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Lin T, Liang R, Song Q, Liao H, Dai M, Jiang T, Tu X, Shu X, Huang X, Ge N, Wan K, Yue J. Development and Validation of PRE-SARC (PREdiction of SARCopenia Risk in Community Older Adults) Sarcopenia Prediction Model. J Am Med Dir Assoc 2024:105128. [PMID: 38977200 DOI: 10.1016/j.jamda.2024.105128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 05/24/2024] [Accepted: 05/27/2024] [Indexed: 07/10/2024]
Abstract
OBJECTIVE Reliable identification of high-risk older adults who are likely to develop sarcopenia is essential to implement targeted preventive measures and follow-up. However, no sarcopenia prediction model is currently available for community use. Our objective was to develop and validate a risk prediction model for calculating the 1-year absolute risk of developing sarcopenia in an aging population. METHODS One prospective population-based cohort of non-sarcopenic individuals aged 60 years or older were used for the development of a sarcopenia risk prediction model and model validation. Sarcopenia was defined according to the 2019 Asian Working Group for Sarcopenia consensus. Stepwise logistic regression was used to identify risk factors for sarcopenia incidence within a 1-year follow-up. Model performance was evaluated using the area under the receiver operating characteristics curve (AUROC) and calibration plot, respectively. RESULTS The development cohort included 1042 older adults, among whom 87 participants developed sarcopenia during a 1-year follow-up. The PRE-SARC (PREdiction of SARCopenia Risk in community older adults) model can accurately predict the 1-year risk of sarcopenia by using 7 easily accessible community-based predictors. The PRE-SARC model performed well in predicting sarcopenia, with an AUROC of 87% (95% CI, 0.83-0.90) and good calibration. Internal validation showed minimal optimism, with an adjusted AUROC of 0.85. The prediction score was categorized into 4 risk groups: low (0%-10%), moderate (>10%-20%), high (>20%-40%), and very high (>40%). The PRE-SARC model has been incorporated into an online risk calculator, which is freely accessible for daily clinical applications (https://sarcopeniariskprediction.shinyapps.io/dynnomapp/). CONCLUSIONS In community-dwelling individuals, the PRE-SARC model can accurately predict 1-year sarcopenia incidence. This model serves as a readily available and free accessible tool to identify older adults at high risk of sarcopenia, thereby facilitating personalized early preventive approaches and optimizing the utilization of health care resources.
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Affiliation(s)
- Taiping Lin
- Department of Geriatrics and National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Rui Liang
- Department of Geriatrics and National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Quhong Song
- Department of Geriatrics and National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Hualong Liao
- Department of Applied Mechanics, College of Architecture and Environment, Sichuan University, Chengdu, Sichuan, China
| | - Miao Dai
- Department of Geriatrics, Jiujiang First People's Hospital, Jiujiang, Jiangxi, China
| | - Tingting Jiang
- Department of Geriatrics and National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Xiangping Tu
- Department of Geriatrics and National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Xiaoyu Shu
- Department of Geriatrics and National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Xiaotao Huang
- Department of Gastroenterology, Jiangyou 903 Hospital, Mianyang, Sichuan, China
| | - Ning Ge
- Department of Geriatrics and National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Ke Wan
- Department of Geriatrics and National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Jirong Yue
- Department of Geriatrics and National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, Sichuan, China.
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Chalkou K, Hamza T, Benkert P, Kuhle J, Zecca C, Simoneau G, Pellegrini F, Manca A, Egger M, Salanti G. Combining randomized and non-randomized data to predict heterogeneous effects of competing treatments. Res Synth Methods 2024; 15:641-656. [PMID: 38501273 DOI: 10.1002/jrsm.1717] [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: 11/07/2022] [Revised: 01/26/2024] [Accepted: 02/16/2024] [Indexed: 03/20/2024]
Abstract
Some patients benefit from a treatment while others may do so less or do not benefit at all. We have previously developed a two-stage network meta-regression prediction model that synthesized randomized trials and evaluates how treatment effects vary across patient characteristics. In this article, we extended this model to combine different sources of types in different formats: aggregate data (AD) and individual participant data (IPD) from randomized and non-randomized evidence. In the first stage, a prognostic model is developed to predict the baseline risk of the outcome using a large cohort study. In the second stage, we recalibrated this prognostic model to improve our predictions for patients enrolled in randomized trials. In the third stage, we used the baseline risk as effect modifier in a network meta-regression model combining AD, IPD randomized clinical trial to estimate heterogeneous treatment effects. We illustrated the approach in the re-analysis of a network of studies comparing three drugs for relapsing-remitting multiple sclerosis. Several patient characteristics influence the baseline risk of relapse, which in turn modifies the effect of the drugs. The proposed model makes personalized predictions for health outcomes under several treatment options and encompasses all relevant randomized and non-randomized evidence.
