Comparison of machine learning clustering algorithms for detecting heterogeneity of treatment effect in acute respiratory distress syndrome: A secondary analysis of three randomised controlled trials
Heterogeneity in Acute Respiratory Distress Syndrome (ARDS), as a consequence of its non-specific definition, has led to a multitude of negative randomised controlled trials (RCTs). Investigators have sought to identify heterogeneity of treatment effect (HTE) in RCTs using clustering algorithms. We...
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| Published in: | EBioMedicine Vol. 74; p. 103697 |
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01.12.2021
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| Abstract | Heterogeneity in Acute Respiratory Distress Syndrome (ARDS), as a consequence of its non-specific definition, has led to a multitude of negative randomised controlled trials (RCTs). Investigators have sought to identify heterogeneity of treatment effect (HTE) in RCTs using clustering algorithms. We evaluated the proficiency of several commonly-used machine-learning algorithms to identify clusters where HTE may be detected.
Five unsupervised: Latent class analysis (LCA), K-means, partition around medoids, hierarchical, and spectral clustering; and four supervised algorithms: model-based recursive partitioning, Causal Forest (CF), and X-learner with Random Forest (XL-RF) and Bayesian Additive Regression Trees were individually applied to three prior ARDS RCTs. Clinical data and research protein biomarkers were used as partitioning variables, with the latter excluded for secondary analyses. For a clustering schema, HTE was evaluated based on the interaction term of treatment group and cluster with day-90 mortality as the dependent variable.
No single algorithm identified clusters with significant HTE in all three trials. LCA, XL-RF, and CF identified HTE most frequently (2/3 RCTs). Important partitioning variables in the unsupervised approaches were consistent across algorithms and RCTs. In supervised models, important partitioning variables varied between algorithms and across RCTs. In algorithms where clusters demonstrated HTE in the same trial, patients frequently interchanged clusters from treatment-benefit to treatment-harm clusters across algorithms. LCA aside, results from all other algorithms were subject to significant alteration in cluster composition and HTE with random seed change. Removing research biomarkers as partitioning variables greatly reduced the chances of detecting HTE across all algorithms.
Machine-learning algorithms were inconsistent in their abilities to identify clusters with significant HTE. Protein biomarkers were essential in identifying clusters with HTE. Investigations using machine-learning approaches to identify clusters to seek HTE require cautious interpretation.
NIGMS R35 GM142992 (PS), NHLBI R35 HL140026 (CSC); NIGMS R01 GM123193, Department of Defense W81XWH-21-1-0009, NIA R21 AG068720, NIDA R01 DA051464 (MMC) |
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| AbstractList | Background: Heterogeneity in Acute Respiratory Distress Syndrome (ARDS), as a consequence of its non-specific definition, has led to a multitude of negative randomised controlled trials (RCTs). Investigators have sought to identify heterogeneity of treatment effect (HTE) in RCTs using clustering algorithms. We evaluated the proficiency of several commonly-used machine-learning algorithms to identify clusters where HTE may be detected. Methods: Five unsupervised: Latent class analysis (LCA), K-means, partition around medoids, hierarchical, and spectral clustering; and four supervised algorithms: model-based recursive partitioning, Causal Forest (CF), and X-learner with Random Forest (XL-RF) and Bayesian Additive Regression Trees were individually applied to three prior ARDS RCTs. Clinical data and research protein biomarkers were used as partitioning variables, with the latter excluded for secondary analyses. For a clustering schema, HTE was evaluated based on the interaction term of treatment group and cluster with day-90 mortality as