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Kulasekera KB, Tholkage S, Kong M. Personalized treatment selection using observational data. J Appl Stat 2022; 50:1115-1127. [PMID: 37009593 PMCID: PMC10062224 DOI: 10.1080/02664763.2021.2019689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Estimating the optimal treatment regime based on individual patient characteristics has been a topic of discussion in many forums. Advanced computational power has added momentum to this discussion over the last two decades and practitioners have been advocating the use of new methods in determining the best treatment. Treatments that are geared toward the 'best' outcome for a patient based on his/her genetic markers and characteristics are of high importance. In this article, we develop an approach to predict the optimal personalized treatment based on observational data. We have used inverse probability of treatment weighted machine learning methods to obtain score functions to predict the optimal treatment. Extensive simulation studies showed that our proposed method has desirable performance in selecting the optimal treatment. We provided a case study to examine the Statin use on cognitive function to illustrate the use of our proposed method.
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Affiliation(s)
- K. B. Kulasekera
- Department of Bioinformatics & Biostatistics, University of Louisville, Louisville, KY, USA
| | - Sudaraka Tholkage
- Department of Bioinformatics & Biostatistics, University of Louisville, Louisville, KY, USA
| | - Maiying Kong
- Department of Bioinformatics & Biostatistics, University of Louisville, Louisville, KY, USA
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Siriwardhana C, Kulasekera KB, Datta S. Personalized treatment selection using data from crossover designs with carry-over effects. Stat Med 2019; 38:5391-5412. [PMID: 31637762 DOI: 10.1002/sim.8372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2018] [Revised: 05/29/2019] [Accepted: 08/24/2019] [Indexed: 11/07/2022]
Abstract
In this work, we propose a semiparametric method for estimating the optimal treatment for a given patient based on individual covariate information for that patient when data from a crossover design are available. Here, we assume there are carry-over effects for patients switching from one treatment to another. For the K treatment (K ≥ 2) scenario, we show that nonparametric estimation of carry-over effects can have the undesirable property that comparison of treatment means can only be done using independent outcome measurements from different groups of patients rather than using available joint measurements for each patient. To overcome this barrier, we compare probabilities of outcome variable of each treatment dominating outcome variables for all other treatments conditional on patient-specific scores constructed from patient covariates. We suggest single-index models as appropriate models connecting outcome variables to covariates and our empirical investigations show that frequencies of correct treatment assignments are highly accurate. The proposed method is also rather robust against departures from a single-index model structure. We also conduct a real data analysis to show the applicability of the proposed procedure.
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Affiliation(s)
- Chathura Siriwardhana
- Department of Quantitative Health Sciences, University of Hawaii John A. Burns School of Medicine, Honolulu, Hawaii
| | - K B Kulasekera
- Department of Bioinformatics & Biostatistics, University of Louisville, Louisville, Kentucky
| | - Somnath Datta
- Department of Biostatistics, University of Florida, Gainesville, Florida
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Siriwardhana C, Datta S, Kulasekera KB. Selection of the optimal personalized treatment from multiple treatments with multivariate outcome measures. J Biopharm Stat 2019; 30:462-480. [PMID: 31691633 DOI: 10.1080/10543406.2019.1684304] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
In this work, we propose a novel method for individualized treatment selection when the treatment response is multivariate. For the K treatment (K ≥2) scenario we compare quantities that are suitable indexes based on outcome variables for each treatment conditional on patient-specific scores constructed from collected covariate measurements. Our method covers any number of treatments and outcome variables, and it can be applied for a broad set of models. The proposed method uses a rank aggregation technique to estimate an ordering of treatments based on ranked lists of treatment performance measures such as smooth conditional means and conditional probability of a response for one treatment dominating others. The method has the flexibility to incorporate patient and clinician preferences to the optimal treatment decision on an individual case basis. A simulation study demonstrates the performance of the proposed method in finite samples. We also present data analyses using HIV and Diabetes clinical trials data to show the applicability of the proposed procedure for real data.
