Susan Murphy - Research

RESEARCH PAPERS

S. Ghosh, Y. Guo, P. Hung, L. Coughlin, E. Bonar, I. Nahum-Shani, M. Walton, S. Murphy (2024). reBandit: Random Effects based Online RL algorithm for Reducing Cannabis Use. (Submitted)
A. Trella, K. Zhang, I. Nahum-Shani, V. Shetty, I. Yan, F. Doshi-Velez, S. Murphy (2024). Monitoring Fidelity of Online Reinforcement Learning Algorithms in Clinical Trials. (Submitted)
A. Trella, W. Dempsey, F. Doshi-Velez, S. Murphy (2024). Non-Stationary Latent Auto-Regressive Bandits. (Submitted)
S. Li, L. Salvat Niell, S. Choi, I. Nahum-Shani, G. Shani, S. Murphy (2023). Dyadic Reinforcement Learning. (Submitted)
K. Zhang, L. Janson, S. Murphy Statistical Inference After Adaptive Sampling for Longitudinal Data.
R. Dwivedi, S. Murphy, D. Shah Counterfactual inference for sequential experimental design. (submitted)
(126) Ghosh, S., Kim, R., Chhabria, P., Dwivedi, R., Klasnja, P., Liao, P., Zhang, K., S. Murphy Did we personalize? Assessing personalization by an online reinforcement learning algorithm using resampling. To appear in Machine Learning
(125) Nahum-Shani, I., Greer, Z.M., Trella, A.L., Zhang, K.W., Carpenter, S.M., Ruenger, D., Elashoff, D., Murphy, S.A., Shetty, V. (2023). Optimizing an adaptive digital oral health intervention for promoting oral self-care behaviors: Micro-randomized trial protocol. To appear in Contemporary Clinical Trials.
(124) Gan, K., Keyvanshokooh, E., Liu, X., S. Murphy Contextual Bandits with Budgeted Information Reveal. Accepted at AISTATS, 2024
(123) Guo, Y., S. Murphy Online learning in bandits with predicted context. Accepted at AISTATS, 2024
(122) Shin, E., Swaroop, S., Pan, W., Murphy, S. and F. Doshi-Velez (2023). Reinforcement Learning Interventions on Boundedly Rational Human Agents in Frictionful Tasks. Accepted at AAMAS2024.
(121) Saengkyongam S., Pfister N., Klasnja P., Murphy S., Peters J. (2023). Effect-Invariant Mechanisms for Policy Generalization. Journal of Machine Learning Research. HeartSteps V1 data
(120) Collins L., Nahum-Shani, I., Guastaferro, K., Strayhorn, J., Vanness, D., Murphy, S.A. (2023). Intervention Optimization: A Paradigm Shift and Its Potential Implications for Clinical Psychology.. Annu Rev Clin Psychol. 2024 Feb 5;. doi: 10.1146/annurev-clinpsy-080822-051119. [Epub ahead of print] Review. PubMed PMID: 38316143.
(119) Shin E., Klasnja, P., Murphy, S.A. Doshi-Velez, F. Online model selection by learning how compositional kernels evolve. Transactions on Machine Learning Research. Forthcoming. NIHMSID: NIHMS1946044.
(118) Carpenter, S.M., Greer, Z.M., Newman, R., Murphy, S.A., Shetty, V., I. Nahum-Shani. Developing Message Strategies to Engage Racial and Ethnic Minority Groups in Digital Oral Self-Care Interventions: Participatory Co-Design Approach.. JMIR Form Res. 2023 Dec 11;7:e49179. doi: 10.2196/49179. PMC10750234.
(117)Golbus JR, Gupta K, Stevens R, Jeganathan VSE, Luff E, Shi J, Dempsey W, Boyden T, Mukherjee B, Kohnstamm S, Taralunga V, Kheterpal V, Murphy S, Klasnja P, Kheterpal S, Nallamothu BK. A Randomized Trial of a Mobile Health Intervention to Augment Cardiac Rehabilitation: The Virtual AppLication- supported ENvironment To INcrease Exercise (VALENTINE) Study. npj Digit. Med. 6, 173 (2023). https://doi.org/10.1038/s41746-023-00921-9 PubMed PMID: 37709933; PubMed Central PMCID: PMC10502072
(116) Lipschitz, J.M., Pike, C.K., Hogan, T.P., Murphy, S.A. and K.E. Burdick. The engagement problem: A review of engagement with digital mental health interventions and recommendations for a path forward. Curr Treat Options Psychiatry. 2023 Sep;10(3):119-135. doi: 10.1007/s40501-023-00297-3. Epub 2023 Aug 25. PMCID: PMC10883589.
