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Bellavia A, Melloni GEM, Park JG, Discacciati A, Murphy SA. Estimating and presenting hazard ratios and absolute risks from a Cox model with complex nonlinear interactions. Am J Epidemiol 2024; 193:1155-1160. [PMID: 38775274 DOI: 10.1093/aje/kwae037] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 02/21/2024] [Accepted: 04/03/2024] [Indexed: 08/06/2024] Open
Abstract
Interaction analysis is a critical component of clinical and public health research and represents a key topic in precision health and medicine. In applied settings, however, interaction assessment is usually limited to the test of a product term in a regression model and to the presentation of results stratified by levels of additional covariates. Stratification of results often relies on categorizing or making linearity assumptions for continuous covariates, with substantial loss of precision and of relevant information. In time-to-event analysis, moreover, interaction assessment is often limited to the multiplicative hazard scale by inclusion of a product term in a Cox regression model, disregarding the clinically relevant information that is captured by the absolute risk scale. In this paper we present a user-friendly procedure, based on the prediction of individual absolute risks from the Cox model, for the estimation and presentation of interactive effects on both the multiplicative and additive scales in survival analysis. We describe how to flexibly incorporate interactions with continuous covariates, which potentially operate in a nonlinear fashion, provide software for replicating our procedure, and discuss different approaches to deriving CIs. The presented approach will allow clinical and public health researchers to assess complex relationships between multiple covariates as they relate to a clinical endpoint, and to provide a more intuitive and precise depiction of the results in applied research papers focusing on interaction and effect stratification.
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Affiliation(s)
- Andrea Bellavia
- TIMI Study Group, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, United States
| | - Giorgio E M Melloni
- TIMI Study Group, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, United States
| | - Jeong-Gun Park
- TIMI Study Group, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, United States
| | - Andrea Discacciati
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, SE-171 77 Stockholm, Sweden
| | - Sabina A Murphy
- TIMI Study Group, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, United States
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2
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Hoogland J, Efthimiou O, Nguyen TL, Debray TPA. Evaluating individualized treatment effect predictions: A model-based perspective on discrimination and calibration assessment. Stat Med 2024. [PMID: 39090523 DOI: 10.1002/sim.10186] [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: 09/13/2022] [Revised: 06/07/2024] [Accepted: 07/16/2024] [Indexed: 08/04/2024]
Abstract
In recent years, there has been a growing interest in the prediction of individualized treatment effects. While there is a rapidly growing literature on the development of such models, there is little literature on the evaluation of their performance. In this paper, we aim to facilitate the validation of prediction models for individualized treatment effects. The estimands of interest are defined based on the potential outcomes framework, which facilitates a comparison of existing and novel measures. In particular, we examine existing measures of discrimination for benefit (variations of the c-for-benefit), and propose model-based extensions to the treatment effect setting for discrimination and calibration metrics that have a strong basis in outcome risk prediction. The main focus is on randomized trial data with binary endpoints and on models that provide individualized treatment effect predictions and potential outcome predictions. We use simulated data to provide insight into the characteristics of the examined discrimination and calibration statistics under consideration, and further illustrate all methods in a trial of acute ischemic stroke treatment. The results show that the proposed model-based statistics had the best characteristics in terms of bias and accuracy. While resampling methods adjusted for the optimism of performance estimates in the development data, they had a high variance across replications that limited their accuracy. Therefore, individualized treatment effect models are best validated in independent data. To aid implementation, a software implementation of the proposed methods was made available in R.
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Affiliation(s)
- J Hoogland
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Epidemiology and Data Science, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - O Efthimiou
- Institute of Primary Health Care (BIHAM), University of Bern, Bern, Switzerland
- Institute of Social and Preventive Medicine (ISPM), University of Bern, Bern, Switzerland
| | - T L Nguyen
- Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - T P A Debray
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Smart Data Analysis and Statistics B.V., Utrecht, The Netherlands
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3
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Jacquemyn X, Van den Eynde J, Chinni BK, Danford DM, Kutty S, Manlhiot C. Computational simulation of the potential improvement in clinical outcomes of cardiovascular diseases with the use of a personalized predictive medicine approach. J Am Med Inform Assoc 2024; 31:1704-1713. [PMID: 38900193 PMCID: PMC11258410 DOI: 10.1093/jamia/ocae136] [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: 09/27/2023] [Revised: 04/29/2024] [Accepted: 05/27/2024] [Indexed: 06/21/2024] Open
Abstract
IMPORTANCE AND OBJECTIVES The current medical paradigm of evidence-based medicine relies on clinical guidelines derived from randomized clinical trials (RCTs), but these guidelines often overlook individual variations in treatment effects. Approaches have been proposed to develop models predicting the effects of individualized management, such as predictive allocation, individualizing treatment allocation. It is currently unknown whether widespread implementation of predictive allocation could result in better population-level outcomes over guideline-based therapy. We sought to simulate the potential effect of predictive allocation using data from previously conducted RCTs. METHODS AND RESULTS Data from 3 RCTs (positive trial, negative trial, trial stopped for futility) in pediatric cardiology were used in a computational simulation study to quantify the potential benefits of a personalized approach based on predictive allocation. Outcomes were compared when using a universal approach vs predictive allocation where each patient was allocated to the treatment associated with the lowest predicted probability of negative outcome. Compared to results from RCTs, predictive allocation yielded absolute risk reductions of 13.8% (95% confidence interval [CI] -1.9 to 29.5), 13.9% (95% CI 4.5-23.2), and 15.6% (95% CI 1.5-29.6), respectively, corresponding to a number needed to treat of 7.3, 7.2, and 6.4. The net benefit of predictive allocation was directly proportional to the performance of the prediction models and disappeared as model performance degraded below an area under the curve of 0.55. DISCUSSION These findings highlight that predictive allocation could result in improved group-level outcomes, particularly when highly predictive models are available. These findings will need to be confirmed in simulations of other trials with varying conditions and eventually in RCTs of predictive vs guideline-based treatment allocation.
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Affiliation(s)
- Xander Jacquemyn
- Blalock-Taussig-Thomas Pediatric and Congenital Heart Center, Department of Pediatrics, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, MD 21282, United States
- Department of Cardiovascular Sciences, KU Leuven & Congenital and Structural Cardiology, UZ Leuven, Leuven, 3000, Belgium
| | - Jef Van den Eynde
- Blalock-Taussig-Thomas Pediatric and Congenital Heart Center, Department of Pediatrics, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, MD 21282, United States
- Department of Cardiovascular Sciences, KU Leuven & Congenital and Structural Cardiology, UZ Leuven, Leuven, 3000, Belgium
| | - Bhargava K Chinni
- Blalock-Taussig-Thomas Pediatric and Congenital Heart Center, Department of Pediatrics, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, MD 21282, United States
| | - David M Danford
- Blalock-Taussig-Thomas Pediatric and Congenital Heart Center, Department of Pediatrics, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, MD 21282, United States
| | - Shelby Kutty
- Blalock-Taussig-Thomas Pediatric and Congenital Heart Center, Department of Pediatrics, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, MD 21282, United States
| | - Cedric Manlhiot
- Blalock-Taussig-Thomas Pediatric and Congenital Heart Center, Department of Pediatrics, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, MD 21282, United States
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4
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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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Bouvier F, Peyrot E, Balendran A, Ségalas C, Roberts I, Petit F, Porcher R. Do machine learning methods lead to similar individualized treatment rules? A comparison study on real data. Stat Med 2024; 43:2043-2061. [PMID: 38472745 DOI: 10.1002/sim.10059] [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: 06/29/2023] [Revised: 01/30/2024] [Accepted: 03/01/2024] [Indexed: 03/14/2024]
Abstract
Identifying patients who benefit from a treatment is a key aspect of personalized medicine, which allows the development of individualized treatment rules (ITRs). Many machine learning methods have been proposed to create such rules. However, to what extent the methods lead to similar ITRs, that is, recommending the same treatment for the same individuals is unclear. In this work, we compared 22 of the most common approaches in two randomized control trials. Two classes of methods can be distinguished. The first class of methods relies on predicting individualized treatment effects from which an ITR is derived by recommending the treatment evaluated to the individuals with a predicted benefit. In the second class, methods directly estimate the ITR without estimating individualized treatment effects. For each trial, the performance of ITRs was assessed by various metrics, and the pairwise agreement between all ITRs was also calculated. Results showed that the ITRs obtained via the different methods generally had considerable disagreements regarding the patients to be treated. A better concordance was found among akin methods. Overall, when evaluating the performance of ITRs in a validation sample, all methods produced ITRs with limited performance, suggesting a high potential for optimism. For non-parametric methods, this optimism was likely due to overfitting. The different methods do not lead to similar ITRs and are therefore not interchangeable. The choice of the method strongly influences for which patients a certain treatment is recommended, drawing some concerns about their practical use.