the dependent variable. Findings: No single algorithm identified clusters with significant HTE in all three trials. LCA, XL-RF, and CF identified HTE most frequently (2/3 RCTs). Important partitioning variables in the unsupervised approaches were consistent across algorithms and RCTs. In supervised models, important partitioning variables varied between algorithms and across RCTs. In algorithms where clusters demonstrated HTE in the same trial, patients frequently interchanged clusters from treatment-benefit to treatment-harm clusters across algorithms. LCA aside, results from all other algorithms were subject to significant alteration in cluster composition and HTE with random seed change. Removing research biomarkers as partitioning variables greatly reduced the chances of detecting HTE across all algorithms. Interpretation: Machine-learning algorithms were inconsistent in their abilities to identify clusters with significant HTE. Protein biomarkers were essential in identifying clusters with HTE. Investigations using machine-learning approaches to identify clusters to seek HTE require cautious interpretation. Funding: NIGMS R35 GM142992 (PS), NHLBI R35 HL140026 (CSC); NIGMS R01 GM123193, Department of Defense W81XWH-21-1-0009, NIA R21 AG068720, NIDA R01 DA051464 (MMC) Heterogeneity in Acute Respiratory Distress Syndrome (ARDS), as a consequence of its non-specific definition, has led to a multitude of negative randomised controlled trials (RCTs). Investigators have sought to identify heterogeneity of treatment effect (HTE) in RCTs using clustering algorithms. We evaluated the proficiency of several commonly-used machine-learning algorithms to identify clusters where HTE may be detected. Five unsupervised: Latent class analysis (LCA), K-means, partition around medoids, hierarchical, and spectral clustering; and four supervised algorithms: model-based recursive partitioning, Causal Forest (CF), and X-learner with Random Forest (XL-RF) and Bayesian Additive Regression Trees were individually applied to three prior ARDS RCTs. Clinical data and research protein biomarkers were used as partitioning variables, with the latter excluded for secondary analyses. For a clustering schema, HTE was evaluated based on the interaction term of treatment group and cluster with day-90 mortality as the dependent variable. No single algorithm identified clusters with significant HTE in all three trials. LCA, XL-RF, and CF identified HTE most frequently (2/3 RCTs). Important partitioning variables in the unsupervised approaches were consistent across algorithms and RCTs. In supervised models, important partitioning variables varied between algorithms and across RCTs. In algorithms where clusters demonstrated HTE in the same trial, patients frequently interchanged clusters from treatment-benefit to treatment-harm clusters across algorithms. LCA aside, results from all other algorithms were subject to significant alteration in cluster composition and HTE with random seed change. Removing research biomarkers as partitioning variables greatly reduced the chances of detecting HTE across all algorithms. Machine-learning algorithms were inconsistent in their abilities to identify clusters with significant HTE. Protein biomarkers were essential in identifying clusters with HTE. Investigations using machine-learning approaches to identify clusters to seek HTE require cautious interpretation. NIGMS R35 GM142992 (PS), NHLBI R35 HL140026 (CSC); NIGMS R01 GM123193, Department of Defense W81XWH-21-1-0009, NIA R21 AG068720, NIDA R01 DA051464 (MMC). AbstractBackgroundHeterogeneity in Acute Respiratory Distress Syndrome (ARDS), as a consequence of its non-specific definition, has led to a multitude of negative randomised controlled trials (RCTs). Investigators have sought to identify heterogeneity of treatment effect (HTE) in RCTs using clustering algorithms. We evaluated the proficiency of several commonly-used machine-learning algorithms to identify clusters where HTE may be detected. MethodsFive unsupervised: Latent class analysis (LCA), K-means, partition around medoids, hierarchical, and spectral