(115) Karine, K., Klasnja, P., Murphy, S., Marlin, B. Assessing the Impact of Context Inference Error and Partial Observability on RL Methods for Just-In-Time Adaptive Interventions.Proc Mach Learn Res. 2023 Aug;216:1047-1057. PMC10506656.
(114) S. Rathnam, S. Parbhoo, W. Pan, S. Murphy and F. Doshi-Velez The Unintended Consequences of Discount Regularization: Improving Regularization in Certainty Equivalence Reinforcement Learning. Accepted at ICML 2023 PMC10472113
(113) E. Cohn, T. Qian, and S. Murphy Sample Size Considerations for Micro-Randomized Trials with Binary Proximal Outcomes. Stat Med. 2023 Jul 20; 42(16): 2777–2796. PMCID: PMC10314739
(112) Trella, A., Zhang, K., Nahum-Shani, I., Shetty, V., Doshi-Velez, F., Murphy, S. Reward Design For An Online Reinforcement Learning Algorithm Supporting Oral Self-Care. . Proc Innov Appl Artif Intell Conf. 2023 Jun 27;37(13):15724-15730. doi: 10.1609/aaai.v37i13.26866. PMC10457015.
(111) Liao, P., Qi, Z., Wan, R., Klasnja, P., Murphy, S.A. (2022) Batch Policy Learning in Average Reward Markov Decision Processes. Ann Stat. 2022 Dec;50(6):3364-3387. doi: 10.1214/22-aos2231. Epub 2022 Dec 21. PMC10072865.
(110) A. Trella, K. Zhang, I. Nahum-Shani, V. Shetty, F. Doshi-Velez, S. Murphy Designing Reinforcement Learning Algorithms for Digital Interventions: Pre-implementation Guidelines. Algorithms 2022, 15(8), 255; https://doi.org/10.3390/a15080255 (special issue on Algorithms in Decision Support Systems). PMC9881427
Nahum-Shani, I., Wetter, D. and S.A. Murphy (2023) Adapting just-in-time interventions to vulnerability and receptivity: Conceptual and methodological considerations, Chapter 7 in Digital Therapeutics for Mental Health and Addiction: State of the Science and Vision for the Future, edited by Nick Jacobson, Tobias Kowatsch, and Lisa Marsch, Elsevier
(109) Coppersmith, D.L., Dempsey, W., Kleiman, E.M., Bentley, K.H., Murphy, S.A., & Nock, M.K. (2022) Just-in-Time Adaptive Interventions for Suicide Prevention: Promise, Challenges, and Future Directions, Psychiatry, DOI: 10.1080/00332747.2022.2092828. 2022. 85:317-333. See here for commentaries!
(108) Bertsimas D., Klasnja, P., Murphy, S., & L. Na (2022) Data-driven Interpretable Policy Construction for Personalized Mobile Health 2022 IEEE International Conference on Digital Health (ICDH), 2022, pp. 13-22, doi: 10.1109/ICDH55609.2022.00010. PMC10645432. Won Best Paper Award!
Qian, T., Cohn, E. and S.A. Murphy,(2022) Statistical Designs for Developing Personalized Mobile Treatment Interventions, Chapter 5 in Digital Therapeutics: Scientific, Statistical, Clinical, and Regulatory Development Aspects , edited by O. Sverdlov and J. van Dam,, Chapman & Hall/CRC
(107) I. Nahum-Shani, S. Shaw, S. Carpenter, S. A. Murphy and C. Yoon Engagement in a Digital Interventions Am Psychol. 2022 Oct;77(7):836-852. doi: 10.1037/amp0000983. PMC9481750
(106) Qian, T., Walton, A., Collins, L.M., Klasnja, P., Lanza, S., Nahum-Shani, I., Rabbi, M., Russell, M., Walton, M.A., Yoo, H. and Murphy, S.A. The Micro-Randomized Trial for Developing Digital Interventions: Experimental Design and Data Analysis Considerations (2022), Psychological Methods, 27(5), 874–894. PMC9276848 https://doi.org/10.1037/met0000283. Older versions are at The Micro-Randomized Trial for Developing Digital Interventions: Experimental Design Considerations and The Micro-Randomized Trial for Developing Digital Interventions: Data Analysis Methods.
S. Rathnam, S. A. Murphy and F. Doshi-Velez Comparison and Unification of Three Regularization Methods in Batch Reinforcement Learning at the ICML Workshop on Learning Theory.
