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Wang G, Wang W, Mangal B, Liao Y, Schneider L, Li Y, Xiong C, McDade E, Kennedy R, Bateman R, Cutter G. Novel non-linear models for clinical trial analysis with longitudinal data: A tutorial using SAS for both frequentist and Bayesian methods. Stat Med 2024; 43:2987-3004. [PMID: 38727205 PMCID: PMC11187662 DOI: 10.1002/sim.10089] [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: 01/16/2024] [Revised: 03/20/2024] [Accepted: 04/14/2024] [Indexed: 06/19/2024]
Abstract
Longitudinal data from clinical trials are commonly analyzed using mixed models for repeated measures (MMRM) when the time variable is categorical or linear mixed-effects models (ie, random effects model) when the time variable is continuous. In these models, statistical inference is typically based on the absolute difference in the adjusted mean change (for categorical time) or the rate of change (for continuous time). Previously, we proposed a novel approach: modeling the percentage reduction in disease progression associated with the treatment relative to the placebo decline using proportional models. This concept of proportionality provides an innovative and flexible method for simultaneously modeling different cohorts, multivariate endpoints, and jointly modeling continuous and survival endpoints. Through simulated data, we demonstrate the implementation of these models using SAS procedures in both frequentist and Bayesian approaches. Additionally, we introduce a novel method for implementing MMRM models (ie, analysis of response profile) using the nlmixed procedure.
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Affiliation(s)
- Guoqiao Wang
- Department of Neurology, School of Medicine, Washington University, St. Louis, 63130, United States
- Division of Biostatistics, School of Medicine, Washington University, St. Louis, 63130, United States
| | - Whedy Wang
- Tenaya Therapeutics, South San Francisco, CA 94080, United States
| | - Brian Mangal
- Solara Consulting Corp., 2311 Swinburne Ave. North Vancouver, BC V7H 3A6, Canada
| | - Yijie Liao
- Neogene Therapeutics, Inc., Santa Monica, 90404, United States
| | - Lon Schneider
- Department of Psychiatry and The Behavioral Sciences, Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, 90033, United States
| | - Yan Li
- Department of Neurology, School of Medicine, Washington University, St. Louis, 63130, United States
| | - Chengjie Xiong
- Division of Biostatistics, School of Medicine, Washington University, St. Louis, 63130, United States
| | - Eric McDade
- Department of Neurology, School of Medicine, Washington University, St. Louis, 63130, United States
| | - Richard Kennedy
- Division of Gerontology, Geriatrics, and Palliative Care, Department of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, 35294, United States
| | - Randall Bateman
- Department of Neurology, School of Medicine, Washington University, St. Louis, 63130, United States
| | - Gary Cutter
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, 35233, United States
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Wang G, Cutter G, Oxtoby NP, Shan G, Wang W, Mangal B, Liao Y, Llibre-Guerra JJ, Li Y, Xiong C, McDade E, Delmar P, Bateman RJ, Schneider L. Statistical considerations when estimating time-saving treatment effects in Alzheimer's disease clinical trials. Alzheimers Dement 2024. [PMID: 39030751 DOI: 10.1002/alz.14035] [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: 01/30/2024] [Revised: 04/23/2024] [Accepted: 05/06/2024] [Indexed: 07/22/2024]
Abstract
INTRODUCTION Estimating treatment effects as time savings in disease progression may be more easily interpretable than assessing the absolute difference or a percentage reduction. In this study, we investigate the statistical considerations of the existing method for estimating time savings and propose alternative complementary methods. METHODS We propose five alternative methods to estimate the time savings from different perspectives. These methods are applied to simulated clinical trial data that mimic or modify the Clinical Dementia Rating Sum of Boxes progression trajectories observed in the Clarity AD lecanemab trial. RESULTS Our study demonstrates that the proposed methods can generate more precise estimates by considering two crucial factors: (1) the absolute difference between treatment arms, and (2) the observed progression rate in the treatment arm. DISCUSSION Quantifying treatment effects as time savings in disease progression offers distinct advantages. To provide comprehensive estimations, it is important to use various methods. HIGHLIGHTS We explore the statistical considerations of the current method for estimating time savings. We proposed alternative methods that provide time savings estimations based on the observed absolute differences. By using various methods, a more comprehensive estimation of time savings can be achieved.