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Affiliation(s)
- Florie Bouvier
- Inserm, INRAE, Center for Research in Epidemiology and StatisticS (CRESS), Université Paris Cité and Université Sorbonne Paris Nord, Paris, France
| | - Etienne Peyrot
- Inserm, INRAE, Center for Research in Epidemiology and StatisticS (CRESS), Université Paris Cité and Université Sorbonne Paris Nord, Paris, France
| | - Alan Balendran
- Inserm, INRAE, Center for Research in Epidemiology and StatisticS (CRESS), Université Paris Cité and Université Sorbonne Paris Nord, Paris, France
| | - Corentin Ségalas
- Bordeaux Population Health Research Center, Université de Bordeaux, Inserm, Bordeaux, France
| | - Ian Roberts
- Clinical Trials Unit, London School of Hygiene & Tropical Medicine, London, UK
| | - François Petit
- Inserm, INRAE, Center for Research in Epidemiology and StatisticS (CRESS), Université Paris Cité and Université Sorbonne Paris Nord, Paris, France
| | - Raphaël Porcher
- Inserm, INRAE, Center for Research in Epidemiology and StatisticS (CRESS), Université Paris Cité and Université Sorbonne Paris Nord, Paris, France
- Centre d'Épidémiologie Clinique, Assistance Publique-Hôpitaux de Paris, Hôtel-Dieu, Paris, France
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Brooks JM, Chapman CG, Chen BK, Floyd SB, Hikmet N. Assessing the properties of patient-specific treatment effect estimates from causal forest algorithms under essential heterogeneity. BMC Med Res Methodol 2024; 24:66. [PMID: 38481139 PMCID: PMC10935905 DOI: 10.1186/s12874-024-02187-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 02/21/2024] [Indexed: 03/17/2024] Open
Abstract
BACKGROUND Treatment variation from observational data has been used to estimate patient-specific treatment effects. Causal Forest Algorithms (CFAs) developed for this task have unknown properties when treatment effect heterogeneity from unmeasured patient factors influences treatment choice - essential heterogeneity. METHODS We simulated eleven populations with identical treatment effect distributions based on patient factors. The populations varied in the extent that treatment effect heterogeneity influenced treatment choice. We used the generalized random forest application (CFA-GRF) to estimate patient-specific treatment effects for each population. Average differences between true and estimated effects for patient subsets were evaluated. RESULTS CFA-GRF performed well across the population when treatment effect heterogeneity did not influence treatment choice. Under essential heterogeneity, however, CFA-GRF yielded treatment effect estimates that reflected true treatment effects only for treated patients and were on average greater than true treatment effects for untreated patients. CONCLUSIONS Patient-specific estimates produced by CFAs are sensitive to why patients in real-world practice make different treatment choices. Researchers using CFAs should develop conceptual frameworks of treatment choice prior to estimation to guide estimate interpretation ex post.
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Affiliation(s)
- John M Brooks
- Center for Effectiveness Research in Orthopaedics - Arnold School of Public Health Greenville, 915 Greene Street #302D, Columbia, SC, 29208-0001, USA.
- University of South Carolina Arnold School of Public Health, Health Services Policy & Management, Columbia, SC, USA.
| | - Cole G Chapman
- Department of Pharmacy Practice and Science Iowa City, University of Iowa, Iowa, USA
- Center for Effectiveness Research in Orthopaedics, Greenville, SC, USA
| | - Brian K Chen
- University of South Carolina Arnold School of Public Health, Health Services Policy & Management, Columbia, SC, USA
- Center for Effectiveness Research in Orthopaedics, Greenville, SC, USA
| | - Sarah B Floyd
- Center for Effectiveness Research in Orthopaedics, Greenville, SC, USA
- Clemson University College of Behavioral Social and Health Sciences, Public Health Sciences, Clemson, South Carolina, USA
| | - Neset Hikmet
- Center for Effectiveness Research in Orthopaedics, Greenville, SC, USA
- Department of Integrated Information Technology, Innovation Think Tank Lab @ USC, University of South Carolina College of Engineering and Computing, Columbia, SC, USA
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AbdulMajeed J, Khatib M, Dulli M, Sioufi S, Al-Khulaifi A, Stone J, Furuya-Kanamori L, Onitilo AA, Doi SAR. Use of conditional estimates of effect in cancer epidemiology: An application to lung cancer treatment. Cancer Epidemiol 2024; 88:102521. [PMID: 38160570 DOI: 10.1016/j.canep.2023.102521] [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: 08/22/2023] [Revised: 12/06/2023] [Accepted: 12/22/2023] [Indexed: 01/03/2024]
Abstract
BACKGROUND In oncology clinical trials, there is the assumption that randomization sufficiently balances confounding covariates and therefore average treatment effects are usually reported. This paper explores the wider benefits provided by conditioning on covariates for reasons other than mitigation of confounding. METHODS We reanalyzed the data from primary randomized controlled trials listed in two meta-analyses to explore the significance of conditioning on smoking status in terms of the effect magnitude of treatment on progression free survival in non-small cell lung cancer. RESULTS The reanalysis revealed that conditioning on smoking status using sub-group analyses provided the closest empiric estimate of individual treatment effect based on smoking status and significantly reduced the heterogeneity of treatment effect observed across studies. In addition, smoking status was determined to be a modifier of the effect of treatment. CONCLUSION Conditioning on prognostic covariates in randomized trials in oncology helps generate the closest empiric estimates of individual treatment benefit, addresses heterogeneity due to varying covariate distributions across trials and facilitates future decision making as well as evidence synthesis. Conditioning using sub-group analyses also allows examination for effect modification in meta-analysis.
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Affiliation(s)
- Jazeel AbdulMajeed
- Department of Population Medicine, College of Medicine, QU Health, Qatar University, Doha, Qatar
| | - Malkan Khatib
- Department of Population Medicine, College of Medicine, QU Health, Qatar University, Doha, Qatar
| | - Mohamad Dulli
- Department of Medicine, Hamad General Hospital, Doha, Qatar
| | | | - Azhar Al-Khulaifi
- Department of Population Medicine, College of Medicine, QU Health, Qatar University, Doha, Qatar
| | - Jennifer Stone
- Joanna Briggs Institute, Faculty of Health and Medical Sciences, University of Adelaide, Australia
| | - Luis Furuya-Kanamori
- UQ Centre for Clinical Research, Faculty of Medicine, The University of Queensland, Herston 4029, Australia
| | - Adedayo A Onitilo
- Department of Oncology, Marshfield Clinic Health System, Marshfield, WI, USA
| | - Suhail A R Doi
- Department of Population Medicine, College of Medicine, QU Health, Qatar University, Doha, Qatar.
