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 On Small-Sample Inference in Multiphase Stepped Wedge Cluster Randomized Trial (MSW-CRT) With Binary Outcomes

2021 ◽  
Author(s):  
Jing Peng ◽  
Abigail Shoben ◽  
Pengyue Zhang ◽  
Philip M. Westgate ◽  
Soledad Fernandez
Keyword(s):  
Randomized Trial ◽  
Type I Error ◽  
Event Rate ◽  
Cluster Randomized Trial ◽  
Small Sample ◽  
Binary Outcomes ◽  
Type I ◽  
Stepped Wedge ◽  
Number Of Clusters ◽  
Cluster Randomized

Abstract BackgroundThe stepped wedge cluster randomized trial (SW-CRT) design is now preferred for many health- related trials because of its flexibility on resource allocation and clinical ethics concerns. However, as a necessary extension of studying multiple interventions, multiphase stepped wedge designs (MSW-CRT) have not been studied adequately. Since estimated intervention effect from Generalized estimating equations (GEE) has a population-average interpretation, valid inference methods for binary outcomes based on GEE are preferred by public health policy makers.MethodsWe form hypothesis testing of add-on effect of a second treatment based on GEE analysis in an MSW-CRT design with limited number of clusters. Four variance-correction estimators are used to adjust the bias of the sandwich estimator. Simulation studies have been used to compare the statistical power and type I error rate of these methods under different correlation matrices.Results We demonstrate that an average estimator with t(I-3) can stably maintain type I error close to the nominal level with limited sample sizes in our settings. We show that power of testing the add-on effect depends on the baseline event rate, effect sizes of two interventions and the number of clusters. Moreover, by changing the design with including more sequences, power benefit can be achieved. ConclusionsFor designing the MSW-CRT, we suggest using more sequences and checking event rate after initiating the first intervention via interim analysis. When the number of clusters is not very large in MSW-CRTs, inference can be conduct using GEE analysis with an average estimator with t(I-3) sampling distribution.

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.

scholarly journals Spread and scale of an electronic deprescribing software to improve health outcomes of older adults living in nursing homes: study protocol for a stepped wedge cluster randomized trial

Trials ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Marc-Eric Nadeau ◽  
Justine L. Henry ◽  
Todd C. Lee ◽  
Émilie Bortolussi-Courval ◽  
Carole Goodine ◽  
...  
Keyword(s):  
Older Adults ◽  
Randomized Trial ◽  
Study Design ◽  
Long Term Care ◽  
Cluster Randomized Trial ◽  
Stepped Wedge ◽  
Term Care ◽  
Medication Reviews ◽  
Cluster Randomized

Abstract Background Medication overload or problematic polypharmacy is a major problem causing widespread harm, particularly to older adults. Taking multiple medications increases the risk of potentially inappropriate medications (PIMs), and residents in long-term care (LTC) are frequently prescribed 10 or more medications at once. One strategy to address this problem is for the physician and/or pharmacist to perform regular medication reviews; however, this process can be complicated and time-consuming. With a prescription review, medications may be decreased, changed, or stopped altogether. MedReviewRx is a software that runs an analysis using deprescribing rules to produce a report to guide medication reviews addressing medication overload for residents in LTC. Methods This study will employ a mixed methods effectiveness-implementation hybrid type 2 study design. To measure effectiveness, a stepped wedge cluster randomized trial design is planned, which allows us to approximate a randomized clinical trial. Approximately 1000 residents living in LTC will be recruited from five facilities in New Brunswick. The study will begin with 3 months of baseline data on rates of deprescribing. Thereafter, every 3 months a new cluster will enter the intervention mode. The intervention consists of medication reviews augmented with the MedReviewRx software, which will be used by staff and clinicians in the facilities. The estimated study duration is 18 months and the main outcome will be the proportion of patients with one or more PIMs deprescribed (reduced/stopped or changed to a safer alternative) in the 90 days following a prescription review. The goal is to study the impact of MedReviewRx on medication overload among older adults living in LTC. In typical fashion of a stepped wedge cluster randomized trial, each cluster acts as an internal control (before and after) as well as a control for the other clusters (external control). Qualitative data collected will include resident/caregiver attitudes towards deprescribing and semi-structured interviews with staff working in the long-term care homes. Discussion This study design addresses issues with seasonality and allows all clusters to participate in the intervention, which is an advantage when the intervention is related to quality improvement. This study will provide valuable information on PIM use, cost savings, and facilitators and challenges associated with medication reviews and deprescribing. This study represents an important step towards understanding and promoting tools to guide safe and rational reduction of PIM use among older adults. Trial registration NCT04762303, Registered February 21, 2021.

Group sequential designs with robust semiparametric recurrent event models

2018 ◽  
Vol 28 (8) ◽  
pp. 2385-2403 ◽  
Author(s):  
Tobias Mütze ◽  
Ekkehard Glimm ◽  
Heinz Schmidli ◽  
Tim Friede
Keyword(s):  
Error Rate ◽  
Joint Distribution ◽  
Recurrent Events ◽  
Type I Error ◽  
Type I ◽  
Sequential Designs ◽  
Group Sequential ◽  
Type I Error Rate ◽  
Sequential Procedures ◽  
Group Sequential Designs

