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Background: Concerns about low accrual have long been a standard part of the discourse on cancer clinical trials, reaching even as far as the news media. Indeed, so many trials are closed before completing accrual that a cottage industry has recently developed creating statistical models to predict trial failure. We previously proposed four methodologic fixes for the current crisis in clinical trials: (1) dramatically reducing the number of eligibility criteria, (2) using data routinely collected in clinical practice for trial endpoints; then lowering barriers to accrual by (3) cluster randomization or (4) staged consent. Methods: We report our practical experience of applying these fixes to randomized trials at Memorial Sloan Kettering Cancer Center. Results: We have completed seven single-center randomized trials, with several more underway and accruing rapidly, with a total accrual approaching 10,000. Many of the trials have compared surgical interventions, an area where trials have traditionally been hard to complete. Only one of these trials was externally funded. While low costs were possible due to the existing research infrastructure at our institution, such infrastructure is common at major cancer centers. Conclusions: Further research on innovative clinical trial designs is warranted, particularly in higher-stakes settings, and in trials of medical and radiotherapy interventions. [ABSTRACT FROM AUTHOR]

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Online marketplace designers frequently run randomized experiments to measure the impact of proposed product changes. However, given that marketplaces are inherently connected, total average treatment effect (TATE) estimates obtained through individual-level randomized experiments may be biased because of violations of the stable unit treatment value assumption, a phenomenon we refer to as "interference bias." Cluster randomization (i.e., the practice of randomizing treatment assignment at the level of "clusters" of similar individuals) is an established experiment design technique for countering interference bias in social networks, but it is unclear ex ante if it will be effective in marketplace settings. In this paper, we use a meta-experiment or "experiment over experiments" conducted on Airbnb to both provide empirical evidence of interference bias in online marketplace settings and assess the viability of cluster randomization as a tool for reducing interference bias in marketplace TATE estimates. Results from our meta-experiment indicate that at least 20% of the TATE estimate produced by an individual-level randomized evaluation of the platform fee increase we study is attributable to interference bias and eliminated through the use of cluster randomization. We also find suggestive, nonstatistically significant evidence that interference bias in seller-side experiments is more severe in demand-constrained geographies and that the efficacy of cluster randomization at reducing interference bias increases with cluster quality. This paper was accepted by Chris Forman, information systems. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2020.01157. [ABSTRACT FROM AUTHOR]

Abstract We evaluated the rate of autism spectrum disorder (ASD) in a group invited to a screening program compared to the rates in two groups who received usual care. The population eligible for screening was all children in Iceland registered for their 30-month well-child visits at primary healthcare centers (PHCs) from March 1, 2016, to October 31, 2017 (N = 7173). The PHCs in the capital area of Reykjavik were the units of cluster randomization. Nine PHCs were selected for intervention (invited group), while eight PHCs received usual care (control group 1). PHCs outside the capital area were without randomization (control group 2). An interdisciplinary team, including a pediatrician contributing with physical and neurological examination, a psychologist evaluating autism symptoms using a diagnostic instrument, and a social worker interviewing the parents, reached a consensus on the clinical diagnosis of ASD according to the ICD-10 diagnostic system. Children in the population were followed up for at least two years and 119 cases were identified. The overall cumulative incidence of ASD was 1.66 (95% confidence interval (CI): 1.37, 1.99). In the invited group the incidence rate was 2.13 (95% CI: 1.60, 2.78); in control group 1, the rate was 1.83 (95% CI: 1.31, 2.50); and in control group 2, the rate was 1.02 (95% CI: 0.66, 1.50). Although the rate of ASD was higher in the invited group than in the control groups, the wide confidence intervals prevented us from concluding definitively that the screening detected ASD more readily than usual care.

This article discusses a study conducted in Peru that aimed to assess the effectiveness of replacing sodium chloride with a salt substitute in reducing blood pressure and the incidence of hypertension in a community-wide setting. The study utilized a stepped-wedge cluster randomized trial design, where the intervention was introduced in a phased manner across different villages. The results showed that the salt substitute was associated with a modest reduction in blood pressure and a lower incidence of hypertension, particularly among individuals with hypertension at baseline. The study suggests that this intervention could be implemented on a population-wide basis. However, it is important to note that the study design used does not provide the same level of evidence as a randomized controlled trial. [Extracted from the article]

We evaluated the rate of autism spectrum disorder (ASD) in a group invited to a screening program compared to the rates in two groups who received usual care. The population eligible for screening was all children in Iceland registered for their 30-month well-child visits at primary healthcare centers (PHCs) from March 1, 2016, to October 31, 2017 (N = 7173). The PHCs in the capital area of Reykjavik were the units of cluster randomization. Nine PHCs were selected for intervention (invited group), while eight PHCs received usual care (control group 1). PHCs outside the capital area were without randomization (control group 2). An interdisciplinary team, including a pediatrician contributing with physical and neurological examination, a psychologist evaluating autism symptoms using a diagnostic instrument, and a social worker interviewing the parents, reached a consensus on the clinical diagnosis of ASD according to the ICD-10 diagnostic system. Children in the population were followed up for at least two years and 119 cases were identified. The overall cumulative incidence of ASD was 1.66 (95% confidence interval (CI): 1.37, 1.99). In the invited group the incidence rate was 2.13 (95% CI: 1.60, 2.78); in control group 1, the rate was 1.83 (95% CI: 1.31, 2.50); and in control group 2, the rate was 1.02 (95% CI: 0.66, 1.50). Although the rate of ASD was higher in the invited group than in the control groups, the wide confidence intervals prevented us from concluding definitively that the screening detected ASD more readily than usual care. [ABSTRACT FROM AUTHOR]

Background: Providing comprehensive and continuous care for patients whose conditions have mental or behavioral components is a central challenge in primary care and an important part of improving universal health coverage. There is a great need for high and routine availability of psychological interventions, but traditional methods for delivering psychotherapy often result in low reach and long wait times. Primary Care Behavioral Health (PCBH) is a method for organizing primary care in which behavioral health staff provide brief, flexible interventions to a large part of the population in active collaboration with other providers. While PCBH holds promise in addressing important challenges, it has not yet been thoroughly evaluated. Methods: This cluster randomized trial will assess 17 primary care centers (PCCs) that are starting a PCBH implementation process. The PCCs will be divided into two groups, with one starting immediate implementation and the other acting as a control, implementing six months later. The purpose of the study is to strengthen the evidence base for PCBH regarding implementation-, organization-, and patient-level outcomes, taking into consideration that there is a partially dependent relationship between the three levels. Patient outcomes (such as increased daily functioning and reduction of symptoms) may be dependent on organizational changes (such as availability of treatment, waiting times and interprofessional teamwork), which in turn requires change in implementation outcomes (most notably, model fidelity). In addition to the main analysis, five secondary analyses will compare groups based on different combinations of randomization and time periods, specifically before and after each center achieves sufficient PCBH fidelity. Discussion: A randomized comparison of PCBH and traditional primary care has, to our knowledge, not been made before. While the naturalistic setting and the intricacies of implementation pose certain challenges, we have designed this study in an effort to evaluate the causal effects of PCBH despite these complex aspects. The results of this project will be helpful in guiding decisions on how to organize the delivery of behavioral interventions and psychological treatment within the context of primary care in Sweden and elsewhere. Trial registration: ClinicalTrials.gov: NCT05335382. Retrospectively registered on March 13th, 2022. [ABSTRACT FROM AUTHOR]

