Studying treatment-effect heterogeneity in precision medicine through induced subgroups

References

• Bailey, K. 1994. Generalizing the results of randomized clinical trials. Controlled Clinical Trials 15 (1):15–23. doi:10.1016/0197-2456(94)90024-8.
   PubMedGoogle Scholar
• Bang, H., and J. M. Robins. 2005. Doubly robust estimation in missing data and causal inference models. Biometrics 61 (4):962–973. doi:10.1111/j.1541-0420.2005.00377.x.
   PubMed Web of Science ®Google Scholar
• Berger, J., X. Wang, and L. Shen. 2014. A Bayesian approach to subgroup identification. Journal of Biopharmaceutical Statistics 24 (1):110–129. doi:10.1080/10543406.2013.856026.
   PubMed Web of Science ®Google Scholar
• Boonacker, C., A. Hoes, K. van Liere-Visser, A. Schilder, and M. Rovers. 2011. A comparison of subgroup analyses in grant applications and publications. American Journal of Epidemiology 174 (2):219–225. doi:10.1093/aje/kwr160.
   PubMed Web of Science ®Google Scholar
• Boulesteix, A.-L., S. Lauer, and M. J. Eugster. 2013. A plea for neutral comparison studies in computational sciences. PloS one 8 (4):e61562. doi:10.1371/journal.pone.0061562.
   PubMed Web of Science ®Google Scholar
• Boulesteix, A.-L., V. Stierle, and A. Hapfelmeier. 2015. Publication bias in methodological computational research. Cancer Informatics 14 (Suppl 5):11–19.
   PubMedGoogle Scholar
• Brookes, S. T., E. Whitely, M. Egger, G. D. Smith, P. A. Mulheran, and T. J. Peters. 2004. Subgroup analyses in randomized trials: Risks of subgroup-specific analyses; power and sample size for the interaction test. Journal of Clinical Epidemiology 57 (3):229–236. doi:10.1016/j.jclinepi.2003.08.009.
   PubMed Web of Science ®Google Scholar
• Byar, D. 1985. Assessing apparent treatment—Covariate interactions in randomized clinical trials. Statistics in Medicine 4 (3):255–263. doi:10.1002/(ISSN)1097-0258.
   PubMed Web of Science ®Google Scholar
• Cai, T., L. Tian, P. Wong, and L. Wei. 2011. Analysis of randomized comparative clinical trial data for personalized treatment selections. Biostatistics 12 (2):270–282. doi:10.1093/biostatistics/kxq060.
   PubMed Web of Science ®Google Scholar
• Chakraborty, B., E. B. Laber, and Y.-Q. Zhao. 2014. Inference about the expected performance of a data-driven dynamic treatment regime. Clinical Trials 11 (4):408–417. doi:10.1177/1740774514537727.
   PubMed Web of Science ®Google Scholar
• Chekroud, A., R. Zotti, Z. Shehzad, R. Gueorguieva, M. Johnson, M. Trivedi, T. Cannon, J. Krystal, and P. Corlett. 2016. Cross-trial prediction of treatment outcome in depression: A machine learning approach. The Lancet Psychiatry 3 (3):243–250. doi:10.1016/S2215-0366(15)00471-X.
   PubMed Web of Science ®Google Scholar
• Cohen, J., P. Cohen, S. West, and L. Aiken. 2013. Applied multiple regression/correlation analysis for the behavioral sciences. Mahwah: Routledge.
   Google Scholar
• Cronbach, L., and R. Snow. 1977. Aptitudes and instructional methods: a handbook for research on interactions. . New York: Irvington Publishers.
   Google Scholar
• Denzin, N. 1978. The research act: a theoretical introduction to sociological methods. New York, NY: McGraw-Hill.
   Google Scholar
• Dixon, D. O., and R. Simon. 1991. Bayesian subset analysis. Biometrics 47 (3):871–881. doi:10.2307/2532645.