clustering; and four supervised algorithms: model-based recursive partitioning, Causal Forest (CF), and X-learner with Random Forest (XL-RF) and Bayesian Additive Regression Trees were individually applied to three prior ARDS RCTs. Clinical data and research protein biomarkers were used as partitioning variables, with the latter excluded for secondary analyses. For a clustering schema, HTE was evaluated based on the interaction term of treatment group and cluster with day-90 mortality as the dependent variable. FindingsNo single algorithm identified clusters with significant HTE in all three trials. LCA, XL-RF, and CF identified HTE most frequently (2/3 RCTs). Important partitioning variables in the unsupervised approaches were consistent across algorithms and RCTs. In supervised models, important partitioning variables varied between algorithms and across RCTs. In algorithms where clusters demonstrated HTE in the same trial, patients frequently interchanged clusters from treatment-benefit to treatment-harm clusters across algorithms. LCA aside, results from all other algorithms were subject to significant alteration in cluster composition and HTE with random seed change. Removing research biomarkers as partitioning variables greatly reduced the chances of detecting HTE across all algorithms. InterpretationMachine-learning algorithms were inconsistent in their abilities to identify clusters with significant HTE. Protein biomarkers were essential in identifying clusters with HTE. Investigations using machine-learning approaches to identify clusters to seek HTE require cautious interpretation. FundingNIGMS R35 GM142992 (PS), NHLBI R35 HL140026 (CSC); NIGMS R01 GM123193, Department of Defense W81XWH-21-1-0009, NIA R21 AG068720, NIDA R01 DA051464 (MMC) Heterogeneity in Acute Respiratory Distress Syndrome (ARDS), as a consequence of its non-specific definition, has led to a multitude of negative randomised controlled trials (RCTs). Investigators have sought to identify heterogeneity of treatment effect (HTE) in RCTs using clustering algorithms. We evaluated the proficiency of several commonly-used machine-learning algorithms to identify clusters where HTE may be detected.BACKGROUNDHeterogeneity in Acute Respiratory Distress Syndrome (ARDS), as a consequence of its non-specific definition, has led to a multitude of negative randomised controlled trials (RCTs). Investigators have sought to identify heterogeneity of treatment effect (HTE) in RCTs using clustering algorithms. We evaluated the proficiency of several commonly-used machine-learning algorithms to identify clusters where HTE may be detected.Five unsupervised: Latent class analysis (LCA), K-means, partition around medoids, hierarchical, and spectral clustering; and four supervised algorithms: model-based recursive partitioning, Causal Forest (CF), and X-learner with Random Forest (XL-RF) and Bayesian Additive Regression Trees were individually applied to three prior ARDS RCTs. Clinical data and research protein biomarkers were used as partitioning variables, with the latter excluded for secondary analyses. For a clustering schema, HTE was evaluated based on the interaction term of treatment group and cluster with day-90 mortality as the dependent variable.METHODSFive unsupervised: Latent class analysis (LCA), K-means, partition around medoids, hierarchical, and spectral clustering; and four supervised algorithms: model-based recursive partitioning, Causal Forest (CF), and X-learner with Random Forest (XL-RF) and Bayesian Additive Regression Trees were individually applied to three prior ARDS RCTs. Clinical data and research protein biomarkers were used as partitioning variables, with the latter excluded for secondary analyses. For a clustering schema, HTE was evaluated based on the interaction term of treatment group and cluster with day-90 mortality as the dependent variable.No single algorithm identified clusters with significant HTE in all three trials. LCA, XL-RF, and CF identified HTE most frequently (2/3 RCTs). Important partitioning variables in the unsupervised approaches were consistent across algorithms and