Recent development on statistical methods for personalized medicine discovery
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
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.
. Recent development on statistical methods for personalized medicine discovery[J]. Frontiers of Medicine, 0, (): 102-110.
Yingqi Zhao, Donglin Zeng. Recent development on statistical methods for personalized medicine discovery. Front Med, 0, (): 102-110.
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
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
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
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
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)
