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Liu W, Zhang B. Joint evaluation of placebo and treatment effects in cluster randomized trials by causal inference models. Contemp Clin Trials 2023; 132:107308. [PMID: 37517684 DOI: 10.1016/j.cct.2023.107308] [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: 04/14/2023] [Revised: 07/14/2023] [Accepted: 07/26/2023] [Indexed: 08/01/2023]
Abstract
The term placebo effect refers to the psychobiological effect of a patient's knowledge or belief of being treated. A placebo effect is patient-driven, which makes it fundamentally different from the usual treatment effect resulting from external actions. In modern clinical research, the presence of a placebo effect is often treated as a nuisance issue, something to be "adjusted away" in estimating a treatment effect of primary interest. However, from a patient-centered perspective, we believe that a possible placebo produces substantial improvements in patient-centered outcomes. Understanding placebo effects is therefore an important part of patient-centered outcomes research. The available methods for estimating placebo effects are designed for individually randomized trials and are not directly applicable to cluster randomized trials (CRTs). There are several challenges in estimating placebo effects in CRTs. A major challenge is the possible presence of interference within clusters, in the sense that a subject's outcome may depend on the beliefs subjects in the same cluster about treatment assignment (mentality) and therefore possible correlation in outcome and mentality among subjects exists in the same cluster. In this article, we extend the previously developed causal inference framework to also encompass CRTs, using the G-Computation and inverse probability weighting (IPW) approaches. We also develop methodologies and further extend the G-Computation and IPW approaches to handle missingness for jointly evaluating placebo effect and treatment-specific effect, specifically in the context of CRTs. The proposed methods are demonstrated in simulation studies and a cluster randomized trial on effect of fermented dairy drink.
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Affiliation(s)
- Wei Liu
- School of Management, Harbin Institutes of Technology, Harbin, China
| | - Bo Zhang
- Department of Neurology and ICCTR Biostatistics and Research Design Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
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2
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Zhang W, Zhang Z, Troendle JF, Liu A. Data-adaptive efficient estimation strategies for biomarker studies embedded in randomized trials. Ann Appl Stat 2022. [DOI: 10.1214/21-aoas1588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- Wei Zhang
- Academy of Mathematics and Systems Science, Chinese Academy of Sciences
| | - Zhiwei Zhang
- National Cancer Institute, National Institutes of Health
| | - James F. Troendle
- National Heart, Lung and Blood Institute, National Institutes of Health
| | - Aiyi Liu
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health
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Laska E, Siegel C, Lin Z. A likely responder approach for the analysis of randomized controlled trials. Contemp Clin Trials 2022; 114:106688. [PMID: 35085831 PMCID: PMC8934276 DOI: 10.1016/j.cct.2022.106688] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 12/03/2021] [Accepted: 01/19/2022] [Indexed: 11/16/2022]
Abstract
OBJECTIVE To further the precision medicine goal of tailoring medical treatment to individual patient characteristics by providing a method of analysis of the effect of test treatment, T, compared to a reference treatment, R, in participants in a RCT who are likely responders to T. METHODS Likely responders to T are individuals whose expected response at baseline exceeds a prespecified minimum. A prognostic score, the expected response predicted as a function of baseline covariates, is obtained at trial completion. It is a balancing score that can be used to match likely responders randomized to T with those randomized to R; the result is comparable treatment groups that have a common covariance distribution. Treatments are compared based on observed outcomes in this enriched sample. The approach is illustrated in a RCT comparing two treatments for opioid use disorder. RESULTS A standard statistical analysis of the opioid use disorder RCT found no treatment difference in the total sample. However, a subset of likely responders to T were identified and in this group, T was statistically superior to R. CONCLUSION The causal treatment effect of T relative to R among likely responders may be more important than the effect in the whole target population. The prognostic score function provides quantitative information to support patient specific treatment decisions regarding T furthering the goal of precision medicine.
