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Steyerberg EW, Nieboer D, Debray TPA, van Houwelingen HC. Assessment of heterogeneity in an individual participant data meta-analysis of prediction models: An overview and illustration. Stat Med 2019; 38:4290-4309. [PMID: 31373722 PMCID: PMC6772012 DOI: 10.1002/sim.8296] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2017] [Revised: 03/23/2019] [Accepted: 06/06/2019] [Indexed: 02/06/2023]
Abstract
Clinical prediction models aim to provide estimates of absolute risk for a diagnostic or prognostic endpoint. Such models may be derived from data from various studies in the context of a meta‐analysis. We describe and propose approaches for assessing heterogeneity in predictor effects and predictions arising from models based on data from different sources. These methods are illustrated in a case study with patients suffering from traumatic brain injury, where we aim to predict 6‐month mortality based on individual patient data using meta‐analytic techniques (15 studies, n = 11 022 patients). The insights into various aspects of heterogeneity are important to develop better models and understand problems with the transportability of absolute risk predictions.
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Affiliation(s)
- Ewout W Steyerberg
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands.,Department of Public Health, Erasmus MC, Rotterdam, The Netherlands
| | - Daan Nieboer
- Department of Public Health, Erasmus MC, Rotterdam, The Netherlands
| | - Thomas P A Debray
- Julius Center for Health Sciences and Primary Care, Utrecht University Medical Center, Utrecht, The Netherlands.,Cochrane Netherlands, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Hans C van Houwelingen
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
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2
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Nguyen TL, Debray TPA. The use of prognostic scores for causal inference with general treatment regimes. Stat Med 2019; 38:2013-2029. [PMID: 30652333 PMCID: PMC6590249 DOI: 10.1002/sim.8084] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2018] [Revised: 12/03/2018] [Accepted: 12/09/2018] [Indexed: 01/29/2023]
Abstract
In nonrandomised studies, inferring causal effects requires appropriate methods for addressing confounding bias. Although it is common to adopt propensity score analysis to this purpose, prognostic score analysis has recently been proposed as an alternative strategy. While both approaches were originally introduced to estimate causal effects for binary interventions, the theory of propensity score has since been extended to the case of general treatment regimes. Indeed, many treatments are not assigned in a binary fashion and require a certain extent of dosing. Hence, researchers may often be interested in estimating treatment effects across multiple exposures. To the best of our knowledge, the prognostic score analysis has not been yet generalised to this case. In this article, we describe the theory of prognostic scores for causal inference with general treatment regimes. Our methods can be applied to compare multiple treatments using nonrandomised data, a topic of great relevance in contemporary evaluations of clinical interventions. We propose estimators for the average treatment effects in different populations of interest, the validity of which is assessed through a series of simulations. Finally, we present an illustrative case in which we estimate the effect of the delay to Aspirin administration on a composite outcome of death or dependence at 6 months in stroke patients.
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Affiliation(s)
- Tri-Long Nguyen
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.,Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen K, Denmark.,Department of Pharmacy, Nîmes University Hospital Centre, Nîmes, France
| | - Thomas P A Debray
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.,Cochrane Netherlands, University Medical Center Utrecht, Utrecht, The Netherlands.,Botnar Research Centre, University of Oxford, Oxford, UK.,Institute of Health Informatics, University College London, London, UK
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3
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Zheng C, Zhou XH. Causal mediation analysis on failure time outcome without sequential ignorability. LIFETIME DATA ANALYSIS 2017; 23:533-559. [PMID: 27464958 PMCID: PMC10360451 DOI: 10.1007/s10985-016-9377-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2015] [Accepted: 07/19/2016] [Indexed: 06/06/2023]
Abstract
Mediation analysis is an important topic as it helps researchers to understand why an intervention works. Most previous mediation analyses define effects in the mean scale and require a binary or continuous outcome. Recently, possible ways to define direct and indirect effects for causal mediation analysis with survival outcome were proposed. However, these methods mainly rely on the assumption of sequential ignorability, which implies no unmeasured confounding. To handle the potential confounding between the mediator and the outcome, in this article, we proposed a structural additive hazard model for mediation analysis with failure time outcome and derived estimators for controlled direct effects and controlled mediator effects. Our methods allow time-varying effects. Simulations showed that our proposed estimator is consistent in the presence of unmeasured confounding while the traditional additive hazard regression ignoring unmeasured confounding produces biased results. We applied our method to the Women's Health Initiative data to study whether the dietary intervention affects breast cancer risk through changing body weight.
