|
|
|
Recent development on statistical methods for personalized medicine discovery |
Yingqi Zhao1, Donglin Zeng2( ) |
| 1. Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, 600 Highland Ave. Madison, WI 53792, USA; 2. Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA |
|
|
|
|
Abstract It is well documented that patients can show significant heterogeneous responses to treatments so the best treatment strategies may require adaptation over individuals and time. Recently, a number of new statistical methods have been developed to tackle the important problem of estimating personalized treatment rules using single-stage or multiple-stage clinical data. In this paper, we provide an overview of these methods and list a number of challenges.
|
| Keywords
dynamic treatment regimes
personalized medicine
reinforcement learning
Q-learning
|
|
Corresponding Author(s):
Zeng Donglin,Email:dzeng@email.unc.edu
|
|
Issue Date: 05 March 2013
|
|
| 1 |
Grünwald V, Hidalgo M. Developing inhibitors of the epidermal growth factor receptor for cancer treatment. J Natl Cancer Inst 2003; 95(12): 851–867
doi: 10.1093/jnci/95.12.851 pmid:12813169
|
| 2 |
Buzdar AU. Role of biologic therapy and chemotherapy in hormone receptor- and HER2-positive breast cancer. Ann Oncol 2009; 20(6): 993–999
doi: 10.1093/annonc/mdn739 pmid:19150946
|
| 3 |
Piper WE, Boroto DR, Joyce AS, McCallum M, Azim HFA. Pattern of alliance and outcome in short-term individual psychotherapy. Psychotherapy 1995; 32(4): 639–647
doi: 10.1037/0033-3204.32.4.639
|
| 4 |
Crits-Christoph P, Siqueland L, Blaine J, Frank A, Luborsky L, Onken LS, Muenz LR, Thase ME, Weiss RD, Gastfriend DR, Woody GE, Barber JP, Butler SF, Daley D, Salloum I, Bishop S, Najavits LM, Lis J, Mercer D, Griffin ML, Moras K, Beck AT. Psychosocial treatments for cocaine dependence: National Institute on Drug Abuse Collaborative Cocaine Treatment Study. Arch Gen Psychiatry 1999; 56(6): 493–502
doi: 10.1001/archpsyc.56.6.493 pmid:10359461
|
| 5 |
Ishigooka J, Murasaki M, Miura S, Group TOLPIS. Olanzapine optimal dose: results of an open-label multicenter study in schizophrenic patients. olanzapine late-phase ii study group. Psychiatry Clin Neurosci 2000; 54(4): 467–478
doi: 10.1046/j.1440-1819.2000.00738.x pmid:10997865
|
| 6 |
Fukuoka M, Wu YL, Thongprasert S, Sunpaweravong P, Leong SS, Sriuranpong V, Chao TY, Nakagawa K, Chu DT, Saijo N, Duffield EL, Rukazenkov Y, Speake G, Jiang H, Armour AA, To KF, Yang JC, Mok TS. Biomarker analyses and final overall survival results from a phase III, randomized, open-label, first-line study of gefitinib versus carboplatin/paclitaxel in clinically selected patients with advanced non-small-cell lung cancer in Asia (IPASS). J Clin Oncol 2011; 29(21): 2866–2874
doi: 10.1200/JCO.2010.33.4235 pmid:21670455
|
| 7 |
Wagner EH, Austin BT, Davis C, Hindmarsh M, Schaefer J, Bonomi A. Improving chronic illness care: translating evidence into action. Health Aff (Millwood) 2001; 20(6): 64–78
doi: 10.1377/hlthaff.20.6.64 pmid:11816692
|
| 8 |
Murphy SA, Oslin DW, Rush AJ, Zhu J ; MCATS. Methodological challenges in constructing effective treatment sequences for chronic psychiatric disorders. Neuropsychopharmacology 2007; 32(2): 257–262
doi: 10.1038/sj.npp.1301241 pmid:17091129
|
| 9 |
Socinski MA, Stinchcombe TE. Duration of first-line chemotherapy in advanced non small-cell lung cancer: less is more in the era of effective subsequent therapies. J Clin Oncol 2007; 25(33): 5155–5157
