1
|
Falck F, Zhu X, Ghalebikesabi S, Kormaksson M, Vandemeulebroecke M, Zhang C, Martin R, Gardiner S, Kwok CH, West DM, Santos L, Tian C, Pang Y, Readie A, Ligozio G, Gandhi KK, Nichols TE, Mallon AM, Kelly L, Ohlssen D, Nicholson G. A framework for longitudinal latent factor modelling of treatment response in clinical trials with applications to Psoriatic Arthritis and Rheumatoid Arthritis. J Biomed Inform 2024; 154:104641. [PMID: 38642627 DOI: 10.1016/j.jbi.2024.104641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Revised: 03/10/2024] [Accepted: 04/11/2024] [Indexed: 04/22/2024]
Abstract
OBJECTIVE Clinical trials involve the collection of a wealth of data, comprising multiple diverse measurements performed at baseline and follow-up visits over the course of a trial. The most common primary analysis is restricted to a single, potentially composite endpoint at one time point. While such an analytical focus promotes simple and replicable conclusions, it does not necessarily fully capture the multi-faceted effects of a drug in a complex disease setting. Therefore, to complement existing approaches, we set out here to design a longitudinal multivariate analytical framework that accepts as input an entire clinical trial database, comprising all measurements, patients, and time points across multiple trials. METHODS Our framework composes probabilistic principal component analysis with a longitudinal linear mixed effects model, thereby enabling clinical interpretation of multivariate results, while handling data missing at random, and incorporating covariates and covariance structure in a computationally efficient and principled way. RESULTS We illustrate our approach by applying it to four phase III clinical trials of secukinumab in Psoriatic Arthritis (PsA) and Rheumatoid Arthritis (RA). We identify three clinically plausible latent factors that collectively explain 74.5% of empirical variation in the longitudinal patient database. We estimate longitudinal trajectories of these factors, thereby enabling joint characterisation of disease progression and drug effect. We perform benchmarking experiments demonstrating our method's competitive performance at estimating average treatment effects compared to existing statistical and machine learning methods, and showing that our modular approach leads to relatively computationally efficient model fitting. CONCLUSION Our multivariate longitudinal framework has the potential to illuminate the properties of existing composite endpoint methods, and to enable the development of novel clinical endpoints that provide enhanced and complementary perspectives on treatment response.
Collapse
Affiliation(s)
- Fabian Falck
- Department of Statistics, University of Oxford, UK; The Alan Turing Institute, London, UK
| | - Xuan Zhu
- Novartis Pharmaceuticals Corporation, East Hanover, United States
| | | | | | | | - Cong Zhang
- China Novartis Institutes for Bio-medical Research CO., Shanghai, China
| | - Ruvie Martin
- Novartis Pharmaceuticals Corporation, East Hanover, United States
| | - Stephen Gardiner
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, UK
| | | | | | | | - Chengeng Tian
- China Novartis Institutes for Bio-medical Research CO., Shanghai, China
| | - Yu Pang
- China Novartis Institutes for Bio-medical Research CO., Shanghai, China
| | - Aimee Readie
- Novartis Pharmaceuticals Corporation, East Hanover, United States
| | - Gregory Ligozio
- Novartis Pharmaceuticals Corporation, East Hanover, United States
| | - Kunal K Gandhi
- Novartis Pharmaceuticals Corporation, East Hanover, United States
| | - Thomas E Nichols
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, UK; Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | | | - Luke Kelly
- School of Mathematical Sciences, University College Cork, Ireland
| | - David Ohlssen
- Novartis Pharmaceuticals Corporation, East Hanover, United States
| | | |
Collapse
|
2
|
Fei T, Hanfelt JJ, Peng L. Latent Class Proportional Hazards Regression with Heterogeneous Survival Data. STATISTICS AND ITS INTERFACE 2023; 17:79-90. [PMID: 38222248 PMCID: PMC10786342 DOI: 10.4310/23-sii785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/16/2024]
Abstract
Heterogeneous survival data are commonly present in chronic disease studies. Delineating meaningful disease subtypes directly linked to a survival outcome can generate useful scientific implications. In this work, we develop a latent class proportional hazards (PH) regression framework to address such an interest. We propose mixture proportional hazards modeling, which flexibly accommodates class-specific covariate effects while allowing for the baseline hazard function to vary across latent classes. Adapting the strategy of nonparametric maximum likelihood estimation, we derive an Expectation-Maximization (E-M) algorithm to estimate the proposed model. We establish the theoretical properties of the resulting estimators. Extensive simulation studies are conducted, demonstrating satisfactory finite-sample performance of the proposed method as well as the predictive benefit from accounting for the heterogeneity across latent classes. We further illustrate the practical utility of the proposed method through an application to a mild cognitive impairment (MCI) cohort in the Uniform Data Set.
Collapse
Affiliation(s)
- Teng Fei
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, 633 3rd Ave, Fl 3, New York, New York 10017, U.S.A
| | - John J Hanfelt
- Department of Biostatistics and Bioinformatics, Emory University, 1518 Clifton Road Northeast, Atlanta, Georgia 30322, U.S.A
| | - Limin Peng
- Department of Biostatistics and Bioinformatics, Emory University, 1518 Clifton Road Northeast, Atlanta, Georgia 30322, U.S.A
| |
Collapse
|
3
|
Guo Y, Sun D, Sun J. Inference of a time-varying coefficient regression model for multivariate panel count data. J MULTIVARIATE ANAL 2022. [DOI: 10.1016/j.jmva.2022.105047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
|
4
|
WONG KINYAU, ZENG DONGLIN, LIN DY. SEMIPARAMETRIC LATENT-CLASS MODELS FOR MULTIVARIATE LONGITUDINAL AND SURVIVAL DATA. Ann Stat 2022; 50:487-510. [PMID: 35813218 PMCID: PMC9269993 DOI: 10.1214/21-aos2117] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/18/2023]
Abstract
In long-term follow-up studies, data are often collected on repeated measures of multivariate response variables as well as on time to the occurrence of a certain event. To jointly analyze such longitudinal data and survival time, we propose a general class of semiparametric latent-class models that accommodates a heterogeneous study population with flexible dependence structures between the longitudinal and survival outcomes. We combine nonparametric maximum likelihood estimation with sieve estimation and devise an efficient EM algorithm to implement the proposed approach. We establish the asymptotic properties of the proposed estimators through novel use of modern empirical process theory, sieve estimation theory, and semiparametric efficiency theory. Finally, we demonstrate the advantages of the proposed methods through extensive simulation studies and provide an application to the Atherosclerosis Risk in Communities study.
Collapse
Affiliation(s)
- KIN YAU WONG
- Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong
| | - DONGLIN ZENG
- Department of Biostatistics, University of North Carolina at Chapel Hill, USA
| | - D. Y. LIN
- Department of Biostatistics, University of North Carolina at Chapel Hill, USA
| |
Collapse
|