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Li Y, Hwang WT, Maude SL, Teachey DT, Frey NV, Myers RM, Barz Leahy A, Liu H, Porter DL, Grupp SA, Shaw PA. Statistical considerations for analyses of time-to-event endpoints in oncology clinical trials: Illustrations with CAR-T immunotherapy studies. Clin Cancer Res 2022; 28:3940-3949. [PMID: 35838646 DOI: 10.1158/1078-0432.ccr-22-0560] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 06/06/2022] [Accepted: 07/13/2022] [Indexed: 11/16/2022]
Abstract
Chimeric antigen receptor T-cell (CAR-T) therapy is an exciting development in the field of cancer immunology and has received a lot of interest in recent years. Many time-to-event (TTE) endpoints related to relapse, disease progression, and remission are analyzed in CAR-T studies to assess treatment efficacy. Definitions of these TTE endpoints are not always consistent, even for the same outcomes (e.g., progression-free survival), which often stems from analysis choices regarding which events to consider as part of the composite endpoint, censoring or competing risk in the analysis. Subsequent therapies such as hematopoietic stem cell transplantation are common but are not treated the same in different studies. Standard survival analysis methods are commonly applied to TTE analyses but often without full consideration of the assumptions inherent in the chosen analysis. We highlight two important issues of TTE analysis that arise in CAR-T studies, as well as in other settings in oncology: the handling of competing risks and assessing the association between a time-varying (post-infusion) exposure and the TTE outcome. We review existing analytical methods, including the cumulative incidence function and regression models for analysis of competing risks, and landmark and time-varying covariate analysis for analysis of post-infusion exposures. We clarify the scientific questions that the different analytical approaches address and illustrate how the application of an inappropriate method could lead to different results using data from multiple published CAR-T studies. Codes for implementing these methods in standard statistical software are provided.
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Affiliation(s)
- Yimei Li
- University of Pennsylvania and The Children's Hospital of Philadelphia, Philadelphia, PA, United States
| | - Wei-Ting Hwang
- University of Pennsylvania, Philadelphia, PA, United States
| | - Shannon L Maude
- Children's Hospital of Philadelphia, Philadelphia, PA, United States
| | - David T Teachey
- Children's Hospital of Philadelphia, Philadelphia, PA, United States
| | - Noelle V Frey
- University of Pennsylvania, Philadelphia, United States
| | - Regina M Myers
- Children's Hospital of Philadelphia, Philadelphia, United States
| | | | - Hongyan Liu
- Children's Hospital of Philadelphia, United States
| | - David L Porter
- University of Pennsylvania, Philadelphia, PA, United States
| | - Stephan A Grupp
- Children's Hospital of Philadelphia, Philadelphia, PA, United States
| | - Pamela A Shaw
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, United States
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Chen WC, Lu N, Wang C, Li H, Song C, Tiwari R, Xu Y, Yue LQ. Propensity score-integrated approach to survival analysis: leveraging external evidence in single-arm studies. J Biopharm Stat 2022; 32:400-413. [PMID: 35675348 DOI: 10.1080/10543406.2022.2080701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
External data, referred to as data external to the traditional clinical study being planned, include but are not limited to real-world data (RWD) and data collected from clinical studies being conducted in the past or in other countries. The external data are sometimes leveraged to augment a single-arm, prospectively designed study when appropriate. In such an application, recently developed propensity score-integrated approaches including PSPP and PSCL can be used for study design and data analysis when the clinical outcomes are binary or continuous. In this paper, the propensity score-integrated Kaplan-Meier (PSKM) method is proposed for a similar situation but the outcome of interest is time-to-event. The propensity score methodology is used to select external subjects that are similar to those in the current study in terms of baseline covariates and to stratify the selected subjects from both data sources into more homogeneous strata. The stratum-specific PSKM estimators are obtained based on all subjects in the stratum with the external data being down-weighted, and then these estimators are combined to obtain an overall PSKM estimator. A simulation study is conducted to assess the performance of the PSKM method, and an illustrative example is presented to demonstrate how to implement the proposed method.
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Affiliation(s)
- Wei-Chen Chen
- Division of Biostatistics, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Baltimore, Maryland, USA
| | - Nelson Lu
- Division of Biostatistics, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Baltimore, Maryland, USA
| | - Chenguang Wang
- Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA
| | - Heng Li
- Division of Biostatistics, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Baltimore, Maryland, USA
| | - Changhong Song
- Division of Biostatistics, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Baltimore, Maryland, USA
| | - Ram Tiwari
- Bristol Myers Squibb, New York, New York, USA
| | - Yunling Xu
- Division of Biostatistics, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Baltimore, Maryland, USA
| | - Lilly Q Yue
- Division of Biostatistics, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Baltimore, Maryland, USA
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Kuper T, Famure O, Greenfield J, Li Y, Ibrahim S, Narang T, Ashwin M, Joseph Kim S. Time-Varying Proteinuria and the Risk of Cardiovascular Disease and Graft Failure in Kidney Transplant Recipients. Prog Transplant 2021; 31:288-297. [PMID: 34839728 DOI: 10.1177/15269248211046011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Introduction: Proteinuria is recognized as an independent risk factor for cardiovascular disease in kidney transplant recipients, but previous studies have not considered the impact of changes in urine protein over time. Research Question and Design: We used time-dependent, multivariable Cox proportional hazards models in this observational cohort study of adult kidney transplant recipients to evaluate whether proteinuria measured by dipstick on random spot urine samples starting from 1-month post-transplant was associated with the risk of major adverse cardiac events and graft loss. Results: A total of 144 major adverse cardiac events, defined as acute myocardial infarction, cerebrovascular accident, revascularization, or all-cause mortality, were observed in 1106 patients over 5728.7 person-years. Any level of proteinuria greater or equal to trace resulted in a two-fold increase in the risk of major adverse cardiac events (hazard ratio 2.00 [95% confidence interval 1.41, 2.84]). This relationship was not found to be dose-dependent (hazard ratios of 2.98, 1.76, 1.63, and 1.54 for trace, 1+, 2+, and 3+ urine protein, respectively). There was an increased risk of graft failure with greater urine protein concentration (hazard ratios 2.22, 2.85, 6.41, and 19.71 for trace, 1+, 2+, and 3+, respectively). Conclusion: Urine protein is associated with major adverse cardiac events and graft loss in kidney transplant recipients. The role of interventions to reduce proteinuria on decreasing the risk of adverse cardiovascular and graft outcomes in kidney transplant recipients requires further study.
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Affiliation(s)
- Tanya Kuper
- Toronto General Hospital, 7989University Health Network, Toronto, Ontario, Canada
| | - Olusegun Famure
- Toronto General Hospital, 7989University Health Network, Toronto, Ontario, Canada
| | - Jamie Greenfield
- Toronto General Hospital, 7989University Health Network, Toronto, Ontario, Canada
| | - Yanhong Li
- Toronto General Hospital, 7989University Health Network, Toronto, Ontario, Canada
| | - Syed Ibrahim
- Toronto General Hospital, 7989University Health Network, Toronto, Ontario, Canada
| | - Tanya Narang
- Toronto General Hospital, 7989University Health Network, Toronto, Ontario, Canada
| | - Monika Ashwin
- Toronto General Hospital, 7989University Health Network, Toronto, Ontario, Canada
| | - S Joseph Kim
- Toronto General Hospital, 7989University Health Network, Toronto, Ontario, Canada.,University of Toronto, Toronto, Ontario, Canada.,St Michael's Hospital, Toronto, Ontario, Canada.,University of Toronto, Toronto, Ontario, Canada
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