On sparse representation for optimal individualized treatment selection with penalized outcome weighted learning

Stat

Volumn 4, Issue 1, 2015, Pages 59-68

  Song, Rui ^a   Kosorok, Michael ^b   Zeng, Donglin ^b   Zhao, Yingqi ^c   Laber, Eric ^a   Yuan, Ming ^c  

^a NORTH CAROLINA STATE UNIVERSITY   (United States)

^b IL   (United States)

^c University of Wisconsin Madison   (United States)

Author keywords

Penalization; Personalized medicine; Support vector machine

Indexed keywords

EID: 84939213760     PISSN: None     EISSN: 20491573     Source Type: Journal    
DOI: 10.1002/sta4.78     Document Type: Article

References (39)

1. The ALLHAT Officers and Coordinators (2002), 'Major outcomes in moderately hypercholesterolemic, hypertensive patients randomized to pravastatin vs usual care: the antihypertensive and lipid-lowering treatment to prevent heart attack trial (ALLHAT-LLT)', JAMA: the Journal of the American Medical Association 288(23), 2998-3007.
2. Biernot P, Moodie EEM (2010), 'A comparison of variable selection approaches for dynamic treatment regimes', The International Journal of Biostatistics 6(1), Article 6. DOI:10.2202/1557-4679.1178.
3. Chakraborty B, Murphy S, Strecher V (2010), 'Inference for non-regular parameters in optimal dynamic treatment regimes', Statistical Methods in Medical Research 19, (3), 317-343.
4. Chen W, Ghosh D, Raghunathan TE, Sargent DJ (2009), 'Bayesian variable selection with joint modeling of categorical and survival outcomes: an application to individualizing chemotherapy treatment in advanced colorectal cancer', Biometrics 65(4), 1030-1040.
5. Cortes C, Vapnik V (1995), 'Support-vector networks, ' In Machine Learning, 20, 273-297.
6. Ernst D, Geurts P, Wehenkel L (2005), 'Tree-based batch mode reinforcement learning', Journal of Machine Learning Research 6, 503-556.
7. Fan J, Li R (2001), 'Variable selection via nonconcave penalized likelihood and its oracle properties', Journal of the American Statistical Association 96(456), 1348-1360.
8. Gail M, Simon R (1985), 'Testing for qualitative interactions between treatment effects and patient subsets', Biometrics 41, 2), 361-372.
9. Gunter L, Zhu J, Murphy SA, 'Variable selection for qualitative interactions', Statistical Methodology 8(2011), 1), 42-55.
10. Keller MB, McCullough JP, Jr., Klein DN, Arnow B, Dunner DL, Gelenberg AJ, Markowitz JC, Nemeroff CB, Russell JM, Thase ME, Trivedi MH, Zajecka J (2000), 'A comparison of nefazodone, the cognitive behavioral analysis system of psychotherapy, and their combination for the treatment of chronic depression', New England Journal of Medicine 342, 1462-1470.
11. Loth M, Davy M, Preux P (2007), 'Sparse temporal difference learning using Lasso', IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning, 2007. ADPRL 2007, IEEE, 352-359.
12. Lu W, Zhang HH, Zeng D (2013), 'Variable selection for optimal treatment decision', Statistical Methods in Medical Research 22, (5), 493-504.
13. Lunceford JK, Davidian M, Tsiatis AA (2002), 'Estimation of survival distributions of treatment policies in two-stage randomization designs in clinical trials', Biometrics 58, 1), 48-57.
14. Moodie EE, Richardson TS, Stephens DA (2007), 'Demystifying optimal dynamic treatment regimes', Biometrics 63, (2), 447-455.
15. Moodie EEM, Platt RW, Kramer MS (2009), 'Estimating response-maximized decision rules with applications to breastfeeding', Journal of the American Statistical Association 104, 155-165.
16. Murphy SA (2003), 'Optimal dynamic treatment regimes', Journal of the Royal Statistical Society: Series B (Statistical Methodology) 65, (2), 331-355.