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Affiliation(s)
- Chathura Siriwardhana
- Department of Quantitative Health Sciences, University of Hawaii John A. Burns School of Medicine, Honolulu, HI, USA
| | - Somnath Datta
- Department of Biostatistics, University of Florida, Gainesville, FL, USA
| | - K B Kulasekera
- Department of Bioinformatics & Biostatistics, University of Louisville, Louisville, KY, USA
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Yan X, Abdia Y, Datta S, Kulasekera KB, Ugiliweneza B, Boakye M, Kong M. Estimation of average treatment effects among multiple treatment groups by using an ensemble approach. Stat Med 2019; 38:2828-2846. [PMID: 30941812 DOI: 10.1002/sim.8146] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2018] [Revised: 12/10/2018] [Accepted: 02/23/2019] [Indexed: 11/08/2022]
Abstract
In observational studies, generalized propensity score (GPS)-based statistical methods, such as inverse probability weighting (IPW) and doubly robust (DR) method, have been proposed to estimate the average treatment effect (ATE) among multiple treatment groups. In this article, we investigate the GPS-based statistical methods to estimate treatment effects from two aspects. The first aspect of our investigation is to obtain an optimal GPS estimation method among four competing GPS estimation methods by using a rank aggregation approach. We further examine whether the optimal GPS-based IPW and DR methods would improve the performance for estimating ATE. It is well known that the DR method is consistent if either the GPS or the outcome models are correctly specified. The second aspect of our investigation is to examine whether the DR method could be improved if we ensemble outcome models. To that end, bootstrap method and rank aggregation method are used to obtain the ensemble optimal outcome model from several competing outcome models, and the resulting outcome model is incorporated into the DR method, resulting in an ensemble DR (enDR) method. Extensive simulation results indicate that the enDR method provides the best performance in estimating the ATE regardless of the method used for estimating GPS. We illustrate our methods using the MarketScan healthcare insurance claims database to examine the treatment effects among three different bones and substitutes used for spinal fusion surgeries. We draw conclusions based on the estimates from the enDR method coupled with the optimal GPS estimation method.
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Affiliation(s)
- Xiaofang Yan
- Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, Kentucky
| | - Younathan Abdia
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Somnath Datta
- Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, Kentucky.,Department of Biostatistics, University of Florida, Gainesville, Florida
| | - K B Kulasekera
- Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, Kentucky
| | | | - Maxwell Boakye
- Department of Neurosurgery, University of Louisville, Louisville, Kentucky
| | - Maiying Kong
- Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, Kentucky
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Siriwardhana C, Kulasekera KB, Datta S. Flexible semi-parametric regression of state occupational probabilities in a multistate model with right-censored data. Lifetime Data Anal 2018; 24:464-491. [PMID: 28819787 PMCID: PMC5816729 DOI: 10.1007/s10985-017-9403-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/24/2016] [Accepted: 07/23/2017] [Indexed: 06/07/2023]
Abstract
Inference for the state occupation probabilities, given a set of baseline covariates, is an important problem in survival analysis and time to event multistate data. We introduce an inverse censoring probability re-weighted semi-parametric single index model based approach to estimate conditional state occupation probabilities of a given individual in a multistate model under right-censoring. Besides obtaining a temporal regression function, we also test the potential time varying effect of a baseline covariate on future state occupation. We show that the proposed technique has desirable finite sample performances and its performance is competitive when compared with three other existing approaches. We illustrate the proposed methodology using two different data sets. First, we re-examine a well-known data set dealing with leukemia patients undergoing bone marrow transplant with various state transitions. Our second illustration is based on data from a study involving functional status of a set of spinal cord injured patients undergoing a rehabilitation program.
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Affiliation(s)
- Chathura Siriwardhana
- Department of Complementary and Integrative Medicine, John A. Burns School of Medicine, University of Hawaii, Honolulu, HI, USA
| | - K B Kulasekera
- Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY, USA
| | - Somnath Datta
- Department of Biostatistics, University of Florida, Gainesville, FL, USA.