RCTs. In supervised models, important partitioning variables varied between algorithms and across RCTs. In algorithms where clusters demonstrated HTE in the same trial, patients frequently interchanged clusters from treatment-benefit to treatment-harm clusters across algorithms. LCA aside, results from all other algorithms were subject to significant alteration in cluster composition and HTE with random seed change. Removing research biomarkers as partitioning variables greatly reduced the chances of detecting HTE across all algorithms.FINDINGSNo single algorithm identified clusters with significant HTE in all three trials. LCA, XL-RF, and CF identified HTE most frequently (2/3 RCTs). Important partitioning variables in the unsupervised approaches were consistent across algorithms and RCTs. In supervised models, important partitioning variables varied between algorithms and across RCTs. In algorithms where clusters demonstrated HTE in the same trial, patients frequently interchanged clusters from treatment-benefit to treatment-harm clusters across algorithms. LCA aside, results from all other algorithms were subject to significant alteration in cluster composition and HTE with random seed change. Removing research biomarkers as partitioning variables greatly reduced the chances of detecting HTE across all algorithms.Machine-learning algorithms were inconsistent in their abilities to identify clusters with significant HTE. Protein biomarkers were essential in identifying clusters with HTE. Investigations using machine-learning approaches to identify clusters to seek HTE require cautious interpretation.INTERPRETATIONMachine-learning algorithms were inconsistent in their abilities to identify clusters with significant HTE. Protein biomarkers were essential in identifying clusters with HTE. Investigations using machine-learning approaches to identify clusters to seek HTE require cautious interpretation.NIGMS R35 GM142992 (PS), NHLBI R35 HL140026 (CSC); NIGMS R01 GM123193, Department of Defense W81XWH-21-1-0009, NIA R21 AG068720, NIDA R01 DA051464 (MMC).FUNDINGNIGMS R35 GM142992 (PS), NHLBI R35 HL140026 (CSC); NIGMS R01 GM123193, Department of Defense W81XWH-21-1-0009, NIA R21 AG068720, NIDA R01 DA051464 (MMC). |
| ArticleNumber | 103697 |
| Author | McAuley, Daniel F Delucchi, Kevin L Spicer, Alexandra Calfee, Carolyn S Churpek, Matthew M Sinha, Pratik |
| Author_xml | – sequence: 1 givenname: Pratik orcidid: 0000-0003-3751-9079 surname: Sinha fullname: Sinha, Pratik email: p.sinha@wustl.edu organization: Division of Clinical and Translational Research, Division of Critical Care, Department of Anesthesia, Washington University School of Medicine, Saint Louis, MO – sequence: 2 givenname: Alexandra surname: Spicer fullname: Spicer, Alexandra organization: Department of Medicine, University of Wisconsin- Madison, Madison, Wisconsin – sequence: 3 givenname: Kevin L surname: Delucchi fullname: Delucchi, Kevin L organization: Department of Psychiatry and Behavioral Sciences; University of California, San Francisco; San Francisco, CA – sequence: 4 givenname: Daniel F surname: McAuley fullname: McAuley, Daniel F organization: Wellcome-Wolfson Institute for Experimental Medicine, Queen's University Belfast – sequence: 5 givenname: Carolyn S surname: Calfee fullname: Calfee, Carolyn S organization: Department of Medicine, Division of Pulmonary, Critical Care, Allergy and Sleep Medicine; University of California, San Francisco; San Francisco, CA – sequence: 6 givenname: Matthew M surname: Churpek fullname: Churpek, Matthew M organization: Department of Medicine, University of Wisconsin- Madison, Madison, Wisconsin |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34861492$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1016/S2213-2600(14)70097-9 10.1007/s00134-018-5378-3 10.1080/01621459.2017.1319839 10.1001/jamainternmed.2016.9125 10.7326/M18-3667 10.1136/bmj.h5651 10.3389/fnins.2016.00267 10.1056/NEJMoa032193 10.1056/NEJMoa062200 10.1164/rccm.201603-0645OC 10.1056/NEJMoa1401520 10.1136/bmj.k4245 10.1016/j.molmed.2014.01.007 10.1016/S2213-2600(18)30177-2 10.1186/1745-6215-10-48 10.1001/jama.2019.5791 10.1038/s41572-019-0069-0 10.1093/bib/bbn058 10.1016/j.chest.2018.04.037 10.1001/jama.298.10.1209 10.1093/bib/bbv018 10.1097/CCM.0000000000004710 10.1073/pnas.1510489113 |
