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Kammar-García A, Fernández-Urrutia LA, Guevara-Díaz JA, Mancilla-Galindo J. Statistical Considerations for the Design and Analysis of Pragmatic Trials in Aging Research. Geriatrics (Basel) 2024; 9:75. [PMID: 38920431 PMCID: PMC11203240 DOI: 10.3390/geriatrics9030075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2024] [Revised: 05/29/2024] [Accepted: 05/30/2024] [Indexed: 06/27/2024] Open
Abstract
Pragmatic trials aim to assess intervention efficacy in usual patient care settings, contrasting with explanatory trials conducted under controlled conditions. In aging research, pragmatic trials are important designs for obtaining real-world evidence in elderly populations, which are often underrepresented in trials. In this review, we discuss statistical considerations from a frequentist approach for the design and analysis of pragmatic trials. When choosing the dependent variable, it is essential to use an outcome that is highly relevant to usual medical care while also providing sufficient statistical power. Besides traditionally used binary outcomes, ordinal outcomes can provide pragmatic answers with gains in statistical power. Cluster randomization requires careful consideration of sample size calculation and analysis methods, especially regarding missing data and outcome variables. Mixed effects models and generalized estimating equations (GEEs) are recommended for analysis to account for center effects, with tools available for sample size estimation. Multi-arm studies pose challenges in sample size calculation, requiring adjustment for design effects and consideration of multiple comparison correction methods. Secondary analyses are common but require caution due to the risk of reduced statistical power and false-discovery rates. Safety data collection methods should balance pragmatism and data quality. Overall, understanding statistical considerations is crucial for designing rigorous pragmatic trials that evaluate interventions in elderly populations under real-world conditions. In conclusion, this review focuses on various statistical topics of interest to those designing a pragmatic clinical trial, with consideration of aspects of relevance in the aging research field.
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Affiliation(s)
- Ashuin Kammar-García
- Dirección de Investigación, Instituto Nacional de Geriatría, Mexico City 10200, Mexico
- Lown Scholars in Cardiovascular Health Program, Departments of Global Health and Population and Epidemiology, Harvard TH Chan School of Public Health, Harvard University, Boston, MA 02115, USA
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Li F, Chen X, Tian Z, Wang R, Heagerty PJ. Planning stepped wedge cluster randomized trials to detect treatment effect heterogeneity. Stat Med 2024; 43:890-911. [PMID: 38115805 DOI: 10.1002/sim.9990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2022] [Revised: 09/22/2023] [Accepted: 11/30/2023] [Indexed: 12/21/2023]
Abstract
Stepped wedge design is a popular research design that enables a rigorous evaluation of candidate interventions by using a staggered cluster randomization strategy. While analytical methods were developed for designing stepped wedge trials, the prior focus has been solely on testing for the average treatment effect. With a growing interest on formal evaluation of the heterogeneity of treatment effects across patient subpopulations, trial planning efforts need appropriate methods to accurately identify sample sizes or design configurations that can generate evidence for both the average treatment effect and variations in subgroup treatment effects. To fill in that important gap, this article derives novel variance formulas for confirmatory analyses of treatment effect heterogeneity, that are applicable to both cross-sectional and closed-cohort stepped wedge designs. We additionally point out that the same framework can be used for more efficient average treatment effect analyses via covariate adjustment, and allows the use of familiar power formulas for average treatment effect analyses to proceed. Our results further sheds light on optimal design allocations of clusters to maximize the weighted precision for assessing both the average and heterogeneous treatment effects. We apply the new methods to the Lumbar Imaging with Reporting of Epidemiology Trial, and carry out a simulation study to validate our new methods.
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Affiliation(s)
- Fan Li
- Department of Biostatistics, Yale University School of Public Health, New Haven, Connecticut, USA
- Center for Methods in Implementation and Prevention Science, Yale University School of Public Health, New Haven, Connecticut, USA
| | - Xinyuan Chen
- Department of Mathematics and Statistics, Mississippi State University, Mississippi State, Mississippi, USA
| | - Zizhong Tian
- Department of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
| | - Rui Wang
- Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, Massachusetts, USA
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Patrick J Heagerty
- Department of Biostatistics, University of Washington, Seattle, Washington, USA
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Ouyang Y, Hemming K, Li F, Taljaard M. Estimating intra-cluster correlation coefficients for planning longitudinal cluster randomized trials: a tutorial. Int J Epidemiol 2023; 52:1634-1647. [PMID: 37196320 PMCID: PMC10555741 DOI: 10.1093/ije/dyad062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2022] [Accepted: 04/26/2023] [Indexed: 05/19/2023] Open
Abstract
It is well-known that designing a cluster randomized trial (CRT) requires an advance estimate of the intra-cluster correlation coefficient (ICC). In the case of longitudinal CRTs, where outcomes are assessed repeatedly in each cluster over time, estimates for more complex correlation structures are required. Three common types of correlation structures for longitudinal CRTs are exchangeable, nested/block exchangeable and exponential decay correlations-the latter two allow the strength of the correlation to weaken over time. Determining sample sizes under these latter two structures requires advance specification of the within-period ICC and cluster autocorrelation coefficient as well as the intra-individual autocorrelation coefficient in the case of a cohort design. How to estimate these coefficients is a common challenge for investigators. When appropriate estimates from previously published longitudinal CRTs are not available, one possibility is to re-analyse data from an available trial dataset or to access observational data to estimate these parameters in advance of a trial. In this tutorial, we demonstrate how to estimate correlation parameters under these correlation structures for continuous and binary outcomes. We first introduce the correlation structures and their underlying model assumptions under a mixed-effects regression framework. With practical advice for implementation, we then demonstrate how the correlation parameters can be estimated using examples and we provide programming code in R, SAS, and Stata. An Rshiny app is available that allows investigators to upload an existing dataset and obtain the estimated correlation parameters. We conclude by identifying some gaps in the literature.