doi: 10.1200/JCO.2007.13.4015 pmid:18024862
|
| 10 |
Flume PA, O’Sullivan BP, Robinson KA, Goss CH, Mogayzel PJ Jr, Willey-Courand DB, Bujan J, Finder J, Lester M, Quittell L, Rosenblatt R, Vender RL, Hazle L, Sabadosa K, Marshall B; Cystic Fibrosis Foundation, Pulmonary Therapies Committee. Cystic fibrosis pulmonary guidelines: chronic medications for maintenance of lung health. Am J Respir Crit Care Med 2007; 176(10): 957–969
doi: 10.1164/rccm.200705-664OC pmid:17761616
|
| 11 |
Hamburg MA, Collins FS. The path to personalized medicine. N Engl J Med 2010; 363(4): 301–304
doi: 10.1056/NEJMp1006304 pmid:20551152
|
| 12 |
Sargent DJ, Conley BA, Allegra C, Collette L. Clinical trial designs for predictive marker validation in cancer treatment trials. J Clin Oncol 2005; 23(9): 2020–2027
doi: 10.1200/JCO.2005.01.112 pmid:15774793
|
| 13 |
Insel TR. Translating scientific opportunity into public health impact: a strategic plan for research on mental illness. Arch Gen Psychiatry 2009; 66(2): 128–133
doi: 10.1001/archgenpsychiatry.2008.540 pmid:19188534
|
| 14 |
Murphy SA. An experimental design for the development of adaptive treatment strategies. Stat Med 2005; 24(10): 1455–1481
doi: 10.1002/sim.2022 pmid:15586395
|
| 15 |
Pocock SJ, Assmann SE, Enos LE, Kasten LE. Subgroup analysis, covariate adjustment and baseline comparisons in clinical trial reporting: current practice and problems. Stat Med 2002; 21(19): 2917–2930
doi: 10.1002/sim.1296 pmid:12325108
|
| 16 |
Wang R, Lagakos SW, Ware JH, Hunter DJ, Drazen JM, Drazen M. Statistics in medicine—reporting of subgroup analyses in clinical trials. N Engl J Med 2007; 357(21): 2189–2194
doi: 10.1056/NEJMsr077003 pmid:18032770
|
| 17 |
Kent D, Hayward R. Subgroup analyses in clinical trials. N Engl J Med 2008; 358(11): 1199
doi: 10.1056/NEJMc073436 pmid:18340666
|
| 18 |
Assmann SF, Pocock SJ, Enos LE, Kasten LE. Subgroup analysis and other (mis)uses of baseline data in clinical trials. Lancet 2000; 355(9209): 1064–1069
doi: 10.1016/S0140-6736(00)02039-0 pmid:10744093
|
| 19 |
Lagakos SW. The challenge of subgroup analyses—reporting without distorting. N Engl J Med 2006; 354(16): 1667–1669
doi: 10.1056/NEJMp068070 pmid:16625007
|
| 20 |
Byar DP. Assessing apparent treatment—covariate interactions in randomized clinical trials. Stat Med 1985; 4(3): 255–263
doi: 10.1002/sim.4780040304 pmid:4059716
|
| 21 |
Sacks FM, Pfeffer MA, Moye LA, Rouleau JL, Rutherford JD, Cole TG, Brown L, Warnica JW, Arnold JM, Wun CC, Davis BR, Braunwald E. The effect of pravastatin on coronary events after myocardial infarction in patients with average cholesterol levels. Cholesterol and Recurrent Events Trial investigators. N Engl J Med 1996; 335(14): 1001–1009
doi: 10.1056/NEJM199610033351401 pmid:8801446
|
| 22 |
Kehl V, Ulm K. Responder identicatifion in clinical trials with censored data. Comput Stat Data Anal 2006; 50(5):1338–1355
doi: 10.1016/j.csda.2004.11.015
|
| 23 |
Eagle KA, Lim MJ, Dabbous OH, Pieper KS, Goldberg RJ, Van de Werf F, Goodman SG, Granger CB, Steg PG, Gore JM, Budaj A, Avezum A, Flather MD, Fox KA; GRACE Investigators. A validated prediction model for all forms of acute coronary syndrome: estimating the risk of 6-month postdischarge death in an international registry. JAMA 2004; 291(22): 2727–2733
