines (0-8-12-16 weeks). The primary outcome, the proportion of hepatitis B surface antigen (HBsAg) positivity in infants aged at 9 months, will be compared between children born before and after HepB-BD introduction. The study will generate data that may assist governments and stakeholders in sub-Saharan Africa to make evidence-based decisions about whether to add HepB-BD into the national immunization programs.

scholarly journals Statistical Properties of Stepped Wedge Cluster-Randomized Trials in Infectious Disease Outbreaks

2020 ◽  
Author(s):  
Lee Kennedy-Shaffer ◽  
Marc Lipsitch
Keyword(s):  
Infectious Disease ◽  
Randomized Trials ◽  
Disease Outbreaks ◽  
Statistical Properties ◽  
Cluster Randomized Trials ◽  
Stepped Wedge ◽  
Infectious Disease Outbreaks ◽  
New Methods ◽  
Stepped Wedge Trials ◽  
Cluster Randomized

ABSTRACTRandomized controlled trials are crucial for the evaluation of interventions such as vaccinations, but the design and analysis of these studies during infectious disease outbreaks is complicated by statistical, ethical, and logistical factors. Attempts to resolve these complexities have led to the proposal of a variety of trial designs, including individual randomization and several types of cluster randomization designs: parallel-arm, ring vaccination, and stepped wedge designs. Because of the strong time trends present in infectious disease incidence, however, methods generally used to analyze stepped wedge trials may not perform well in these settings. Using simulated outbreaks, we evaluate various designs and analysis methods, including recently proposed methods for analyzing stepped wedge trials, to determine the statistical properties of these methods. While new methods for analyzing stepped wedge trials can provide some improvement over previous methods, we find that they still lag behind parallel-arm cluster-randomized trials and individually-randomized trials in achieving adequate power to detect intervention effects. We also find that these methods are highly sensitive to the weighting of effect estimates across time periods. Despite the value of new methods, stepped wedge trials still have statistical disadvantages compared to other trial designs in epidemic settings.

Investigating the Effect of Aspect-Oriented Refactoring on the Unit Testing Effort of Classes: An Empirical Evaluation

2017 ◽  
Vol 27 (05) ◽  
pp. 749-789 ◽  
Author(s):  
Mourad Badri ◽  
Aymen Kout ◽  
Linda Badri
Keyword(s):  
Statistical Tests ◽  
Source Code ◽  
Empirical Evaluation ◽  
Software Systems ◽  
Test Cases ◽  
Unit Testing ◽  
Unit Test ◽  
Testing Effort ◽  
Using Data ◽  
The Impact

This paper aims at investigating empirically the effect of aspect-oriented (AO) refactoring on the unit testability of classes in object-oriented software. The unit testability of classes has been addressed from the perspective of the unit testing effort, and particularly from the perspective of the unit test cases (TCs) construction. We investigated, in fact, different research questions: (1) the impact of AO refactoring on source code attributes (size, complexity, coupling, cohesion and inheritance), attributes that are mostly related to the unit testability of classes, (2) the impact of AO refactoring on unit test code attributes (size, assertions, invocations and data creation), attributes that are indicators of the effort involved to write the code of unit TCs, and (3) the relationships between the variations observed after AO refactoring in both source code and unit test code attributes. We used in the study different techniques: correlation analysis, statistical tests and linear regression. We performed an empirical evaluation using data collected from three well-known open source (Java) software systems (JHOTDRAW, HSQLBD and PETSTORE) that have been refactored using AO programming (AspectJ). Results suggest that: (1) overall, the effort involved in the construction of unit TCs of refactored classes has been reduced, (2) the variations of source code attributes have more impact on methods invocation between unit TCs, and finally (3) the variations of unit test code attributes are more influenced by the variation of the complexity of refactored classes compared to the other class attributes.

Inference for the treatment effect in longitudinal cluster randomized trials when treatment effect heterogeneity is ignored

2021 ◽  
pp. 096228022110417
Author(s):  
Rhys Bowden ◽  
Andrew B Forbes ◽  
Jessica Kasza
Keyword(s):  
Treatment Effect ◽  
Randomized Trials ◽  
Error Rates ◽  
Type I ◽  
Cluster Randomized Trials ◽  
Stepped Wedge ◽  
Treatment Effect Heterogeneity ◽  
Cluster Randomized ◽  
Effect Heterogeneity ◽  
The Impact

In cluster-randomized trials, sometimes the effect of the intervention being studied differs between clusters, commonly referred to as treatment effect heterogeneity. In the analysis of stepped wedge and cluster-randomized crossover trials, it is possible to include terms in outcome regression models to allow for such treatment effect heterogeneity yet this is not frequently considered. Outside of some simulation studies of specific cases where the outcome is binary, the impact of failing to include terms for treatment effect heterogeneity on the variance of the treatment effect estimator is unknown. We analytically examine the impact of failing to include terms for treatment effect heterogeneity on the variance of the treatment effect estimator, when outcomes are continuous. Using analysis of variance and feasible generalized least squares we provide expressions for this variance. For both the cluster-randomized crossover design and the stepped wedge design, our analytic derivations indicate that failing to include treatment effect heterogeneity results in the estimates for variance of the treatment effect that are too small, leading to inflation of type I error rates. We therefore recommend assessing the sensitivity of sample size calculations and conclusions drawn from the analysis of cluster randomized trials to the inclusion of treatment effect heterogeneity.