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Oikonomou EK, Khera R. Machine learning in precision diabetes care and cardiovascular risk prediction. Cardiovasc Diabetol 2023; 22:259. [PMID: 37749579 PMCID: PMC10521578 DOI: 10.1186/s12933-023-01985-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 09/07/2023] [Indexed: 09/27/2023] Open
Abstract
Artificial intelligence and machine learning are driving a paradigm shift in medicine, promising data-driven, personalized solutions for managing diabetes and the excess cardiovascular risk it poses. In this comprehensive review of machine learning applications in the care of patients with diabetes at increased cardiovascular risk, we offer a broad overview of various data-driven methods and how they may be leveraged in developing predictive models for personalized care. We review existing as well as expected artificial intelligence solutions in the context of diagnosis, prognostication, phenotyping, and treatment of diabetes and its cardiovascular complications. In addition to discussing the key properties of such models that enable their successful application in complex risk prediction, we define challenges that arise from their misuse and the role of methodological standards in overcoming these limitations. We also identify key issues in equity and bias mitigation in healthcare and discuss how the current regulatory framework should ensure the efficacy and safety of medical artificial intelligence products in transforming cardiovascular care and outcomes in diabetes.
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Affiliation(s)
- Evangelos K Oikonomou
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.
- Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA.
- Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT, USA.
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, 195 Church St, 6th floor, New Haven, CT, 06510, USA.
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Trischitta V, Menzaghi C, Copetti M. Unveiling Novel Markers and Modeling Clinical Prediction of Treatment Effects Are Equally Important for Implementing Precision Therapeutics. Diabetes 2023; 72:1057-1059. [PMID: 37471601 DOI: 10.2337/dbi22-0039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 04/28/2023] [Indexed: 07/22/2023]
Affiliation(s)
- Vincenzo Trischitta
- Research Unit of Diabetes and Endocrine Diseases, Fondazione IRCCS "Casa Sollievo della Sofferenza," San Giovanni Rotondo, Italy
- Department of Experimental Medicine, "Sapienza" University, Rome, Italy
| | - Claudia Menzaghi
- Research Unit of Diabetes and Endocrine Diseases, Fondazione IRCCS "Casa Sollievo della Sofferenza," San Giovanni Rotondo, Italy
| | - Massimiliano Copetti
- Biostatistics Unit, Fondazione IRCCS "Casa Sollievo della Sofferenza," San Giovanni Rotondo, Italy
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Maas CCHM, Kent DM, Hughes MC, Dekker R, Lingsma HF, van Klaveren D. Performance metrics for models designed to predict treatment effect. BMC Med Res Methodol 2023; 23:165. [PMID: 37422647 PMCID: PMC10329397 DOI: 10.1186/s12874-023-01974-w] [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: 11/18/2022] [Accepted: 06/10/2023] [Indexed: 07/10/2023] Open
Abstract
BACKGROUND Measuring the performance of models that predict individualized treatment effect is challenging because the outcomes of two alternative treatments are inherently unobservable in one patient. The C-for-benefit was proposed to measure discriminative ability. However, measures of calibration and overall performance are still lacking. We aimed to propose metrics of calibration and overall performance for models predicting treatment effect in randomized clinical trials (RCTs). METHODS Similar to the previously proposed C-for-benefit, we defined observed pairwise treatment effect as the difference between outcomes in pairs of matched patients with different treatment assignment. We match each untreated patient with the nearest treated patient based on the Mahalanobis distance between patient characteristics. Then, we define the Eavg-for-benefit, E50-for-benefit, and E90-for-benefit as the average, median, and 90th quantile of the absolute distance between the predicted pairwise treatment effects and local-regression-smoothed observed pairwise treatment effects. Furthermore, we define the cross-entropy-for-benefit and Brier-for-benefit as the logarithmic and average squared distance between predicted and observed pairwise treatment effects. In a simulation study, the metric values of deliberately "perturbed models" were compared to those of the data-generating model, i.e., "optimal model". To illustrate these performance metrics, different modeling approaches for predicting treatment effect are applied to the data of the Diabetes Prevention Program: 1) a risk modelling approach with restricted cubic splines; 2) an effect modelling approach including penalized treatment interactions; and 3) the causal forest. RESULTS As desired, performance metric values of "perturbed models" were consistently worse than those of the "optimal model" (Eavg-for-benefit ≥ 0.043 versus 0.002, E50-for-benefit ≥ 0.032 versus 0.001, E90-for-benefit ≥ 0.084 versus 0.004, cross-entropy-for-benefit ≥ 0.765 versus 0.750, Brier-for-benefit ≥ 0.220 versus 0.218). Calibration, discriminative ability, and overall performance of three different models were similar in the case study. The proposed metrics were implemented in a publicly available R-package "HTEPredictionMetrics". CONCLUSION The proposed metrics are useful to assess the calibration and overall performance of models predicting treatment effect in RCTs.
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Affiliation(s)
- C C H M Maas
- Department of Public Health, Erasmus University Medical Center, Doctor Molewaterplein 40, 3015 GD, Rotterdam, Netherlands.
| | - D M Kent
- Predictive Analytics and Comparative Effectiveness Center, Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, USA
| | - M C Hughes
- Predictive Analytics and Comparative Effectiveness Center, Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, USA
| | - R Dekker
- Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, Netherlands
| | - H F Lingsma
- Department of Public Health, Erasmus University Medical Center, Doctor Molewaterplein 40, 3015 GD, Rotterdam, Netherlands
| | - D van Klaveren
- Department of Public Health, Erasmus University Medical Center, Doctor Molewaterplein 40, 3015 GD, Rotterdam, Netherlands
- Predictive Analytics and Comparative Effectiveness Center, Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, USA
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Luo Y, Chalkou K, Funada S, Salanti G, Furukawa TA. Estimating Patient-Specific Relative Benefit of Adding Biologics to Conventional Rheumatoid Arthritis Treatment: An Individual Participant Data Meta-Analysis. JAMA Netw Open 2023; 6:e2321398. [PMID: 37389866 PMCID: PMC10314313 DOI: 10.1001/jamanetworkopen.2023.21398] [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: 12/21/2022] [Accepted: 05/16/2023] [Indexed: 07/01/2023] Open
Abstract
Importance Current evidence remains ambiguous regarding whether biologics should be added to conventional treatment of rheumatoid arthritis for specific patients, which may cause potential overuse or treatment delay. Objectives To estimate the benefit of adding biologics to conventional antirheumatic drugs for the treatment of rheumatoid arthritis given baseline characteristics. Data Sources Cochrane CENTRAL, Scopus, MEDLINE, and the World Health Organization International Clinical Trials Registry Platform were searched for articles published from database inception to March 2, 2022. Study Selection Randomized clinical trials comparing certolizumab plus conventional antirheumatic drugs with placebo plus conventional drugs were selected. Data Extraction and Synthesis Individual participant data of the prespecified outcomes and covariates were acquired from the Vivli database. A 2-stage model was fitted to estimate patient-specific relative outcomes of adding certolizumab vs conventional drugs only. Stage 1 was a penalized logistic regression model to estimate the baseline expected probability of the outcome regardless of treatment using baseline characteristics. Stage 2 was a bayesian individual participant data meta-regression model to estimate the relative outcomes for a particular baseline expected probability. Patient-specific results were displayed interactively on an application based on a 2-stage model. Main Outcomes and Measures The primary outcome was low disease activity or remission at 3 months, defined by 3 disease activity indexes (ie, Disease Activity Score based on the evaluation of 28 joints, Clinical Disease Activity Index, or Simplified Disease Activity Index). Results Individual participant data were obtained from 3790 patients (2996 female [79.1%] and 794 male [20.9%]; mean [SD] age, 52.7 [12.3] years) from 5 large randomized clinical trials for moderate to high activity rheumatoid arthritis with usable data for 22 prespecified baseline covariates. Overall, adding certolizumab was associated with a higher probability of reaching low disease activity. The odds ratio for patients with an average baseline expected probability of the outcome was 6.31 (95% credible interval, 2.22-15.25). However, the benefits differed in patients with different baseline characteristics. For example, the estimated risk difference was smaller than 10% for patients with either low or high baseline expected probability. Conclusions and Relevance In this individual participant data meta-analysis, adding certolizumab was associated with more effectiveness for rheumatoid arthritis in general. However, the benefit was uncertain for patients with low or high baseline expected probability, for whom other evaluations were necessary. The interactive application displaying individual estimates may help with treatment selection.
