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Moodie EEM, Coulombe J, Danieli C, Renoux C, Shortreed SM. Privacy-preserving estimation of an optimal individualized treatment rule: a case study in maximizing time to severe depression-related outcomes. LIFETIME DATA ANALYSIS 2022; 28:512-542. [PMID: 35499604 PMCID: PMC10805063 DOI: 10.1007/s10985-022-09554-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 03/08/2022] [Indexed: 06/14/2023]
Abstract
Estimating individualized treatment rules-particularly in the context of right-censored outcomes-is challenging because the treatment effect heterogeneity of interest is often small, thus difficult to detect. While this motivates the use of very large datasets such as those from multiple health systems or centres, data privacy may be of concern with participating data centres reluctant to share individual-level data. In this case study on the treatment of depression, we demonstrate an application of distributed regression for privacy protection used in combination with dynamic weighted survival modelling (DWSurv) to estimate an optimal individualized treatment rule whilst obscuring individual-level data. In simulations, we demonstrate the flexibility of this approach to address local treatment practices that may affect confounding, and show that DWSurv retains its double robustness even when performed through a (weighted) distributed regression approach. The work is motivated by, and illustrated with, an analysis of treatment for unipolar depression using the United Kingdom's Clinical Practice Research Datalink.
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Affiliation(s)
- Erica E M Moodie
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, QC, Canada.
| | - Janie Coulombe
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, QC, Canada
| | - Coraline Danieli
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, QC, Canada
| | - Christel Renoux
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, QC, Canada
- Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, QC, Canada
- Department of Neurology and Neurosurgery, McGill University, Montréal, QC, Canada
| | - Susan M Shortreed
- Biostatistics Unit, Kaiser Permanente Washington Health Research Institute, Seattle, USA
- Biostatistics Department, University of Washington, Seattle, USA
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Danieli C, Moodie EEM. Preserving data privacy when using multi-site data to estimate individualized treatment rules. Stat Med 2022; 41:1627-1643. [PMID: 35088914 DOI: 10.1002/sim.9318] [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: 06/14/2021] [Revised: 12/20/2021] [Accepted: 12/21/2021] [Indexed: 11/05/2022]
Abstract
Precision medicine is a rapidly expanding area of health research wherein patient level information is used to inform treatment decisions. A statistical framework helps to formalize the individualization of treatment decisions that characterize personalized management plans. Numerous methods have been proposed to estimate individualized treatment rules that optimize expected patient outcomes, many of which have desirable properties such as robustness to model misspecification. However, while individual data are essential in this context, there may be concerns about data confidentiality, particularly in multi-center studies where data are shared externally. To address this issue, we compared two approaches to privacy preservation: (i) data pooling, which is a covariate microaggregation technique and (ii) distributed regression. These approaches were combined with the doubly robust yet user-friendly method of dynamic weighted ordinary least squares to estimate individualized treatment rules. In simulations, we extensively evaluated the performance of the methods in estimating the parameters of the decision rule under different assumptions. The results demonstrate that double robustness is not maintained in data pooling setting and that this can result in bias, whereas the distributed regression provides good performance. We illustrate the methods via an analysis of optimal Warfarin dosing using data from the International Warfarin Consortium.
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Affiliation(s)
- Coraline Danieli
- Department of Epidemiology, Biostatistics and Occupational Health, Research Institute of the McGill University Health Centre, McGill University, Montreal, QC, Canada
| | - Erica E M Moodie
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada
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Guan Q, Reich BJ, Laber EB. A spatiotemporal recommendation engine for malaria control. Biostatistics 2021; 23:1023-1038. [PMID: 33838029 DOI: 10.1093/biostatistics/kxab010] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2020] [Revised: 03/09/2021] [Accepted: 03/14/2021] [Indexed: 11/13/2022] Open
Abstract
Malaria is an infectious disease affecting a large population across the world, and interventions need to be efficiently applied to reduce the burden of malaria. We develop a framework to help policy-makers decide how to allocate limited resources in realtime for malaria control. We formalize a policy for the resource allocation as a sequence of decisions, one per intervention decision, that map up-to-date disease related information to a resource allocation. An optimal policy must control the spread of the disease while being interpretable and viewed as equitable to stakeholders. We construct an interpretable class of resource allocation policies that can accommodate allocation of resources residing in a continuous domain and combine a hierarchical Bayesian spatiotemporal model for disease transmission with a policy-search algorithm to estimate an optimal policy for resource allocation within the pre-specified class. The estimated optimal policy under the proposed framework improves the cumulative long-term outcome compared with naive approaches in both simulation experiments and application to malaria interventions in the Democratic Republic of the Congo.