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Affiliation(s)
- Konstantina Chalkou
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Graduate School for Health Sciences, University of Bern, Bern, Switzerland
- Department of Clinical Research, University of Bern, Bern, Switzerland
| | - Tasnim Hamza
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Graduate School for Health Sciences, University of Bern, Bern, Switzerland
| | - Pascal Benkert
- Department of Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Jens Kuhle
- Multiple Sclerosis Centre, Neurologic Clinic and Policlinic, Department of Head, Spine and Neuromedicine, University Hospital Basel, University of Basel, Basel, Switzerland
- Multiple Sclerosis Centre, Neurologic Clinic and Policlinic, Department of Biomedicine, University Hospital Basel, University of Basel, Basel, Switzerland
- Multiple Sclerosis Centre, Neurologic Clinic and Policlinic, Department of Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience (RC2NB), University Hospital, University of Basel, Basel, Switzerland
| | - Chiara Zecca
- Multiple Sclerosis Center, Neurocenter of Southern Switzerland, EOC, Lugano, Switzerland
- Faculty of Biomedical Sciences, Università della Svizzera Italiana, Lugano, Switzerland
| | | | | | - Andrea Manca
- Centre for Health Economics, University of York, York, UK
| | - Matthias Egger
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Georgia Salanti
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
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Sakr AM, Mansmann U, Havla J, Ön BI, Ön BI. Framework for personalized prediction of treatment response in relapsing-remitting multiple sclerosis: a replication study in independent data. BMC Med Res Methodol 2024; 24:138. [PMID: 38914938 PMCID: PMC11194862 DOI: 10.1186/s12874-024-02264-9] [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: 08/28/2023] [Accepted: 06/13/2024] [Indexed: 06/26/2024] Open
Abstract
BACKGROUND Individualizing and optimizing treatment of relapsing-remitting multiple sclerosis patients is a challenging problem, which would benefit from a clinically valid decision support. Stühler et al. presented black box models for this aim which were developed and internally evaluated in a German registry but lacked external validation. METHODS In patients from the French OFSEP registry, we independently built and validated models predicting being free of relapse and free of confirmed disability progression (CDP), following the methodological roadmap and predictors reported by Stühler. Hierarchical Bayesian models were fit to predict the outcomes under 6 disease-modifying treatments given the individual disease course up to the moment of treatment change. Data was temporally split on 2017, and models were developed in patients treated earlier (n = 5517). Calibration curves, discrimination, mean squared error (MSE) and relative percentage of root MSE (RMSE%) were assessed by external validation of models in more-recent patients (n = 3768). Non-Bayesian fixed-effects GLMs were also applied and their outcomes were compared to these of the Bayesian ones. For both, we modelled the number of on-therapy relapses with a negative binomial distribution, and CDP occurrence with a binomial distribution. RESULTS The performance of our temporally-validated relapse model (MSE: 0.326, C-Index: 0.639) is potentially superior to that of Stühler's (MSE: 0.784, C-index: 0.608). Calibration plots revealed miscalibration. Our CDP model (MSE: 0.072, C-Index: 0.777) was also better than its counterpart (MSE: 0.131, C-index: 0.554). Results from non-Bayesian fixed-effects GLM models were similar to the Bayesian ones. CONCLUSIONS The relapse and CDP models rebuilt and externally validated in independent data could compare and strengthen the credibility of the Stühler models. Their model-building strategy was replicable.