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Affiliation(s)
- Yan Luo
- Department of Health Promotion and Human Behavior, School of Public Health, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Population Health and Policy Research Unit, Medical Education Center, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Konstantina Chalkou
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Graduate School for Health Sciences, University of Bern, Bern, Switzerland
| | - Satoshi Funada
- Department of Health Promotion and Human Behavior, School of Public Health, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Department of Preventive Medicine and Public Health, School of Medicine, Keio University, Tokyo, Japan
| | - Georgia Salanti
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
| | - Toshi A. Furukawa
- Department of Health Promotion and Human Behavior, School of Public Health, Graduate School of Medicine, Kyoto University, Kyoto, Japan
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12
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Xia Y, Gustafson P, Sadatsafavi M. Methodological concerns about "concordance-statistic for benefit" as a measure of discrimination in predicting treatment benefit. Diagn Progn Res 2023; 7:10. [PMID: 37189162 DOI: 10.1186/s41512-023-00147-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 04/17/2023] [Indexed: 05/17/2023] Open
Abstract
Prediction algorithms that quantify the expected benefit of a given treatment conditional on patient characteristics can critically inform medical decisions. Quantifying the performance of treatment benefit prediction algorithms is an active area of research. A recently proposed metric, the concordance statistic for benefit (cfb), evaluates the discriminative ability of a treatment benefit predictor by directly extending the concept of the concordance statistic from a risk model with a binary outcome to a model for treatment benefit. In this work, we scrutinize cfb on multiple fronts. Through numerical examples and theoretical developments, we show that cfb is not a proper scoring rule. We also show that it is sensitive to the unestimable correlation between counterfactual outcomes and to the definition of matched pairs. We argue that measures of statistical dispersion applied to predicted benefits do not suffer from these issues and can be an alternative metric for the discriminatory performance of treatment benefit predictors.
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Affiliation(s)
- Yuan Xia
- Department of Statistics, University of British Columbia, Vancouver, Canada.
| | - Paul Gustafson
- Department of Statistics, University of British Columbia, Vancouver, Canada
| | - Mohsen Sadatsafavi
- Respiratory Evaluation Sciences Program, Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, Canada
- Faculty of Medicine, University of British Columbia, Vancouver, Canada
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13
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Efthimiou O, Hoogland J, Debray TP, Seo M, Furukawa TA, Egger M, White IR. Measuring the performance of prediction models to personalize treatment choice. Stat Med 2023; 42:1188-1206. [PMID: 36700492 PMCID: PMC7615726 DOI: 10.1002/sim.9665] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 11/07/2022] [Accepted: 12/31/2022] [Indexed: 01/27/2023]
Abstract
When data are available from individual patients receiving either a treatment or a control intervention in a randomized trial, various statistical and machine learning methods can be used to develop models for predicting future outcomes under the two conditions, and thus to predict treatment effect at the patient level. These predictions can subsequently guide personalized treatment choices. Although several methods for validating prediction models are available, little attention has been given to measuring the performance of predictions of personalized treatment effect. In this article, we propose a range of measures that can be used to this end. We start by defining two dimensions of model accuracy for treatment effects, for a single outcome: discrimination for benefit and calibration for benefit. We then amalgamate these two dimensions into an additional concept, decision accuracy, which quantifies the model's ability to identify patients for whom the benefit from treatment exceeds a given threshold. Subsequently, we propose a series of performance measures related to these dimensions and discuss estimating procedures, focusing on randomized data. Our methods are applicable for continuous or binary outcomes, for any type of prediction model, as long as it uses baseline covariates to predict outcomes under treatment and control. We illustrate all methods using two simulated datasets and a real dataset from a trial in depression. We implement all methods in the R package predieval. Results suggest that the proposed measures can be useful in evaluating and comparing the performance of competing models in predicting individualized treatment effect.
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Affiliation(s)
- Orestis Efthimiou
- Institute of Social and Preventive Medicine (ISPM), University of BernBernSwitzerland
- Institute of Primary Health Care (BIHAM), University of BernBernSwitzerland
- Department of PsychiatryUniversity of OxfordOxfordUK
| | - Jeroen Hoogland
- Julius Center for Health Sciences and Primary CareUniversity Medical Center Utrecht, Utrecht UniversityUtrechtThe Netherlands
- Department of Epidemiology and Data ScienceAmsterdam University Medical CentersAmsterdamThe Netherlands
| | - Thomas P.A. Debray
- Julius Center for Health Sciences and Primary CareUniversity Medical Center Utrecht, Utrecht UniversityUtrechtThe Netherlands
- Smart Data Analysis and Statistics B.V.UtrechtThe Netherlands
| | - Michael Seo
- Institute of Social and Preventive Medicine (ISPM), University of BernBernSwitzerland
- Graduate School for Health SciencesUniversity of BernBernSwitzerland
| | - Toshiaki A. Furukawa
- Departments of Health Promotion and Human Behavior and of Clinical EpidemiologyKyoto University Graduate School of Medicine/School of Public HealthKyotoJapan
| | - Matthias Egger
- Institute of Social and Preventive Medicine (ISPM), University of BernBernSwitzerland
- Centre for Infectious Disease Epidemiology and Research, Faculty of Health SciencesUniversity of Cape TownCape TownSouth Africa
- Population Health Sciences, Bristol Medical SchoolUniversity of BristolBristolUK
| | - Ian R. White
- MRC Clinical Trials Unit at UCLUniversity College LondonLondonUK
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14
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Chalkou K, Vickers AJ, Pellegrini F, Manca A, Salanti G. Decision Curve Analysis for Personalized Treatment Choice between Multiple Options. Med Decis Making 2023; 43:337-349. [PMID: 36511470 PMCID: PMC10021120 DOI: 10.1177/0272989x221143058] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 11/03/2022] [Indexed: 12/15/2022]
Abstract
BACKGROUND Decision curve analysis can be used to determine whether a personalized model for treatment benefit would lead to better clinical decisions. Decision curve analysis methods have been described to estimate treatment benefit using data from a single randomized controlled trial. OBJECTIVES Our main objective is to extend the decision curve analysis methodology to the scenario in which several treatment options exist and evidence about their effects comes from a set of trials, synthesized using network meta-analysis (NMA). METHODS We describe the steps needed to estimate the net benefit of a prediction model using evidence from studies synthesized in an NMA. We show how to compare personalized versus one-size-fit-all treatment decision-making strategies, such as "treat none" or "treat all patients with a specific treatment" strategies. First, threshold values for each included treatment need to be defined (i.e., the minimum risk difference compared with control that renders a treatment worth taking). The net benefit per strategy can then be plotted for a plausible range of threshold values to reveal the most clinically useful strategy. We applied our methodology to an NMA prediction model for relapsing-remitting multiple sclerosis, which can be used to choose between natalizumab, dimethyl fumarate, glatiramer acetate, and placebo. RESULTS We illustrated the extended decision curve analysis methodology using several threshold value combinations for each available treatment. For the examined threshold values, the "treat patients according to the prediction model" strategy performs either better than or close to the one-size-fit-all treatment strategies. However, even small differences may be important in clinical decision making. As the advantage of the personalized model was not consistent across all thresholds, improving the existing model (by including, for example, predictors that will increase discrimination) is needed before advocating its clinical usefulness. CONCLUSIONS This novel extension of decision curve analysis can be applied to NMA-based prediction models to evaluate their use to aid treatment decision making. HIGHLIGHTS Decision curve analysis is extended into a (network) meta-analysis framework.Personalized models predicting treatment benefit are evaluated when several treatment options are available and evidence about their effects comes from a set of trials.Detailed steps to compare personalized versus one-size-fit-all treatment decision-making strategies are outlined.This extension of decision curve analysis can be applied to (network) meta-analysis-based prediction models to evaluate their use to aid treatment decision making.