doi: 10.1001/jama.291.22.2727 pmid:15187054
|
| 24 |
Marlowe DB, Festinger DS, Dugosh KL, Lee PA, Benasutti KM. Adapting judicial supervision to the risk level of drug offenders: Discharge and 6-month outcomes from a prospective matching study. Drug Alcohol Depend 2007;88 (Suppl 2):S4–13
pmid:17071020
|
| 25 |
Cai T, Tian L, Uno H, Solomon SD, Wei LJ. Calibrating parametric subject-specific risk estimation. Biometrika 2010; 97(2): 389–404
doi: 10.1093/biomet/asq012 pmid:23049123
|
| 26 |
Qian M, Murphy SA. Performance guarantees for individualized treatment rules. Ann Stat 2011; 39(2): 1180–1210
doi: 10.1214/10-AOS864 pmid:21666835
|
| 27 |
Robins J. Correcting for non-compliance in randomized trials using structural nested mean models. Commun Stat 1994; 23(8): 2379–2412
doi: 10.1080/03610929408831393
|
| 28 |
Murphy SA. Optimal dynamic treatment regimes. J R Stat Soc, B 2003; 65(2): 331–355
doi: 10.1111/1467-9868.00389
|
| 29 |
Rosenwald A, Wright G, Chan WC, Connors JM, Campo E, Fisher RI, Gascoyne RD, Muller-Hermelink HK, Smeland EB, Giltnane JM, Hurt EM, Zhao H, Averett L, Yang L, Wilson WH, Jaffe ES, Simon R, Klausner RD, Powell J, Duffey PL, Longo DL, Greiner TC, Weisenburger DD, Sanger WG, Dave BJ, Lynch JC, Vose J, Armitage JO, Montserrat E, López-Guillermo A, Grogan TM, Miller TP, LeBlanc M, Ott G, Kvaloy S, Delabie J, Holte H, Krajci P, Stokke T, Staudt LM; Lymphoma/Leukemia Molecular Profiling Project. The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma. N Engl J Med 2002; 346(25): 1937–1947
doi: 10.1056/NEJMoa012914 pmid:12075054
|
| 30 |
van’t Veer LJ, Bernards R. Enabling personalized cancer medicine through analysis of gene-expression patterns. Nature 2008; 452(7187): 564–570
doi: 10.1038/nature06915 pmid:18385730
|
| 31 |
Murphy SA. A generalization error for q-learning. J Mach Learn Res 2005; 6: 1073–1097
pmid:16763665
|
| 32 |
Zhao YQ, Zeng D, Rush AJ, Kosorok MR. Estimating individualized treatment rules using outcome weighted learning. J Am Stat Assoc 2012; 107(499):1106–1118
doi: 10.1080/01621459.2012.695674
|
| 33 |
Lavori P W, Dawson R. A design for testing clinical strategies: biased adaptive withinsubject randomization. J R Stat Soc [Ser A] 2000; 163: 29–38
doi: 10.1111/1467-985X.00154
|
| 34 |
Lavori PW, Dawson R. Dynamic treatment regimes: practical design considerations. Clin Trials 2004; 1(1): 9–20
doi: 10.1191/1740774S04cn002oa pmid:16281458
|
| 35 |
Dawson R, Lavori PW. Placebo-free designs for evaluating new mental health treatments: the use of adaptive treatment strategies. Stat Med 2004; 23(21): 3249–3262
doi: 10.1002/sim.1920 pmid:15490427
|
| 36 |
Thall PF, Millikan RE, Sung HG. Evaluating multiple treatment courses in clinical trials. Stat Med 2000; 19(8): 1011–1028
doi: 10.1002/(SICI)1097-0258(20000430)19:8<1011::AID-SIM414>3.0.CO;2-M pmid:10790677
|
| 37 |
Schneider LS, Tariot PN, Lyketsos CG, Dagerman KS, Davis KL, Davis S, Hsiao JK, Jeste DV, Katz IR, Olin JT, Pollock BG, Rabins PV, Rosenheck RA, Small GW, Lebowitz B, Lieberman JA. National institute of mental health clinical antipsychotic trials of intervention effectiveness (catie): Alzheimer disease trial methodology. Am J Geriatr Psychiatry 2001; 9(4): 346–360
pmid:11739062
|
| 38 |