   PubMed Web of Science ®Google Scholar
• Dusseldorp, E., C. Conversano, and B. Van Os. 2010. Combining an additive and tree-based regression model simultaneously: Stima. Journal of Computational and Graphical Statistics 19 (3):514–530. doi:10.1198/jcgs.2010.06089.
   Web of Science ®Google Scholar
• Dusseldorp, E., and I. Van Mechelen. 2014. Qualitative interaction trees: A tool to identify qualitative treatment–Subgroup interactions. Statistics in Medicine 33 (2):219–237. doi:10.1002/sim.5933.
   PubMed Web of Science ®Google Scholar
• Efron, B. 1983. Estimating the error rate of a prediction rule: Improvement on cross-validation. Journal of the American Statistical Association 78 (382):316–331. doi:10.1080/01621459.1983.10477973.
   Web of Science ®Google Scholar
• Efron, B., and R. Tibshirani. 1997. Improvements on cross-validation: The 632+ bootstrap method. Journal of the American Statistical Association 92 (438):548–560.
   Web of Science ®Google Scholar
• FDA. 2013. Paving the way for personalized medicine: fda’s role in a new era of medical product development. https://www.fdanews.com/ext/resources/files/10/10-28-13-Personalized-Medicine.pdf
   Google Scholar
• Feinstein, A. 1998. The problem of cogent subgroups: A clinicostatistical tragedy. Journal of Clinical Epidemiology 51 (4):297–299. doi:10.1016/S0895-4356(98)00004-3.
   PubMed Web of Science ®Google Scholar
• Foster, J., J. Taylor, N. Kaciroti, and B. Nan. 2014. Simple subgroup approximations to optimal treatment regimes from randomized clinical trial data. Biostatistics 16 (2):368–382. doi:10.1093/biostatistics/kxu049.
   PubMed Web of Science ®Google Scholar
• Foster, J., J. Taylor, and S. Ruberg. 2011. Subgroup identification from randomized clinical trial data. Statistics in Medicine 30 (24):2867–2880. doi:10.1002/sim.v30.24.
   PubMed Web of Science ®Google Scholar
• Foster, J. C., B. Nan, L. Shen, N. Kaciroti, and J. M. Taylor. 2016. Permutation testing for treatment–Covariate interactions and subgroup identification. Statistics in Biosciences 8 (1):77–98. doi:10.1007/s12561-015-9125-9.
   PubMed Web of Science ®Google Scholar
• Friedman, J., T. Hastie, and R. Tibshirani. 2009. The elements of statistical learning, volume 1. Berlin: Springer series in statistics Springer.
   Google Scholar
• Fu, H., J. Zhou, and D. Faries. 2016. Estimating optimal treatment regimes via subgroup identification in randomized control trials and observational studies. Statistics in Medicine 35 (19):3285–3302. doi:10.1002/sim.6920.
   PubMed Web of Science ®Google Scholar
• Gail, M., and R. Simon. 1985. Testing for qualitative interactions between treatment effects and patient subsets. Biometrics 21 (2):361–372. doi:10.2307/2530862.
   Google Scholar
• Hamburg, M., and F. Collins. 2010. The path to personalized medicine. New England Journal of Medicine 363 (4):301–304. doi:10.1056/NEJMoa1011205.
   PubMed Web of Science ®Google Scholar
• Henderson, N. C., T. A. Louis, G. L. Rosner, and R. Varadhan. 2017. Individualized treatment effects with censored data via fully nonparametric Bayesian accelerated failure time models. arXiv preprint arXiv:1706.06611.
   Google Scholar
• Horizon 2020. 2015. Health, demographic change and wellbeing. https://ec.europa.eu/programmes/horizon2020/en/h2020-section/health-demographic-change-and-wellbeing
   Google Scholar
• Huang, Y., and Y. Fong. 2014. Identifying optimal biomarker combinations for treatment selection via a robust kernel method. Biometrics 70 (4):891–901. doi:10.1111/biom.12204.