Complete randomization balances covariates on average, but covariate imbalance often exists in finite samples. Rerandomization can ensure covariate balance in the realized experiment by discarding the undesired treatment assignments. Many field experiments in public health and social sciences assign the treatment at the cluster level due to logistical constraints or policy considerations. Moreover, they are frequently combined with re-randomization in the design stage. We define cluster rerandomization as a cluster-randomized experiment compounded with rerandomization to balance covariates at the individual or cluster level. Existing asymptotic theory can only deal with rerandomization with treatments assigned at the individual level, leaving that for cluster rerandomization an open problem. To fill the gap, we provide a design-based theory for cluster rerandomization. Moreover, we compare two cluster rerandomization schemes that use prior information on the importance of the covariates: one based on the weighted Euclidean distance and the other based on the Mahalanobis distance with tiers of covariates. We demonstrate that the former dominates the latter with optimal weights and orthogonalized covariates. Last but not least, we discuss the role of covariate adjustment in the analysis stage, and recommend covariate-adjusted procedures that can be conveniently implemented by least squares with the associated robust standard errors. [ABSTRACT FROM AUTHOR]

Abstract Background Post-exposure prophylaxis (PEP) is a well-established strategy for the prevention of infectious diseases, in which recently exposed people take a short course of medication to prevent infection. The primary objective of the COVID-19 Ring-based Prevention Trial with lopinavir/ritonavir (CORIPREV-LR) is to evaluate the efficacy of a 14-day course of oral lopinavir/ritonavir as PEP against COVID-19 among individuals with a high-risk exposure to a confirmed case. Methods This is an open-label, multicenter, 1:1 cluster-randomized trial of LPV/r 800/200 mg twice daily for 14 days (intervention arm) versus no intervention (control arm), using an adaptive approach to sample size calculation. Participants will be individuals aged > 6 months with a high-risk exposure to a confirmed COVID-19 case within the past 7 days. A combination of remote and in-person study visits at days 1, 7, 14, 35, and 90 includes comprehensive epidemiological, clinical, microbiologic, and serologic sampling. The primary outcome is microbiologically confirmed COVID-19 infection within 14 days after exposure, defined as a positive respiratory tract specimen for SARS-CoV-2 by polymerase chain reaction. Secondary outcomes include safety, symptomatic COVID-19, seropositivity, hospitalization, respiratory failure requiring ventilator support, mortality, psychological impact, and health-related quality of life. Additional analyses will examine the impact of LPV/r on these outcomes in the subset of participants who test positive for SARS-CoV-2 at baseline. To detect a relative risk reduction of 40% with 80% power at α = 0.05, assuming the secondary attack rate in ring members (p 0) = 15%, 5 contacts per case and intra-class correlation coefficient (ICC) = 0.05, we require 110 clusters per arm, or 220 clusters overall and approximately 1220 enrollees after accounting for 10% loss-to-follow-up. We will modify the sample size target after 60 clusters, based on preliminary estimates of p 0, ICC, and cluster size and consider switching to an alternative drug after interim analyses and as new data emerges. The primary analysis will be a generalized linear mixed model with logit link to estimate the effect of LPV/r on the probability of infection. Participants who test positive at baseline will be excluded from the primary analysis but will be maintained for additional analyses to examine the impact of LPV/r on early treatment. Discussion Harnessing safe, existing drugs such as LPV/r as PEP could provide an important tool for control of the COVID-19 pandemic. Novel aspects of our design include the ring-based prevention approach, and the incorporation of remote strategies for conducting study visits and biospecimen collection. Trial registration This trial was registered at www.ClinicalTrials.gov ( NCT04321174 ) on March 25, 2020.

Abstract For most chronic medical conditions, multiple medications are available and prescribers often have limited evidence about which therapy is likely to be the most effective and safe for an individual patient. As many patients are exposed every day to medicines that may be less effective than available alternatives, this is of public health importance. Cluster randomised trials of prescribing policy offer an opportunity to rapidly obtain evidence of comparative effectiveness and safety. These trials can pose a low risk to patients and cause minimal disruption to usual care. Despite the potential scientific value of this approach, there remain valid concerns about consent, medication switching and the use of routinely collected data in research. We discuss these concerns with reference to an ongoing pilot study (Evaluating Diuretics in Normal Care (EVIDENCE) - a cluster randomised evaluation of hypertension prescribing policy, ISRCTN 46635087, registered 11 August 2017).

Abstract Background The rising number of nursing home (NH) residents and their increasingly complex treatment needs pose a challenge to the German health care system. In Germany, there is no specialized geriatric medical care for NH residents. Nursing staff and general practitioners (GPs) in particular have to compensate for the additional demand, which is compounded by organizational and structural hurdles. As a result, avoidable emergency calls and hospital admissions occur. In the SaarPHIR project (Saarländische PflegeHeimversorgung Integriert Regelhaft), a complex intervention focusing on a medical care concept was developed in a participatory practice-based approach involving NH representatives and GPs. The complex intervention addresses the collaboration between nurses and GPs and aims to help restructure and optimize the existing daily care routine. It is expected to improve the medical care of geriatric patients in NHs and reduce stressful, costly hospital admissions. The intervention was pilot-tested during the first 12 months of the project. In the present study, its effectiveness, cost-effectiveness, and safety will be evaluated. Methods The study is a cluster-randomized controlled trial, comparing an intervention group with a control group. The intervention includes a concept of interprofessional collaboration, in which GPs group into regional cooperating teams. Teams are encouraged to cooperate more closely with NH staff and to provide on-call schedules, pre-weekend visits, joint team meetings, joint documentation, and improved medication safety. At least 32 NHs in Saarland, Germany (with at least 50 residents each) will be included and monitored for 12 months. The primary endpoint is hospitalization. Secondary endpoints are quality of life, quality of care, and medication safety. The control group receives treatment as usual. Process evaluation and health economic evaluation accompany the study. The data set contains claims data from German statutory health insurance companies as well as primary data. Analysis will be conducted using a generalized linear mixed model. Conclusion A reduction in hospital admissions of NH residents and relevant changes in secondary endpoints are expected. In turn, these will have a positive impact on the economic assessment. Trial registration German Clinical Trials Register: DRKS00017129 . Registered on 23 April 2019. https://www.drks.de/drks_web/setLocale_EN.do .

