1
|
Park H, Petkova E, Tarpey T, Ogden RT. Functional additive models for optimizing individualized treatment rules. Biometrics 2023; 79:113-126. [PMID: 34704622 PMCID: PMC9043034 DOI: 10.1111/biom.13586] [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: 10/15/2020] [Revised: 10/01/2021] [Accepted: 10/14/2021] [Indexed: 11/29/2022]
Abstract
A novel functional additive model is proposed, which is uniquely modified and constrained to model nonlinear interactions between a treatment indicator and a potentially large number of functional and/or scalar pretreatment covariates. The primary motivation for this approach is to optimize individualized treatment rules based on data from a randomized clinical trial. We generalize functional additive regression models by incorporating treatment-specific components into additive effect components. A structural constraint is imposed on the treatment-specific components in order to provide a class of additive models with main effects and interaction effects that are orthogonal to each other. If primary interest is in the interaction between treatment and the covariates, as is generally the case when optimizing individualized treatment rules, we can thereby circumvent the need to estimate the main effects of the covariates, obviating the need to specify their form and thus avoiding the issue of model misspecification. The methods are illustrated with data from a depression clinical trial with electroencephalogram functional data as patients' pretreatment covariates.
Collapse
Affiliation(s)
- Hyung Park
- Division of Biostatistics, Department of Population Health, New York University, New York, NY 10016, USA
| | - Eva Petkova
- Division of Biostatistics, Department of Population Health, New York University, New York, NY 10016, USA
| | - Thaddeus Tarpey
- Division of Biostatistics, Department of Population Health, New York University, New York, NY 10016, USA
| | - R. Todd Ogden
- Department of Biostatistics, Columbia University, New York, NY 10032, USA
| |
Collapse
|
2
|
Wang W, Sun Y, Wang HJ. Latent group detection in functional partially linear regression models. Biometrics 2023; 79:280-291. [PMID: 34482542 DOI: 10.1111/biom.13557] [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: 06/27/2020] [Revised: 08/06/2021] [Accepted: 08/19/2021] [Indexed: 11/28/2022]
Abstract
In this paper, we propose a functional partially linear regression model with latent group structures to accommodate the heterogeneous relationship between a scalar response and functional covariates. The proposed model is motivated by a salinity tolerance study of barley families, whose main objective is to detect salinity tolerant barley plants. Our model is flexible, allowing for heterogeneous functional coefficients while being efficient by pooling information within a group for estimation. We develop an algorithm in the spirit of the K-means clustering to identify latent groups of the subjects under study. We establish the consistency of the proposed estimator, derive the convergence rate and the asymptotic distribution, and develop inference procedures. We show by simulation studies that the proposed method has higher accuracy for recovering latent groups and for estimating the functional coefficients than existing methods. The analysis of the barley data shows that the proposed method can help identify groups of barley families with different salinity tolerant abilities.
Collapse
Affiliation(s)
- Wu Wang
- Center for Applied Statistics and School of Statistics, Renmin University of China, Beijing, China
| | - Ying Sun
- Statistics Program, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Huixia Judy Wang
- Department of Statistics, The George Washington University, Washington, DC, USA
| |
Collapse
|
3
|
Li Z, Chen J, Laber E, Liu F, Baumgartner R. Optimal Treatment Regimes: A Review and Empirical Comparison. Int Stat Rev 2023. [DOI: 10.1111/insr.12536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
Affiliation(s)
- Zhen Li
- Department of Statistics North Carolina State University Raleigh 27607 NC USA
| | - Jie Chen
- Department of Biometrics Overland Pharmaceuticals Dover 19901 DE USA
| | - Eric Laber
- Department of Statistical Science, Department of Biostatistics and Bioinformatics Duke University Durham 27708 NC USA
| | - Fang Liu
- Biostatistics and Research Decision Sciences Merck & Co., Inc. Kenilworth NJ 07033 USA
| | - Richard Baumgartner
- Biostatistics and Research Decision Sciences Merck & Co., Inc. Kenilworth NJ 07033 USA
| |
Collapse
|
4
|
Park H, Petkova E, Tarpey T, Ogden RT. A sparse additive model for treatment effect-modifier selection. Biostatistics 2022; 23:412-429. [PMID: 32808656 PMCID: PMC9308457 DOI: 10.1093/biostatistics/kxaa032] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Revised: 07/05/2020] [Accepted: 07/10/2020] [Indexed: 11/26/2023] Open
Abstract
Sparse additive modeling is a class of effective methods for performing high-dimensional nonparametric regression. This article develops a sparse additive model focused on estimation of treatment effect modification with simultaneous treatment effect-modifier selection. We propose a version of the sparse additive model uniquely constrained to estimate the interaction effects between treatment and pretreatment covariates, while leaving the main effects of the pretreatment covariates unspecified. The proposed regression model can effectively identify treatment effect-modifiers that exhibit possibly nonlinear interactions with the treatment variable that are relevant for making optimal treatment decisions. A set of simulation experiments and an application to a dataset from a randomized clinical trial are presented to demonstrate the method.