Rush AJ, Fava M, Wisniewski SR, Lavori PW, Trivedi MH, Sackeim HA, Thase ME, Nierenberg AA, Quitkin FM, Kashner TM, Kupfer DJ, Rosenbaum JF, Alpert J, Stewart JW, McGrath PJ, Biggs MM, Shores-Wilson K, Lebowitz BD, Ritz L, Niederehe G; STAR*D Investigators Group. Sequenced treatment alternatives to relieve depression (STAR*D): rationale and design. Control Clin Trials 2004; 25(1): 119–142
doi: 10.1016/S0197-2456(03)00112-0 pmid:15061154
|
| 39 |
Strecher VJ, McClure JB, Alexander GL, Chakraborty B, Nair VN, Konkel JM, Greene SM, Collins LM, Carlier CC, Wiese CJ, Little RJ, Pomerleau CS, Pomerleau OF. Web-based smoking cessation components and tailoring depth: Results of a randomized trial. Am J Prev Med 2008; 34: 373–381
doi: 10.1016/j.amepre.2007.12.024 pmid:18407003
|
| 40 |
Murphy SA, van der Laan MJ, Robins JM, CPPRG. Marginal mean models for dynamic regimes. J Am Stat Assoc 2001; 96(456): 1410–1423
doi: 10.1198/016214501753382327 pmid:20019887
|
| 41 |
Thall PF, Sung HG, Estey EH. Selecting therapeutic strategies based on efficacy and death in multicourse clinical trials. J Am Stat Assoc 2002; 97(457): 29–39
doi: 10.1198/016214502753479202
|
| 42 |
Thall PF, Wooten LH, Logothetis CJ, Millikan RE, Tannir NM. Bayesian and frequentist two-stage treatment strategies based on sequential failure times subject to interval censoring. Stat Med 2007; 26(26): 4687–4702
doi: 10.1002/sim.2894 pmid:17427204
|
| 43 |
Bellman R. Dynamic Programming. Princeton: Princeton University Press, 1957
|
| 44 |
Sutton RS, Barto AG. Reinforcement Learning I: Introduction. Cambridge, MA: MIT Press, 1998
|
| 45 |
Watkins CJCH. Learning from delayed rewards. . Cambridge, UK: Kings College, 1989
|
| 46 |
Zhao Y, Kosorok MR, Zeng D. Reinforcement learning design for cancer clinical trials. Stat Med 2009; 28(26): 3294–3315
doi: 10.1002/sim.3720 pmid:19750510
|
| 47 |
Zhao Y, Zeng D, Socinski MA, Kosorok MR. Reinforcement learning strategies for clinical trials in nonsmall cell lung cancer. Biometrics 2011; 67(4): 1422–1433
doi: 10.1111/j.1541-0420.2011.01572.x pmid:21385164
|
| 48 |
Blatt D, Murphy SA, Zhu J. A-learning for approximate planning. 2004. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1. 76.5799&rep=rep1&type=pdf (Accessed on December 1, 2012)
|
| 49 |
Robins JM. Optimal structural nested models for optimal sequential decisions. In: Proceedings of the Second Seattle Symposium on Biostatistics. Springer , 2004:189–326
|
| 50 |
Moodie EEM, Richardson TS, Stephens DA. Demystifying optimal dynamic treatment regimes. Biometrics 2007; 63(2): 447–455
doi: 10.1111/j.1541-0420.2006.00686.x pmid:17688497
|
| 51 |
Chakraborty B, Murphy SA, Strecher V. Inference for non-regular parameters in optimal dynamic treatment regimes. Stat Methods Med Res 2010; 19(3): 317–343
doi: 10.1177/0962280209105013 pmid:19608604
|
| 52 |
Schulte PJ, Tsiatis AA, Laber EB, Davidian M. Q and A-learning methods for estimating optimal dynamic treatment regimes. 2012.http://arxiv.org/abs/1202.4177 (Accessed on December 1, 2012)
|
| 53 |
Goldberg Y, Kosorok MR. Q-learning with censored data. Ann Stat 2012; 40(1): 529–560
doi: 10.1214/12-AOS968 pmid:22754029
|
| 54 |
Laber EB, Qian M, Lizotte D, Pelham WE, Murphy S. Statistical inference in dynamic treatment regimes. Revision of Univ. of Michigan, Statistics Department Technical Report 506 2011. http://arxiv.org/abs/1006.5831(Accessed on December 1, 2012)
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
| |
Shared |
|
|
|
|
| |
Discussed |
|
|
|
|