Abstract Background More than half of women in India are anemic. Anemia can result in fatigue, poor work productivity, higher risk of pre-term delivery, and maternal mortality. The Indian government has promoted the use of iron-folic acid supplements (IFA) for the prevention and treatment of anemia for the past five decades, but uptake remains low and anemia prevalence high. Current programs target individual-level barriers among pregnant women and adolescents, but a more comprehensive approach that targets multiple levels among all women of reproductive age is needed to increase uptake of IFA and iron-rich foods. Methods The Reduction in Anemia through Normative Innovations (RANI) project is a norms-based intervention to reduce anemia among women of reproductive age. We will evaluate the intervention through a clustered randomized controlled trial in Odisha, India. We will collect data at three time points (baseline, midline, and end line). For the study, we selected 89 clusters of villages, which we randomized into treatment and control on a 1:1 basis. The treatment arm will receive the RANI project components while the control arm will receive usual care. Fifteen clusters (40–41 villages) were selected and 4000 women (2000 in each arm) living in the selected clusters will be randomly selected to take part in data collection. Women in both study arms will have their hemoglobin concentrations measured. They will also complete in-person surveys about their knowledge, attitudes, perceptions of iron folic acid supplements, and nutritional intake. We will also select a smaller cohort of 300 non-pregnant women (150 in each arm) from this cohort for additional physical activity and cognitive testing. We will conduct both within- and between-group comparisons (treatment and control) at baseline, midline and end line using t-tests. We will also conduct structural equation modeling to examine how much each factor accounts for IFA use and hemoglobin levels. Discussion This RCT will enable us to examine whether a social norms-based intervention can increase uptake of iron folic acid supplements and iron rich foods to reduce anemia. Trial registration This trial was registered with Clinical Trial Registry- India (CTRI) (CTRI/2018/10/016186) on 29 October 2018.

Abstract Background In cluster-randomized controlled trials (C-RCTs), covariate-constrained randomization (CCR) methods efficiently control imbalance in multiple baseline cluster-level variables, but the choice of imbalance metric to define the subset of “adequately balanced” possible allocation schemes for C-RCTs involving more than two arms and continuous variables is unclear. In an ongoing three-armed C-RCT, we chose the min(three Kruskal–Wallis [KW] test P values) > 0.30 as our metric. We use simulation studies to explore the performance of this and other metrics of baseline variable imbalance in CCR. Methods We simulated three continuous variables across three arms under varying allocation ratios and assumptions. We compared the performance of min(analysis of variance [ANOVA] P value) > 0.30, min(KW P value) > 0.30, multivariate analysis of variance (MANOVA) P value > 0.30, min(nine possible t test P values) > 0.30, and min(Wilcoxon rank-sum [WRS] P values) > 0.30. Results Pairwise comparison metrics (t test and WRS) tended to be the most conservative, providing the smallest subset of allocation schemes (10%–13%) meeting criteria for acceptable balance. Sensitivity of the min(t test P values) > 0.30 for detecting non-trivial imbalance was 100% for both hypothetical and resampled simulation scenarios. The KW criterion maintained higher sensitivity than both the MANOVA and ANOVA criteria (89% to over 99%) but was not as sensitive as pairwise criteria. Conclusions Our criterion, the KW P value > 0.30, to signify “acceptable” balance was not the most conservative, but it appropriately identified imbalance in the majority of simulations. Since all are related, CCR algorithms involving any of these imbalance metrics for continuous baseline variables will ensure robust simultaneous control over multiple continuous baseline variables, but we recommend care in determining the threshold of “acceptable” levels of (im)balance. Trial registration This trial is registered on ClinicalTrials.gov (initial post: December 1, 2016; identifier: NCT02979444).

The reduced efficiency of the cluster randomized trial design may be compensated by implementing a multi-period design. The trial then becomes longitudinal, with a risk of intermittently missing observations and dropout. This paper studies the effect of missing data on design efficiency in trials where the periods are the days of the week and clusters are followed for at least one week. The multilevel model with a decaying correlation structure is used to relate outcome to period and treatment condition. The variance of the treatment effect estimator is used to measure efficiency. When there is no data loss, efficiency increases with increasing number of subjects per day and number of weeks. Different weekly measurement schemes are used to evaluate the impact of planned missing data designs: the loss of efficiency due to measuring on fewer days is largest for few subjects per day and few weeks. Dropout is modeled by the Weibull survival function. The loss of efficiency due to dropout increases when more clusters drop out during the course of the trial, especially if the risk of dropout is largest at the beginning of the trial. The largest loss is observed for few subjects per day and a large number of weeks. An example of the effect of waiting room environments in reducing stress in dental care shows how different design options can be compared. An R Shiny app allows researchers to interactively explore various design options and to choose the best design for their trial. [ABSTRACT FROM AUTHOR]

BackgroundThe best treatment option for end-stage renal disease is usually a transplant, preferably a live donor kidney transplant (LDKT). The most effective ways to educate kidney transplant candidates about the risks, benefits, and process of LDKT remain unknown.Methods/designWe report the protocol of the Enhancing Living Donor Kidney Transplant Education (ELITE) Study, a cluster randomized trial of an educational intervention to be implemented during initial transplant evaluation at a large, suburban U.S. transplant center. Five hundred potential transplant candidates are cluster randomized (by date of visit) to receive either: (1) standard-of-care ("usual") transplant education, or (2) intensive education that is based upon the Explore Transplant series of educational materials. Intensive transplant education includes viewing an educational video about LDKT, receiving print education, and meeting with a transplant educator. The primary outcome consists of knowledge of the benefits, risks, and process of LDKT, assessed one week after the transplant evaluation. As a secondary outcome, knowledge and understanding of LDKT are assessed 3 months after the evaluation. Additional secondary outcomes, assessed one week and 3 months after the evaluation, include readiness, self-efficacy, and decisional balance regarding transplant and LDKT, with differences assessed by race. Although the unit of randomization is the date of the transplant evaluation visit, the unit of analysis will be the individual potential transplant candidate.DiscussionThe ELITE Study will help to determine how education in a transplant center can best be designed to help Black and non-Black patients learn about the option of LDKT.Trial registrationClinicaltrials.gov number NCT01261910.

BackgroundThe need for evidence about the effectiveness of therapeutics and other medical practices has triggered new interest in methods for comparative effectiveness research.ObjectiveDescribe an approach to comparative effectiveness research involving cluster randomized trials in networks of hospitals, health plans, or medical practices with centralized administrative and informatics capabilities.Research designWe discuss the example of an ongoing cluster randomized trial to prevent methicillin-resistant Staphylococcus aureus (MRSA) infection in intensive care units (ICUs). The trial randomizes 45 hospitals to: (a) screening cultures of ICU admissions, followed by Contact Precautions if MRSA-positive, (b) screening cultures of ICU admissions followed by decolonization if MRSA-positive, or (c) universal decolonization of ICU admissions without screening.SubjectsAll admissions to adult ICUs.MeasuresThe primary outcome is MRSA-positive clinical cultures occurring >or=2 days following ICU admission. Secondary outcomes include blood and urine infection caused by MRSA (and, separately, all pathogens), as well as the development of resistance to decolonizing agents.ResultsRecruitment of hospitals is complete. Data collection will end in Summer 2011.ConclusionsThis trial takes advantage of existing personnel, procedures, infrastructure, and information systems in a large integrated hospital network to conduct a low-cost evaluation of prevention strategies under usual practice conditions. This approach is applicable to many comparative effectiveness topics in both inpatient and ambulatory settings.