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Affiliation(s)
- Guoqiao Wang
- Department of Neurology, Division of Biostatistics, School of Medicine, Washington University, St. Louis, Missouri, USA
- Division of Biostatistics, Washington University, School of Medicine, St. Louis, Missouri, USA
| | - Gary Cutter
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Neil P Oxtoby
- Department of Computer Science, University College London, London, UK
| | - Guogen Shan
- Department of Biostatistics, University of Florida, Gainesville, Florida, USA
| | - Whedy Wang
- Tenaya Therapeutics, South San Francisco, California, USA
| | - Brian Mangal
- Solara Consulting Corp., North Vancouver, British Columbia, Canada
| | - Yijie Liao
- Neogene Therapeutics, Inc., Santa Monica, California, USA
| | - Jorge J Llibre-Guerra
- Department of Neurology, Division of Biostatistics, School of Medicine, Washington University, St. Louis, Missouri, USA
| | - Yan Li
- Department of Neurology, Division of Biostatistics, School of Medicine, Washington University, St. Louis, Missouri, USA
| | - Chengjie Xiong
- Division of Biostatistics, Washington University, School of Medicine, St. Louis, Missouri, USA
| | - Eric McDade
- Department of Neurology, Division of Biostatistics, School of Medicine, Washington University, St. Louis, Missouri, USA
| | - Paul Delmar
- F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Randall J Bateman
- Department of Neurology, Division of Biostatistics, School of Medicine, Washington University, St. Louis, Missouri, USA
| | - Lon Schneider
- Department of Psychiatry and The Behavioral Sciences, Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
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Wang G, Li Y, Xiong C, Benzinger TLS, Gordon BA, Hassenstab J, Aschenbrenner AJ, McDade E, Clifford DB, Libre‐Guerra JJ, Shi X, Mummery CJ, van Dyck CH, Lah JJ, Honig LS, Day G, Ringman JM, Brooks WS, Fox NC, Suzuki K, Levin J, Jucker M, Delmar P, Bittner T, Bateman RJ. Examining amyloid reduction as a surrogate endpoint through latent class analysis using clinical trial data for dominantly inherited Alzheimer's disease. Alzheimers Dement 2024; 20:2698-2706. [PMID: 38400532 PMCID: PMC11032558 DOI: 10.1002/alz.13735] [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: 09/20/2023] [Revised: 12/18/2023] [Accepted: 01/18/2024] [Indexed: 02/25/2024]
Abstract
INTRODUCTION Increasing evidence suggests that amyloid reduction could serve as a plausible surrogate endpoint for clinical and cognitive efficacy. The double-blind phase 3 DIAN-TU-001 trial tested clinical and cognitive declines with increasing doses of solanezumab or gantenerumab. METHODS We used latent class (LC) analysis on data from the Dominantly Inherited Alzheimer Network Trials Unit 001 trial to test amyloid positron emission tomography (PET) reduction as a potential surrogate biomarker. RESULTS LC analysis categorized participants into three classes: amyloid no change, amyloid reduction, and amyloid growth, based on longitudinal amyloid Pittsburgh compound B PET standardized uptake value ratio data. The amyloid-no-change class was at an earlier disease stage for amyloid amounts and dementia. Despite similar baseline characteristics, the amyloid-reduction class exhibited reductions in the annual decline rates compared to the amyloid-growth class across multiple biomarker, clinical, and cognitive outcomes. DISCUSSION LC analysis indicates that amyloid reduction is associated with improved clinical outcomes and supports its use as a surrogate biomarker in clinical trials. HIGHLIGHTS We used latent class (LC) analysis to test amyloid reduction as a surrogate biomarker. Despite similar baseline characteristics, the amyloid-reduction class exhibited remarkably better outcomes compared to the amyloid-growth class across multiple measures. LC analysis proves valuable in testing amyloid reduction as a surrogate biomarker in clinical trials lacking significant treatment effects.
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Affiliation(s)
- Guoqiao Wang
- Washington University, School of MedicineSt. LouisMissouriUSA
| | - Yan Li
- Washington University, School of MedicineSt. LouisMissouriUSA
| | - Chengjie Xiong
- Washington University, School of MedicineSt. LouisMissouriUSA
| | | | - Brian A. Gordon
- Washington University, School of MedicineSt. LouisMissouriUSA
| | | | | | - Eric McDade
- Washington University, School of MedicineSt. LouisMissouriUSA
| | | | | | - Xinyu Shi
- Washington University, School of MedicineSt. LouisMissouriUSA
| | | | | | - James J. Lah
- Emory University Medical CenterAtlantaGeorgiaUSA
| | | | - Gregg Day
- Mayo Clinic JacksonvilleJacksonvilleFloridaUSA
| | - John M. Ringman
- Department of NeurologyKeck School of Medicine of USCLos AngelesCaliforniaUSA
| | - William S. Brooks
- Neuroscience Research Australia, Randwick NSW Australia, and School of Clinical MedicineUniversity of New South WalesRandwickNew South WalesAustralia
| | - Nick C. Fox
- Dementia Research CentreUniversity College LondonLondonUK
| | | | - Johannes Levin
- Department of NeurologyLudwig‐Maximilians‐Universität MünchenMunichGermany
- German Center for Neurodegenerative DiseasesMunichGermany
- Munich Cluster for Systems Neurology (SyNergy)MunichGermany
| | - Mathias Jucker
- Department of Cellular NeurologyHertie Institute for Clinical Brain ResearchUniversity of TübingenTübingenGermany
- German Center for Neurodegenerative Diseases (DZNE)TübingenGermany
| | | | - Tobias Bittner
- F.Hoffmann‐LaRoche, Ltd.BaselSwitzerland
- Genentech, Inc., a member of the Roche GroupSouth San FranciscoCaliforniaUSA