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Affiliation(s)
- Anna Maria Sakr
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Faculty of Medicine, LMU Munich, Marchioninistrasse 15, Munich, 81377, Germany.
- Pettenkofer School of Public Health, Elisabeth-Winterhalter-Weg 6, Munich, 81377, Germany.
| | - Ulrich Mansmann
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Faculty of Medicine, LMU Munich, Marchioninistrasse 15, Munich, 81377, Germany
- Pettenkofer School of Public Health, Elisabeth-Winterhalter-Weg 6, Munich, 81377, Germany
| | - Joachim Havla
- Institute of Clinical Neuroimmunology, University Hospital, LMU Munich, Marchioninistrasse 15, Munich, 81377, Germany
| | - Begum Irmak Ön
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Faculty of Medicine, LMU Munich, Marchioninistrasse 15, Munich, 81377, Germany
- Pettenkofer School of Public Health, Elisabeth-Winterhalter-Weg 6, Munich, 81377, Germany
| | - Begum Irmak Ön
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Faculty of Medicine, LMU Munich, Marchioninistrasse 15, Munich, 81377, Germany
- Pettenkofer School of Public Health, Elisabeth-Winterhalter-Weg 6, Munich, 81377, Germany
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Keogh RH, Van Geloven N. Prediction Under Interventions: Evaluation of Counterfactual Performance Using Longitudinal Observational Data. Epidemiology 2024; 35:329-339. [PMID: 38630508 DOI: 10.1097/ede.0000000000001713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/19/2024]
Abstract
Predictions under interventions are estimates of what a person's risk of an outcome would be if they were to follow a particular treatment strategy, given their individual characteristics. Such predictions can give important input to medical decision-making. However, evaluating the predictive performance of interventional predictions is challenging. Standard ways of evaluating predictive performance do not apply when using observational data, because prediction under interventions involves obtaining predictions of the outcome under conditions that are different from those that are observed for a subset of individuals in the validation dataset. This work describes methods for evaluating counterfactual performance of predictions under interventions for time-to-event outcomes. This means we aim to assess how well predictions would match the validation data if all individuals had followed the treatment strategy under which predictions are made. We focus on counterfactual performance evaluation using longitudinal observational data, and under treatment strategies that involve sustaining a particular treatment regime over time. We introduce an estimation approach using artificial censoring and inverse probability weighting that involves creating a validation dataset mimicking the treatment strategy under which predictions are made. We extend measures of calibration, discrimination (c-index and cumulative/dynamic AUCt) and overall prediction error (Brier score) to allow assessment of counterfactual performance. The methods are evaluated using a simulation study, including scenarios in which the methods should detect poor performance. Applying our methods in the context of liver transplantation shows that our procedure allows quantification of the performance of predictions supporting crucial decisions on organ allocation.
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Affiliation(s)
- Ruth H Keogh
- From the Department of Medical Statistics, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Nan Van Geloven
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