Abstract Background The prevention of postoperative complications is of prime importance after complex elective abdominal operations. Preoperative patient education may prevent postoperative complications and improve patients’ wellbeing, but evidence for its efficacy is poor. The aims of the PEDUCAT trial were (a) to assess the impact of preoperative patient education on postoperative complications and patient-reported outcomes in patients scheduled for elective complex visceral surgery and (b) to evaluate the feasibility of cluster randomization in this setting. Methods Adult patients (age ≥ 18 years) scheduled for elective major visceral surgery were randomly assigned in clusters to attend a preoperative education seminar or to the control group receiving the department’s standard care. Outcome measures were the postoperative complications pneumonia, deep vein thrombosis (DVT), pulmonary embolism, burst abdomen, and in-hospital fall, together with patient-reported outcomes (postoperative pain, anxiety and depression, patient satisfaction, quality of life), length of hospital stay (LOS), and postoperative mortality within 30 days after the index operation. Statistical analysis was primarily by intention to treat. Results In total 244 patients (60 clusters) were finally included (intervention group 138 patients; control group 106 patients). Allocation of hospital wards instead of individual patients facilitated study conduct and reduced confusion about group assignment. In the intervention and control groups respectively, pneumonia occurred in 7.4% versus 8.3% (p = 0.807), pulmonary embolism in 1.6% versus 1.0% (p = 0.707), burst abdomen in 4.2% versus 1.0% (p = 0.165), and in-hospital falls in 0.0% versus 4.2% of patients (p = 0.024). DVT did not occur in any of the patients. Mortality rates (1.4% versus 1.9%, p = 0.790) and LOS (14.2 (+/− 12.0) days versus 16.1 (+/− 15.0) days, p = 0.285) were also similar in the intervention and control groups. Conclusions Cluster randomization was feasible in the setting of preoperative patient education and reduced the risk of contamination effects. The results of this trial indicate good postoperative outcomes in patients undergoing major visceral surgery without superiority of preoperative patient education compared to standard patient care at a high-volume center. However, preoperative patient education is a helpful instrument not only for teaching patients but also for training the nursing staff. Trial registration German Clinical Trials Registry, DRKS00004226. Registered on 23 October 2012. Registered 8 days after the first enrollment.

Background: Post-exposure prophylaxis (PEP) is a well-established strategy for the prevention of infectious diseases, in which recently exposed people take a short course of medication to prevent infection. The primary objective of the COVID-19 Ring-based Prevention Trial with lopinavir/ritonavir (CORIPREV-LR) is to evaluate the efficacy of a 14-day course of oral lopinavir/ritonavir as PEP against COVID-19 among individuals with a high-risk exposure to a confirmed case.Methods: This is an open-label, multicenter, 1:1 cluster-randomized trial of LPV/r 800/200 mg twice daily for 14 days (intervention arm) versus no intervention (control arm), using an adaptive approach to sample size calculation. Participants will be individuals aged > 6 months with a high-risk exposure to a confirmed COVID-19 case within the past 7 days. A combination of remote and in-person study visits at days 1, 7, 14, 35, and 90 includes comprehensive epidemiological, clinical, microbiologic, and serologic sampling. The primary outcome is microbiologically confirmed COVID-19 infection within 14 days after exposure, defined as a positive respiratory tract specimen for SARS-CoV-2 by polymerase chain reaction. Secondary outcomes include safety, symptomatic COVID-19, seropositivity, hospitalization, respiratory failure requiring ventilator support, mortality, psychological impact, and health-related quality of life. Additional analyses will examine the impact of LPV/r on these outcomes in the subset of participants who test positive for SARS-CoV-2 at baseline. To detect a relative risk reduction of 40% with 80% power at α = 0.05, assuming the secondary attack rate in ring members (p0) = 15%, 5 contacts per case and intra-class correlation coefficient (ICC) = 0.05, we require 110 clusters per arm, or 220 clusters overall and approximately 1220 enrollees after accounting for 10% loss-to-follow-up. We will modify the sample size target after 60 clusters, based on preliminary estimates of p0, ICC, and cluster size and consider switching to an alternative drug after interim analyses and as new data emerges. The primary analysis will be a generalized linear mixed model with logit link to estimate the effect of LPV/r on the probability of infection. Participants who test positive at baseline will be excluded from the primary analysis but will be maintained for additional analyses to examine the impact of LPV/r on early treatment.Discussion: Harnessing safe, existing drugs such as LPV/r as PEP could provide an important tool for control of the COVID-19 pandemic. Novel aspects of our design include the ring-based prevention approach, and the incorporation of remote strategies for conducting study visits and biospecimen collection.Trial Registration: This trial was registered at www.ClinicalTrials.gov ( NCT04321174 ) on March 25, 2020. [ABSTRACT FROM AUTHOR]

For most chronic medical conditions, multiple medications are available and prescribers often have limited evidence about which therapy is likely to be the most effective and safe for an individual patient. As many patients are exposed every day to medicines that may be less effective than available alternatives, this is of public health importance. Cluster randomised trials of prescribing policy offer an opportunity to rapidly obtain evidence of comparative effectiveness and safety. These trials can pose a low risk to patients and cause minimal disruption to usual care. Despite the potential scientific value of this approach, there remain valid concerns about consent, medication switching and the use of routinely collected data in research. We discuss these concerns with reference to an ongoing pilot study (Evaluating Diuretics in Normal Care (EVIDENCE) - a cluster randomised evaluation of hypertension prescribing policy, ISRCTN 46635087, registered 11 August 2017). [ABSTRACT FROM AUTHOR]

Background: The rising number of nursing home (NH) residents and their increasingly complex treatment needs pose a challenge to the German health care system. In Germany, there is no specialized geriatric medical care for NH residents. Nursing staff and general practitioners (GPs) in particular have to compensate for the additional demand, which is compounded by organizational and structural hurdles. As a result, avoidable emergency calls and hospital admissions occur. In the SaarPHIR project (Saarländische PflegeHeimversorgung Integriert Regelhaft), a complex intervention focusing on a medical care concept was developed in a participatory practice-based approach involving NH representatives and GPs. The complex intervention addresses the collaboration between nurses and GPs and aims to help restructure and optimize the existing daily care routine. It is expected to improve the medical care of geriatric patients in NHs and reduce stressful, costly hospital admissions. The intervention was pilot-tested during the first 12 months of the project. In the present study, its effectiveness, cost-effectiveness, and safety will be evaluated.Methods: The study is a cluster-randomized controlled trial, comparing an intervention group with a control group. The intervention includes a concept of interprofessional collaboration, in which GPs group into regional cooperating teams. Teams are encouraged to cooperate more closely with NH staff and to provide on-call schedules, pre-weekend visits, joint team meetings, joint documentation, and improved medication safety. At least 32 NHs in Saarland, Germany (with at least 50 residents each) will be included and monitored for 12 months. The primary endpoint is hospitalization. Secondary endpoints are quality of life, quality of care, and medication safety. The control group receives treatment as usual. Process evaluation and health economic evaluation accompany the study. The data set contains claims data from German statutory health insurance companies as well as primary data. Analysis will be conducted using a generalized linear mixed model.Conclusion: A reduction in hospital admissions of NH residents and relevant changes in secondary endpoints are expected. In turn, these will have a positive impact on the economic assessment.Trial Registration: German Clinical Trials Register: DRKS00017129. Registered on 23 April 2019. https://www.drks.de/drks_web/setLocale_EN.do. [ABSTRACT FROM AUTHOR]