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Affiliation(s)
- Eugene Laska
- Department of Psychiatry, New York University Grossman School of Medicine, One Park Avenue, New York, NY 10016, USA; Department of Population Health, Division of Biostatistics, NYU Grossman School of Medicine, 180 Madison Avenue, New York, NY 10016, USA.
| | - Carole Siegel
- Department of Psychiatry, New York University Grossman School of Medicine, One Park Avenue, New York, NY 10016, USA; Department of Population Health, Division of Biostatistics, NYU Grossman School of Medicine, 180 Madison Avenue, New York, NY 10016, USA.
| | - Ziqiang Lin
- Department of Psychiatry, New York University Grossman School of Medicine, One Park Avenue, New York, NY 10016, USA.
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Ma S, Huang J, Zhang Z, Liu M. Exploration of Heterogeneous Treatment Effects via Concave Fusion. Int J Biostat 2019; 16:ijb-2018-0026. [PMID: 31541601 DOI: 10.1515/ijb-2018-0026] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2018] [Accepted: 07/26/2019] [Indexed: 11/15/2022]
Abstract
Understanding treatment heterogeneity is essential to the development of precision medicine, which seeks to tailor medical treatments to subgroups of patients with similar characteristics. One of the challenges of achieving this goal is that we usually do not have a priori knowledge of the grouping information of patients with respect to treatment effect. To address this problem, we consider a heterogeneous regression model which allows the coefficients for treatment variables to be subject-dependent with unknown grouping information. We develop a concave fusion penalized method for estimating the grouping structure and the subgroup-specific treatment effects, and derive an alternating direction method of multipliers algorithm for its implementation. We also study the theoretical properties of the proposed method and show that under suitable conditions there exists a local minimizer that equals the oracle least squares estimator based on a priori knowledge of the true grouping information with high probability. This provides theoretical support for making statistical inference about the subgroup-specific treatment effects using the proposed method. The proposed method is illustrated in simulation studies and illustrated with real data from an AIDS Clinical Trials Group Study.
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Affiliation(s)
- Shujie Ma
- Department of Statistics, University of California at Riverside, Riverside, California 92521, USA
| | - Jian Huang
- Department of Statistics and Actuarial Science, University of Iowa, Iowa City, USA
| | - Zhiwei Zhang
- Department of Statistics, University of California at Riverside, Riverside, California 92521, USA
| | - Mingming Liu
- Department of Statistics, University of California at Riverside, Riverside, California, USA
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Liu W, Zhang Z, Nie L, Soon G. A Case Study in Personalized Medicine: Rilpivirine Versus Efavirenz for Treatment-Naive HIV Patients. J Am Stat Assoc 2018. [DOI: 10.1080/01621459.2017.1280404] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Wei Liu
- Department of Mathematics, Harbin Institute of Technology, Harbin, P. R. China
| | - Zhiwei Zhang
- Department of Statistics, University of California, Riverside, CA
| | - Lei Nie
- Division of Biometrics V, Office of Biostatistics, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, MD
| | - Guoxing Soon
- Division of Biometrics IV, Office of Biostatistics, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, MD
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Zhang Z, Chen R, Soon G, Zhang H. Treatment evaluation for a data-driven subgroup in adaptive enrichment designs of clinical trials. Stat Med 2017; 37:1-11. [PMID: 28948633 DOI: 10.1002/sim.7497] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2016] [Revised: 05/29/2017] [Accepted: 08/24/2017] [Indexed: 11/09/2022]
Abstract
Adaptive enrichment designs (AEDs) of clinical trials allow investigators to restrict enrollment to a promising subgroup based on an interim analysis. Most of the existing AEDs deal with a small number of predefined subgroups, which are often unknown at the design stage. The newly developed Simon design offers a great deal of flexibility in subgroup selection (without requiring pre-defined subgroups) but does not provide a procedure for estimating and testing treatment efficacy for the selected subgroup. This article proposes a 2-stage AED which does not require predefined subgroups but requires a prespecified algorithm for choosing a subgroup on the basis of baseline covariate information. Having a prespecified algorithm for subgroup selection makes it possible to use cross-validation and bootstrap methods to correct for the resubstitution bias in estimating treatment efficacy for the selected subgroup. The methods are evaluated and compared in a simulation study mimicking actual clinical trials of human immunodeficiency virus infection.