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| Keywords | LCA Clustering machine learning RCTs ARDS Heterogeneity of treatment effect |
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| References | Greene, Giffin, Greene, Moore (bib0021) 2016; 17 Wiedemann, Wheeler, Bernard, Thompson (bib0017) 2006; 354 Sinha, Delucchi, Thompson, McAuley, Matthay, Calfee (bib0012) 2018; 44 Athey, Imbens (bib0014) 2016; 113 Kent, Paulus, van Klaveren, D'Agostino, Goodman, Hayward (bib0024) 2020; 172 Calfee, Delucchi, Sinha, Matthay, Hackett, Shankar-Hari (bib0010) 2018; 6 Marshall (bib0003) 2014; 20 Calfee, Delucchi, Parsons, Thompson, Ware, Matthay (bib0009) 2014; 2 Andreopoulos, An, Wang, Schroeder (bib0019) 2009; 10 Varadhan, Seeger (bib0005) 2013 Wager, Athey (bib0015) 2018; 113 Burke, Sussman, Kent, Hayward (bib0006) 2015; 351 Kent, Hayward (bib0002) 2007; 298 Lipton ZC. The Doctor Just Won't Accept That!2017 November 01, 2017:[arXiv:1711.08037 p.]. Available from Brower, Lanken, MacIntyre, Matthay, Morris, Ancukiewicz (bib0016) 2004; 351 Sanchez-Pinto, Luo, Churpek (bib0022) 2018; 154 Famous, Delucchi, Ware, Kangelaris, Liu, Thompson (bib0011) 2017; 195 Feng, Wallace, Grissom, Iyyer, Rodriguez, Boyd-Graber (bib0025) 2018 McCoy, Min, Linzen (bib0026) 2020 Kent, Kitsios (bib0001) 2009; 10 . Wallach, Sullivan, Trepanowski, Sainani, Steyerberg, Ioannidis (bib0007) 2017; 177 Tonekaboni, Joshi, McCradden, Goldenberg (bib0029) 2019 Truwit, Bernard, Steingrub, Matthay (bib0018) 2014; 370 Kent, Steyerberg, van Klaveren (bib0008) 2018; 363 Lessov-Schlaggar, Rubin, Schlaggar (bib0023) 2016; 10 Lahav O, Mastronarde N, van der Schaar M. What is Interpretable? Using Machine Learning to Design Interpretable Decision-Support Systems2018 November 01, 2018:[arXiv:1811.10799 p.]. Available from Matthay, Zemans, Zimmerman, Arabi, Beitler, Mercat (bib0004) 2019; 5 Seymour, Kennedy, Wang, Chang, Elliott, Xu (bib0013) 2019; 321 Sinha, Calfee, Delucchi (bib0020) 2021; 49 Kent (10.1016/j.ebiom.2021.103697_bib0001) 2009; 10 Famous (10.1016/j.ebiom.2021.103697_bib0011) 2017; 195 Kent (10.1016/j.ebiom.2021.103697_bib0008) 2018; 363 Calfee (10.1016/j.ebiom.2021.103697_bib0010) 2018; 6 Truwit (10.1016/j.ebiom.2021.103697_bib0018) 2014; 370 10.1016/j.ebiom.2021.103697_bib0027 10.1016/j.ebiom.2021.103697_bib0028 Kent (10.1016/j.ebiom.2021.103697_bib0024) 2020; 172 Matthay (10.1016/j.ebiom.2021.103697_bib0004) 2019; 5 Sanchez-Pinto (10.1016/j.ebiom.2021.103697_bib0022) 2018; 154 McCoy (10.1016/j.ebiom.2021.103697_bib0026) 2020 Calfee (10.1016/j.ebiom.2021.103697_bib0009) 2014; 2 Athey (10.1016/j.ebiom.2021.103697_bib0014) 2016; 113 Seymour (10.1016/j.ebiom.2021.103697_bib0013) 2019; 321 Brower (10.1016/j.ebiom.2021.103697_bib0016) 2004; 351 Andreopoulos (10.1016/j.ebiom.2021.103697_bib0019) 2009; 10 Varadhan (10.1016/j.ebiom.2021.103697_bib0005) 2013 Sinha (10.1016/j.ebiom.2021.103697_bib0012) 2018; 44 Burke (10.1016/j.ebiom.2021.103697_bib0006) 2015; 351 Feng (10.1016/j.ebiom.2021.103697_bib0025) 2018 Wiedemann (10.1016/j.ebiom.2021.103697_bib0017) 2006; 354 Greene (10.1016/j.ebiom.2021.103697_bib0021) 2016; 17 Lessov-Schlaggar (10.1016/j.ebiom.2021.103697_bib0023) 2016; 10 Sinha (10.1016/j.ebiom.2021.103697_bib0020) 2021; 49 Kent (10.1016/j.ebiom.2021.103697_bib0002) 2007; 298 Wallach (10.1016/j.ebiom.2021.103697_bib0007) 2017; 177 Marshall (10.1016/j.ebiom.2021.103697_bib0003) 2014; 20 Tonekaboni (10.1016/j.ebiom.2021.103697_bib0029) 2019 Wager (10.1016/j.ebiom.2021.103697_bib0015) 2018; 113 |