   PubMed Web of Science ®Google Scholar
• Imai, K., Ratkovic, M. 2013. Estimating treatment effect heterogeneity in randomized program evaluation. The Annals of Applied Statistics 7(1):443–470. doi:10.1214/12-AOAS593.
   Web of Science ®Google Scholar
• Kehl, V., and K. Ulm. 2006. Responder identification in clinical trials with censored data. Computational Statistics & Data Analysis 50 (5):1338–1355. doi:10.1016/j.csda.2004.11.015.
   Web of Science ®Google Scholar
• Kimball, A. 1957. Errors of the third kind in statistical consulting. Journal of the American Statistical Association 52 (278):133–142. doi:10.1080/01621459.1957.10501374.
   Web of Science ®Google Scholar
• Kravitz, R., N. Duan, and J. Braslow. 2004. Evidence-based medicine, heterogeneity of treatment effects, and the trouble with averages. Milbank Quarterly 82 (4):661–687. doi:10.1111/milq.2004.82.issue-4.
   PubMed Web of Science ®Google Scholar
• Laber, E., and Y. Zhao. 2015. Tree-based methods for individualized treatment regimes. Biometrika 102 (3):501–514. doi:10.1093/biomet/asv028.
   PubMed Web of Science ®Google Scholar
• Laber, E. B., D. J. Lizotte, M. Qian, W. E. Pelham, and S. A. Murphy. 2014. Dynamic treatment regimes: Technical challenges and applications. Electronic Journal of Statistics 8 (1):1225. doi:10.1214/14-EJS920.
   PubMed Web of Science ®Google Scholar
• Lagakos, S. 2006. The challenge of subgroup analyses-reporting without distorting. New England Journal of Medicine 354 (16):1667–1669. doi:10.1056/NEJMp068070.
   PubMed Web of Science ®Google Scholar
• Lee, S., M. Lei, and G. Brody. 2015. Confidence intervals for distinguishing ordinal and disordinal interactions in multiple regression. Psychological Methods 20 (2):245. doi:10.1037/met0000033.
   PubMed Web of Science ®Google Scholar
• Lipkovich, I., and A. Dmitrienko. 2014. Biomarker identification in clinical trials. New York, NY: Chapman and Hall/CRC Press.
   Google Scholar
• Lipkovich, I., A. Dmitrienko, J. Denne, and G. Enas. 2011. Subgroup identification based on differential effect search – A recursive partitioning method for establishing response to treatment in patient subpopulations. Statistics in Medicine 30 (21):2601–2621.
   PubMed Web of Science ®Google Scholar
• Lipkovich, I., A. Dmitrienko, and S. R. D’Agostino. 2017. Tutorial in biostatistics: Data-driven subgroup identification and analysis in clinical trials. Statistics in Medicine 36 (1):136–196. doi:10.1002/sim.7064.
   PubMed Web of Science ®Google Scholar
• Loh, W., X. He, and M. Man. 2015. A regression tree approach to identifying subgroups with differential treatment effects. Statistics in Medicine 34 (11):1818–1833. doi:10.1002/sim.v34.11.
   PubMed Web of Science ®Google Scholar
• Lu, W., H. Zhang, and D. Zeng. 2011. Variable selection for optimal treatment decision. Statistical Methods in Medical Research 22 (5):493–504. doi:10.1177/0962280211428383.
   PubMed Web of Science ®Google Scholar
• Luedtke, A. R., Van Der Laan, M. J. 2016. Statistical inference for the mean outcome under a possibly non-unique optimal treatment strategy. The Annals of Statistics 44(2):713–742. doi:10.1214/15-AOS1384.
   PubMed Web of Science ®Google Scholar
• National Institute for Health and Clinical Excellence. 2011. Generalised anxiety disorder and panic disorder in adults: Management. NICE guideline (CG113).