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Garbett NC, Brock GN, Chaires JB, Mekmaysy CS, DeLeeuw L, Sivils KL, Harley JB, Rovin BH, Kulasekera KB, Jarjour WN. Characterization and classification of lupus patients based on plasma thermograms. PLoS One 2017; 12:e0186398. [PMID: 29149219 PMCID: PMC5693473 DOI: 10.1371/journal.pone.0186398] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2016] [Accepted: 09/29/2017] [Indexed: 11/22/2022] Open
Abstract
Objective Plasma thermograms (thermal stability profiles of blood plasma) are being utilized as a new diagnostic approach for clinical assessment. In this study, we investigated the ability of plasma thermograms to classify systemic lupus erythematosus (SLE) patients versus non SLE controls using a sample of 300 SLE and 300 control subjects from the Lupus Family Registry and Repository. Additionally, we evaluated the heterogeneity of thermograms along age, sex, ethnicity, concurrent health conditions and SLE diagnostic criteria. Methods Thermograms were visualized graphically for important differences between covariates and summarized using various measures. A modified linear discriminant analysis was used to segregate SLE versus control subjects on the basis of the thermograms. Classification accuracy was measured based on multiple training/test splits of the data and compared to classification based on SLE serological markers. Results Median sensitivity, specificity, and overall accuracy based on classification using plasma thermograms was 86%, 83%, and 84% compared to 78%, 95%, and 86% based on a combination of five antibody tests. Combining thermogram and serology information together improved sensitivity from 78% to 86% and overall accuracy from 86% to 89% relative to serology alone. Predictive accuracy of thermograms for distinguishing SLE and osteoarthritis / rheumatoid arthritis patients was comparable. Both gender and anemia significantly interacted with disease status for plasma thermograms (p<0.001), with greater separation between SLE and control thermograms for females relative to males and for patients with anemia relative to patients without anemia. Conclusion Plasma thermograms constitute an additional biomarker which may help improve diagnosis of SLE patients, particularly when coupled with standard diagnostic testing. Differences in thermograms according to patient sex, ethnicity, clinical and environmental factors are important considerations for application of thermograms in a clinical setting.
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Affiliation(s)
- Nichola C. Garbett
- James Graham Brown Cancer Center, Department of Medicine, University of Louisville, Louisville, KY, United States of America
- * E-mail:
| | - Guy N. Brock
- Department of Bioinformatics and Biostatistics, School of Public Health and Information Sciences, University of Louisville, Louisville, KY, United States of America
| | - Jonathan B. Chaires
- James Graham Brown Cancer Center, Department of Medicine, University of Louisville, Louisville, KY, United States of America
| | - Chongkham S. Mekmaysy
- James Graham Brown Cancer Center, Department of Medicine, University of Louisville, Louisville, KY, United States of America
| | - Lynn DeLeeuw
- James Graham Brown Cancer Center, Department of Medicine, University of Louisville, Louisville, KY, United States of America
| | - Kathy L. Sivils
- Arthritis and Clinical Immunology Program, Oklahoma Medical Research Foundation, Oklahoma City, OK, United States of America
| | - John B. Harley
- U.S. Department of Veterans Affairs Medical Center, Cincinnati, OH, United States of America
- The Center for Autoimmune Genomics and Etiology, Department of Pediatrics, Cincinnati Children’s Hospital Medical Center & University of Cincinnati, Cincinnati, OH, United States of America
| | - Brad H. Rovin
- Nephrology Division, The Ohio State University Wexner Medical Center, Columbus, OH, United States of America
| | - K. B. Kulasekera
- Department of Bioinformatics and Biostatistics, School of Public Health and Information Sciences, University of Louisville, Louisville, KY, United States of America
| | - Wael N. Jarjour
- Division of Rheumatology and Immunology, Department of Internal Medicine, The Ohio State University Wexner Medical Center, Columbus, OH, United States of America
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Siriwardhana C, Zhao M, Datta S, Kulasekera KB. A probability based method for selecting the optimal personalized treatment from multiple treatments. Stat Methods Med Res 2017; 28:749-760. [DOI: 10.1177/0962280217735701] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
In this work we propose a method for optimal treatment assignment based on individual covariate information for a patient. For the K treatment ([Formula: see text]) scenario, we compare quantities that are suitable surrogates to true conditional probabilities of outcome variable of each treatment dominating outcome variables for all other treatments conditional on patient specific scores constructed from patient-specific covariates. As opposed to methods based on conditional means, our method can be applied for a broad set of models and error structures. Furthermore, the proposed method has very desirable large sample properties. We suggest Single Index Models as appropriate models connecting outcome variables to covariates and our empirical investigations show that correct treatment assignments are highly accurate. The proposed method is also rather robust against departures from a Single Index Model structure. Furthermore, selection of a treatment using the proposed metric appears to incur no losses in terms of the average reward for cases when two treatments are close in terms of this metric. We also conduct a real data analysis to show the applicability of the proposed procedure. This analysis highlights possible gains both in terms of average response and survival time if one were to use the proposed method.