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Affiliation(s)
- Zhiwei Zhang
- Department of Statistics, University of California at Riverside, Riverside, California, USA
| | - Ruizhe Chen
- Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Guoxing Soon
- Division of Biometrics IV, Office of Biostatistics, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland, USA
| | - Hui Zhang
- Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
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Zhang Z, Ma S, Nie L, Soon G. A Quantitative Concordance Measure for Comparing and Combining Treatment Selection Markers. Int J Biostat 2017; 13:/j/ijb.ahead-of-print/ijb-2016-0064/ijb-2016-0064.xml. [PMID: 28343164 DOI: 10.1515/ijb-2016-0064] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Motivated by an HIV example, we consider how to compare and combine treatment selection markers, which are essential to the notion of precision medicine. The current literature on precision medicine is focused on evaluating and optimizing treatment regimes, which can be obtained by dichotomizing treatment selection markers. In practice, treatment decisions are based not only on efficacy but also on safety, cost and individual preference, making it difficult to choose a single cutoff value for all patients in all settings. It is therefore desirable to have a statistical framework for comparing and combining treatment selection markers without dichotomization. We provide such a framework based on a quantitative concordance measure, which quantifies the extent to which higher marker values are predictive of larger treatment effects. For a given marker, the proposed concordance measure can be estimated from clinical trial data using a U-statistic, which can incorporate auxiliary covariate information through an augmentation term. For combining multiple markers, we propose to maximize the estimated concordance measure among a specified family of combination markers. A cross-validation procedure can be used to remove any re-substitution bias in assessing the quality of an optimized combination marker. The proposed methodology is applied to the HIV example and evaluated in simulation studies.
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Lamont A, Lyons MD, Jaki T, Stuart E, Feaster DJ, Tharmaratnam K, Oberski D, Ishwaran H, Wilson DK, Van Horn ML. Identification of predicted individual treatment effects in randomized clinical trials. Stat Methods Med Res 2016; 27:142-157. [DOI: 10.1177/0962280215623981] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
In most medical research, treatment effectiveness is assessed using the average treatment effect or some version of subgroup analysis. The practice of individualized or precision medicine, however, requires new approaches that predict how an individual will respond to treatment, rather than relying on aggregate measures of effect. In this study, we present a conceptual framework for estimating individual treatment effects, referred to as predicted individual treatment effects. We first apply the predicted individual treatment effect approach to a randomized controlled trial designed to improve behavioral and physical symptoms. Despite trivial average effects of the intervention, we show substantial heterogeneity in predicted individual treatment response using the predicted individual treatment effect approach. The predicted individual treatment effects can be used to predict individuals for whom the intervention may be most effective (or harmful). Next, we conduct a Monte Carlo simulation study to evaluate the accuracy of predicted individual treatment effects. We compare the performance of two methods used to obtain predictions: multiple imputation and non-parametric random decision trees. Results showed that, on average, both predictive methods produced accurate estimates at the individual level; however, the random decision trees tended to underestimate the predicted individual treatment effect for people at the extreme and showed more variability in predictions across repetitions compared to the imputation approach. Limitations and future directions are discussed.
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Affiliation(s)
- Andrea Lamont
- Department of Psychology, Barnwell College, University of South Carolina, Columbia, USA
| | - Michael D Lyons
- Department of Psychology, University of Houston, Houston, USA
| | - Thomas Jaki
- Department of Mathematics and Statistics, Lancaster University, Lancaster, UK
| | - Elizabeth Stuart
- Department of Mental Health, Department of Biostatistics, and Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins, Baltimore, MD, USA
| | - Daniel J Feaster
- Department of Public Health Sciences, Division of Biostatistics, University of Miami, Miami, FL, USA
| | | | - Daniel Oberski
- Department of Methodology and Statistics, Tilburg University, Tilburg, the Netherlands
| | - Hemant Ishwaran
- Department of Public Health Sciences, Division of Biostatistics, University of Miami, Miami, FL, USA
| | - Dawn K Wilson
- Department of Psychology, Barnwell College, University of South Carolina, Columbia, USA
| | - M Lee Van Horn
- Department of Individual, Family and Community Education, University of New Mexico, Albuquerque, NM, USA
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