E. Shin, P. Klasnja, S. A. Murphy and F. Doshi-Velez Online structural kernel selection for mobile health at the ICML Workshop on Interpretable Machine Learning in Healthcare, 2021.
(105) K. Zhang, L. Janson and Murphy, S.A.Statistical Inference with M-Estimators on Adaptively Collected Data. Adv Neural Inf Process Syst. 2021 Dec; 34: 7460–74711. PMC9232184
(104) Psihogios, A.M., Rabbi, M., Ahmed, A., McKelvey, E.R., Li, Y., Laurenceau, J., Hunger, S.P., Fleisher, L., Pai, A., Schwartz, L.A., Murphy, S.A., Barakat, L.P.. Understanding Adolescent and Young Adult 6-Mercaptopurine Adherence and mHealth Engagement During Cancer Treatment: Protocol for Ecological Momentary Assessment. JMIR Res Protoc. 2021 Oct 22;10(10):e32789. doi: 10.2196/32789. PMC8571686.
(103) Battalio SL, Conroy DE, Dempsey W, Liao P, Menictas M, Murphy S, Nahum-Shani I, Qian T, Kumar S, Spring B. Sense2Stop: A Micro-randomized Trial Using Wearable Sensors to Optimize a Just-In-Time-Adaptive Stress Management Intervention for Smoking Relapse Prevention. Contemp Clin Trials. 2021 Oct;109:106534. doi: 10.1016/j.cct.2021.106534. Epub 2021 Aug 8. PMID: 34375749; PMCID: PMC8556307.
(102) Nahum-Shani, I., Potter, L.N., Lam, C.Y., Yap, J., Moreno, A., Stoffel, R., Wu, Z., Wan, N., Dempsey, W., Kumar, S., Ertin, E., Murphy, S.A., Rehg, J., Wetter, D.W. The Mobile-Assistance for Regulating Smoking (MARS) Micro-Randomized Trial Design Protocol. Contemp Clin Trials. 2021 Nov;110:106513. doi: 10.1016/j.cct.2021.106513. Epub 2021 Jul 24. PubMed PMID: 34314855; PubMed Central PMCID: PMC8824313.
(101) Yao, J., Brunskill, E., Pan, W., Murphy, S.A., Doshi-Velez, F., Power-Constrained Bandits. Proceedings of Machine Learning Research 2021; 149:209-259.
Menictas, M., Tomkins, S. and Murphy, S.A., Streamlined Empirical Bayes Fitting of Linear Mixed Models in Mobile Health. See also Fast Physical Activity Suggestions: Efficient Hyperparameter Learning in Mobile Health.
(100)Tomkins, S., Liao, P., Klasnja, P. and Murphy, S.A., IntelligentPooling: Practical Thompson Sampling for mHealth. Mach Learn (2021). https://doi.org/10.1007/s10994-021-05995-8. PMCID:PMC849236. See also Tomkins, S., Liao, P., Klasnja, P., Yeung, S. and Murphy, S.A. Rapidly Personalizing Mobile Health Treatment Policies with Limited Data.
(99)Nahum-Shani, I., Rabbi, M., Yap, J., Philyaw-Kotov, M.L., Klasnja, P., Bonar, E.E., Cunningham, R.M., Murphy, S.A. and M.A. Walton. Translating Strategies for Promoting Engagement in Mobile Health: A Proof-of-Concept Micro-Randomized Trial. Health Psychology, 40(12), 974–987. https://doi.org/10.1037/hea0001101 PMCID: PMC8738098.

o Yap, J., Rabbi, M., Philyaw-Kotov, M., Walton, M., Murphy, S., & Nahum-Shani, I. (2020a). SARA: Availability and Missing Data Summaries.

o Yap, J., Rabbi, M., Philyaw-Kotov, M., Walton, M., Murphy, S., & Nahum-Shani, I. (2020b). SARA: Results.

o An early version of this paper and documentation won the MIDAS Reproducibility Challenge Category D:Robustness! Announcement at MIDAS Website.