| References_xml | – volume: 113 start-page: 7353 year: 2016 end-page: 7360 ident: bib0014 article-title: Recursive partitioning for heterogeneous causal effects publication-title: Proc Natl Acad Sci U S A – reference: Lipton ZC. The Doctor Just Won't Accept That!2017 November 01, 2017:[arXiv:1711.08037 p.]. Available from: – volume: 195 start-page: 331 year: 2017 end-page: 338 ident: bib0011 article-title: Acute respiratory distress syndrome subphenotypes respond differently to randomized fluid management strategy publication-title: Am J Respir Crit Care Med – volume: 20 start-page: 195 year: 2014 end-page: 203 ident: bib0003 article-title: Why have clinical trials in sepsis failed? publication-title: Trends Mol Med – volume: 351 start-page: h5651 year: 2015 ident: bib0006 article-title: Three simple rules to ensure reasonably credible subgroup analyses publication-title: BMJ – volume: 354 start-page: 2564 year: 2006 end-page: 2575 ident: bib0017 article-title: Comparison of two fluid-management strategies in acute lung injury publication-title: N Engl J Med – volume: 351 start-page: 327 year: 2004 end-page: 336 ident: bib0016 article-title: Higher versus lower positive end-expiratory pressures in patients with the acute respiratory distress syndrome publication-title: N Engl J Med – volume: 172 start-page: 35 year: 2020 end-page: 45 ident: bib0024 article-title: The predictive approaches to treatment effect heterogeneity (PATH) statement publication-title: Ann Intern Med – year: 2020 ident: bib0026 article-title: BERTs of a feather do not generalize together: Large variability in generalization across models with similar test set performance publication-title: ArXiv – year: 2018 ident: bib0025 article-title: Pathologies of neural models make interpretation difficult publication-title: EMNLP – volume: 6 start-page: 691 year: 2018 end-page: 698 ident: bib0010 article-title: Acute respiratory distress syndrome subphenotypes and differential response to simvastatin: secondary analysis of a randomised controlled trial publication-title: Lancet Respir Med – volume: 44 start-page: 1859 year: 2018 end-page: 1869 ident: bib0012 article-title: Latent class analysis of ARDS subphenotypes: a secondary analysis of the statins for acutely injured lungs from sepsis (SAILS) study publication-title: Intensive Care Med – volume: 10 start-page: 297 year: 2009 end-page: 314 ident: bib0019 article-title: A roadmap of clustering algorithms: finding a match for a biomedical application publication-title: Brief Bioinform – volume: 10 start-page: 48 year: 2009 ident: bib0001 article-title: Against pragmatism: on efficacy, effectiveness and the real world publication-title: Trials – volume: 298 start-page: 1209 year: 2007 end-page: 1212 ident: bib0002 article-title: Limitations of applying summary results of clinical trials to individual patients: the need for risk stratification publication-title: JAMA – volume: 154 start-page: 1239 year: 2018 end-page: 1248 ident: bib0022 article-title: Big data and data science in critical care publication-title: Chest – volume: 10 start-page: 267 year: 2016 ident: bib0023 article-title: The fallacy of univariate solutions to complex systems problems publication-title: Front Neurosci – volume: 5 start-page: 18 year: 2019 ident: bib0004 article-title: Acute respiratory distress syndrome publication-title: Nat Rev Dis Primers – volume: 321 start-page: 2003 year: 2019 end-page: 2017 ident: bib0013 article-title: Derivation, validation, and potential treatment implications of novel clinical phenotypes for sepsis publication-title: JAMA – volume: 363 start-page: k4245 year: 2018 ident: bib0008 article-title: Personalized evidence based medicine: predictive approaches to heterogeneous treatment effects publication-title: BMJ – volume: 2 start-page: 611 year: 2014 end-page: 620 ident: bib0009 article-title: Subphenotypes in acute respiratory distress syndrome: latent class analysis of data from two randomised controlled trials publication-title: Lancet Respir Med – volume: 17 start-page: 43 year: 2016 end-page: 50 ident: bib0021 article-title: Adapting bioinformatics curricula for big data publication-title: Brief Bioinform – volume: 177 start-page: 554 year: 2017 end-page: 560 ident: bib0007 article-title: Evaluation of evidence of statistical support and corroboration of subgroup claims in randomized clinical trials publication-title: JAMA Intern Med – reference: . – reference: Lahav O, Mastronarde N, van der Schaar M. What is Interpretable? Using Machine Learning to Design Interpretable Decision-Support Systems2018 November 01, 2018:[arXiv:1811.10799 p.]