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Original Articles

Design and analysis considerations for comparing dynamic treatment regimens with binary outcomes from sequential multiple assignment randomized trials

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Pages 1628-1651 | Received 24 Feb 2017, Accepted 16 Sep 2017, Published online: 12 Oct 2017

References

  • A. Agresti, Categorical Data Analysis, John Wiley & Sons, Hoboken, NJ, 2013.
  • D. Almirall, S.N. Compton, M. Gunlicks-Stoessel, N. Duan, and S.A. Murphy, Designing a pilot sequential multiple assignment randomized trial for developing an adaptive treatment strategy, Stat. Med. 31 (2012), pp. 1887–1902. doi: 10.1002/sim.4512
  • D. Almirall, C. DiStefano, Y.C. Chang, S. Shire, A. Kaiser, X. Lu, I. Nahum-Shani, R. Landa, P. Mathy, and C. Kasari, Longitudinal effects of adaptive interventions with a speech-generating device in minimally verbal children with ASD, J. Clin. Child. Adolesc. Psychol. 35 (2016), pp. 1595–1615.
  • D. Almirall, I. Nahum-Shani, N. Sherwood, and S. Murphy, Introduction to smart designs for the development of adaptive interventions: With application to weight loss research, Transl. Behav. Med. 4 (2014), pp. 260–274. doi:10.1007/s13142-014-0265-0.
  • S.F. Auyeung, Q. Long, E.B. Royster, S. Murthy, M.D. McNutt, D. Lawson, A. Miller, A. Manatunga, and D.L. Musselman, Sequential multiple-assignment randomized trial design of neurobehavioral treatment for patients with metastatic malignant melanoma undergoing high-dose interferon-alpha therapy, Clin. Trials 6 (2009), pp. 480–490. doi: 10.1177/1740774509344633
  • O. Bembom and M.J. van der Laan, Statistical methods for analyzing sequentially randomized trials, J. Natl. Cancer Inst. 99 (2007), pp. 1577–1582. doi: 10.1093/jnci/djm185
  • O. Bembom and M.J. van der Laan, Analyzing sequentially randomized trials based on causal effect models for realistic individualized treatment rules, Stat. Med. 27 (2008), pp. 3689–3716. doi: 10.1002/sim.3268
  • M. Campbell, S. Julious, and D. Altmani, Estimating sample sizes for binary, ordered categorical, and continuous outcomes in two group comparisons, Br. Med. J. 311 (1995), pp. 1145–1148. doi: 10.1136/bmj.311.7013.1145
  • L. Collins, S. Murphy, and K. Bierman, A conceptual framework for adaptive preventive interventions, Prev. Sci. 5 (2004), pp. 185–196. doi: 10.1023/B:PREV.0000037641.26017.00
  • R. Dawson and P. Lavori, Sample size calculations for evaluating treatment policies in multi-stage designs, Clin. Trials 7 (2010), pp. 643–652. doi: 10.1177/1740774510376418
  • E. Demidenko, Sample size determination for logistic regression revisited, Stat. Med. 26 (2007), pp. 3385–3397. doi:10.1002/sim.2771.
  • E. Demidenko, Sample size and optimal design for logistic regression with binary interaction, Stat. Med. 27 (2008), pp. 36–46. doi: 10.1002/sim.2980
  • A. Ertefaie, K. Deng, A.T. Wagner, and S.A. Murphy, qlaci R package for using q-learning to construct adaptive interventions using data from a SMART (Version 1.0) (2014). Available at methodology.psu.edu, The Methodology Center, Penn State, University Park.
  • W. Feng and A.S. Wahed, Supremum weighted log-rank test and sample size for comparing two-stage adaptive treatment strategies, Biometrika 95 (2008), pp. 695–707. doi: 10.1093/biomet/asn025
  • M.R. Kosorok and E.E.M. Moodie (eds.), Adaptive Treatment Strategies in Practice: Planning Trials and Analyzing Data for Personalized Medicine, ASA-SIAM Series on Statistics and Applied Probability, SIAM, Philadelphia, ASA, Alexandria, VA, 2016.