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Affiliation(s)
- Yongdong Ouyang
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada
| | - Karla Hemming
- Institute of Applied Health Research, The University of Birmingham, Birmingham, UK
| | - Fan Li
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
- Center for Methods in Implementation and Prevention Science, Yale School of Public Health, New Haven, CT, USA
| | - Monica Taljaard
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada
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Maleyeff L, Li F, Haneuse S, Wang R. Assessing exposure-time treatment effect heterogeneity in stepped-wedge cluster randomized trials. Biometrics 2023; 79:2551-2564. [PMID: 36416302 PMCID: PMC10203056 DOI: 10.1111/biom.13803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Accepted: 11/16/2022] [Indexed: 11/24/2022]
Abstract
A stepped-wedge cluster randomized trial (CRT) is a unidirectional crossover study in which timings of treatment initiation for clusters are randomized. Because the timing of treatment initiation is different for each cluster, an emerging question is whether the treatment effect depends on the exposure time, namely, the time duration since the initiation of treatment. Existing approaches for assessing exposure-time treatment effect heterogeneity either assume a parametric functional form of exposure time or model the exposure time as a categorical variable, in which case the number of parameters increases with the number of exposure-time periods, leading to a potential loss in efficiency. In this article, we propose a new model formulation for assessing treatment effect heterogeneity over exposure time. Rather than a categorical term for each level of exposure time, the proposed model includes a random effect to represent varying treatment effects by exposure time. This allows for pooling information across exposure-time periods and may result in more precise average and exposure-time-specific treatment effect estimates. In addition, we develop an accompanying permutation test for the variance component of the heterogeneous treatment effect parameters. We conduct simulation studies to compare the proposed model and permutation test to alternative methods to elucidate their finite-sample operating characteristics, and to generate practical guidance on model choices for assessing exposure-time treatment effect heterogeneity in stepped-wedge CRTs.
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Affiliation(s)
- Lara Maleyeff
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Fan Li
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, USA
- Center for Methods in Implementation and Prevention Science, Yale School of Public Health, New Haven, Connecticut, USA
| | - Sebastien Haneuse
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Rui Wang
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA
- Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, Massachusetts, USA
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Li F, Kasza J, Turner EL, Rathouz PJ, Forbes AB, Preisser JS. Generalizing the information content for stepped wedge designs: A marginal modeling approach. Scand Stat Theory Appl 2023; 50:1048-1067. [PMID: 37601275 PMCID: PMC10434823 DOI: 10.1111/sjos.12615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Accepted: 09/02/2022] [Indexed: 11/30/2022]
Abstract
Stepped wedge trials are increasingly adopted because practical constraints necessitate staggered roll-out. While a complete design requires clusters to collect data in all periods, resource and patient-centered considerations may call for an incomplete stepped wedge design to minimize data collection burden. To study incomplete designs, we expand the metric of information content to discrete outcomes. We operate under a marginal model with general link and variance functions, and derive information content expressions when data elements (cells, sequences, periods) are omitted. We show that the centrosymmetric patterns of information content can hold for discrete outcomes with the variance-stabilizing link function. We perform numerical studies under the canonical link function, and find that while the patterns of information content for cells are approximately centrosymmetric for all examined underlying secular trends, the patterns of information content for sequences or periods are more sensitive to the secular trend, and may be far from centrosymmetric.