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Affiliation(s)
- Qian Guan
- Department of Statistics, North Carolina State University, 2311 Stinson Dr. Raleigh, NC 27695-8203, USA
| | - Brian J Reich
- Department of Statistics, North Carolina State University, 2311 Stinson Dr. Raleigh, NC 27695-8203, USA
| | - Eric B Laber
- Department of Statistics, North Carolina State University, 2311 Stinson Dr. Raleigh, NC 27695-8203, USA
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Affiliation(s)
- Juliana Schulz
- Department of Decision Sciences, HEC Montréal, Montreal, QC, Canada
| | - Erica E. M. Moodie
- Department of Epidemiology, Biostatistics, and Occupational Health, University of McGill, Montreal, QC, Canada
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Moodie EEM, Krakow EF. Precision medicine: Statistical methods for estimating adaptive treatment strategies. Bone Marrow Transplant 2020; 55:1890-1896. [PMID: 32286507 DOI: 10.1038/s41409-020-0871-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Revised: 03/10/2020] [Accepted: 03/11/2020] [Indexed: 11/09/2022]
Abstract
SERIES EDITORS' NOTE The beauty of science is that all the important things are unpredictable. Freeman Dyson In the typescript which follows, Moodie and Krakow tackle the topical issue of precision medicine and statistical methods for estimating adaptive treatment strategies. This may be the most difficult typescript in our series so far for non-statisticians to understand. It even has equations! But please bear with the authors and give it a chance. One needs not to understand the equations to get the thrust of the strategy.Precision medicine as we discuss elsewhere, is misnamed. In statistics and mathematics precision refers to getting the same answer again and again. It does not mean getting the correct answer, the term for which is accuracy, not precision. However, precision is the current buzz word so there's no point trying to get this straight. When we think about precision we need to consider two elements, reproducibility and replicability. Reproducibility means you give me your data and computer code and I come to the same conclusion you did. Replicability is another matter. I try to replicate your experiment and hopefully reach the same conclusion. In medicine, replicability is obviously more important than reproducibility but things which cannot be reproduced are unlikely to be replicated.As the authors discuss, one can think about precision medicine as one does a family vacation. A best vacation depends on several co-variates: where you live, your prior travel experiences, advice from family and friends, online reviews, Wikitravel, cost, your travel budget, if you have kids and many other co-variates. Consequently, there is unlikely to be a best vacation for everyone. Yours might be a week at the Ritz Carlton Cancun with dinner at Careyes and ours, a week at the Pfister Hotel in Milwaukee with dinner at Mader's German Restaurant (bring simvastatin). Similarly, it is unlikely there is a best therapy of acute myeloid leukemia, a best donor, a best conditioning regimen, a best posttransplant immune suppressive regimen etc. and certainly no best combination of these co-variates for your patient.The question Moodie and Krakow tackle is how we can determine the best therapy or combination of therapies for someone receiving a haematopoietic cell transplant. Although the default answer is typically: randomized clinical trials are the gold standard, these inform us of the outcome of a cohort of subjects, not individuals. In many instances, although a new therapy may be shown to be better than an old one in a controlled randomized trial the benefit is not uniformly distributed. Some subjects in the experimental cohort may do worse with the new therapy compared with controls, others better. The question is who are the winners and losers? We cannot do a controlled randomized trial of one person. Moodie and Krakow discuss statistical tools to help us sort this out.Again, please do not be put off by the equations; forgetaboutit. The overriding message is not so complex, and important. We are always standing by on twitter @BMTStats to help. But don't confuse us with Match.com. And, by the way, Freeman Dyson was a professor at the Institute for Advanced Studies at Princeton but never got his PhD.Robert Peter Gale, Imperial College London, and Mei-Jie Zhang, Medical College of Wisconsin, Center for International Blood and Marrow Research (CIBMTR).