  • T. Habermann, E. Weller, V. Morrison, R. Gascoyne, P. Cassileth, J. Cohn, S. Dakhil, B. Woda, R. Fisher, B. Peterson, and S. Horning, Rituximab-CHOP verses CHOP alone or with maintenance Rituximab in older patients with diffuse large B-cell lymphoma, J. Clin. Oncol. 24 (2006), pp. 3121–3127. doi: 10.1200/JCO.2005.05.1003
  • M. Hernán, B. Brumback, and J. Robins, Marginal structural models to estimate the causal effect of zidovudine on the survival of hiv-positive men, Epidemiology 11 (2000), pp. 561–570. doi: 10.1097/00001648-200009000-00012
  • S.T. Holloway, E.B. Laber, K.A. Linn, B. Zhang, M. Davidian, and A.A. Tsiatis, DynTxRegime: Methods for estimating dynamic treatment regimes (2015). Available at https://CRAN.R-project.org/package=DynTxRegime, R package version 2.1.
  • H.E. Jones, K.E. O'Grady, and M. Tuten, Reinforcement-based treatment improves the maternal treatment and neonatal outcomes of pregnant patients enrolled in comprehensive care treatment, The American Journal on Addictions 20 (2011), pp. 196–204. doi:10.1111/j.1521-0391.2011.00119.x.
  • C. Kasari, A. Kaiser, K. Goods, J. Nietfeld, P. Mathy, R. Landa, S. Murphy, and D. Almirall, Communication interventions for minimally verbal children with autism: A sequential multiple assignment randomized trial, J. Am. Acad. Child. Adolesc. Psychiatry. 53 (2014), pp. 635–646. Available at http://www.sciencedirect.com/science/article/pii/S08908567140%01634. doi: 10.1016/j.jaac.2014.01.019
  • A. Kilbourne, D. Almirall, D. Eisenberg, J. Waxmonsky, D. Goodrich, J. Fortney, J. Kircher, L. Solberg, M. Main, J. Kyle, S. Murphy, K. Nord, and M. Thomas, Protocol: Adaptive implementation of effective programs trial (adept): cluster randomized smart trial comparing a standard versus enhanced implementation strategy to improve outcomes of a mood disorders program, Implement. Sci. 9 (2014), pp. 132. doi: 10.1186/s13012-014-0132-x
  • P.W. Lavori and R. Dawson, A design for testing clinical strategies: Biased individually tailored within-subject randomization, J. R. Statist. Soc. A 163 (2000), pp. 29–38. doi: 10.1111/1467-985X.00154
  • P.W. Lavori, R. Dawson, and A.J. Rush, Flexible treatment strategies in chronic disease: Clinical research implications, Biol. Psychol. 48 (2000), pp. 605–614. doi: 10.1016/S0006-3223(00)00946-X
  • P.W. Lavori, R. Dawson, and A.J. Rush, Dynamic treatment regimes: Practical design considerations, Clin. Trials 1 (2004), pp. 9–20. doi: 10.1191/1740774S04cn002oa
  • H. Lei, I. Nahum-Shani, K. Lynch, D. Oslin, and S.A. Murphy, A ‘SMART’ design for building individualized treatment sequences, Annu. Rev. Clin. Psychol. 8 (2012), pp. 21–48. doi: 10.1146/annurev-clinpsy-032511-143152
  • Z. Li and S.A. Murphy, Sample size formulae for two-stage random trials with survival outcomes, Biometrika 98 (2011), pp. 503–518. doi: 10.1093/biomet/asr019
  • X. Lu, I. Nahum-Shani, C. Kasari, D.W. Oslin, W.E. Pelham, G. Fabiano, and D. Almirall, Comparing dynamic treatment regimes using repeated-measures outcomes: Modeling considerations in smart studies, Stat. Med. 45 (2016), pp. 442–456.