In cluster randomized trials (CRTs), the outcome of interest is often a count at the cluster level. This occurs, for example, in evaluating an intervention with the outcome being the number of infections of a disease such as HIV or dengue or the number of hospitalizations in the cluster. Standard practice analyzes these counts through cluster outcome rates using an appropriate denominator (eg, population size). However, such denominators are sometimes unknown, particularly when the counts depend on a passive community surveillance system. We consider direct comparison of the counts without knowledge of denominators, relying on randomization to balance denominators. We also focus on permutation tests to allow for small numbers of randomized clusters. However, such approaches are subject to bias when there is differential ascertainment of counts across arms, a situation that may occur in CRTs that cannot implement blinded interventions. We suggest the use of negative control counts as a method to remove, or reduce, this bias, discussing the key properties necessary for an effective negative control. A current example of such a design is the recent extension of test‐negative designs to CRTs testing community‐level interventions. Via simulation, we compare the performance of new and standard estimators based on CRTs with negative controls to approaches that only use the original counts. When there is no differential ascertainment by intervention arm, the count‐only approaches perform comparably to those using debiasing negative controls. However, under even modest differential ascertainment, the count‐only estimators are no longer reliable. [ABSTRACT FROM AUTHOR]

The subject of this work is multiple group random coefficients regression models with several treatments and one control group. Such models are often used for studies with cluster randomized trials. We investigate A-, D- and E-optimal designs for estimation and prediction of fixed and random treatment effects, respectively, and illustrate the obtained results by numerical examples. [ABSTRACT FROM AUTHOR]

Background: More than half of women in India are anemic. Anemia can result in fatigue, poor work productivity, higher risk of pre-term delivery, and maternal mortality. The Indian government has promoted the use of iron-folic acid supplements (IFA) for the prevention and treatment of anemia for the past five decades, but uptake remains low and anemia prevalence high. Current programs target individual-level barriers among pregnant women and adolescents, but a more comprehensive approach that targets multiple levels among all women of reproductive age is needed to increase uptake of IFA and iron-rich foods.Methods: The Reduction in Anemia through Normative Innovations (RANI) project is a norms-based intervention to reduce anemia among women of reproductive age. We will evaluate the intervention through a clustered randomized controlled trial in Odisha, India. We will collect data at three time points (baseline, midline, and end line). For the study, we selected 89 clusters of villages, which we randomized into treatment and control on a 1:1 basis. The treatment arm will receive the RANI project components while the control arm will receive usual care. Fifteen clusters (40-41 villages) were selected and 4000 women (2000 in each arm) living in the selected clusters will be randomly selected to take part in data collection. Women in both study arms will have their hemoglobin concentrations measured. They will also complete in-person surveys about their knowledge, attitudes, perceptions of iron folic acid supplements, and nutritional intake. We will also select a smaller cohort of 300 non-pregnant women (150 in each arm) from this cohort for additional physical activity and cognitive testing. We will conduct both within- and between-group comparisons (treatment and control) at baseline, midline and end line using t-tests. We will also conduct structural equation modeling to examine how much each factor accounts for IFA use and hemoglobin levels.Discussion: This RCT will enable us to examine whether a social norms-based intervention can increase uptake of iron folic acid supplements and iron rich foods to reduce anemia.Trial Registration: This trial was registered with Clinical Trial Registry- India (CTRI) (CTRI/2018/10/016186) on 29 October 2018. [ABSTRACT FROM AUTHOR]

Background: Evidence from prospectively designed studies to guide on-site monitoring practices for randomized trials is limited. A cluster randomized study, nested within the Strategic Timing of AntiRetroviral Treatment (START) trial, was conducted to evaluate on-site monitoring. Methods: Sites were randomized to either annual on-site monitoring or no on-site monitoring. All sites were centrally monitored, and local monitoring was carried out twice each year. Randomization was stratified by country and projected enrollment in START. The primary outcome was a participant-level composite outcome including components for eligibility errors, consent violations, use of antiretroviral treatment not recommended by protocol, late reporting of START primary and secondary clinical endpoints (defined as the event being reported more than 6 months from occurrence), and data alteration and fraud. Logistic regression fixed effect hierarchical models were used to compare on-site versus no on-site monitoring for the primary composite outcome and its components. Odds ratios and 95% confidence intervals comparing on-site monitoring versus no on-site monitoring are cited. Results: In total, 99 sites (2107 participants) were randomized to receive annual on-site monitoring and 97 sites (2264 participants) were randomized to be monitored only centrally and locally. The two monitoring groups were well balanced at entry. In the on-site monitoring group, 469 annual on-site monitoring visits were conducted, and 134 participants (6.4%) in 56 of 99 sites (57%) had a primary monitoring outcome. In the no on-site monitoring group, 85 participants (3.8%) in 34 of 97 sites (35%) had a primary monitoring outcome (odds ratio = 1.7; 95% confidence interval: 1.1–2.7; p = 0.03). Informed consent violations accounted for most outcomes in each group (56 vs 41 participants). The largest odds ratio was for eligibility violations (odds ratio = 12.2; 95% confidence interval: 1.8–85.2; p = 0.01). The number of participants with a late START primary endpoint was similar for each monitoring group (23 vs 16 participants). Late START grade 4 and unscheduled hospitalization events were found for 34 participants in the on-site monitoring group and 19 participants in the no on-site monitoring group (odds ratio = 2.0; 95% confidence interval: 1.1–3.7; p = 0.02). There were no cases of data alteration or fraud. Based on the travel budget for on-site monitoring and the hours spent conducting on-site monitoring, the estimated cost of on-site monitoring was over US$2 million. Conclusion: On-site monitoring led to the identification of more eligibility and consent violations and START clinical events being reported more than 6 months from occurrence as compared to no on-site monitoring. Considering the nature of the excess monitoring outcomes identified at sites receiving on-site monitoring, as well as the cost of on-site monitoring, the value to the START study was limited. [ABSTRACT FROM AUTHOR]

In stepped wedge designs (SWD), clusters are randomized to the time period during which new patients will receive the intervention under study in a sequential rollout over time. By the study's end, patients at all clusters receive the intervention, eliminating ethical concerns related to withholding potentially efficacious treatments. This is a practical option in many large-scale public health implementation settings. Little statistical theory for these designs exists for binary outcomes. To address this, we utilized a maximum likelihood approach and developed numerical methods to determine the asymptotic power of the SWD for binary outcomes. We studied how the power of a SWD for detecting risk differences varies as a function of the number of clusters, cluster size, the baseline risk, the intervention effect, the intra-cluster correlation coefficient, and the time effect. We studied the robustness of power to the assumed form of the distribution of the cluster random effects, as well as how power is affected by variable cluster size. % SWD power is sensitive to neither, in contrast to the parallel cluster randomized design which is highly sensitive to variable cluster size. We also found that the approximate weighted least square approach of Hussey and Hughes (2007, Design and analysis of stepped wedge cluster randomized trials. Contemporary Clinical Trials 28, 182-191) for binary outcomes under-estimates the power in some regions of the parameter spaces, and over-estimates it in others. The new method was applied to the design of a large-scale intervention program on post-partum intra-uterine device insertion services for preventing unintended pregnancy in the first 1.5 years following childbirth in Tanzania, where it was found that the previously available method under-estimated the power. [ABSTRACT FROM AUTHOR]