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Affiliation(s)
- Chathura Siriwardhana
- Department of Complementary & Integrative Medicine, John A. Burns School of Medicine, University of Hawaii, HI, USA
| | - Meng Zhao
- Department of Bioinformatics & Biostatistics, University of Louisville, Louisville, KY, USA
| | - Somnath Datta
- Department of Biostatistics, University of Florida, Gainesville, FL, USA
| | - KB Kulasekera
- Department of Bioinformatics & Biostatistics, University of Louisville, Louisville, KY, USA
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Abdia Y, Kulasekera KB, Datta S, Boakye M, Kong M. Propensity scores based methods for estimating average treatment effect and average treatment effect among treated: A comparative study. Biom J 2017; 59:967-985. [PMID: 28436047 DOI: 10.1002/bimj.201600094] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2016] [Revised: 10/31/2016] [Accepted: 12/31/2016] [Indexed: 11/10/2022]
Abstract
Propensity score based statistical methods, such as matching, regression, stratification, inverse probability weighting (IPW), and doubly robust (DR) estimating equations, have become popular in estimating average treatment effect (ATE) and average treatment effect among treated (ATT) in observational studies. Propensity score is the conditional probability receiving a treatment assignment with given covariates, and propensity score is usually estimated by logistic regression. However, a misspecification of the propensity score model may result in biased estimates for ATT and ATE. As an alternative, the generalized boosting method (GBM) has been proposed to estimate the propensity score. GBM uses regression trees as weak predictors and captures nonlinear and interactive effects of the covariate. For GBM-based propensity score, only IPW methods have been investigated in the literature. In this article, we provide a comparative study of the commonly used propensity score based methods for estimating ATT and ATE, and examine their performances when propensity score is estimated by logistic regression and GBM, respectively. Extensive simulation results indicate that the estimators for ATE and ATT may vary greatly due to different methods. We concluded that (i) regression may not be suitable for estimating ATE and ATT regardless of the estimation method of propensity score; (ii) IPW and stratification usually provide reliable estimates of ATT when propensity score model is correctly specified; (iii) the estimators of ATE based on stratification, IPW, and DR are close to the underlying true value of ATE when propensity score is correctly specified by logistic regression or estimated using GBM.
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Affiliation(s)
- Younathan Abdia
- Department of Bioinformatics and Biostatisitcs, University of Louisville, Louisville, KY, USA
| | - K B Kulasekera
- Department of Bioinformatics and Biostatisitcs, University of Louisville, Louisville, KY, USA
| | - Somnath Datta
- Department of Bioinformatics and Biostatisitcs, University of Louisville, Louisville, KY, USA.,Department of Biostatistics, University of Florida, Gainesville, FL, USA
| | - Maxwell Boakye
- Department of Neurosurgery, University of Louisville, Louisville, KY, USA
| | - Maiying Kong
- Department of Bioinformatics and Biostatisitcs, University of Louisville, Louisville, KY, USA
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Abstract
Problems with censored data arise quite frequently in reliability applications. Estimation of the reliability function is usually of concern. Reliability function estimators proposed by Kaplan and Meier (1958), Breslow (1972), are generally used when dealing with censored data. These estimators have the known properties of being asymptotically unbiased, uniformly strongly consistent, and weakly convergent to the same Gaussian process, when properly normalized. We study the properties of the smoothed Kaplan-Meier estimator with a suitable kernel function in this paper. The smooth estimator is compared with the Kaplan-Meier and Breslow estimators for large sample sizes giving an exact expression for an appropriately normalized difference of the mean square error (MSE) of the two estimators. This quantifies the deficiency of the Kaplan-Meier estimator in comparison to the smoothed version. We also obtain a non-asymptotic bound on an expected L1-type error under weak conditions. Some simulations are carried out to examine the performance of the suggested method.
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Affiliation(s)
- K B Kulasekera
- Department of Mathematical Sciences, Clemson University, Clemson, SC 29634-1907, USA
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Kulasekera KB. Crossing points of failure rates. COMMUN STAT-THEOR M 1999. [DOI: 10.1080/03610929908832284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Kulasekera KB, Condra LW. Reliability Improvement with Design of Experiments. J Am Stat Assoc 1995. [DOI: 10.2307/2291184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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