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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, Switzerland
| | - Andrew J. Vickers
- Department of Epidemiology and Biostatistics,
Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | | | - Andrea Manca
- Centre for Health Economics, University of
York, York, UK
| | - Georgia Salanti
- Institute of Social and Preventive Medicine,
University of Bern, Bern, Switzerland
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15
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Rekkas A, van Klaveren D, Ryan PB, Steyerberg EW, Kent DM, Rijnbeek PR. A standardized framework for risk-based assessment of treatment effect heterogeneity in observational healthcare databases. NPJ Digit Med 2023; 6:58. [PMID: 36991144 DOI: 10.1038/s41746-023-00794-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 03/10/2023] [Indexed: 03/31/2023] Open
Abstract
Treatment effects are often anticipated to vary across groups of patients with different baseline risk. The Predictive Approaches to Treatment Effect Heterogeneity (PATH) statement focused on baseline risk as a robust predictor of treatment effect and provided guidance on risk-based assessment of treatment effect heterogeneity in a randomized controlled trial. The aim of this study is to extend this approach to the observational setting using a standardized scalable framework. The proposed framework consists of five steps: (1) definition of the research aim, i.e., the population, the treatment, the comparator and the outcome(s) of interest; (2) identification of relevant databases; (3) development of a prediction model for the outcome(s) of interest; (4) estimation of relative and absolute treatment effect within strata of predicted risk, after adjusting for observed confounding; (5) presentation of the results. We demonstrate our framework by evaluating heterogeneity of the effect of thiazide or thiazide-like diuretics versus angiotensin-converting enzyme inhibitors on three efficacy and nine safety outcomes across three observational databases. We provide a publicly available R software package for applying this framework to any database mapped to the Observational Medical Outcomes Partnership Common Data Model. In our demonstration, patients at low risk of acute myocardial infarction receive negligible absolute benefits for all three efficacy outcomes, though they are more pronounced in the highest risk group, especially for acute myocardial infarction. Our framework allows for the evaluation of differential treatment effects across risk strata, which offers the opportunity to consider the benefit-harm trade-off between alternative treatments.
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Affiliation(s)
- Alexandros Rekkas
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands.
| | - David van Klaveren
- Department of Public Health, Erasmus University Medical Center, Rotterdam, The Netherlands
- Predictive Analytics and Comparative Effectiveness (PACE) Center, Institute for Clinical Research and Health Policy Studies (ICRHPS), Tufts Medical Center, Boston, MA, USA
| | - Patrick B Ryan
- Janssen Research and Development, 125 Trenton Harbourton Road, Titusville, NJ, 08560, USA
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York, USA
| | - Ewout W Steyerberg
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - David M Kent
- Predictive Analytics and Comparative Effectiveness (PACE) Center, Institute for Clinical Research and Health Policy Studies (ICRHPS), Tufts Medical Center, Boston, MA, USA
| | - Peter R Rijnbeek
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
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16
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Rekkas A, Rijnbeek PR, Kent DM, Steyerberg EW, van Klaveren D. Estimating individualized treatment effects from randomized controlled trials: a simulation study to compare risk-based approaches. BMC Med Res Methodol 2023; 23:74. [PMID: 36977990 PMCID: PMC10045909 DOI: 10.1186/s12874-023-01889-6] [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: 05/17/2022] [Accepted: 03/15/2023] [Indexed: 03/30/2023] Open
Abstract
BACKGROUND Baseline outcome risk can be an important determinant of absolute treatment benefit and has been used in guidelines for "personalizing" medical decisions. We compared easily applicable risk-based methods for optimal prediction of individualized treatment effects. METHODS We simulated RCT data using diverse assumptions for the average treatment effect, a baseline prognostic index of risk, the shape of its interaction with treatment (none, linear, quadratic or non-monotonic), and the magnitude of treatment-related harms (none or constant independent of the prognostic index). We predicted absolute benefit using: models with a constant relative treatment effect; stratification in quarters of the prognostic index; models including a linear interaction of treatment with the prognostic index; models including an interaction of treatment with a restricted cubic spline transformation of the prognostic index; an adaptive approach using Akaike's Information Criterion. We evaluated predictive performance using root mean squared error and measures of discrimination and calibration for benefit. RESULTS The linear-interaction model displayed optimal or close-to-optimal performance across many simulation scenarios with moderate sample size (N = 4,250; ~ 785 events). The restricted cubic splines model was optimal for strong non-linear deviations from a constant treatment effect, particularly when sample size was larger (N = 17,000). The adaptive approach also required larger sample sizes. These findings were illustrated in the GUSTO-I trial. CONCLUSIONS An interaction between baseline risk and treatment assignment should be considered to improve treatment effect predictions.
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Affiliation(s)
- Alexandros Rekkas
- Department of Medical Informatics, Erasmus Medical Center, P.O. Box 2040, 3000, CA, Rotterdam, The Netherlands.
| | - Peter R Rijnbeek
- Department of Medical Informatics, Erasmus Medical Center, P.O. Box 2040, 3000, CA, Rotterdam, The Netherlands
| | - David M Kent
- Predictive Analytics and Comparative Effectiveness Center, Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, USA
| | - Ewout W Steyerberg
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - David van Klaveren
- Department of Public Health, Erasmus Medical Center, Rotterdam, The Netherlands
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17
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Bosso L, Espejo T, Taffé P, Caillet-Bois D, Christen T, Berna C, Hugli O. Analgesic and Anxiolytic Effects of Virtual Reality During Minor Procedures in an Emergency Department: A Randomized Controlled Study. Ann Emerg Med 2023; 81:84-94. [PMID: 35641354 DOI: 10.1016/j.annemergmed.2022.04.015] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 04/06/2022] [Accepted: 04/12/2022] [Indexed: 02/04/2023]
Abstract
STUDY OBJECTIVE We aimed to assess the analgesic and anxiolytic efficacy of distraction, a nonpharmacologic intervention provided by 3-dimensional (3D) virtual reality (VR) compared with that provided by 2-dimensional (2D) VR during minor emergency department (ED) procedures. METHODS This randomized controlled study conducted in the ED of a teaching hospital included patients aged more than or equal to 18 years undergoing minor procedures. The patients watched the same computer-generated VR world either in 3D in a head-mounted display (intervention) or in 2D on a laptop screen (control). Our main outcomes were pain and anxiety during the procedure, assessed on a 100-mm visual analog scale. Secondary outcomes included the impression of telepresence in the computer-generated world assessed using the Igroup Presence Questionnaire, and the prevalence and intensity of cybersickness measured on a 100-mm visual analog scale. RESULTS The final analysis included 117 patients. The differences in median procedural pain and anxiety levels between the 2D and 3D VR groups were not significant: -3 mm (95% confidence interval [CI] -14 to 8) and -4 mm (95% CI -15 to 3), respectively; the difference in telepresence was 2.0 point (95% CI 0 to 2.0), and the proportion difference of cybersickness was -4% (95% CI -22 to 14), with an intensity difference of -5 mm (95% CI -9 to 3). CONCLUSION During minor procedures in adult patients in the ED, distraction by viewing a 3D virtual world in a head-mounted VR display did not result in lower average levels of procedural pain and anxiety than that by 2D viewing on a screen despite a higher sense of telepresence. There were no significant differences in the prevalence and intensity of cybersickness between the 2 groups.