Abstract Background The number of clusters in a cluster randomized trial is often low. It is therefore likely random assignment of clusters to treatment conditions results in covariate imbalance. There are no studies that quantify the consequences of covariate imbalance in cluster randomized trials on parameter and standard error bias and on power to detect treatment effects. Methods The consequences of covariance imbalance in unadjusted and adjusted linear mixed models are investigated by means of a simulation study. The factors in this study are the degree of imbalance, the covariate effect size, the cluster size and the intraclass correlation coefficient. The covariate is binary and measured at the cluster level; the outcome is continuous and measured at the individual level. Results The results show covariate imbalance results in negligible parameter bias and small standard error bias in adjusted linear mixed models. Ignoring the possibility of covariate imbalance while calculating the sample size at the cluster level may result in a loss in power of at most 25 % in the adjusted linear mixed model. The results are more severe for the unadjusted linear mixed model: parameter biases up to 100 % and standard error biases up to 200 % may be observed. Power levels based on the unadjusted linear mixed model are often too low. The consequences are most severe for large clusters and/or small intraclass correlation coefficients since then the required number of clusters to achieve a desired power level is smallest. Conclusions The possibility of covariate imbalance should be taken into account while calculating the sample size of a cluster randomized trial. Otherwise more sophisticated methods to randomize clusters to treatments should be used, such as stratification or balance algorithms. All relevant covariates should be carefully identified, be actually measured and included in the statistical model to avoid severe levels of parameter and standard error bias and insufficient power levels.

Clinical trials may apply or use a sequential introduction of a new treatment to determine its efficacy or effectiveness with respect to a control treatment. The reasons for choosing a particular switch design have different origins. For instance, they may be implemented for ethical or logistic reasons or for studying disease-modifying effects. Large-scale pragmatic trials with complex interventions often use stepped wedge designs (SWDs), where all participants start at the control group, and during the trial, the control treatment is switched to the new intervention at different moments. They typically use cross-sectional data and cluster randomization. On the other hand, new drugs for inhibition of cognitive decline in Alzheimer's or Parkinson's disease typically use delayed start designs (DSDs). Here, participants start in a parallel group design and at a certain moment in the trial, (part of) the control group switches to the new treatment. The studies are longitudinal in nature, and individuals are being randomized. Statistical methods for these unidirectional switch designs (USD) are quite complex and incomparable, and they have been developed by various authors under different terminologies, model specifications, and assumptions. This imposes unnecessary barriers for researchers to compare results or choose the most appropriate method for their own needs. This paper provides an overview of past and current statistical developments for the USDs (SWD and DSD). All designs are formulated in a unified framework of treatment patterns to make comparisons between switch designs easier. The focus is primarily on statistical models, methods of estimation, sample size calculation, and optimal designs for estimation of the treatment effect. Other relevant open issues are being discussed as well to provide suggestions for future research in USDs. [ABSTRACT FROM AUTHOR]

Data from cluster randomized trials do not always have a pure hierarchical structure. For instance, students are nested within schools that may be crossed by neighborhoods, and soldiers are nested within army units that may be crossed by mental health–care professionals. It is important that the random cross-classification is taken into account while planning a cluster randomized trial. This article presents sample size equations, such that a desired power level is achieved for the test on treatment effect. Furthermore, it also presents optimal sample sizes given a budgetary constraint, with a special focus on conditional optimal designs where one of the sample sizes is fixed beforehand. The optimal design methodology is illustrated using a postdeployment training to reduce ill-health in armed forces personnel. [ABSTRACT FROM AUTHOR]

I discuss three common practices that obfuscate or invalidate the statistical analysis of randomized controlled interventions in applied linguistics. These are (a) checking whether randomization produced groups that are balanced on a number of possibly relevant covariates, (b) using repeated measures ANOVA to analyze pretest-posttest designs, and (c) using traditional significance tests to analyze interventions in which whole groups were assigned to the conditions (cluster randomization). The first practice is labeled superfluous, and taking full advantage of important covariates regardless of balance is recommended. The second is needlessly complicated, and analysis of covariance is recommended as a more powerful alternative. The third produces dramatic inferential errors, which are largely, though not entirely, avoided when mixed-effects modeling is used. This discussion is geared towards applied linguists who need to design, analyze, or assess intervention studies or other randomized controlled trials. Statistical formalism is kept to a minimum throughout.

An increasing number of studies are being performed in educational research to evaluate new teaching methods and approaches. These studies could be performed more efficiently and deliver more convincing results if they more strictly applied and complied with recognized standards of scientific studies. Such an approach could substantially increase the quality in particular of prospective, two-arm (intervention) studies that aim to compare two different teaching methods. A key standard in such studies is randomization, which can minimize systematic bias in study findings; such bias may result if the two study arms are not structurally equivalent. If possible, educational research studies should also achieve this standard, although this is not yet generally the case. Some difficulties and concerns exist, particularly regarding organizational and methodological aspects. An important point to consider in educational research studies is that usually individuals cannot be randomized, because of the teaching situation, and instead whole groups have to be randomized (so-called “cluster randomization”). Compared with studies with individual randomization, studies with cluster randomization normally require (significantly) larger sample sizes and more complex methods for calculating sample size. Furthermore, cluster-randomized studies require more complex methods for statistical analysis. The consequence of the above is that a competent expert with respective special knowledge needs to be involved in all phases of cluster-randomized studies.Studies to evaluate new teaching methods need to make greater use of randomization in order to achieve scientifically convincing results. Therefore, in this article we describe the general principles of cluster randomization and how to implement these principles, and we also outline practical aspects of using cluster randomization in prospective, two-arm comparative educational research studies.

Purpose:To study the effectiveness of Exergames in communal psychiatry for persons with severe mental illness, a randomized cluster study was performed. The hypothesis was to increase physical activity habits to improve somatic health. To identify factors promoting or impeding the use of the Exergames. Methods:Assessments of BMI, blood pressure, physical fitness, SF36, GAF and social interactions were studied at baseline and 10 months. An integrated methods design using content analysis of focus group interviews was integrated with a statistical analysis. Forty-three persons were randomized to the intervention and 30 to the control group. The qualitative interviews included 18 users, 11 staffs and one technical assistant. Results:There were no significant between-group changes in physical activity behaviours or somatic health parameters after 10 months. Only 5% of the intervention group made systematic use of the intervention. Technological difficulties and staff attitudes were found to be barriers. The Exergames were perceived as technically complicated. The staff did not see playing TV games as important and negative attitudes were found. Conclusions:Exergames was not a successful intervention to increase physical activity behaviours in persons with severe mental illness in the community. Exergames and motivation for physical activity in this group is problematic.Implications for rehabilitationThere are difficulties to change passive physical activity habits for persons with severe mental illness, living in sheltered housing conditions in the community due to negative symptoms with depression, low motivation and bad self -confidence.An exergame intervention was not successful in this group of persons. No somatic health benefits were found.Simple physical activities and offering different choices meeting different user needs should be offered.Ensuring user and staff engagement, good technical knowledge and good monitoring is a need for a successful intervention, if Exergames are offered as an alternative physical activity. [ABSTRACT FROM PUBLISHER]