Information growth for sequential monitoring of clinical trials with a stepped wedge cluster randomized design and unknown intracluster correlation

2020 ◽  
Vol 17 (2) ◽  
pp. 176-183
Author(s):  
Siobhan P Brown ◽  
Abigail B Shoben
Keyword(s):  
Study Design ◽  
Type I Error ◽  
Type I ◽  
Intracluster Correlation ◽  
Stepped Wedge ◽  
Group Sequential ◽  
New Therapy ◽  
Cluster Randomized ◽  
The Impact ◽  
Information Growth

Background/aims In a stepped wedge study design, study clusters usually start with the baseline treatment and then cross over to the intervention at randomly determined times. Such designs are useful when the intervention must be delivered at the cluster level and are becoming increasingly common in practice. In these trials, if the outcome is death or serious morbidity, one may have an ethical imperative to monitor the trial and stop before maximum enrollment if the new therapy is proven to be beneficial. In addition, because formal monitoring allows for the stoppage of trials when a significant benefit for new therapy has been ruled out, their use can make a research program more efficient. However, use of the stepped wedge cluster randomized study design complicates the implementation of standard group sequential monitoring methods. Both the correlation of observations introduced by the clustered randomization and the timing of crossover from one treatment to the other impact the rate of information growth, an important component of an interim analysis. Methods We simulated cross-sectional stepped wedge study data in order to evaluate the impact of sequential monitoring on the Type I error and power when the true intracluster correlation is unknown. We studied the impact of varying intracluster correlations, treatment effects, methods of estimating the information growth, and boundary shapes. Results While misspecified information growth can impact both the Type I error and power of a study in some settings, we observed little inflation of the Type I error and only moderate reductions in power across a range of misspecified information growth patterns in our simulations. Conclusion Taking the study design into account and using either an estimate of the intracluster correlation from the ongoing study or other data in the same clusters should allow for easy implementation of group sequential methods in future stepped wedge designs.

scholarly journals Sample size calculation for stepped-wedge cluster-randomized trials with more than two levels of clustering

2019 ◽  
Vol 16 (3) ◽  
pp. 225-236 ◽  
Author(s):  
Steven Teerenstra ◽  
Monica Taljaard ◽  
Anja Haenen ◽  
Anita Huis ◽  
Femke Atsma ◽  
...  
Keyword(s):  
Hand Hygiene ◽  
Sample Size ◽  
Sample Size Calculation ◽  
Correlation Coefficients ◽  
Stepped Wedge ◽  
Change Trial ◽  
Stepped Wedge Trials ◽  
Hygiene Compliance ◽  
Standard Designs ◽  
Cluster Randomized

Background/Aims: Power and sample size calculation formulas for stepped-wedge trials with two levels (subjects within clusters) are available. However, stepped-wedge trials with more than two levels are possible. An example is the CHANGE trial which randomizes nursing homes (level 4) consisting of nursing home wards (level 3) in which nurses (level 2) are observed with respect to their hand hygiene compliance during hand hygiene opportunities (level 1) in the care of patients. We provide power and sample size methods for such trials and illustrate these in the setting of the CHANGE trial. Methods: We extend the original sample size methodology derived for stepped-wedge trials based on a random intercepts model, to accommodate more than two levels of clustering. We derive expressions that can be used to determine power and sample size for p levels of clustering in terms of the variances at each level or, alternatively, in terms of intracluster correlation coefficients. We consider different scenarios, depending on whether the same units in a particular level are repeatedly measured as a cohort sample or whether different units are measured cross-sectionally. Results: A simple variance inflation factor is obtained that can be used to calculate power and sample size for continuous and by approximation for binary and rate outcomes. It is the product of (1) variance inflation due to the multilevel structure and (2) variance inflation due to the stepped-wedge manner of assigning interventions over time. Standard and non-standard designs (i.e. so-called “hybrid designs” and designs with more, less, or no data collection when the clusters are all in the control or are all in the intervention condition) are covered. Conclusions: The formulas derived enable power and sample size calculations for multilevel stepped-wedge trials. For the two-, three-, and four-level case of the standard stepped wedge, we provide programs to facilitate these calculations.

Sign in / Sign up

Export Citation Format

Share Document