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Affiliation(s)
- Luca Bosso
- Faculty of Biology and Medicine, University of Lausanne, Lausanne, Vaud, Switzerland
| | - Tanguy Espejo
- Faculty of Biology and Medicine, University of Lausanne, Lausanne, Vaud, Switzerland
| | - Patrick Taffé
- Center for Primary Care and Public Health (Unisanté), DFRI/Division of Biostatistics, Lausanne, Vaud, Switzerland
| | - David Caillet-Bois
- Emergency Department, Lausanne University Hospital, Lausanne, Vaud, Switzerland
| | - Thierry Christen
- Department of Plastic and Hand Surgery, Lausanne University Hospital, Lausanne, Vaud, Switzerland
| | - Chantal Berna
- Centre for Integrative and Complementary Medicine and Pain Centers, Lausanne University Hospital & Lausanne University, Lausanne, Vaud, Switzerland
| | - Olivier Hugli
- Emergency Department, Lausanne University Hospital, Lausanne, Vaud, Switzerland.
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18
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Ge X, Peng Y, Tu D. A generalized single‐index linear threshold model for identifying treatment‐sensitive subsets based on multiple covariates and longitudinal measurements. CAN J STAT 2022. [DOI: 10.1002/cjs.11737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Xinyi Ge
- Department of Mathematics and Statistics Queen's University Kingston Ontario Canada
| | - Yingwei Peng
- Departments of Mathematics and Statistics & Public Health Sciences Queen's University Kingston Ontario Canada
| | - Dongsheng Tu
- Departments of Mathematics and Statistics & Public Health Sciences and Canadian Cancer Trials Group Queen's University Kingston Ontario Canada
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19
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Abstract
The odds ratio (OR) has been misunderstood in evidence based medicine and clinical epidemiology. Currently, "noncollapsibility" is considered a problem with interpretation of the OR and it is thought that the OR is rarely the parameter of interest for causal inference or interpretation of effect modification. The current focus on the relative risk (RR) and risk difference (RD) suffers from an important limitation: they are not solely measures of effect and vary numerically with baseline risk. In this paper, generalized linear models are examined in terms of the three binary effect measures commonly used in epidemiology to demonstrate that ORs may be the only way to interpret effect modification and have properties that should make them the parameter of interest for causal inference. We look forward to discussion, debate, and counter-views on this issue from the epidemiology community.
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Affiliation(s)
- Suhail A Doi
- Department of Population Medicine, College of Medicine, QU Health, Qatar University, Doha, Qatar
| | - Jazeel Abdulmajeed
- Department of Population Medicine, College of Medicine, QU Health, Qatar University, Doha, Qatar
- Primary Health Care Corporation, Doha, Qatar
| | - Chang Xu
- Department of Population Medicine, College of Medicine, QU Health, Qatar University, Doha, Qatar
- Ministry of Education Key Laboratory for Population Health Across-life Cycle & School of Public Health, Anhui Medical University, Hefei, China
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20
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Brooks JM, Chapman CG, Floyd SB, Chen BK, Thigpen CA, Kissenberth M. Assessing the ability of an instrumental variable causal forest algorithm to personalize treatment evidence using observational data: the case of early surgery for shoulder fracture. BMC Med Res Methodol 2022; 22:190. [PMID: 35818028 PMCID: PMC9275148 DOI: 10.1186/s12874-022-01663-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2022] [Accepted: 06/20/2022] [Indexed: 11/24/2022] Open
Abstract
Background Comparative effectiveness research (CER) using observational databases has been suggested to obtain personalized evidence of treatment effectiveness. Inferential difficulties remain using traditional CER approaches especially related to designating patients to reference classes a priori. A novel Instrumental Variable Causal Forest Algorithm (IV-CFA) has the potential to provide personalized evidence using observational data without designating reference classes a priori, but the consistency of the evidence when varying key algorithm parameters remains unclear. We investigated the consistency of IV-CFA estimates through application to a database of Medicare beneficiaries with proximal humerus fractures (PHFs) that previously revealed heterogeneity in the effects of early surgery using instrumental variable estimators. Methods IV-CFA was used to estimate patient-specific early surgery effects on both beneficial and detrimental outcomes using different combinations of algorithm parameters and estimate variation was assessed for a population of 72,751 fee-for-service Medicare beneficiaries with PHFs in 2011. Classification and regression trees (CART) were applied to these estimates to create ex-post reference classes and the consistency of these classes were assessed. Two-stage least squares (2SLS) estimators were applied to representative ex-post reference classes to scrutinize the estimates relative to known 2SLS properties. Results IV-CFA uncovered substantial early surgery effect heterogeneity across PHF patients, but estimates for individual patients varied with algorithm parameters. CART applied to these estimates revealed ex-post reference classes consistent across algorithm parameters. 2SLS estimates showed that ex-post reference classes containing older, frailer patients with more comorbidities, and lower utilizers of healthcare were less likely to benefit and more likely to have detriments from higher rates of early surgery. Conclusions IV-CFA provides an illuminating method to uncover ex-post reference classes of patients based on treatment effects using observational data with a strong instrumental variable. Interpretation of treatment effect estimates within each ex-post reference class using traditional CER methods remains conditional on the extent of measured information in the data. Supplementary Information The online version contains supplementary material available at 10.1186/s12874-022-01663-0.
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Affiliation(s)
- John M Brooks
- Center for Effectiveness Research in Orthopaedics - Arnold School of Public Health Greenville, 915 Greene Street #302D, 29208, Columbia, SC, 29208-0001, USA. .,Health Services Policy & Management, University of South Carolina Arnold School of Public Health, Columbia, USA.