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Affiliation(s)
- Cheng Zheng
- Joseph J. Zilber School of Public Health, University of Wisconsin, 1240 North 10th Street, Room 378, Milwaukee, WI, 53205, USA.
| | - Xiao-Hua Zhou
- Department of Biostatistics, University of Washington, Seattle, WA, USA
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4
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Kamarudin AN, Cox T, Kolamunnage-Dona R. Time-dependent ROC curve analysis in medical research: current methods and applications. BMC Med Res Methodol 2017; 17:53. [PMID: 28388943 PMCID: PMC5384160 DOI: 10.1186/s12874-017-0332-6] [Citation(s) in RCA: 435] [Impact Index Per Article: 62.1] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2016] [Accepted: 03/28/2017] [Indexed: 01/30/2023] Open
Abstract
BACKGROUND ROC (receiver operating characteristic) curve analysis is well established for assessing how well a marker is capable of discriminating between individuals who experience disease onset and individuals who do not. The classical (standard) approach of ROC curve analysis considers event (disease) status and marker value for an individual as fixed over time, however in practice, both the disease status and marker value change over time. Individuals who are disease-free earlier may develop the disease later due to longer study follow-up, and also their marker value may change from baseline during follow-up. Thus, an ROC curve as a function of time is more appropriate. However, many researchers still use the standard ROC curve approach to determine the marker capability ignoring the time dependency of the disease status or the marker. METHODS We comprehensively review currently proposed methodologies of time-dependent ROC curves which use single or longitudinal marker measurements, aiming to provide clarity in each methodology, identify software tools to carry out such analysis in practice and illustrate several applications of the methodology. We have also extended some methods to incorporate a longitudinal marker and illustrated the methodologies using a sequential dataset from the Mayo Clinic trial in primary biliary cirrhosis (PBC) of the liver. RESULTS From our methodological review, we have identified 18 estimation methods of time-dependent ROC curve analyses for censored event times and three other methods can only deal with non-censored event times. Despite the considerable numbers of estimation methods, applications of the methodology in clinical studies are still lacking. CONCLUSIONS The value of time-dependent ROC curve methods has been re-established. We have illustrated the methods in practice using currently available software and made some recommendations for future research.
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Affiliation(s)
| | - Trevor Cox
- Department of Biostatistics, University of Liverpool, Liverpool, L69 3GL, UK
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5
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Debray TPA, Riley RD, Rovers MM, Reitsma JB, Moons KGM. Individual participant data (IPD) meta-analyses of diagnostic and prognostic modeling studies: guidance on their use. PLoS Med 2015; 12:e1001886. [PMID: 26461078 PMCID: PMC4603958 DOI: 10.1371/journal.pmed.1001886] [Citation(s) in RCA: 81] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Affiliation(s)
- Thomas P A Debray
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands; The Dutch Cochrane Centre, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Richard D Riley
- Research Institute for Primary Care and Health Sciences, Keele University, Staffordshire, The United Kingdom
| | - Maroeska M Rovers
- Radboud Institute for Health Sciences, Radboudumc Nijmegen, The Netherlands
| | - Johannes B Reitsma
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands; The Dutch Cochrane Centre, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Karel G M Moons
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands; The Dutch Cochrane Centre, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
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6
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Shen Y, Cai T, Chen Y, Yang Y, Chen J. Retrospective likelihood-based methods for analyzing case-cohort genetic association studies. Biometrics 2015; 71:960-8. [PMID: 26177343 DOI: 10.1111/biom.12342] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2014] [Revised: 04/01/2015] [Accepted: 04/01/2015] [Indexed: 11/27/2022]
Abstract
The case cohort (CCH) design is a cost-effective design for assessing genetic susceptibility with time-to-event data especially when the event rate is low. In this work, we propose a powerful pseudo-score test for assessing the association between a single nucleotide polymorphism (SNP) and the event time under the CCH design. The pseudo-score is derived from a pseudo-likelihood which is an estimated retrospective likelihood that treats the SNP genotype as the dependent variable and time-to-event outcome and other covariates as independent variables. It exploits the fact that the genetic variable is often distributed independent of covariates or only related to a low-dimensional subset. Estimates of hazard ratio parameters for association can be obtained by maximizing the pseudo-likelihood. A unique advantage of our method is that it allows the censoring distribution to depend on covariates that are only measured for the CCH sample while not requiring the knowledge of follow-up or covariate information on subjects not selected into the CCH sample. In addition to these flexibilities, the proposed method has high relative efficiency compared with commonly used alternative approaches. We study large sample properties of this method and assess its finite sample performance using both simulated and real data examples.