   Google Scholar
• Negassa, A., A. Ciampi, M. Abrahamowicz, S. Shapiro, and J. Boivin. 2005. Tree-structured subgroup analysis for censored survival data: Validation of computationally inexpensive model selection criteria. Statistics and Computing 15 (3):231–239. doi:10.1007/s11222-005-1311-z.
   Web of Science ®Google Scholar
• Ondra, T., A. Dmitrienko, T. Friede, A. Graf, F. Miller, N. Stallard, and M. Posch. 2016. Methods for identification and confirmation of targeted subgroups in clinical trials: A systematic review. Journal of Biopharmaceutical Statistics 26 (1):99–119. doi:10.1080/10543406.2015.1092034.
   PubMed Web of Science ®Google Scholar
• Pan, G., and D. Wolfe. 1997. Test for qualitative interaction of clinical significance. Statistics in Medicine 16 (14):1645–1652. doi:10.1002/(ISSN)1097-0258.
   PubMed Web of Science ®Google Scholar
• Potthoff, R. F., B. L. Peterson, and S. L. George. 2001. Detecting treatment-by-centre interaction in multi-centre clinical trials. Statistics in Medicine 20 (2):193–213.
   PubMed Web of Science ®Google Scholar
• Qian, M., and S. Murphy. 2011. Performance guarantees for individualized treatment rules. Annals of Statistics 39 (2):1180. doi:10.1214/10-AOS864.
   PubMed Web of Science ®Google Scholar
• Rothwell, P. 1995. Can overall results of clinical trials be applied to all patients? The Lancet 345 (8965):1616–1619. doi:10.1016/S0140-6736(95)90120-5.
   PubMed Web of Science ®Google Scholar
• Rothwell, P. 2005. Subgroup analysis in randomised controlled trials: Importance, indications, and interpretation. The Lancet 365 (9454):176–186. doi:10.1016/S0140-6736(05)17709-5.
   PubMed Web of Science ®Google Scholar
• Royal Australian and New Zealand College of Psychiatrists Clinical Practice Guidelines Team for Panic Disorder and Agoraphobia. 2003. Australian and New Zealand clinical practice guidelines for the treatment of panic disorder and agoraphobia. Australian & New Zealand Journal of Psychiatry 37(3):641–656. doi:10.1080/j.1440-1614.2003.01254.x.
   PubMedGoogle Scholar
• Royston, P., and W. Sauerbrei. 2004. A new approach to modelling interactions between treatment and continuous covariates in clinical trials by using fractional polynomials. Statistics in Medicine 23 (16):2509–2525. doi:10.1002/(ISSN)1097-0258.
   PubMed Web of Science ®Google Scholar
• Rush, A., H. Kraemer, H. Sackeim, M. Fava, M. Trivedi, E. Frank, P. Ninan, M. Thase, A. Gelenberg, D. Kupfer, et al. 2006. Report by the ACNP task force on response and remission in major depressive disorder. Neuropsychopharmacology 31 (9):1841–1853.
   PubMed Web of Science ®Google Scholar
• Rush, A., M. Trivedi, H. Ibrahim, T. Carmody, B. Arnow, D. Klein, J. Markowitz, P. Ninan, S. Kornstein, R. Manber et al. 2003. The 16-item quick inventory of depressive symptomatology (QIDS), clinician rating (QIDS-C), and self-report (QIDS-SR): A psychometric evaluation in patients with chronic major depression. Biological Psychiatry 54(5):573–583. doi:10.1016/S0006-3223(02)01866-8.
   PubMed Web of Science ®Google Scholar
• Rush, A., M. Trivedi, J. Stewart, A. Nierenberg, M. Fava, B. Kurian, D. Warden, D. Morris, J. Luther, M. Husain et al. 2011. Combining medications to enhance depression outcomes (co-med): Acute and long-term outcomes of a single-blind randomized study. American Journal of Psychiatry 168(7):689–701. doi:10.1176/appi.ajp.2011.11020284.