Background: The Stroke Canada Optimization of Rehabilitation by Evidence-Implementation Trial (SCORE-IT) showed that a facilitated knowledge translation (KT) approach to implementing a stroke rehabilitation guideline was more likely than passive strategies to improve functional walking capacity, but not gross manual dexterity, among patients in rehabilitation hospitals. This paper presents the results of a planned process evaluation designed to assess whether the type and number of recommended treatments implemented by stroke teams in each group would help to explain the results related to patient outcomes.Methods: As part of a cluster randomized trial, 20 rehabilitation units were stratified by language and allocated to a facilitated or passive KT intervention group. Sites in the facilitated group received the guideline with treatment protocols and funding for a part-time nurse and therapist facilitator who attended a 2-day training workshop and promoted guideline implementation for 16 months. Sites in the passive group received the guideline excluding treatment protocols. As part of a process evaluation, nurses, and occupational and physical therapists, blinded to study hypotheses, were asked to record their implementation of 18 recommended treatments targeting motor function, postural control and mobility using individualized patient checklists after treatment sessions for 2 weeks pre- and post-intervention. The percentage of patients receiving each treatment pre- and post-intervention and between groups was compared after adjusting for clustering and covariates in a random-effects logistic regression analysis.Results: Data on treatment implementation from nine and eight sites in the facilitated and passive KT group, respectively, were available for analysis. The facilitated KT intervention was associated with improved implementation of sit-to-stand (p = 0.028) and walking (p = 0.043) training while the passive KT intervention was associated with improved implementation of standing balance training (p = 0.037), after adjusting for clustering at patient and provider levels and covariates.Conclusions: Despite multiple strategies and resources, the facilitated KT intervention was unsuccessful in improving integration of 18 treatments concurrently. The facilitated approach may not have adequately addressed barriers to integrating numerous treatments simultaneously and complex treatments that were unfamiliar to providers.Trial Registration: Unique identifier- NCT00359593. [ABSTRACT FROM AUTHOR]

Stepped wedge designs are increasingly commonplace and advantageous for cluster randomized trials when it is both unethical to assign placebo, and it is logistically difficult to allocate an intervention simultaneously to many clusters. We study marginal mean models fit with generalized estimating equations for assessing treatment effectiveness in stepped wedge cluster randomized trials. This approach has advantages over the more commonly used mixed models that (1) the population-average parameters have an important interpretation for public health applications and (2) they avoid untestable assumptions on latent variable distributions and avoid parametric assumptions about error distributions, therefore, providing more robust evidence on treatment effects. However, cluster randomized trials typically have a small number of clusters, rendering the standard generalized estimating equation sandwich variance estimator biased and highly variable and hence yielding incorrect inferences. We study the usual asymptotic generalized estimating equation inferences (i.e., using sandwich variance estimators and asymptotic normality) and four small-sample corrections to generalized estimating equation for stepped wedge cluster randomized trials and for parallel cluster randomized trials as a comparison. We show by simulation that the small-sample corrections provide improvement, with one correction appearing to provide at least nominal coverage even with only 10 clusters per group. These results demonstrate the viability of the marginal mean approach for both stepped wedge and parallel cluster randomized trials. We also study the comparative performance of the corrected methods for stepped wedge and parallel designs, and describe how the methods can accommodate interval censoring of individual failure times and incorporate semiparametric efficient estimators. [ABSTRACT FROM AUTHOR]

Examine the impact of programs led by community health workers on health and function in older adults with arthritis and other health conditions. We conducted a cluster-randomized trial of the Arthritis Foundation Exercise Program (AFEP) enhanced with the '10 Keys'™ to Healthy Aging compared with the AFEP program at 54 sites in 462 participants (mean age 73 years, 88 % women, 80 % white). Trained Community health workers delivered the 10-week programs. Outcomes assessed after 6 months included physical performance [Short Physical Performance Battery (SPPB)], Western Ontario and McMaster Universities (WOMAC) Osteoarthritis Index, and preventive health behaviors. Both groups experienced improvements. Performance improved by 0.3 SPPB points in the AFEP/'10 Keys'™ group and 0.5 in AFEP alone; WOMAC scores declined by 3.0 and 3.9 points respectively. More participants had controlled hypertension at 6 months in both groups (60.1 % baseline to 76.7 % in AFEP/10 Keys and from 76.5 to 84.9 % in AFEP alone) and greater diabetes control (from 15.0 to 34.9 and 15.5 to 34.1 %, respectively). These community-based programs showed similar improvements in preventive health, mobility and arthritis outcomes. [ABSTRACT FROM AUTHOR]

Background The addition of specialty palliative care to standard oncology care improves outcomes for patients with advanced cancer and their caregivers, but many lack access to specialty care services. Primary palliative care—meaning basic palliative care services provided by clinicians who are not palliative care specialists—is an alternative approach that has not been rigorously evaluated. Methods A cluster randomized, controlled trial of the CONNECT (Care management by Oncology Nurses to address supportive care needs) intervention, an oncology nurse-led care management approach to providing primary palliative care for patients with advanced cancer and their family caregivers, is currently underway at 16 oncology practices in Western Pennsylvania. Existing oncology nurses are trained to provide symptom management and emotional support, engage patients and families in advance care planning, and coordinate appropriate care using evidence-based care management strategies. The trial will assess the impact of CONNECT versus standard oncology care on patient quality of life (primary outcome), symptom burden, and mood; caregiver burden and mood; and healthcare resource use. Discussion This trial addresses the need for more accessible models of palliative care by evaluating an intervention led by oncology nurses that can be widely disseminated in community oncology settings. The design confronts potential biases in palliative care research by randomizing at the practice level to avoid contamination, enrolling patients prior to informing them of group allocation, and conducting blinded outcome assessments. By collecting patient, caregiver, and healthcare utilization outcomes, the trial will enable understanding of the full range of a primary palliative care intervention's impact. [ABSTRACT FROM AUTHOR]

Background: Counselling on health-related lifestyles is key to the prevention and management of chronic diseases. After comprehensive study of determinants of its delivery in general practice and strategies to improve, we composed a tailored improvement program, which included communication skills training, online patient information, and a clinical protocol for managing depressive symptoms. Our aim was to assess the effectiveness of this program on professional performance and outcomes in cardiovascular patients.Methods: A two-arm cluster randomized trial in 34 general practices involving 34 nurses was conducted. The primary outcome was an aggregated score of a positive score on lifestyle counselling delivered and an appropriate action on depressive symptoms. Secondary outcomes included the various elements of the primary outcome, vascular risk factors (extracted from patient records), and patient-reported lifestyle behaviors. Data were collected from medical records and a written survey among included patients.Results: A sample of 1782 patients with recorded cardiovascular disease or high cardiovascular risk was available at follow-up at 6 months. No impact on the primary outcome was found; lifestyle counselling was recorded in a minority of patients (11.4 % in the intervention group and 10.3 % in the control group). An effect was found on a secondary outcome: patients' physical activity level increased (B 0.18; 95 % CI 0.02-0.35) on a seven-point scale.Conclusions: The tailored improvement program showed no effect on the primary outcome. This challenges the methodology of tailoring. More involvement of the targeted health care professionals might offer ways to develop more effective implementation programs. Physical activity might be the lifestyle issue that can be more easily changed than smoking or dietary habits.Trial Registration: Nederlands Trial register NTR4069. [ABSTRACT FROM AUTHOR]