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Pajewski NM, Donohue MC, Raman R, Espeland MA. Ascertainment and Statistical Issues for Randomized Trials of Cardiovascular Interventions for Cognitive Impairment and Dementia. Hypertension 2024; 81:45-53. [PMID: 37732473 PMCID: PMC10840823 DOI: 10.1161/hypertensionaha.123.19941] [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] [Indexed: 09/22/2023]
Abstract
There has been considerable progress in the prevention and treatment of cardiovascular disease, reducing the population burden of cardiovascular morbidity and mortality. Recently, some randomized trials, including the SPRINT (Systolic Blood Pressure Intervention Trial), have suggested that improvements in cardiovascular risk factors may also slow cognitive decline and reduce the eventual development of dementia. Unfortunately, the randomized trial template that has been used repeatedly to successfully demonstrate reductions in major adverse cardiac events faces several design and analytic obstacles when applied in the context of cognitive decline and dementia. Here, we review these obstacles, motivated by SPRINT and the context of selecting an appropriate cognitive end point for future preventive randomized trials. A few options are available, spanning neuropsychological test scores or composites reflecting specific domains of cognitive function, adjudicated cognitive impairment, or potentially physiological biomarkers. This choice entails considerations around statistical power, modes of ascertainment, the clinical relevance of treatment effects, a myriad of statistical issues (interval censoring, missing data, the competing risk of death, practice effects, etc), as well as ethical considerations around equipoise. Collectively, these considerations indicate that trials aiming to mitigate the cardiovascular contribution to cognitive decline and dementia will generally need to be large, inclusive of a wide age range of older adults, and with multiple years of follow-up.
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Affiliation(s)
- Nicholas M. Pajewski
- Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC
| | - Michael C. Donohue
- Alzheimer’s Therapeutic Research Institute, Keck School of Medicine of the University of Southern California, San Diego, CA
| | - Rema Raman
- Alzheimer’s Therapeutic Research Institute, Keck School of Medicine of the University of Southern California, San Diego, CA
| | - Mark A. Espeland
- Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC
- Section of Gerontology and Geriatric Medicine, Wake Forest University School of Medicine, Winston-Salem, NC
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Donohue M, Langford O, Insel P, van Dyck C, Petersen R, Craft S, Sethuraman G, Raman R, Aisen P. Natural cubic splines for the analysis of Alzheimer's clinical trials. Pharm Stat 2023; 22:508-519. [PMID: 36627206 PMCID: PMC10191992 DOI: 10.1002/pst.2285] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 12/09/2022] [Accepted: 12/12/2022] [Indexed: 01/12/2023]
Abstract
Mixed model repeated measures (MMRM) is the most common analysis approach used in clinical trials for Alzheimer's disease and other progressive diseases measured with continuous outcomes over time. The model treats time as a categorical variable, which allows an unconstrained estimate of the mean for each study visit in each randomized group. Categorizing time in this way can be problematic when assessments occur off-schedule, as including off-schedule visits can induce bias, and excluding them ignores valuable information and violates the intention to treat principle. This problem has been exacerbated by clinical trial visits which have been delayed due to the COVID19 pandemic. As an alternative to MMRM, we propose a constrained longitudinal data analysis with natural cubic splines that treats time as continuous and uses test version effects to model the mean over time. Compared to categorical-time models like MMRM and models that assume a proportional treatment effect, the spline model is shown to be more parsimonious and precise in real clinical trial datasets, and has better power and Type I error in a variety of simulation scenarios.
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Affiliation(s)
- M.C. Donohue
- Alzheimer’s Therapeutic Research Institute, University of Southern California California, USA
| | - O. Langford
- Alzheimer’s Therapeutic Research Institute, University of Southern California California, USA
| | - P. Insel
- Department of Psychiatry, University of California, San Francisco, California, USA
| | - C.H. van Dyck
- Alzheimer’s Disease Research Unit, Yale School of Medicine, Connecticut, USA
| | | | - S. Craft
- Department of Internal Medicine–Geriatrics, Wake Forest School of Medicine, North Carolina, USA
| | - G. Sethuraman
- Alzheimer’s Therapeutic Research Institute, University of Southern California California, USA
| | - R. Raman
- Alzheimer’s Therapeutic Research Institute, University of Southern California California, USA
| | - P.S. Aisen
- Alzheimer’s Therapeutic Research Institute, University of Southern California California, USA
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Raket LL. Progression models for repeated measures: Estimating novel treatment effects in progressive diseases. Stat Med 2022; 41:5537-5557. [DOI: 10.1002/sim.9581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 08/21/2022] [Accepted: 09/05/2022] [Indexed: 11/09/2022]
Affiliation(s)
- Lars Lau Raket
- Novo Nordisk A/S Søborg Denmark
- Clinical Memory Research Unit, Department of Clinical Sciences Lund University Lund Sweden
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