| | - Cole G Chapman
- Department of Pharmacy Practice and Science, University of Iowa, Iowa City, USA.,Center for Effectiveness Research in Orthopaedics, Greenville, USA
| | - Sarah B Floyd
- Center for Effectiveness Research in Orthopaedics, Greenville, USA.,Clemson University College of Behavioral Social and Health Sciences, Public Health Sciences, Clemson, USA
| | - Brian K Chen
- Health Services Policy & Management, University of South Carolina Arnold School of Public Health, Columbia, USA.,Center for Effectiveness Research in Orthopaedics, Greenville, USA
| | - Charles A Thigpen
- Center for Effectiveness Research in Orthopaedics, Greenville, USA.,ATI Physical Therapy, Greenville, USA
| | - Michael Kissenberth
- Center for Effectiveness Research in Orthopaedics, Greenville, USA.,Prisma Health, Steadman Hawkins Clinic of the Carolinas, Greenville, USA
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21
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Hogervorst MA, Vreman RA, Mantel-Teeuwisse AK, Goettsch WG. Reported Challenges in Health Technology Assessment of Complex Health Technologies. VALUE IN HEALTH : THE JOURNAL OF THE INTERNATIONAL SOCIETY FOR PHARMACOECONOMICS AND OUTCOMES RESEARCH 2022; 25:992-1001. [PMID: 35667787 DOI: 10.1016/j.jval.2021.11.1356] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 10/06/2021] [Accepted: 11/09/2021] [Indexed: 06/15/2023]
Abstract
OBJECTIVES With complex health technologies entering the market, methods for health technology assessment (HTA) may require changes. This study aimed to identify challenges in HTA of complex health technologies. METHODS A survey was sent to European HTA organizations participating in European Network for HTA (EUnetHTA). The survey contained open questions and used predefined potentially complex health technologies and 7 case studies to identify types of complex health technologies and challenges faced during HTA. The survey was validated, tested for reliability by an expert panel, and pilot tested before dissemination. RESULTS A total of 22 HTA organizations completed the survey (67%). Advanced therapeutic medicinal products (ATMPs) and histology-independent therapies were considered most challenging based on the predefined complex health technologies and case studies. For the case studies, more than half of the reported challenges were "methodological," equal in relative effectiveness assessments as in cost-effectiveness assessments. Through the open questions, we found that most of these challenges actually rooted in data unavailability. Data were reported as "absent," "insufficient," "immature," or "low quality" by 18 of 20 organizations (90%), in particular data on quality of life. Policy and organizational challenges and challenges because of societal or political pressure were reported by 8 (40%) and 4 organizations (20%), respectively. Modeling issues were reported least often (n = 2, 4%). CONCLUSIONS Most challenges in HTA of complex health technologies root in data insufficiencies rather than in the complexity of health technologies itself. As the number of complex technologies grows, the urgency for new methods and policies to guide HTA decision making increases.
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Affiliation(s)
- Milou A Hogervorst
- Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, The Netherlands; National Health Care Institute, Diemen, The Netherlands
| | - Rick A Vreman
- Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, The Netherlands; National Health Care Institute, Diemen, The Netherlands
| | - Aukje K Mantel-Teeuwisse
- Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, The Netherlands
| | - Wim G Goettsch
- Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, The Netherlands; National Health Care Institute, Diemen, The Netherlands.
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22
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Prediction of treatment outcome in clinical trials under a personalized medicine perspective. Sci Rep 2022; 12:4115. [PMID: 35260665 PMCID: PMC8904517 DOI: 10.1038/s41598-022-07801-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 02/22/2022] [Indexed: 11/10/2022] Open
Abstract
A central problem in most data-driven personalized medicine scenarios is the estimation of heterogeneous treatment effects to stratify individuals into subpopulations that differ in their susceptibility to a particular disease or response to a specific treatment. In this work, with an illustrative example on type 2 diabetes we showed how the increasing ability to access and analyzed open data from randomized clinical trials (RCTs) allows to build Machine Learning applications in a framework of personalized medicine. An ensemble machine learning predictive model is first developed and then applied to estimate the expected treatment response according to the medication that would be prescribed. Machine learning is quickly becoming indispensable to bridge science and clinical practice, but it is not sufficient on its own. A collaborative effort is requested to clinicians, statisticians, and computer scientists to strengthen tools built on machine learning to take advantage of this evidence flow.
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23
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Meid AD, Gerharz A, Groll A. Machine learning for tumor growth inhibition: Interpretable predictive models for transparency and reproducibility. CPT Pharmacometrics Syst Pharmacol 2022; 11:257-261. [PMID: 35104394 PMCID: PMC8923723 DOI: 10.1002/psp4.12761] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 10/15/2021] [Accepted: 10/20/2021] [Indexed: 12/30/2022] Open
Affiliation(s)
- Andreas D. Meid
- Department of Clinical Pharmacology and Pharmacoepidemiology University of Heidelberg Heidelberg Germany
| | | | - Andreas Groll
- Department of Statistics TU Dortmund University Dortmund Germany
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24
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Kessler RC, Luedtke A. Pragmatic Precision Psychiatry-A New Direction for Optimizing Treatment Selection. JAMA Psychiatry 2021; 78:1384-1390. [PMID: 34550327 DOI: 10.1001/jamapsychiatry.2021.2500] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
IMPORTANCE Clinical trials have identified numerous prescriptive predictors of mental disorder treatment response, ie, predictors of which treatments are best for which patients. However, none of these prescriptive predictors is strong enough alone to guide precision treatment planning. This has prompted growing interest in developing precision treatment rules (PTRs) that combine information across multiple prescriptive predictors, but this work has been much less successful in psychiatry than some other areas of medicine. Study designs and analysis schemes used in research on PTR development in other areas of medicine are reviewed, key challenges for implementing similar studies of mental disorders are highlighted, and recent methodological advances to address these challenges are described here. OBSERVATIONS Discovering prescriptive predictors requires large samples. Three approaches have been used in other areas of medicine to do this: conduct very large randomized clinical trials, pool individual-level results across multiple smaller randomized clinical trials, and develop preliminary PTRs in large observational treatment samples that are then tested in smaller randomized clinical trials. The third approach is most feasible for research on mental disorders. This approach requires working with large real-world observational electronic health record databases; carefully selecting samples to emulate trials; extracting information about prescriptive predictors from electronic health records along with other inexpensive data augmentation strategies; estimating preliminary PTRs in the observational data using appropriate methods; implementing pragmatic trials to validate the preliminary PTRs; and iterating between subsequent observational studies and quality improvement pragmatic trials to refine and expand the PTRs. New statistical methods exist to address the methodological challenges of implementing this approach. CONCLUSIONS AND RELEVANCE Advances in pragmatic precision psychiatry will require moving beyond the current focus on randomized clinical trials and adopting an iterative discovery-confirmation process that integrates observational and experimental studies in real-world clinical populations.
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Affiliation(s)
- Ronald C Kessler
- Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts
| | - Alex Luedtke
- Department of Statistics, University of Washington, Seattle, Washington.,Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
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25
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Lou C, Habes M, Illenberger NA, Ezzati A, Lipton RB, Shaw PA, Stephens-Shields AJ, Akbari H, Doshi J, Davatzikos C, Shinohara RT. Leveraging machine learning predictive biomarkers to augment the statistical power of clinical trials with baseline magnetic resonance imaging. Brain Commun 2021; 3:fcab264. [PMID: 34806001 PMCID: PMC8600962 DOI: 10.1093/braincomms/fcab264] [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: 04/14/2021] [Revised: 09/03/2021] [Accepted: 09/17/2021] [Indexed: 11/12/2022] Open
Abstract
A key factor in designing randomized clinical trials is the sample size required to achieve a particular level of power to detect the benefit of a treatment. Sample size calculations depend upon the expected benefits of a treatment (effect size), the accuracy of measurement of the primary outcome, and the level of power specified by the investigators. In this study, we show that radiomic models, which leverage complex brain MRI patterns and machine learning, can be utilized in clinical trials with protocols that incorporate baseline MR imaging to significantly increase statistical power to detect treatment effects. Akin to the historical control paradigm, we propose to utilize a radiomic prediction model to generate a pseudo-control sample for each individual in the trial of interest. Because the variability of expected outcome across patients can mask our ability to detect treatment effects, we can increase the power to detect a treatment effect in a clinical trial by reducing that variability through using radiomic predictors as surrogates. We illustrate this method with simulations based on data from two cohorts in different neurologic diseases, Alzheimer's disease and glioblastoma multiforme. We present sample size requirements across a range of effect sizes using conventional analysis and models that include a radiomic predictor. For our Alzheimer's disease cohort, at an effect size of 0.35, total sample size requirements for 80% power declined from 246 to 212 for the endpoint cognitive decline. For our glioblastoma multiforme cohort, at an effect size of 1.65 with the endpoint survival time, total sample size requirements declined from 128 to 74. This methodology can decrease the required sample sizes by as much as 50%, depending on the strength of the radiomic predictor. The power of this method grows with increased accuracy of radiomic prediction, and furthermore, this method is most helpful when treatment effect sizes are small. Neuroimaging biomarkers are a powerful and increasingly common suite of tools that are, in many cases, highly predictive of disease outcomes. Here, we explore the possibility of using MRI-based radiomic biomarkers for the purpose of improving statistical power in clinical trials in the contexts of brain cancer and prodromal Alzheimer's disease. These methods can be applied to a broad range of neurologic diseases using a broad range of predictors of outcome to make clinical trials more efficient.