17. Murphy SA, 'An experimental design for the development of adaptive treatment strategies', Statistics in Medicine 24(2005), 10), 1455-1481.
18. Murphy SA, van der Laan MJ, Robins JM, CPPRG (2001), 'Marginal Mean Models for Dynamic Regimes', Journal of the American Statistical Association 96, 1410-1423.
19. Park C, Kim K, Myung R, Koo J (2012), 'Oracle properties of SCAD-penalized support vector machine', Journal of Statistical Planning and Inference 142, (8), 2257-2270.
20. Piantadosi S, Gail MH (1993), 'A comparison of the power of two tests for qualitative interactions', Statistics in Medicine 12, (13), 1239-1248.
21. Qian M, Murphy SA, 'Performance guarantees for individualized treatment rules', Annals of Statistics 39(2011), 2), 1180-1210.
22. Reynolds CF, Dew MA, Pollock BG, Mulsant BH, Frank E, Miller MD, Houck PR, Mazumdar S, Butters MA, Stack JA, et al. (2006), 'Maintenance treatment of major depression in old age', New England Journal of Medicine 354, (11), 1130-1138.
23. Robins JM (2004), 'Optimal structural nested models for optimal sequential decisions', Proceedings of the Second Seattle Symposium in Biostatistics, Springer, 189-326.
24. Song R, Wang W, Zeng D, Kosorok MR, 'Penalized Q-learning for dynamic treatment regimes', Statistica Sinica (2014). DOI: 10.5705/ss.2012.364.
25. Tibshirani R (1996), 'Regression shrinkage and selection via the lasso', Journal of the Royal Statistical Society: Series B (Statistical Methodology) 58, (1), 267-288.
26. Thall PF, Millikan RE, Sung HG (2000), 'Evaluating multiple treatment courses in clinical trials', Statistics in Medicine 19, (8), 1011-1028.
27. Thall PF, Sung HG, Estey EH (2002), 'Selecting therapeutic strategies based on efficacy and death in multicourse clinical trials', Journal of the American Statistical Association 97, (457), 29-39.
28. Thall PF, Wooten LH, Logothetis CJ, Millikan RE, Tannir NM (2007), 'Bayesian and frequentist two-stage treatment strategies based on sequential failure times subject to interval censoring', Statistics in Medicine 26, (26), 4687-4702.
29. Wahed A, Tsiatis A (2006), 'Semiparametric efficient estimation of survival distributions in two-stage randomisation designs in clinical trials with censored data', Biometrika 93, (1), 163-177.
30. Wahed AS, Tsiatis AA (2004), 'Optimal estimator for the survival distribution and related quantities for treatment policies in two-stage randomised designs in clinical trials', Biometrics 60, (5), 124-133.
31. Watkins CJ (1989), 'Learning from delayed rewards', Ph.D. Thesis, University of Cambridge, England.
32. Watkins CJ, Dayan P (1992), 'Q-learning', Machine Learning 8, (3-4), 279-292.
33. Wu S, Zou H, Yuan M (2008), 'Structured variable selection in support vector machines', Electronic Journal of Statistics 2, 103-117.
34. Yan X (2004), 'Test for qualitative interaction in equivalence trials when the number of centres is large', Statistics in Medicine 23, (5), 711-722.
35. Zhang B, Tsiatis AA, Laber EB, Davidian M (2012), 'A robust method for estimating optimal treatment regimes', Biometrics 68, (4), 1010-1018.
36. Zhao Y, Zeng D, Rush AJ, Kosorok MR (2012), 'Estimating individualized treatment rules using outcome weighted learning', Journal of the American Statistical Association 107, (499), 1106-1118.
37. Zhao Y, Zeng D, Socinski MA, Kosorok MR (2011), 'Reinforcement learning strategies for clinical trials in nonsmall cell lung cancer', Biometrics 67, (4), 1422-1433.
38. Zhu J, Rosset S, Hastie T, Tibshirani R (2003), '1-Norm support vector machines', Neural Information Processing Systems, MIT Press, 16.
39. Zou H, Li R (2008), 'One-step sparse estimates in nonconcave penalized likelihood models', The Annals of Statistics 36, 1509-1533.