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Deisenhofer AK, Barkham M, Beierl ET, Schwartz B, Aafjes-van Doorn K, Beevers CG, Berwian IM, Blackwell SE, Bockting CL, Brakemeier EL, Brown G, Buckman JEJ, Castonguay LG, Cusack CE, Dalgleish T, de Jong K, Delgadillo J, DeRubeis RJ, Driessen E, Ehrenreich-May J, Fisher AJ, Fried EI, Fritz J, Furukawa TA, Gillan CM, Gómez Penedo JM, Hitchcock PF, Hofmann SG, Hollon SD, Jacobson NC, Karlin DR, Lee CT, Levinson CA, Lorenzo-Luaces L, McDanal R, Moggia D, Ng MY, Norris LA, Patel V, Piccirillo ML, Pilling S, Rubel JA, Salazar-de-Pablo G, Schleider JL, Schnurr PP, Schueller SM, Siegle GJ, Uher R, Watkins E, Webb CA, Wiltsey Stirman S, Wynants L, Youn SJ, Zilcha-Mano S, Lutz W, Cohen ZD. Implementing precision methods in personalizing psychological therapies: Barriers and possible ways forward. Behav Res Ther 2024; 172:104443. [PMID: 38086157 DOI: 10.1016/j.brat.2023.104443] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 11/21/2023] [Accepted: 11/27/2023] [Indexed: 12/26/2023]
Affiliation(s)
| | | | | | | | | | | | | | | | - Claudi L Bockting
- AmsterdamUMC, Department of Psychiatry, Research Program Amsterdam Public Health and Centre for Urban Mental Health, University of Amsterdam, the Netherlands
| | | | | | | | | | | | | | - Kim de Jong
- Leiden University, Institute of Psychology, USA
| | | | | | | | | | | | | | - Jessica Fritz
- University of Cambridge, UK; Philipps University of Marburg, Germany
| | | | - Claire M Gillan
- School of Psychology, Trinity College Institute for Neuroscience, And Global Brain Health Institute, Trinity College Dublin, USA
| | | | | | | | | | | | | | | | | | | | | | | | - Mei Yi Ng
- Florida International University, USA
| | | | | | | | | | | | | | - Jessica L Schleider
- Stony Brook University and Feinberg School of Medicine Northwestern University, USA
| | - Paula P Schnurr
- National Center for PTSD and Geisel School of Medicine at Dartmouth, USA
| | | | | | | | | | | | | | | | - Soo Jeong Youn
- Reliant Medical Group, OptumCare and Harvard Medical School, USA
| | | | | | - Zachary D Cohen
- University of California, Los Angeles and University of Arizona, USA.
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Lv Y, Bai W, Zhu X, Xue H, Zhao J, Zhuge Y, Sun J, Zhang C, Ding P, Jiang Z, Zhu X, Ren W, Li Y, Zhang K, Zhang W, Li K, Wang Z, Luo B, Li X, Yang Z, Guo W, Xia D, Xie H, Pan Y, Yin Z, Fan D, Han G. Development and validation of a prognostic score to identify the optimal candidate for preemptive TIPS in patients with cirrhosis and acute variceal bleeding. Hepatology 2024; 79:118-134. [PMID: 37594323 DOI: 10.1097/hep.0000000000000548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Accepted: 06/12/2023] [Indexed: 08/19/2023]
Abstract
BACKGROUND AND AIM Baveno VII workshop recommends the use of preemptive TIPS (p-TIPS) in patients with cirrhosis and acute variceal bleeding (AVB) at high- risk of treatment failure. However, the criteria defining "high-risk" have low clinical accessibility or include subjective variables. We aimed to develop and externally validate a model for better identification of p-TIPS candidates. APPROACH AND RESULTS The derivation cohort included 1554 patients with cirrhosis and AVB who were treated with endoscopy plus drug (n = 1264) or p-TIPS (n = 290) from 12 hospitals in China between 2010 and 2017. We first used competing risk regression to develop a score for predicting 6-week and 1-year mortality in patients treated with endoscopy plus drugs, which included age, albumin, bilirubin, international normalized ratio, white blood cell, creatinine, and sodium. The score was internally validated with the bootstrap method, which showed good discrimination (6 wk/1 y concordance-index: 0.766/0.740) and calibration, and outperformed other currently available models. In the second stage, the developed score was combined with treatment and their interaction term to predicate the treatment effect of p-TIPS (mortality risk difference between treatment groups) in the whole derivation cohort. The estimated treatment effect of p-TIPS varied substantially among patients. The prediction model had good discriminative ability (6 wk/1 y c -for-benefit: 0.696/0.665) and was well calibrated. These results were confirmed in the validation dataset of 445 patients with cirrhosis with AVB from 6 hospitals in China between 2017 and 2019 (6-wk/1-y c-for-benefit: 0.675/0.672). CONCLUSIONS We developed and validated a clinical prediction model that can help to identify individuals who will benefit from p-TIPS, which may guide clinical decision-making.