   PubMed Web of Science ®Google Scholar
• Schulte, P., A. Tsiatis, E. Laber, and M. Davidian. 2014. Q-and a-learning methods for estimating optimal dynamic treatment regimes. Statistical Science: A Review Journal of the Institute of Mathematical Statistics 29 (4):640.
   PubMed Web of Science ®Google Scholar
• Serafini, G., M. Pompili, P. Fusar-Poli, G. Porfiri, G. Giordano, S. Ferracuti, P. Girardi, and R. Tatarelli. 2011. Bupropion and panic disorder: Case report and review of the literature. The Journal of Neuropsychiatry and Clinical Neurosciences 23 (2):E47–E50. doi:10.1176/jnp.23.2.jnpe47.
   PubMed Web of Science ®Google Scholar
• Shaffer, J. 1991. Probability of directional errors with disordinal (qualitative) interaction. Psychometrika 56 (1):29–38.
   Web of Science ®Google Scholar
• Sheehan, D., J. Davidson, T. Manschreck, and J. Van Wyck Fleet. 1983. Lack of efficacy of a new antidepressant (bupropion) in the treatment of panic disorder with phobias. Journal of Clinical Psychopharmacology 3 (1):28–31. doi:10.1097/00004714-198302000-00006.
   PubMed Web of Science ®Google Scholar
• Shelton, R., and M. Trivedi. 2011. Moderators of antidepressant response in major depression. The Journal of Clinical Psychiatry 72 (10):32. doi:10.4088/JCP.09m05440blu.
   Google Scholar
• Sies, A., and I. Van Mechelen. 2017. Comparing four methods for estimating tree-based treatment regimes. The International Journal of Biostatistics 13 (1). doi:10.1515/ijb-2016-0068.
   PubMedGoogle Scholar
• Simon, N., N. Emmanuel, J. Ballenger, J. Worthington, G. Kinrys, N. Korbly, F. Farach, and M. Pollack. 2002. Bupropion sustained release for panic disorder. Psychopharmacology Bulletin 37 (4):66–72.
   Google Scholar
• Sleight, P. 2000. Debate: Subgroup analyses in clinical trials: Fun to look at-but don’t believe them. Current Controlled Trials Cardiovascular Medicine 1 (1):25–27. doi:10.1186/CVM-1-1-025.
   PubMedGoogle Scholar
• Souery, D., P. Oswald, I. Massat, U. Bailer, J. Bollen, K. Demyttenaere, S. Kasper, Y. Lecrubier, S. Montgomery, A. Serretti et al. 2007. Clinical factors associated with treatment resistance in major depressive disorder: Results from a European multicenter study. Journal of Clinical Psychiatry 68(7):1062–1070. doi:10.4088/JCP.v68n0713.
   PubMed Web of Science ®Google Scholar
• Su, X., C. Tsai, H. Wang, D. Nickerson, and B. Li. 2009. Subgroup analysis via recursive partitioning. The Journal of Machine Learning Research 10:141–158.
   Web of Science ®Google Scholar
• Su, X., T. Zhou, X. Yan, J. Fan, and S. Yang. 2008. Interaction trees with censored survival data. The International Journal of Biostatistics 4 (1). doi:10.2202/1557-4679.1071.
   PubMedGoogle Scholar
• The White House. 2015. FACT SHEET: President Obama’s Precision Medicine Initiative.
   Google Scholar
• Therneau, T., B. Atkinson, and B. Ripley. 2015. Package ‘rpart’.
   Google Scholar
• Tian, L., A. Alizadeh, A. Gentles, and R. Tibshirani. 2014. A simple method for estimating interactions between a treatment and a large number of covariates. Journal of the American Statistical Association 109 (508):1517–1532. doi:10.1080/01621459.2014.951443.