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Affiliation(s)
- Yuanyuan Shen
- Department of Biostatistics, Harvard University, Boston, MA 02115
| | - Tianxi Cai
- Department of Biostatistics, Harvard University, Boston, MA 02115
| | - Yu Chen
- Department of Population Health, New York University School of Medicine, New York, NY 10016
| | - Ying Yang
- Department of Mathematical Sciences, Tsinghua University, Beijing 100084, P. R. China
| | - Jinbo Chen
- Department of Biostatistics and Epidemiology, University of Pennsylvania, School of Medicine, Philadelphia, PA 19104
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7
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Alotaibi R, Fiaccone R, Henderson R, Stare J. Explained variation for recurrent event data. Biom J 2015; 57:571-91. [PMID: 25899247 DOI: 10.1002/bimj.201300143] [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: 07/18/2013] [Revised: 02/06/2015] [Accepted: 02/14/2015] [Indexed: 11/07/2022]
Abstract
Although there are many suggested measures of explained variation for single-event survival data, there has been little attention to explained variation for recurrent event data. We describe an existing rank-based measure and we investigate a new statistic based on observed and expected event count processes. Both methods can be used for all models. Adjustments for missing data are proposed and demonstrated through simulation to be effective. We compare the population values of the two statistics and illustrate their use in comparing an array of non-nested models for data on recurrent episodes of infant diarrhoea.
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Affiliation(s)
- Refah Alotaibi
- Princess Norah Bint Abdulrahman University, Riyadh 11635, Saudi Arabia
| | - Rosemeire Fiaccone
- Statistics Department, Federal University of Bahia, Salvador, Bahia 40170-110, Brazil
| | - Robin Henderson
- School of Mathematics & Statistics, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
| | - Janez Stare
- Institute for Biostatistics and Medical Informatics, Faculty of Medicine, University of Ljubljana, Ljubljana 1000, Slovenia
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8
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Cheng Y, Li J. Time-dependent diagnostic accuracy analysis with censored outcome and censored predictor. J Stat Plan Inference 2015. [DOI: 10.1016/j.jspi.2014.08.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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9
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Lorent M, Giral M, Foucher Y. Net time-dependent ROC curves: a solution for evaluating the accuracy of a marker to predict disease-related mortality. Stat Med 2014; 33:2379-89. [DOI: 10.1002/sim.6079] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2013] [Revised: 10/15/2013] [Accepted: 12/05/2013] [Indexed: 01/27/2023]
Affiliation(s)
- Marine Lorent
- SPHERE EA 4275 Biostatistics, Clinical Research and Subjective Measurements in Health Sciences; University of Nantes; 1 rue Gaston Veil 44035 Nantes France
| | - Magali Giral
- Transplantation, Urology and Nephrology Institute (ITUN); Nantes Hospital and University; Inserm U1064, 30 Bd. Jean Monnet 44093 Nantes France
| | - Yohann Foucher
- SPHERE EA 4275 Biostatistics, Clinical Research and Subjective Measurements in Health Sciences; University of Nantes; 1 rue Gaston Veil 44035 Nantes France
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10
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Review and comparison of ROC curve estimators for a time-dependent outcome with marker-dependent censoring. Biom J 2013; 55:687-704. [DOI: 10.1002/bimj.201200045] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2012] [Revised: 02/18/2013] [Accepted: 04/17/2013] [Indexed: 11/07/2022]
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