  • K.A. Lynn, E.B. Laber, and L.A. Stefanski, iqlearn: Interactive q-learning in r, J. Stat. Softw. 64 (2015), pp. 1–25.
  • M.V. Mateos, A. Orio, J. Martínez-López, N. Gutiérrez, A.I. Teruel, R. de Paz, J. García-Lara na, E. Bengoechea, A. Martín, J.D. Mediavilla, L. Palomera, F. de Arriba, Y. González, J.M. Hernández, A. Sureda, J.L. Bello, J. Bargay, F.J. Peñalver, J.M. Ribera, M.L. Martín-Mateos, R. García-Sanz, M.T. Cibeira, M.L.M. Ramos, M.B. Vidriales, B. Paiva, M.A. Montalbán, J.J. Lahuerta, J. Bladé, and J.F.S. Miguel, Bortezomib, melphalan, and prednisone versus Bortezomib, thalidomide, and prednisone as induction therapy followed by maintenance treatment with Bortezomib and thalidomide versus Bortezomib and prednisone in elderly patients with untreated multiple myeloma: a randomised trial, Lancet Oncol 11 (2010), pp. 934–941. doi: 10.1016/S1470-2045(10)70187-X
  • K.K. Matthay, C.P. Reynolds, R.C. Seeger, H. Shimada, E.S. Adkins, D. Haas-Kogan, R.B. Gerbing, W.B. London, and J.G. Villablanca, Long-term results for children with high-risk neuroblastoma treated on a randomized trial of myeloablative therapy followed by 13-cis-retinoic acid: A children's oncology group study, J. Clin. Oncol. 27 (2009), pp. 1007–1013. doi: 10.1200/JCO.2007.13.8925
  • K.K. Matthay, J.G. Villablanca, R.C. Seeger, D.O. Stram, R.E. Harris, N.K. Ramsay, P. Swift, H. Shimada, C.T. Black, G.M. Brodeur, R.B. Gerbing, and C.P. Reynolds, Treatment of high-risk neuroblastoma with intensive chemotherapy, radiotherapy, autologous bone marrow transplantation, and 13-cis-retinoic acid, N. Engl. J. Med. 341 (1999), pp. 1165–1173. doi: 10.1056/NEJM199910143411601
  • S. Murphy, An experimental design for the development of adaptive treatment strategies, Stat. Med. 24 (2005), pp. 1455–1481. doi: 10.1002/sim.2022
  • S. Murphy and D. Almirall, Dynamic treatment regimens, in Encyclopedia of Medical Decision Making, M.W. Kattan, ed., Sage Publications, Thousand Oaks, CA, 2009, pp. 419–422.
  • S.A. Murphy, M.J. van der Laan, J.M. Robins, and CPPRG, Marginal mean models for dynamic regimes, J. Am. Stat. Assoc. 96 (2001), pp. 1410–1423. doi: 10.1198/016214501753382327
  • I. Nahum-Shani, M. Qian, D. Almirall, W. Pelham, B. Gnagy, G. Fabiano, J. Waxmonsky, J. Yu, and S. Murphy, Experimental design and primary data analysis for developing adaptive interventions, Psychol. Methods 17 (2012), pp. 457–477. doi: 10.1037/a0029372
  • A.I. Oetting, J.A. Levy, R.D. Weiss, and S.A. Murphy, Statistical methodology for a SMART design in the development of adaptive treatment strategies, in Causality and Psychopathology: Finding the Determinants of Disorders and their Cures, P. Shrout, K. Keyes, and K. Ornstein, eds., Chapter 8, American Psychopathological Association, American Psychiatric Publishing, Inc., Arlington, VA, 2011, pp. 179–205.
  • S.B. Ogbagaber, J. Karp, and A.S. Wahed, Design of sequentially randomized trials for testing adaptive treatment strategies, Stat. Med. 35 (2016), pp. 840–858. doi: 10.1002/sim.6747.
  • L. Orellana, A. Rotnitzky, and J. Robins, Dynamic regime marginal structural mean models for estimation of optimal dynamic treatment regimes, part i: Main content, Int. J. Biostat. 6 (2010), pp. 8.