Collapse
Affiliation(s)
- Hyung Park
- Division of Biostatistics, Department of Population Health, New York University, New York, NY, USA and Department of Biostatistics, Columbia University, New York, NY, USA
| | - Eva Petkova
- Division of Biostatistics, Department of Population Health, New York University, New York, NY, USA and Department of Biostatistics, Columbia University, New York, NY, USA
| | - Thaddeus Tarpey
- Division of Biostatistics, Department of Population Health, New York University, New York, NY, USA and Department of Biostatistics, Columbia University, New York, NY, USA
| | - R Todd Ogden
- Division of Biostatistics, Department of Population Health, New York University, New York, NY, USA and Department of Biostatistics, Columbia University, New York, NY, USA
| |
Collapse
|
5
|
Zhang X, Xue W, Wang Q. Covariate balancing functional propensity score for functional treatments in cross-sectional observational studies. Comput Stat Data Anal 2021. [DOI: 10.1016/j.csda.2021.107303] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
|
6
|
Goldfeld KS, Wu D, Tarpey T, Liu M, Wu Y, Troxel AB, Petkova E. Prospective individual patient data meta-analysis: Evaluating convalescent plasma for COVID-19. Stat Med 2021; 40:5131-5151. [PMID: 34164838 PMCID: PMC8441650 DOI: 10.1002/sim.9115] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2020] [Revised: 06/09/2021] [Accepted: 06/09/2021] [Indexed: 12/12/2022]
Abstract
As the world faced the devastation of the COVID‐19 pandemic in late 2019 and early 2020, numerous clinical trials were initiated in many locations in an effort to establish the efficacy (or lack thereof) of potential treatments. As the pandemic has been shifting locations rapidly, individual studies have been at risk of failing to meet recruitment targets because of declining numbers of eligible patients with COVID‐19 encountered at participating sites. It has become clear that it might take several more COVID‐19 surges at the same location to achieve full enrollment and to find answers about what treatments are effective for this disease. This paper proposes an innovative approach for pooling patient‐level data from multiple ongoing randomized clinical trials (RCTs) that have not been configured as a network of sites. We present the statistical analysis plan of a prospective individual patient data (IPD) meta‐analysis (MA) from ongoing RCTs of convalescent plasma (CP). We employ an adaptive Bayesian approach for continuously monitoring the accumulating pooled data via posterior probabilities for safety, efficacy, and harm. Although we focus on RCTs for CP and address specific challenges related to CP treatment for COVID‐19, the proposed framework is generally applicable to pooling data from RCTs for other therapies and disease settings in order to find answers in weeks or months, rather than years.
Collapse
Affiliation(s)
- Keith S Goldfeld
- Division of Biostatistics, Department of Population Health, New York University Grossman School of Medicine, New York, New York, USA
| | - Danni Wu
- Division of Biostatistics, Department of Population Health, New York University Grossman School of Medicine, New York, New York, USA
| | - Thaddeus Tarpey
- Division of Biostatistics, Department of Population Health, New York University Grossman School of Medicine, New York, New York, USA
| | - Mengling Liu
- Division of Biostatistics, Department of Population Health, New York University Grossman School of Medicine, New York, New York, USA.,Department of Environmental Medicine, New York University Grossman School of Medicine, New York, New York, USA
| | - Yinxiang Wu
- Division of Biostatistics, Department of Population Health, New York University Grossman School of Medicine, New York, New York, USA
| | - Andrea B Troxel
- Division of Biostatistics, Department of Population Health, New York University Grossman School of Medicine, New York, New York, USA
| | - Eva Petkova
- Division of Biostatistics, Department of Population Health, New York University Grossman School of Medicine, New York, New York, USA.,Nathan Kline Institute for Psychiatric Research, Orangeburg, New York, USA
| |
Collapse
|
7
|
Petkova E, Park H, Ciarleglio A, Todd Ogden R, Tarpey T. Optimising treatment decision rules through generated effect modifiers: a precision medicine tutorial. BJPsych Open 2019; 6:e2. [PMID: 31791433 PMCID: PMC7001471 DOI: 10.1192/bjo.2019.85] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Revised: 10/18/2019] [Accepted: 10/20/2019] [Indexed: 01/01/2023] Open
Abstract
This tutorial introduces recent developments in precision medicine for estimating treatment decision rules. The objective of these developments is to advance personalised healthcare by identifying an optimal treatment option for each individual patient based on each patient's characteristics. The methods detailed in this tutorial define composite variables from the patient measures that can be viewed as 'biosignatures' for differential treatment response, which we have termed 'generated effect modifiers'. In contrast to most machine learning approaches to precision medicine, these biosignatures are derived from linear and non-linear regression models and thus have the advantage of easy visualisation and ready interpretation. The methods are illustrated using examples from randomised clinical trials.
Collapse
Affiliation(s)
- Eva Petkova
- Professor, Departments of Population Health and Child and Adolescent Psychiatry, New York University School of Medicine and Nathan S. Kline Institute for Psychiatric Research, USA
| | - Hyung Park
- Post-doctoral Fellow, Department of Population Health, New York University School of Medicine, USA
| | - Adam Ciarleglio
- Assistant Professor, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, George Washington University, USA
| | - R. Todd Ogden
- Professor, Department of Biostatistics, Columbia University Mailman School of Public Health, USA
| | - Thaddeus Tarpey
- Professor, Department of Population Health, New York University School of Medicine, USA
| |
Collapse
|