Robust semiparametric models for recurrent events have received increasing attention in the analysis of clinical trials in a variety of diseases including chronic heart failure. In comparison to parametric recurrent event models, robust semiparametric models are more flexible in that neither the baseline event rate nor the process inducing between-patient heterogeneity needs to be specified in terms of a specific parametric statistical model. However, implementing group sequential designs in the robust semiparametric model is complicated by the fact that the sequence of Wald statistics does not follow asymptotically the canonical joint distribution. In this manuscript, we propose two types of group sequential procedures for a robust semiparametric analysis of recurrent events. The first group sequential procedure is based on the asymptotic covariance of the sequence of Wald statistics and it guarantees asymptotic control of the type I error rate. The second procedure is based on the canonical joint distribution and does not guarantee asymptotic type I error rate control but is easy to implement and corresponds to the well-known standard approach for group sequential designs. Moreover, we describe how to determine the maximum information when planning a clinical trial with a group sequential design and a robust semiparametric analysis of recurrent events. We contrast the operating characteristics of the proposed group sequential procedures in a simulation study motivated by the ongoing phase 3 PARAGON-HF trial (ClinicalTrials.gov identifier: NCT01920711) in more than 4600 patients with chronic heart failure and a preserved ejection fraction. We found that both group sequential procedures have similar operating characteristics and that for some practically relevant scenarios, the group sequential procedure based on the canonical joint distribution has advantages with respect to the control of the type I error rate. The proposed method for calculating the maximum information results in appropriately powered trials for both procedures.

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 Statistical Monitoring of Clinical Trials Using Brownian Bridges

2016 ◽  
Vol 6 (1) ◽  
pp. 142
Author(s):  
Qiang Zhang ◽  
Michael R. Kosorok
Keyword(s):  
Clinical Trials ◽  
Type I Error ◽  
Brownian Bridge ◽  
Type I ◽  
Group Sequential Design ◽  
Group Sequential ◽  
Cancer Trial ◽  
Statistical Monitoring ◽  
Brownian Bridges ◽  
Group Sequential Methods

The Brownian bridge is not yet used widely in the statistical monitoring of clinical trials. In this paper, we investigate properties of the Brownian bridge and formally derive monitoring rules from these results. We will present four related main methods: (1). derivation of group sequential boundaries; (2). calculation of conditional power; (3). a new alpha spending function and (4). repeated confidence intervals, all under a Brownian bridge framework. Simulation results show that the type I error rate is well controlled and power is satisfactory for the group sequential design. We apply the proposed methods to monitor the interim results from the Beta Blocker Heart Attack Trial (BHAT) and a Head and Neck cancer trial with comparisons to the commonly used monitoring tools. Overall, the proposed methods when used together as one framework are more powerful and sensitive to interim positive and negative trends that are clinically meaningful and lead to timely early stopping with potentially more savings on sample sizes, time and costs. These tools are valuable additions to the existing group sequential methods which can be utilized in trial design, routine monitoring, and to answer important questions from data monitoring committees.

scholarly journals Group sequential designs for stepped-wedge cluster randomised trials

2017 ◽  
Vol 14 (5) ◽  
pp. 507-517 ◽  
Author(s):  
Michael J Grayling ◽  
James MS Wason ◽  
Adrian P Mander
Keyword(s):  
Randomised Trial ◽  
Cluster Randomised Trial ◽  
Type I ◽  
Randomised Trials ◽  
Early Stopping ◽  
Stepped Wedge ◽  
Group Sequential ◽  
Interim Analyses ◽  
Cluster Randomised Trials ◽  
Cluster Randomised

Background/Aims: The stepped-wedge cluster randomised trial design has received substantial attention in recent years. Although various extensions to the original design have been proposed, no guidance is available on the design of stepped-wedge cluster randomised trials with interim analyses. In an individually randomised trial setting, group sequential methods can provide notable efficiency gains and ethical benefits. We address this by discussing how established group sequential methodology can be adapted for stepped-wedge designs. Methods: Utilising the error spending approach to group sequential trial design, we detail the assumptions required for the determination of stepped-wedge cluster randomised trials with interim analyses. We consider early stopping for efficacy, futility, or efficacy and futility. We describe first how this can be done for any specified linear mixed model for data analysis. We then focus on one particular commonly utilised model and, using a recently completed stepped-wedge cluster randomised trial, compare the performance of several designs with interim analyses to the classical stepped-wedge design. Finally, the performance of a quantile substitution procedure for dealing with the case of unknown variance is explored. Results: We demonstrate that the incorporation of early stopping in stepped-wedge cluster randomised trial designs could reduce the expected sample size under the null and alternative hypotheses by up to 31% and 22%, respectively, with no cost to the trial’s type-I and type-II error rates. The use of restricted error maximum likelihood estimation was found to be more important than quantile substitution for controlling the type-I error rate. Conclusion: The addition of interim analyses into stepped-wedge cluster randomised trials could help guard against time-consuming trials conducted on poor performing treatments and also help expedite the implementation of efficacious treatments. In future, trialists should consider incorporating early stopping of some kind into stepped-wedge cluster randomised trials according to the needs of the particular trial.

How Does Polytomous Item Bias Affect Total-group Survey Score Comparisons?

2015 ◽  
Vol 46 (3) ◽  
pp. 586-603 ◽  
Author(s):  
Ma Dolores Hidalgo ◽  
Isabel Benítez ◽  
Jose-Luis Padilla ◽  
Juana Gómez-Benito
Keyword(s):  
Effect Size ◽  
Type I Error ◽  
Error Rates ◽  
T Test ◽  
Type I ◽  
Polytomous Items ◽  
Item Functioning ◽  
Scale Scores ◽  
Comparative Group ◽  
The Impact

The growing use of scales in survey questionnaires warrants the need to address how does polytomous differential item functioning (DIF) affect observed scale score comparisons. The aim of this study is to investigate the impact of DIF on the type I error and effect size of the independent samples t-test on the observed total scale scores. A simulation study was conducted, focusing on potential variables related to DIF in polytomous items, such as DIF pattern, sample size, magnitude, and percentage of DIF items. The results showed that DIF patterns and the number of DIF items affected the type I error rates and effect size of t-test values. The results highlighted the need to analyze DIF before making comparative group interpretations.

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