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Recent development on statistical methods for personalized medicine discovery

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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.

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References

  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

    Article  PubMed  Google Scholar 

  2. Buzdar AU. Role of biologic therapy and chemotherapy in hormone receptor- and HER2-positive breast cancer. Ann Oncol 2009; 20(6): 993–999

    Article  PubMed  CAS  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  PubMed  CAS  Google Scholar 

  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

    Article  PubMed  CAS  Google Scholar 

  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

    Article  PubMed  CAS  Google Scholar 

  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

    Article  CAS  Google Scholar 

  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

    Article  PubMed  CAS  Google Scholar 

  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

    Article  PubMed  CAS  Google Scholar 

  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

    Article  PubMed  CAS  Google Scholar 

  11. Hamburg MA, Collins FS. The path to personalized medicine. N Engl J Med 2010; 363(4): 301–304

    Article  PubMed  CAS  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  14. Murphy SA. An experimental design for the development of adaptive treatment strategies. Stat Med 2005; 24(10): 1455–1481

    Article  PubMed  CAS  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  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

    Article  PubMed  CAS  Google Scholar 

  17. Kent D, Hayward R. Subgroup analyses in clinical trials. N Engl J Med 2008; 358(11): 1199

    Article  PubMed  CAS  Google Scholar 

  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

    Article  PubMed  CAS  Google Scholar 

  19. Lagakos SW. The challenge of subgroup analyses-reporting without distorting. N Engl J Med 2006; 354(16): 1667–1669

    Article  PubMed  CAS  Google Scholar 

  20. Byar DP. Assessing apparent treatment-covariate interactions in randomized clinical trials. Stat Med 1985; 4(3): 255–263

    Article  PubMed  CAS  Google Scholar 

  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

    Article  PubMed  CAS  Google Scholar 

  22. Kehl V, Ulm K. Responder identicatifion in clinical trials with censored data. Comput Stat Data Anal 2006; 50(5):1338–1355

    Article  Google Scholar 

  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

    Article  PubMed  CAS  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  25. Cai T, Tian L, Uno H, Solomon SD, Wei LJ. Calibrating parametric subject-specific risk estimation. Biometrika 2010; 97(2): 389–404

    Article  PubMed  Google Scholar 

  26. Qian M, Murphy SA. Performance guarantees for individualized treatment rules. Ann Stat 2011; 39(2): 1180–1210

    Article  PubMed  Google Scholar 

  27. Robins J. Correcting for non-compliance in randomized trials using structural nested mean models. Commun Stat 1994; 23(8): 2379–2412

    Article  Google Scholar 

  28. Murphy SA. Optimal dynamic treatment regimes. J R Stat Soc, B 2003; 65(2): 331–355

    Article  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  30. van’t Veer LJ, Bernards R. Enabling personalized cancer medicine through analysis of gene-expression patterns. Nature 2008; 452(7187): 564–570

    Article  Google Scholar 

  31. Murphy SA. A generalization error for q-learning. J Mach Learn Res 2005; 6: 1073–1097

    PubMed  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  34. Lavori PW, Dawson R. Dynamic treatment regimes: practical design considerations. Clin Trials 2004; 1(1): 9–20

    Article  PubMed  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  36. Thall PF, Millikan RE, Sung HG. Evaluating multiple treatment courses in clinical trials. Stat Med 2000; 19(8): 1011–1028

    Article  PubMed  CAS  Google Scholar 

  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

    PubMed  CAS  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  43. Bellman R. Dynamic Programming. Princeton: Princeton University Press, 1957

    Google Scholar 

  44. Sutton RS, Barto AG. Reinforcement Learning I: Introduction. Cambridge, MA: MIT Press, 1998

    Google Scholar 

  45. Watkins CJCH. Learning from delayed rewards. Cambridge, UK: Kings College, 1989

    Google Scholar 

  46. Zhao Y, Kosorok MR, Zeng D. Reinforcement learning design for cancer clinical trials. Stat Med 2009; 28(26): 3294–3315

    Article  PubMed  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  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)

    Google Scholar 

  49. Robins JM. Optimal structural nested models for optimal sequential decisions. In: Proceedings of the Second Seattle Symposium on Biostatistics. Springer, 2004:189–326

    Chapter  Google Scholar 

  50. Moodie EEM, Richardson TS, Stephens DA. Demystifying optimal dynamic treatment regimes. Biometrics 2007; 63(2): 447–455

    Article  PubMed  CAS  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  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)

    Google Scholar 

  53. Goldberg Y, Kosorok MR. Q-learning with censored data. Ann Stat 2012; 40(1): 529–560

    Article  PubMed  Google Scholar 

  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)

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Authors and Affiliations

  1. Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, 600 Highland Ave., Madison, WI, 53792, USA

    Yingqi Zhao

  2. Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA

    Donglin Zeng

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  1. Yingqi Zhao
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Correspondence to Donglin Zeng.

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Zhao, Y., Zeng, D. Recent development on statistical methods for personalized medicine discovery. Front. Med. 7, 102–110 (2013). https://doi.org/10.1007/s11684-013-0245-7

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