  • D. Oslin, Managing alcoholism in people who do not respond to naltrexone (EXTEND). Available at http://clinicaltrials.gov/ct2/show/NCT00115037?term=oslin&rank=8 (2005).
  • W. Pelham and G. Fabiano, Evidence-based psychosocial treatments for attention-deficit/ hyperactivity disorder, J. Clin. Child. Adolesc. Psychol. 37 (2008), pp. 184–214. doi: 10.1080/15374410701818681
  • J. Robins, A new approach to causal inference in mortality studies with sustained exposure periods- application to control of the healthy worker survivor effect., Math. Model. 7 (1986), pp. 1393–1512. doi: 10.1016/0270-0255(86)90088-6
  • J.M. Robins, Association, causation, and marginal structural models, Synthese 121 (1999), pp. 151–179. doi: 10.1023/A:1005285815569
  • S.M. Shortreed, E. Laber, T. Scott Stroup, J. Pineau, and S.A. Murphy, A multiple imputation strategy for sequential multiple assignment randomized trials, Stat. Med. 33 (2014), pp. 4202–4214. doi:10.1002/sim.6223.
  • R.M. Stone, D.T. Berg, S.L. George, R.K. Dodge, P.A. Paciucci, P.P. Schulman, E.J. Lee, J.O. Moore, B.L. Powell, M.R. Baer, C.D. Bloomfield, and C.A. Schiffer, Postremission therapy in older patients with de novo acute myeloid leukemia: A randomized trial comparing mitoxantrone and intermediate-dose cytarabine with standard-dose cytarabine, Blood 98 (2001), pp. 548–53. doi: 10.1182/blood.V98.3.548
  • R.M. Stone, D.T. Berg, S.L. George, R.K. Dodge, P.A. Paciucci, P.P. Schulman, E.J. Lee, J.O. Moore, B.L. Powell, M.R. Baer, and C.A. Schiffer, Granulocyte-macrophage colony-stimulating factor after initial chemotherapy for elderly patients with primary acute myelogenous leukemia, N. Engl. J. Med. 332 (1995), pp. 1671–1677. doi: 10.1056/NEJM199506223322503
  • X. Tang and M. Melguizo, DTR: An R package for estimation and comparison of survival outcomes of dynamic treatment regimes, J. Stat. Softw. 65 (2015), pp. 1–28. Available at http://www.jstatsoft.org/v65/i07/. doi: 10.18637/jss.v065.i07
  • D. Tummarello, D. Mari, F. Graziano, P. Isidori, G. Cetto, F. Pasini, A. Santo, and R. Cellerino, A randomized, controlled phase iii study of cyclophosphamide, doxorubicin, and vincristine with etoposide (CAV-E) or teniposide (CAV-T),followed by recombinant interferon-alpha maintenance therapy or observation, in small cell lung carcinoma patients with complete responses, Cancer 80 (1997), pp. 2222–2229. doi: 10.1002/(SICI)1097-0142(19971215)80:12<2222::AID-CNCR2>3.0.CO;2-W
  • M. Wallace, E. Moodie, and D. Stephens, Dynamic treatment regimen estimation via regression-based techniques: Introducing r package dtrreg, Journal of Statistical Software (2016) (accepted).
  • J. Xin, B. Chakraborty, and E.B. Laber, qLearn: Estimation and inference for Q-learning (2012). Available at https://CRAN.R-project.org/package=qLearn, R package version 1.0.
  • Y. Zhang, listdtr: List-Based Rules for Dynamic Treatment Regimes (2016). Available at https://CRAN.R-project.org/package=listdtr, R package version 1.0.
  • S.L. Zeger, K-Y. Liang, and P.S. Albert, Models for longitudinal data: A generalized estimating equation approach, Biometrics 44 (1988), pp. 1049–1060. Available at http://www.jstor.org/stable/2531734. doi: 10.2307/2531734

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