Background: The number of clusters in a cluster randomized trial is often low. It is therefore likely random assignment of clusters to treatment conditions results in covariate imbalance. There are no studies that quantify the consequences of covariate imbalance in cluster randomized trials on parameter and standard error bias and on power to detect treatment effects.Methods: The consequences of covariance imbalance in unadjusted and adjusted linear mixed models are investigated by means of a simulation study. The factors in this study are the degree of imbalance, the covariate effect size, the cluster size and the intraclass correlation coefficient. The covariate is binary and measured at the cluster level; the outcome is continuous and measured at the individual level.Results: The results show covariate imbalance results in negligible parameter bias and small standard error bias in adjusted linear mixed models. Ignoring the possibility of covariate imbalance while calculating the sample size at the cluster level may result in a loss in power of at most 25 % in the adjusted linear mixed model. The results are more severe for the unadjusted linear mixed model: parameter biases up to 100 % and standard error biases up to 200 % may be observed. Power levels based on the unadjusted linear mixed model are often too low. The consequences are most severe for large clusters and/or small intraclass correlation coefficients since then the required number of clusters to achieve a desired power level is smallest.Conclusions: The possibility of covariate imbalance should be taken into account while calculating the sample size of a cluster randomized trial. Otherwise more sophisticated methods to randomize clusters to treatments should be used, such as stratification or balance algorithms. All relevant covariates should be carefully identified, be actually measured and included in the statistical model to avoid severe levels of parameter and standard error bias and insufficient power levels. [ABSTRACT FROM AUTHOR]

Background: Many interventions against infectious diseases have geographically diffuse effects. This leads to contamination between arms in cluster-randomized trials (CRTs). Pathogen elimination is the goal of many intervention programs against infectious agents, but contamination means that standard CRT designs and analyses do not provide inferences about the potential of interventions to interrupt pathogen transmission at maximum scale-up.Methods: A generic model of disease transmission was used to simulate infections in stepped wedge cluster-randomized trials (SWCRTs) of a transmission-reducing intervention, where the intervention has spatially diffuse effects. Simulations of such trials were then used to examine the potential of such designs for providing generalizable causal inferences about the impact of such interventions, including measurements of the contamination effects. The simulations were applied to the geography of Rusinga Island, Lake Victoria, Kenya, the site of the SolarMal trial on the use of odor-baited mosquito traps to eliminate Plasmodium falciparum malaria. These were used to compare variants in the proposed SWCRT designs for the SolarMal trial.Results: Measures of contamination effects were found that could be assessed in the simulated trials. Inspired by analyses of trials of insecticide-treated nets against malaria when applied to the geography of the SolarMal trial, these measures were found to be robust to different variants of SWCRT design. Analyses of the likely extent of contamination effects supported the choice of cluster size for the trial.Conclusions: The SWCRT is an appropriate design for trials that assess the feasibility of local elimination of a pathogen. The effects of incomplete coverage can be estimated by analyzing the extent of contamination between arms in such trials, and the estimates also support inferences about causality. The SolarMal example illustrates how generic transmission models incorporating spatial smoothing can be used to simulate such trials for a power calculation and optimization of cluster size and randomization strategies. The approach is applicable to a range of infectious diseases transmitted via environmental reservoirs or via arthropod vectors. [ABSTRACT FROM AUTHOR]

Background: Childhood anemia is highly prevalent worldwide. Improving the hemoglobin level of preschool age children could yield substantial benefits in cognitive and psychosocial development and overall health. While evidence-based recommendations for reducing childhood anemia in high anemia prevalence countries are available, there is no experimental evidence of community centered education and counseling programs, as a route to improved acceptance of iron supplements, demonstrating beneficial effects on anemia outcomes. We report on the evaluation protocol of a complex educational intervention led by the community lay health worker (LHW) and delivered to mothers of 12-59-month-old anemic children living in and visiting village day care centers in a large district of southern India.Methods and Design: The study is designed as a cluster randomized controlled trial. The intervention is based on the social cognitive theory and aims to promote among mothers, anemia awareness, dietary modifications to increase iron intake in the child, and recognition of the need for enhanced adherence to supplemental iron in the anemic child. From 270 eligible villages in the study area, a sample of 60 villages will be randomized to intervention [n = 30] or to treatment as usual [n = 30] of the study. LHWs in the intervention arm will be trained to administer the following intervention components to mothers of anemic children: 1] monthly distribution of Iron and folic acid (IFA) supplements to mothers of anemic children, and 2] five monthly counseling sessions of mothers of anemic children covering: a] anemia awareness education b] IFA adherence counseling and assessment, c] dietary modification to improve iron intake, and d] hygiene and sanitation. LHWs in the control arm will distribute IFA to mothers of anemic children as in the intervention arm but will not provide monthly education and counseling support. The primary outcome is the difference between the two experimental groups in anemia cure rates of children found to be anemic at baseline. Secondary outcomes, assessed as differences between all participants in both experimental groups, are: change in mothers' knowledge regarding anemia; 24 hour dietary iron intake; net improvement in individual hemoglobin values; serum ferritin; and the difference in overall cluster level childhood anemia prevalence. All outcomes will be measured 6 months after the start of the intervention. Multilevel linear and logistic regression models will be used to analyze differences between intervention and control groups in outcome variables.Discussion: This trial is designed to evaluate the effectiveness of an intervention intended to improve anemia cure rates in anemic children living in villages of Chamarajnagar, Karnataka a large district in south India. The extensive study of secondary endpoints will be used to identify possible weak points in the compliance to intervention delivery and uptake. This evaluation is one of the few large randomized trials evaluating the impact of an education and counseling intervention to reduce childhood anemia prevalence.Trial Registration: This trial was registered with ISRCTN.com (identifier: ISRCTN68413407) on 17 September 2013. [ABSTRACT FROM AUTHOR]

Background: The use of cluster randomized trials (CRTs) is increasing, along with the variety in their design and analysis. The simplest approach for their sample size calculation is to calculate the sample size assuming individual randomization and inflate this by a design effect to account for randomization by cluster. The assumptions of a simple design effect may not always be met; alternative or more complicated approaches are required. Methods: We summarise a wide range of sample size methods available for cluster randomized trials. For those familiar with sample size calculations for individually randomized trials but with less experience in the clustered case, this manuscript provides formulae for a wide range of scenarios with associated explanation and recommendations. For those with more experience, comprehensive summaries are provided that allow quick identification of methods for a given design, outcome and analysis method. Results: We present first those methods applicable to the simplest two-arm, parallel group, completely randomized design followed by methods that incorporate deviations from this design such as: variability in cluster sizes; attrition; non-compliance; or the inclusion of baseline covariates or repeated measures. The paper concludes with methods for alternative designs. Conclusions: There is a large amount of methodology available for sample size calculations in CRTs. This paper gives the most comprehensive description of published methodology for sample size calculation and provides an important resource for those designing these trials. [ABSTRACT FROM AUTHOR]