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Lonsdale H, Gray GM, Ahumada LM, Yates HM, Varughese A, Rehman MA. The Perioperative Human Digital Twin. Anesth Analg 2022; 134:885-892. [PMID: 35299215 DOI: 10.1213/ane.0000000000005916] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- Hannah Lonsdale
- From the Department of Anesthesiology and Critical Care Medicine, Johns Hopkins Hospital, Baltimore, Maryland
| | | | - Luis M Ahumada
- Center for Pediatric Data Science and Analytics Methodology
| | - Hannah M Yates
- Department of Anesthesia and Pain Medicine, Johns Hopkins All Children's Hospital, St Petersburg, Florida
| | - Anna Varughese
- Department of Anesthesia and Pain Medicine, Johns Hopkins All Children's Hospital, St Petersburg, Florida
| | - Mohamed A Rehman
- Department of Anesthesia and Pain Medicine, Johns Hopkins All Children's Hospital, St Petersburg, Florida
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Mboup B, Blanche P, Latouche A. On evaluating how well a biomarker can predict treatment response with survival data. Pharm Stat 2020; 19:410-423. [PMID: 31943737 DOI: 10.1002/pst.2002] [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: 12/10/2018] [Revised: 12/09/2019] [Accepted: 12/16/2019] [Indexed: 11/10/2022]
Abstract
One of the objectives of personalized medicine is to take treatment decisions based on a biomarker measurement. Therefore, it is often interesting to evaluate how well a biomarker can predict the response to a treatment. To do so, a popular methodology consists of using a regression model and testing for an interaction between treatment assignment and biomarker. However, the existence of an interaction is not sufficient for a biomarker to be predictive. It is only necessary. Hence, the use of the marker-by-treatment predictiveness curve has been recommended. In addition to evaluate how well a single continuous biomarker predicts treatment response, it can further help to define an optimal threshold. This curve displays the risk of a binary outcome as a function of the quantiles of the biomarker, for each treatment group. Methods that assume a binary outcome or rely on a proportional hazard model for a time-to-event outcome have been proposed to estimate this curve. In this work, we propose some extensions for censored data. They rely on a time-dependent logistic model, and we propose to estimate this model via inverse probability of censoring weighting. We present simulations results and three applications to prostate cancer, liver cirrhosis, and lung cancer data. They suggest that a large number of events need to be observed to define a threshold with sufficient accuracy for clinical usefulness. They also illustrate that when the treatment effect varies with the time horizon which defines the outcome, then the optimal threshold also depends on this time horizon.
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Affiliation(s)
- Bassirou Mboup
- INSERM, Institut Curie, PSL Research University, Paris, France
| | - Paul Blanche
- Department of Cardiology, Copenhagen University Hospital Herlev and Gentofte, Hellerup, Denmark.,Department of Public Health, Section of Biostatistics, University of Copenhagen, Copenhagen, Denmark.,Department of Cardiology, The Heart Centre, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Aurélien Latouche
- INSERM, Institut Curie, PSL Research University, Paris, France.,Department of Mathematics and Statistics, Conservatoire National des Arts et Métiers, Paris, France
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Galaznik A, Reich C, Klebanov G, Khoma Y, Allakhverdiiev E, Hather G, Shou Y. Predicting Outcomes in Patients With Diffuse Large B-Cell Lymphoma Treated With Standard of Care. Cancer Inform 2019; 18:1176935119835538. [PMID: 30906191 PMCID: PMC6421613 DOI: 10.1177/1176935119835538] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2019] [Accepted: 01/29/2019] [Indexed: 01/17/2023] Open
Abstract
In diffuse large B-cell lymphoma (DLBCL), predictive modeling may contribute to targeted drug development by enrichment of the study populations enrolled in clinical trials of DLBCL investigational drugs to include patients with lower likelihood of responding to standard of care. In clinical practice, predictive modeling has the potential to optimize therapy choices in DLBCL. The objectives of this study were to create a model for predicting health outcomes in patients with DLBCL treated with standard of care and determine informative predictors of health outcomes for patients with DLBCL. This was a retrospective observational study using data extracted from the IMS Health Database between September 2007 and April 2015. Patients were ⩾18 years of age with a DLBCL diagnosis. The index date was the date of the first DLBCL diagnosis. Patients were followed until outcome occurrence, defined as progression to a later line of therapy after ⩾60 days from the end of a previous therapy or stem cell transplantation. Patients were categorized into three cohorts depending on the post-index observation period: ⩽1 year, ⩽3 years, or ⩽5 years. Lasso logistic regression (LASSO), Naive Bayes, gradient-boosting machine (GBM), random forest (RF), and neural network models were performed for each cohort. The best-performing algorithms were predictive models based on GBM and observation periods ⩽1 and ⩽3 years after index date. Informative predictors included myocardial imaging, DLBCL stage IV, bronchiolar and renal disease, a chemotherapy regimen, and exposure to diphenhydramine and vasoprotectives on or before the first DLBCL diagnosis. These predictive models may be applied to targeted drug development and have the potential to optimize therapy choices in DLBCL. They were generated efficiently using a large number of independent variables readily available in standard insurance claims or electronic health record data systems.
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Affiliation(s)
- Aaron Galaznik
- Millennium Pharmaceuticals, Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Limited, Cambridge, MA, USA
| | - Christian Reich
- IMS Health, Danbury, CT, USA.,Odysseus Data Services, Inc., Cambridge, MA, USA
| | | | - Yuriy Khoma
- Odysseus Data Services, Inc., Cambridge, MA, USA.,Lviv Polytechnic National University, Lviv, Ukraine
| | | | - Greg Hather
- Millennium Pharmaceuticals, Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Limited, Cambridge, MA, USA
| | - Yaping Shou
- Millennium Pharmaceuticals, Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Limited, Cambridge, MA, USA
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