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Affiliation(s)
- Yong Lv
- Department of Liver Diseases and Digestive Interventional Radiology, National Clinical Research Center for Digestive Diseases and Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, China
| | - Wei Bai
- Department of Liver Diseases and Digestive Interventional Radiology, National Clinical Research Center for Digestive Diseases and Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, China
- Department of Liver Diseases and Interventional Radiology, Xi'an International Medical Center Hospital of Digestive Diseases, Northwest University, Xi'an, China
| | - Xuan Zhu
- Department of Gastroenterology, First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Hui Xue
- Department of Gastroenterology, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Jianbo Zhao
- Department of Interventional Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Yuzheng Zhuge
- Department of Gastroenterology, Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Junhui Sun
- Hepatobiliary and Pancreatic Intervention Center, Division of Hepatobiliary and Pancreatic Surgery, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Chunqing Zhang
- Department of Gastroenterology, Shandong Provincial Hospital affiliated to Shandong University, Jinan, China
| | - Pengxu Ding
- Department of Vascular and Endovascular Surgery, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Zaibo Jiang
- Department of interventional Radiology, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Xiaoli Zhu
- Department of interventional Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Weixin Ren
- Department of Interventional Radiology, First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
| | - Yingchun Li
- Department of Interventional Radiology, Second Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Kewei Zhang
- Department of Vascular Surgery, Henan Provincial People's Hospital, Zhengzhou, China
| | - Wenguang Zhang
- Department of Interventional Radiology, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Kai Li
- Department of Liver Diseases and Digestive Interventional Radiology, National Clinical Research Center for Digestive Diseases and Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, China
| | - Zhengyu Wang
- Department of Liver Diseases and Digestive Interventional Radiology, National Clinical Research Center for Digestive Diseases and Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, China
- Department of Liver Diseases and Interventional Radiology, Xi'an International Medical Center Hospital of Digestive Diseases, Northwest University, Xi'an, China
| | - Bohan Luo
- Department of Liver Diseases and Digestive Interventional Radiology, National Clinical Research Center for Digestive Diseases and Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, China
- Department of Liver Diseases and Interventional Radiology, Xi'an International Medical Center Hospital of Digestive Diseases, Northwest University, Xi'an, China
| | - Xiaomei Li
- Department of Liver Diseases and Digestive Interventional Radiology, National Clinical Research Center for Digestive Diseases and Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, China
- Department of Liver Diseases and Interventional Radiology, Xi'an International Medical Center Hospital of Digestive Diseases, Northwest University, Xi'an, China
| | - Zhiping Yang
- State Key Laboratory of Cancer Biology, National Clinical Research Center for Digestive Diseases and Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, China
| | - Wengang Guo
- Department of Liver Diseases and Digestive Interventional Radiology, National Clinical Research Center for Digestive Diseases and Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, China
- Department of Liver Diseases and Interventional Radiology, Xi'an International Medical Center Hospital of Digestive Diseases, Northwest University, Xi'an, China
| | - Dongdong Xia
- Department of Liver Diseases and Digestive Interventional Radiology, National Clinical Research Center for Digestive Diseases and Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, China
- Department of Liver Diseases and Interventional Radiology, Xi'an International Medical Center Hospital of Digestive Diseases, Northwest University, Xi'an, China
| | - Huahong Xie
- State Key Laboratory of Cancer Biology, National Clinical Research Center for Digestive Diseases and Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, China
| | - Yanglin Pan
- State Key Laboratory of Cancer Biology, National Clinical Research Center for Digestive Diseases and Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, China
| | - Zhanxin Yin
- Department of Liver Diseases and Digestive Interventional Radiology, National Clinical Research Center for Digestive Diseases and Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, China
- Department of Liver Diseases and Interventional Radiology, Xi'an International Medical Center Hospital of Digestive Diseases, Northwest University, Xi'an, China
| | - Daiming Fan
- State Key Laboratory of Cancer Biology, National Clinical Research Center for Digestive Diseases and Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, China