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Affiliation(s)
- Fan Li
- Department of Biostatistics, Yale University School of Public Health, New Haven, Connecticut, USA
- Center for Methods in Implementation and Prevention Science, Yale University, New Haven, Connecticut, USA
| | - Jessica Kasza
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Elizabeth L. Turner
- Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, USA
| | - Paul J. Rathouz
- Department of Population Health, The University of Texas at Austin, Austin, Texas, USA
| | - Andrew B. Forbes
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - John S. Preisser
- Department of Epidemiology, Gillings School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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Cook K, Lu W, Wang R. Marginal proportional hazards models for clustered interval-censored data with time-dependent covariates. Biometrics 2023; 79:1670-1685. [PMID: 36314377 DOI: 10.1111/biom.13787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Accepted: 10/18/2022] [Indexed: 11/29/2022]
Abstract
The Botswana Combination Prevention Project was a cluster-randomized HIV prevention trial whose follow-up period coincided with Botswana's national adoption of a universal test and treat strategy for HIV management. Of interest is whether, and to what extent, this change in policy modified the preventative effects of the study intervention. To address such questions, we adopt a stratified proportional hazards model for clustered interval-censored data with time-dependent covariates and develop a composite expectation maximization algorithm that facilitates estimation of model parameters without placing parametric assumptions on either the baseline hazard functions or the within-cluster dependence structure. We show that the resulting estimators for the regression parameters are consistent and asymptotically normal. We also propose and provide theoretical justification for the use of the profile composite likelihood function to construct a robust sandwich estimator for the variance. We characterize the finite-sample performance and robustness of these estimators through extensive simulation studies. Finally, we conclude by applying this stratified proportional hazards model to a re-analysis of the Botswana Combination Prevention Project, with the national adoption of a universal test and treat strategy now modeled as a time-dependent covariate.
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Affiliation(s)
- Kaitlyn Cook
- Program in Statistical and Data Sciences, Smith College, Northampton, Massachusetts, USA
- Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, Massachusetts, USA
| | - Wenbin Lu
- Department of Statistics, North Carolina State University, Raleigh, North Carolina, USA
| | - Rui Wang
- Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, Massachusetts, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
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Hershey MS, Chang CR, Sotos-Prieto M, Fernandez-Montero A, Cash SB, Christophi CA, Folta SC, Muegge C, Kleinschmidt V, Moffatt S, Mozaffarian D, Kales SN. Effect of a Nutrition Intervention on Mediterranean Diet Adherence Among Firefighters: A Cluster Randomized Clinical Trial. JAMA Netw Open 2023; 6:e2329147. [PMID: 37589978 PMCID: PMC10436136 DOI: 10.1001/jamanetworkopen.2023.29147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 07/09/2023] [Indexed: 08/18/2023] Open
Abstract
Importance US firefighters are a working population at risk of chronic diseases, including obesity, cardiovascular disease, and cancer. This risk may be mitigated by a healthy diet. Objective To evaluate the effect of a Mediterranean nutrition intervention using a behavioral/environmental approach (firefighter/fire station/home) at the individual participant level. Design, Setting, and Participants This 12-month cluster randomized clinical trial included US career firefighters from fire stations and homes within 2 Indiana fire departments. Participants were randomized by fire station to either Mediterranean diet or control (usual care). The study was conducted from October 2016 to December 2019, and data were analyzed in November 2022. Intervention For the first 12 months of the study, firefighters located at fire stations randomized to the intervention group were provided with access to supermarket discounts and free samples of Mediterranean diet foods, online nutrition education platforms, email announcements and reminders, family and peer education and support, and chef demonstrations. Firefighters in fire stations allocated to the control group received no intervention and were instructed to follow their usual diet. Main Outcomes and Measures Change in dietary habits at 12 months as measured by a modified Mediterranean diet score (range, 0 to 51 points) at baseline and 6-month and 12-month follow-up. Cardiometabolic parameters were secondary outcomes. Results Of 485 included firefighters, 458 (94.4%) were male, and the mean (SD) age was 47 (7.5) years. A total of 241 firefighters (27 fire stations) were randomized to the Mediterranean nutrition intervention, and 244 (25 fire stations) were randomized to usual diet. Outcomes were analyzed using generalized linear mixed models for modified Mediterranean diet score at 6 months (n = 336) and 12 months (n = 260), adjusting for baseline age, sex, race and ethnicity, fire department, physical activity, and waist circumference. In the intervention group compared with the control group, the modified Mediterranean diet score significantly increased by 2.01 points (95% CI, 0.62-3.40; P = .005) at 6 months and by 2.67 points (95% CI, 1.14-4.20; P = .001) at 12 months. Among secondary outcomes, changes in cardiometabolic risk factors were not statistically significant at 1 year. Results from analyses with multilevel multiple imputation for missingness were similar. Conclusions and Relevance In this Mediterranean nutrition intervention of multicomponent behavioral/environmental changes, career firefighters had increased adherence to a Mediterranean diet. Trial Registration ClinicalTrials.gov Identifier: NCT02941757.