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Affiliation(s)
- Carolyn Lou
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA.,Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
| | - Mohamad Habes
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
| | - Nicholas A Illenberger
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
| | - Ali Ezzati
- Department of Neurology, Albert Einstein College of Medicine, New York City, New York, 10461, USA
| | - Richard B Lipton
- Department of Neurology, Albert Einstein College of Medicine, New York City, New York, 10461, USA
| | - Pamela A Shaw
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
| | - Alisa J Stephens-Shields
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
| | - Hamed Akbari
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
| | - Jimit Doshi
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA.,Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA.,Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
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26
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Hoogland J, IntHout J, Belias M, Rovers MM, Riley RD, E. Harrell Jr F, Moons KGM, Debray TPA, Reitsma JB. A tutorial on individualized treatment effect prediction from randomized trials with a binary endpoint. Stat Med 2021; 40:5961-5981. [PMID: 34402094 PMCID: PMC9291969 DOI: 10.1002/sim.9154] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Revised: 06/08/2021] [Accepted: 07/19/2021] [Indexed: 12/23/2022]
Abstract
Randomized trials typically estimate average relative treatment effects, but decisions on the benefit of a treatment are possibly better informed by more individualized predictions of the absolute treatment effect. In case of a binary outcome, these predictions of absolute individualized treatment effect require knowledge of the individual's risk without treatment and incorporation of a possibly differential treatment effect (ie, varying with patient characteristics). In this article, we lay out the causal structure of individualized treatment effect in terms of potential outcomes and describe the required assumptions that underlie a causal interpretation of its prediction. Subsequently, we describe regression models and model estimation techniques that can be used to move from average to more individualized treatment effect predictions. We focus mainly on logistic regression-based methods that are both well-known and naturally provide the required probabilistic estimates. We incorporate key components from both causal inference and prediction research to arrive at individualized treatment effect predictions. While the separate components are well known, their successful amalgamation is very much an ongoing field of research. We cut the problem down to its essentials in the setting of a randomized trial, discuss the importance of a clear definition of the estimand of interest, provide insight into the required assumptions, and give guidance with respect to modeling and estimation options. Simulated data illustrate the potential of different modeling options across scenarios that vary both average treatment effect and treatment effect heterogeneity. Two applied examples illustrate individualized treatment effect prediction in randomized trial data.
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Affiliation(s)
- Jeroen Hoogland
- Julius Center for Health Sciences and Primary Care, University Medical Center UtrechtUtrecht UniversityUtrechtthe Netherlands
| | - Joanna IntHout
- Radboud Institute for Health Sciences (RIHS)Radboud University Medical CenterNijmegenthe Netherlands
| | - Michail Belias
- Radboud Institute for Health Sciences (RIHS)Radboud University Medical CenterNijmegenthe Netherlands
| | - Maroeska M. Rovers
- Radboud Institute for Health Sciences (RIHS)Radboud University Medical CenterNijmegenthe Netherlands
| | | | - Frank E. Harrell Jr
- Department of BiostatisticsVanderbilt University School of MedicineNashvilleTennesseeUSA
| | - Karel G. M. Moons
- Julius Center for Health Sciences and Primary Care, University Medical Center UtrechtUtrecht UniversityUtrechtthe Netherlands
- Cochrane Netherlands, University Medical Center UtrechtUtrecht UniversityUtrechtthe Netherlands
| | - Thomas P. A. Debray
- Julius Center for Health Sciences and Primary Care, University Medical Center UtrechtUtrecht UniversityUtrechtthe Netherlands
- Cochrane Netherlands, University Medical Center UtrechtUtrecht UniversityUtrechtthe Netherlands
| | - Johannes B. Reitsma
- Julius Center for Health Sciences and Primary Care, University Medical Center UtrechtUtrecht UniversityUtrechtthe Netherlands
- Cochrane Netherlands, University Medical Center UtrechtUtrecht UniversityUtrechtthe Netherlands
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27
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Chalkou K, Steyerberg E, Egger M, Manca A, Pellegrini F, Salanti G. A two-stage prediction model for heterogeneous effects of treatments. Stat Med 2021; 40:4362-4375. [PMID: 34048066 PMCID: PMC9291845 DOI: 10.1002/sim.9034] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2020] [Revised: 04/19/2021] [Accepted: 04/21/2021] [Indexed: 12/23/2022]
Abstract
Treatment effects vary across different patients, and estimation of this variability is essential for clinical decision‐making. We aimed to develop a model estimating the benefit of alternative treatment options for individual patients, extending a risk modeling approach in a network meta‐analysis framework. We propose a two‐stage prediction model for heterogeneous treatment effects by combining prognosis research and network meta‐analysis methods where individual patient data are available. In the first stage, a prognostic model to predict the baseline risk of the outcome. In the second stage, we use the baseline risk score from the first stage as a single prognostic factor and effect modifier in a network meta‐regression model. We apply the approach to a network meta‐analysis of three randomized clinical trials comparing the relapses in Natalizumab, Glatiramer Acetate, and Dimethyl Fumarate, including 3590 patients diagnosed with relapsing‐remitting multiple sclerosis. We find that the baseline risk score modifies the relative and absolute treatment effects. Several patient characteristics, such as age and disability status, impact the baseline risk of relapse, which in turn moderates the benefit expected for each of the treatments. For high‐risk patients, the treatment that minimizes the risk of relapse in 2 years is Natalizumab, whereas Dimethyl Fumarate might be a better option for low‐risk patients. Our approach can be easily extended to all outcomes of interest and has the potential to inform a personalized treatment approach.
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Affiliation(s)
- Konstantina Chalkou
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
| | - Ewout Steyerberg
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Matthias Egger
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.,Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Andrea Manca
- Centre for Health Economics, University of York, York, UK
| | | | - Georgia Salanti
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
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28
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Diamond JA, Schussheim AE, Phillips RA. Another Nudge to Overcome the Treatment-Risk Paradox in Blood Pressure Management. J Am Coll Cardiol 2021; 77:1991-1993. [PMID: 33888248 DOI: 10.1016/j.jacc.2021.03.230] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 03/15/2021] [Indexed: 12/29/2022]
Affiliation(s)
| | - Adam E Schussheim
- Bridgeport Hospital, Yale-New Haven Health System, Bridgeport, Connecticut, USA
| | - Robert A Phillips
- Houston Methodist, Houston, Texas, USA; Weill Cornell College of Medicine, New York, New York, USA
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29
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Personalized Medicine and Cognitive Behavioral Therapies for Depression: Small Effects, Big Problems, and Bigger Data. Int J Cogn Ther 2020. [DOI: 10.1007/s41811-020-00094-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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