| | - Guohong Han
- Department of Liver Diseases and Digestive Interventional Radiology, National Clinical Research Center for Digestive Diseases and Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, China
- Department of Liver Diseases and Interventional Radiology, Xi'an International Medical Center Hospital of Digestive Diseases, Northwest University, Xi'an, China
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7
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van Klaveren D, Maas CCHM, Kent DM. Measuring the performance of prediction models to personalize treatment choice: Defining observed and predicted pairwise treatment effects. Stat Med 2023; 42:4514-4515. [PMID: 37828811 DOI: 10.1002/sim.9719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 03/13/2023] [Indexed: 10/14/2023]
Affiliation(s)
- David van Klaveren
- Department of Public Health, Erasmus MC University Medical Center, Rotterdam, The Netherlands
- Predictive Analytics and Comparative Effectiveness (PACE) Center at the Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, Massachusetts, USA
| | - Carolien C H M Maas
- Department of Public Health, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - David M Kent
- Predictive Analytics and Comparative Effectiveness (PACE) Center at the Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, Massachusetts, USA
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8
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Venkatasubramaniam A, Mateen BA, Shields BM, Hattersley AT, Jones AG, Vollmer SJ, Dennis JM. Comparison of causal forest and regression-based approaches to evaluate treatment effect heterogeneity: an application for type 2 diabetes precision medicine. BMC Med Inform Decis Mak 2023; 23:110. [PMID: 37328784 PMCID: PMC10276367 DOI: 10.1186/s12911-023-02207-2] [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: 11/07/2022] [Accepted: 06/01/2023] [Indexed: 06/18/2023] Open
Abstract
OBJECTIVE Precision medicine requires reliable identification of variation in patient-level outcomes with different available treatments, often termed treatment effect heterogeneity. We aimed to evaluate the comparative utility of individualized treatment selection strategies based on predicted individual-level treatment effects from a causal forest machine learning algorithm and a penalized regression model. METHODS Cohort study characterizing individual-level glucose-lowering response (6 month reduction in HbA1c) in people with type 2 diabetes initiating SGLT2-inhibitor or DPP4-inhibitor therapy. Model development set comprised 1,428 participants in the CANTATA-D and CANTATA-D2 randomised clinical trials of SGLT2-inhibitors versus DPP4-inhibitors. For external validation, calibration of observed versus predicted differences in HbA1c in patient strata defined by size of predicted HbA1c benefit was evaluated in 18,741 patients in UK primary care (Clinical Practice Research Datalink). RESULTS Heterogeneity in treatment effects was detected in clinical trial participants with both approaches (proportion predicted to have a benefit on SGLT2-inhibitor therapy over DPP4-inhibitor therapy: causal forest: 98.6%; penalized regression: 81.7%). In validation, calibration was good with penalized regression but sub-optimal with causal forest. A strata with an HbA1c benefit > 10 mmol/mol with SGLT2-inhibitors (3.7% of patients, observed benefit 11.0 mmol/mol [95%CI 8.0-14.0]) was identified using penalized regression but not causal forest, and a much larger strata with an HbA1c benefit 5-10 mmol with SGLT2-inhibitors was identified with penalized regression (regression: 20.9% of patients, observed benefit 7.8 mmol/mol (95%CI 6.7-8.9); causal forest 11.6%, observed benefit 8.7 mmol/mol (95%CI 7.4-10.1). CONCLUSIONS Consistent with recent results for outcome prediction with clinical data, when evaluating treatment effect heterogeneity researchers should not rely on causal forest or other similar machine learning algorithms alone, and must compare outputs with standard regression, which in this evaluation was superior.
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Affiliation(s)
| | - Bilal A Mateen
- The Alan Turing Institute, British Library, 96 Euston Road, London, NW1 2DB, UK
- University College London, Institute of Health Informatics, 222 Euston Rd, London, NW1 2DA, UK
| | - Beverley M Shields
- University of Exeter Medical School, Institute of Biomedical & Clinical Science, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter, EX2 5DW, UK
| | - Andrew T Hattersley
- University of Exeter Medical School, Institute of Biomedical & Clinical Science, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter, EX2 5DW, UK
| | - Angus G Jones
- University of Exeter Medical School, Institute of Biomedical & Clinical Science, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter, EX2 5DW, UK
| | | | - John M Dennis
- University of Exeter Medical School, Institute of Biomedical & Clinical Science, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter, EX2 5DW, UK.
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