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Affiliation(s)
- Maria Soledad Hershey
- Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, Massachusetts
| | - Chia-Rui Chang
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts
| | - Mercedes Sotos-Prieto
- Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, Massachusetts
- Department of Preventive Medicine and Public Health, School of Medicine, Universidad Autónoma de Madrid, Madrid, Spain
- Centro de Investigación Biomédica en Red of Epidemiology and Public Health, Madrid, Spain
- Campus of International Excellence (CEI) Universidad Autónoma de Madrid (UAM), Spanish National Research Council (CSIC), Madrid, Spain
| | - Alejandro Fernandez-Montero
- Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, Massachusetts
- Department of Occupational Medicine, University of Navarra, Pamplona, Spain
- Instituto de Investigación Sanitaria de Navarra (IdiSNA), Pamplona, Spain
| | - Sean B. Cash
- Friedman School of Nutrition Science and Policy, Tufts University, Boston, Massachusetts
| | - Costas A. Christophi
- Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, Massachusetts
- Cyprus International Institute for Environmental and Public Health, Cyprus University of Technology, Lemesos, Cyprus
| | - Sara C. Folta
- Friedman School of Nutrition Science and Policy, Tufts University, Boston, Massachusetts
| | - Carolyn Muegge
- National Institute for Public Safety Health, Indianapolis, Indiana
| | | | - Steven Moffatt
- National Institute for Public Safety Health, Indianapolis, Indiana
| | - Dariush Mozaffarian
- Friedman School of Nutrition Science and Policy, Tufts University, Boston, Massachusetts
| | - Stefanos N. Kales
- Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, Massachusetts
- Occupational Medicine, Cambridge Hospital, Harvard Medical School, Cambridge, Massachusetts
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Marrie RA, Sormani MP, Apap Mangion S, Bovis F, Cheung WY, Cutter GR, Feys P, Hill MD, Koch MW, McCreary M, Mowry EM, Park JJH, Piehl F, Salter A, Chataway J. Improving the efficiency of clinical trials in multiple sclerosis. Mult Scler 2023; 29:1136-1148. [PMID: 37555492 PMCID: PMC10413792 DOI: 10.1177/13524585231189671] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 06/01/2023] [Accepted: 06/12/2023] [Indexed: 08/10/2023]
Abstract
BACKGROUND Phase 3 clinical trials for disease-modifying therapies in relapsing-remitting multiple sclerosis (RRMS) have utilized a limited number of conventional designs with a high degree of success. However, these designs limit the types of questions that can be addressed, and the time and cost required. Moreover, trials involving people with progressive multiple sclerosis (MS) have been less successful. OBJECTIVE The objective of this paper is to discuss complex innovative trial designs, intermediate and composite outcomes and to improve the efficiency of trial design in MS and broaden questions that can be addressed, particularly as applied to progressive MS. METHODS We held an international workshop with experts in clinical trial design. RESULTS Recommendations include increasing the use of complex innovative designs, developing biomarkers to enrich progressive MS trial populations, prioritize intermediate outcomes for further development that target therapeutic mechanisms of action other than peripherally mediated inflammation, investigate acceptability to people with MS of data linkage for studying long-term outcomes of clinical trials, use Bayesian designs to potentially reduce sample sizes required for pediatric trials, and provide sustained funding for platform trials and registries that can support pragmatic trials. CONCLUSION Novel trial designs and further development of intermediate outcomes may improve clinical trial efficiency in MS and address novel therapeutic questions.
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Affiliation(s)
- Ruth Ann Marrie
- Departments of Internal Medicine and Community Health Sciences, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
| | - Maria Pia Sormani
- Department of Health Sciences, University of Genoa, Genoa, Italy/IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Sean Apap Mangion
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Francesca Bovis
- Department of Health Sciences, University of Genoa, Genoa, Italy
| | - Winson Y Cheung
- Department of Oncology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Gary R Cutter
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Peter Feys
- REVAL Rehabilitation Research Center, REVAL, Faculty of Rehabilitation Sciences, Hasselt University, Hasselt, Belgium/Universitair MS Centrum, UMSC, Hasselt, Belgium
| | - Michael D Hill
- Departments of Clinical Neurosciences, Community Health Sciences, Medicine, and Radiology, Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Marcus Werner Koch
- Departments of Clinical Neurosciences, Community Health Sciences, Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Morgan McCreary
- Department of Neurology, Section on Statistical Planning and Analysis, UT Southwestern Medical Center, Dallas, TX, USA
| | - Ellen M Mowry
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jay JH Park
- Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, ON, Canada
| | - Fredrik Piehl
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Amber Salter
- Department of Neurology, Section on Statistical Planning and Analysis, UT Southwestern Medical Center, Dallas, TX, USA
| | - Jeremy Chataway
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK/National Institute for Health Research, University College London Hospitals, Biomedical Research Centre, London, UK/Medical Research Council Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, University College London, London, UK
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