   PubMed Web of Science ®Google Scholar
• Tunis, S., J. Benner, and M. McClellan. 2010. Comparative effectiveness research: Policy context, methods development and research infrastructure. Statistics in Medicine 29 (19):1963–1976. doi:10.1002/sim.v29:19.
   PubMed Web of Science ®Google Scholar
• Xu, Y., M. Yu, Y. Zhao, Q. Li, S. Wang, and J. Shao. 2015. Regularized outcome weighted subgroup identification for differential treatment effects. Biometrics 71 (3):645–653. doi:10.1111/biom.12322.
   PubMed Web of Science ®Google Scholar
• Young, S. J. 1996. Panic associated with combining fluoxetine and bupropion. The Journal of Clinical Psychiatry 57 (4):177.
   PubMed Web of Science ®Google Scholar
• Zeileis, A., T. Hothorn, and K. Hornik. 2008. Model-based recursive partitioning. Journal of Computational and Graphical Statistics 17 (2):492–514. doi:10.1198/106186008X319331.
   Web of Science ®Google Scholar
• Zhang, B., A. Tsiatis, E. Laber, and M. Davidian. 2012b. A robust method for estimating optimal treatment regimes. Biometrics 68 (4):1010–1018. doi:10.1111/j.1541-0420.2012.01763.x.
   PubMed Web of Science ®Google Scholar
• Zhang, B., A. Tsiatis, M. Davidian, M. Zhang, and E. Laber. 2012a. Estimating optimal treatment regimes from a classification perspective. Stat 1 (1):103–114. doi:10.1002/sta.411.
   PubMedGoogle Scholar
• Zhang, Y., E. Laber, A. Tsiatis, and M. Davidian. 2015. Using decision lists to construct interpretable and parsimonious treatment regimes. Biometrics 71 (4):895–904. doi:10.1111/biom.12354.
   PubMed Web of Science ®Google Scholar
• Zhang, Y., E. B. Laber, A. Tsiatis, and M. Davidian. 2016. Interpretable dynamic treatment regimes. arXiv preprint arXiv:1606.01472.
   Google Scholar
• Zhao, L., L. Tian, T. Cai, B. Claggett, and L. Wei. 2013. Effectively selecting a target population for a future comparative study. Journal of the American Statistical Association 108 (502):527–539. doi:10.1080/01621459.2013.763566.
   PubMed Web of Science ®Google Scholar
• Zhao, Y., D. Zeng, A. Rush, and M. Kosorok. 2012. Estimating individualized treatment rules using outcome weighted learning. Journal of the American Statistical Association 107 (499):1106–1118. doi:10.1080/01621459.2012.682532.
   PubMed Web of Science ®Google Scholar
• Zhao, Y., D. Zeng, E. Laber, R. Song, M. Yuan, and M. Kosorok. 2015. Doubly robust learning for estimating individualized treatment with censored data. Biometrika 102 (1):151–168. doi:10.1093/biomet/asu050.
   PubMed Web of Science ®Google Scholar
• Zhu, R., Y. Zhao, G. Chen, S. Ma, and H. Zhao. 2016. Greedy outcome weighted tree learning of optimal personalized treatment rules. Biometrics 73 (2):391-400.
   Google Scholar
• Zimmerman, M., and I. Chelminski. 2006. A scale to screen for DSM-IV axis I disorders in psychiatric out-patients: Performance of the psychiatric diagnostic screening questionnaire. Psychological Medicine 36 (11):1601–1611. doi:10.1017/S0033291706008257.
   PubMed Web of Science ®Google Scholar
• Zimmerman, M., and J. Mattia. 2001. A self-report scale to help make psychiatric diagnoses: The psychiatric diagnostic screening questionnaire. Archives of General Psychiatry 58 (8):787–794. doi:10.1001/archpsyc.58.8.787.
   PubMed Web of Science ®Google Scholar