. Available from: – year: 2013 ident: bib0005 article-title: Estimation and reporting of heterogeneity of treatment effects publication-title: Developing a Protocol for Observational Comparative Effectiveness Research: A User's Guide – start-page: 359 year: 2019 end-page: 380 ident: bib0029 article-title: What clinicians want: contextualizing explainable machine learning for clinical end use publication-title: Proceedings of the 4th Machine Learning for Healthcare Conference; Proceedings of Machine Learning Research: PMLR – volume: 113 start-page: 1228 year: 2018 end-page: 1242 ident: bib0015 article-title: Estimation and inference of heterogeneous treatment effects using random forests publication-title: J Am Stat Assoc – volume: 49 start-page: e63 year: 2021 end-page: e79 ident: bib0020 article-title: Practitioner's guide to latent class analysis: methodological considerations and common pitfalls publication-title: Crit Care Med – volume: 370 start-page: 2191 year: 2014 end-page: 2200 ident: bib0018 article-title: Rosuvastatin for sepsis-associated acute respiratory distress syndrome publication-title: N Engl J Med – volume: 2 start-page: 611 issue: 8 year: 2014 ident: 10.1016/j.ebiom.2021.103697_bib0009 article-title: Subphenotypes in acute respiratory distress syndrome: latent class analysis of data from two randomised controlled trials publication-title: Lancet Respir Med doi: 10.1016/S2213-2600(14)70097-9 – volume: 44 start-page: 1859 issue: 11 year: 2018 ident: 10.1016/j.ebiom.2021.103697_bib0012 article-title: Latent class analysis of ARDS subphenotypes: a secondary analysis of the statins for acutely injured lungs from sepsis (SAILS) study publication-title: Intensive Care Med doi: 10.1007/s00134-018-5378-3 – year: 2018 ident: 10.1016/j.ebiom.2021.103697_bib0025 article-title: Pathologies of neural models make interpretation difficult publication-title: EMNLP – volume: 113 start-page: 1228 issue: 523 year: 2018 ident: 10.1016/j.ebiom.2021.103697_bib0015 article-title: Estimation and inference of heterogeneous treatment effects using random forests publication-title: J Am Stat Assoc doi: 10.1080/01621459.2017.1319839 – volume: 177 start-page: 554 issue: 4 year: 2017 ident: 10.1016/j.ebiom.2021.103697_bib0007 article-title: Evaluation of evidence of statistical support and corroboration of subgroup claims in randomized clinical trials publication-title: JAMA Intern Med doi: 10.1001/jamainternmed.2016.9125 – year: 2013 ident: 10.1016/j.ebiom.2021.103697_bib0005 article-title: Estimation and reporting of heterogeneity of treatment effects – volume: 172 start-page: 35 issue: 1 year: 2020 ident: 10.1016/j.ebiom.2021.103697_bib0024 article-title: The predictive approaches to treatment effect heterogeneity (PATH) statement publication-title: Ann Intern Med doi: 10.7326/M18-3667 – volume: 351 start-page: h5651 year: 2015 ident: 10.1016/j.ebiom.2021.103697_bib0006 article-title: Three simple rules to ensure reasonably credible subgroup analyses publication-title: BMJ doi: 10.1136/bmj.h5651 – volume: 10 start-page: 267 year: 2016 ident: 10.1016/j.ebiom.2021.103697_bib0023 article-title: The fallacy of univariate solutions to complex systems problems publication-title: Front Neurosci doi: 10.3389/fnins.2016.00267 – volume: 351 start-page: 327 issue: 4 year: 2004 ident: 10.1016/j.ebiom.2021.103697_bib0016 article-title: Higher versus lower positive end-expiratory pressures in patients with the acute respiratory distress syndrome publication-title: N Engl J Med doi: 10.1056/NEJMoa032193 – ident: 10.1016/j.ebiom.2021.103697_bib0027 – volume: 354 start-page: 2564 issue: 24 year: 2006 ident: 10.1016/j.ebiom.2021.103697_bib0017 article-title: Comparison of two fluid-management strategies in acute lung injury publication-title: N Engl J Med doi: 10.1056/NEJMoa062200 – start-page: 359 year: 2019 ident: 10.1016/j.ebiom.2021.103697_bib0029 article-title: What clinicians want: contextualizing explainable machine learning for clinical end use – year: 2020 ident: 10.1016/j.ebiom.2021.103697_bib0026 article-title: BERTs of a feather do not generalize together: Large variability in generalization across models with similar test set performance publication-title: ArXiv – volume: 195 start-page: 331 issue: 3 year: 2017 ident: 10.1016/j.ebiom.2021.103697_bib0011 article-title: Acute respiratory distress syndrome subphenotypes respond differently to randomized fluid management strategy publication-title: Am J Respir Crit Care Med doi: 10.1164/rccm.201603-0645OC – volume: 370 start-page: 2191 issue: 23 year: 2014 ident: 10.1016/j.ebiom.2021.103697_bib0018 article-title: Rosuvastatin for sepsis-associated acute respiratory distress syndrome publication-title: N Engl J Med doi: 10.1056/NEJMoa1401520 – volume: 363 start-page: k4245 year: 2018 ident: 10.1016/j.ebiom.2021.103697_bib0008 article-title: Personalized evidence based medicine: predictive approaches to heterogeneous treatment effects publication-title: BMJ doi: 10.1136/bmj.k4245 – volume: 20 start-page: 195 issue: 4 year: 2014 ident: 10.1016/j.ebiom.2021.103697_bib0003 article-title: Why have clinical trials in sepsis failed? publication-title: Trends Mol Med doi: 10.1016/j.molmed.2014.01.007 – volume: 6 start-page: 691 issue: 9 year: 2018 ident: 10.1016/j.ebiom.2021.103697_bib0010 article-title: Acute respiratory distress syndrome subphenotypes and differential response to simvastatin: secondary analysis of a randomised controlled trial publication-title: Lancet Respir Med doi: 10.1016/S2213-2600(18)30177-2 – volume: 10 start-page: 48 year: 2009 ident: 10.1016/j.ebiom.2021.103697_bib0001 article-title: Against pragmatism: on efficacy, effectiveness and the real world publication-title: Trials doi: 10.1186/1745-6215-10-48 – volume: 321 start-page: 2003 issue: 20 year: 2019 ident: 10.1016/j.ebiom.2021.103697_bib0013 article-title: Derivation, validation, and potential treatment implications of novel clinical phenotypes for sepsis publication-title: JAMA doi: 10.1001/jama.2019.5791 – volume: 5 start-page: 18 issue: 1 year: 2019 ident: 10.1016/j.ebiom.2021.103697_bib0004 article-title: Acute respiratory distress syndrome publication-title: Nat Rev Dis Primers doi: 10.1038/s41572-019-0069-0 – volume: 10 start-page: 297 issue: 3 year: 2009 ident: 10.1016/j.ebiom.2021.103697_bib0019 article-title: A roadmap of clustering algorithms: finding a match for a biomedical application publication-title: Brief Bioinform doi: 10.1093/bib/bbn058 – volume: 154 start-page: 1239 issue: 5 year: 2018 ident: 10.1016/j.ebiom.2021.103697_bib0022 article-title: Big data and data science in critical care publication-title: Chest doi: 10.1016/j.chest.2018.04.037 – ident: 10.1016/j.ebiom.2021.103697_bib0028 – volume: 298 start-page: 1209 issue: 10 year: 2007 ident: 10.1016/j.ebiom.2021.103697_bib0002 article-title: Limitations of applying summary results of clinical trials to individual patients: the need for risk stratification publication-title: JAMA doi: 10.1001/jama.298.10.1209 – volume: 17 start-page: 43 issue: 1 year: 2016 ident: 10.1016/j.ebiom.2021.103697_bib0021 article-title: Adapting bioinformatics curricula for big data publication-title: Brief Bioinform doi: 10.1093/bib/bbv018 – volume: 49 start-page: e63 issue: 1 year: 2021 ident: 10.1016/j.ebiom.2021.103697_bib0020 article-title: Practitioner's guide to latent class analysis: methodological considerations and common pitfalls publication-title: Crit Care Med doi: 10.1097/CCM.0000000000004710 – volume: 113 start-page: 7353 issue: 27 year: 2016 ident: 10.1016/j.ebiom.2021.103697_bib0014 article-title: Recursive partitioning for heterogeneous causal effects publication-title: Proc Natl Acad Sci U S A doi: 10.1073/pnas.1510489113 |
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| SubjectTerms | Advanced Basic Science ARDS Clustering Heterogeneity of treatment effect Internal Medicine LCA machine learning RCTs Research paper |
| Title | Comparison of machine learning clustering algorithms for detecting heterogeneity of treatment effect in acute respiratory distress syndrome: A secondary analysis of three randomised controlled trials |
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