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Affiliation(s)
- Erica E M Moodie
- Department of Epidemiology and Biostatistics, McGill University, 1020 Pine Ave W, Montreal, QC, H3A 1A2, Canada
| | - Elizabeth F Krakow
- Fred Hutchinson Cancer Research Center and University of Washington, 1100 Fairview Ave N, Seattle, WA, 98109, USA.
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Villain L, Commenges D, Pasin C, Prague M, Thiébaut R. Adaptive protocols based on predictions from a mechanistic model of the effect of IL7 on CD4 counts. Stat Med 2018; 38:221-235. [PMID: 30259533 DOI: 10.1002/sim.7957] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2018] [Revised: 07/05/2018] [Accepted: 08/18/2018] [Indexed: 12/16/2022]
Abstract
In human immunodeficiency virus-infected patients, antiretroviral therapy suppresses the viral replication, which is followed in most patients by a restoration of CD4+ T cells pool. For patients who fail to do so, repeated injections of exogenous interleukin 7 (IL7) are experimented. The IL7 is a cytokine that is involved in the T cell homeostasis and the INSPIRE study has shown that injections of IL7 induced a proliferation of CD4+ T cells. Phase I/II INSPIRE 2 and 3 studies have evaluated a protocol in which a first cycle of three IL7 injections is followed by a new cycle at each visit when the patient has less than 550 CD4 cells/μL. Restoration of the CD4 concentration has been demonstrated, but the long-term best adaptive protocol is yet to be determined. A mechanistic model of the evolution of CD4 after IL7 injections has been developed, which is based on a system of ordinary differential equations and includes random effects. Based on the estimation of this model, we use a Bayesian approach to forecast the dynamics of CD4 in new patients. We propose four prediction-based adaptive protocols of injections to minimize the time spent under 500 CD4 cells/μL for each patient, without increasing the number of injections received too much. We show that our protocols significantly reduce the time spent under 500 CD4 over a period of two years, without increasing the number of injections. These protocols have the potential to increase the efficiency of this therapy.
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Affiliation(s)
- Laura Villain
- University of Bordeaux, Inserm, Bordeaux, Population Health Research Center, Team SISTM, Bordeaux, France.,INRIA Bordeaux Sud Ouest, Talence, France.,Vaccine Research Institute (VRI), Hôpital Henri Mondor, Créteil, France
| | - Daniel Commenges
- University of Bordeaux, Inserm, Bordeaux, Population Health Research Center, Team SISTM, Bordeaux, France.,INRIA Bordeaux Sud Ouest, Talence, France.,Vaccine Research Institute (VRI), Hôpital Henri Mondor, Créteil, France
| | - Chloé Pasin
- University of Bordeaux, Inserm, Bordeaux, Population Health Research Center, Team SISTM, Bordeaux, France.,INRIA Bordeaux Sud Ouest, Talence, France.,Vaccine Research Institute (VRI), Hôpital Henri Mondor, Créteil, France
| | - Mélanie Prague
- University of Bordeaux, Inserm, Bordeaux, Population Health Research Center, Team SISTM, Bordeaux, France.,INRIA Bordeaux Sud Ouest, Talence, France.,Vaccine Research Institute (VRI), Hôpital Henri Mondor, Créteil, France
| | - Rodolphe Thiébaut
- University of Bordeaux, Inserm, Bordeaux, Population Health Research Center, Team SISTM, Bordeaux, France.,INRIA Bordeaux Sud Ouest, Talence, France.,Vaccine Research Institute (VRI), Hôpital Henri Mondor, Créteil, France
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Kim S, Wong WK. Discussion on Optimal treatment allocations in space and time for on-line control of an emerging infectious disease. J R Stat Soc Ser C Appl Stat 2018. [PMID: 30270943 DOI: 10.1111/rssc.12266] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Seongho Kim
- Biostatistics Core, Karmanos Cancer Institute, Department of Oncology, School of Medicine, Wayne State University, Detroit, MI 48201
| | - Weng Kee Wong
- Department of Biostatistics, UCLA School of Public Health, Los Angeles, CA 90095
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G-Estimation of Structural Nested Models: Recent Applications in Two Subfields of Epidemiology. CURR EPIDEMIOL REP 2016. [DOI: 10.1007/s40471-016-0081-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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