(98) Zhang, K.W., Janson, L. and Murphy, S.A., Inference for Batched Bandits. 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Vancouver, Canada. PubMed Central PMCID: PMC8734616
(97) Qian, T., Yoo, H., Klasnja, P., Almirall, D. and Murphy, S.A., (2021) Estimating Time-Varying Causal Excursion Effects in Mobile Health with Binary Outcomes with discussion. Biometrika Volume 108, Issue 3, September 2021, Pages 507–527. DOI 10.1093/biomet/asaa070. Rejoinder. Biometrika Volume 108, Issue 3, September 2021, Pages 551–555
Li, S., Psihogios, A.M., McKelvey E.R., Ahmed, A., Rabbi, M. and S.A. Murphy. Micro-Randomized Trials for Promoting Engagement in Mobile Health Data Collection: Adolescent/Young Adult Oral Chemotherapy Adherence as an Example. Current Opinion in Systems Biology, (2020) PubMed Central PMCID: PMC7437990.
Bidargaddi, N., Schrader, G., Klasnja, P., Licinio, J. and S.A. Murphy. Designing m-Health interventions for precision mental health support. Translational Psychiatry (2020)10:222 https://doi.org/10.1038/s41398-020-00895-2
Carpenter, S.M., Menictas, M., Nahum-Shani, I., Wetter, D.W., and Murphy, S.A. Developments in Mobile Health Just-in-Time Adaptive Interventions for Addiction Science. Current Addiction Reports Published online: 27 June, 2020. PMCID: PMC7968352
(96) Kroska, E.B., Hoel, S., Victory, A., Murphy, S.A., McInnis, M.G., Stowe, Z.N. and A. Cochran. Optimizing Acceptance and Commitment Therapy Microintervention via a Mobile App with Two Cohorts: Protocol for Micro-Randomized Trials, JMIR Res Protoc. 2020 Sep 23;9(9):e17086. doi: 10.2196/17086. PubMed PMID: 32965227; PubMed Central PMCID: PMC7542401. doi:10.2196/17086
(95) Liao, P., Klasnja, P. and Murphy, S.A., Off-Policy Estimation of Long-Term Average Outcomes with Applications to Mobile Health. J Am Stat Assoc. 2021;116(533):382-391. doi: 10.1080/01621459.2020.1807993. Epub 2020 Oct 1. PubMed PMID: 33814653; PubMed Central PMCID: PMC8014957
(94) Liao, P., Greenewald K., Klasnja, P. and Murphy, S.A., Personalized HeartSteps: A Reinforcement Learning Algorithm for Optimizing Physical Activity. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies March 2020 Article No.: 18. https://doi.org/10.1145/3381007 PubMed PMCID: PMC8439432
Rabbi, M., Philyaw-Kotov, M., Li, J., Li, K., Rothman, B., Giragosian, L., Reyes, M., Gadway, H., Cunningham, R., Bonar, E., Nahum-Shani, I., Walton, M., Murphy, S. and Klasnja, P. (n.d.). Translating behavioral theory into features of mHealth interventions: A case study of SARA, a mHealth tool for substance-use research. Submitted
Wongvibulsin S, Martin SS, Saria S, Zeger SL, Murphy S. An Individualized, Data-Driven Digital Approach for Precision Behavior Change.. Am J Lifestyle Med.. (2020) 14 (3): 289-293. doi: 10.1177/1559827619843489. eCollection 2020 May-Jun. Review. PubMed PMID: 32477031; PubMed Central PMCID: PMC7232899.
(93) Rabbi, M., Li, K., Yan, H.Y., Hall, K., Klasnja, P. and Murphy, S.A., ReVibe: A context-assisted evening recall approach to improve self-report adherence. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies December 2019 Article No.: 149. https://doi.org/10.1145/3369806. PubMed Central PMCID: PMC8218636.
Menictas M, Rabbi M, Klasnja P, and Murphy, S.A., Artificial Intelligence Decision Making in Mobile Health The Biochemist. Biochem (Lond). (2020) 41 (5): 20-24 NIHMS ID NIHMS1055067 https://doi.org/10.1042/BIO04105020
(92) Dempsey, W., Liao, P., Kumar, S. and Murphy, S.A., The stratified micro-randomized trial design: sample size considerations for testing nested causal effects of time-varying treatments Annals of Applied Statistics. (2020) 14 (2): 661-684. PMC8049613
(91) Qian, T., Klasnja, P. and Murphy, S.A., Linear mixed models with endogenous covariates: modeling sequential treatments with application to a mobile health study. Statistical Science with discussion. (2020) 35, (3): 375-390. Rejoinder pgs: 400-403. PMC7596885
(90) Seewald NJ, Smith SN, Lee AJ, Klasnja P, Murphy, S.A. Practical Considerations for Data Collection and Management in Mobile Health Micro-randomized Trials. Stat Biosci. 2019 Jul;11(2):355-370. doi: 10.1007/s12561-018-09228-w. Epub 2019 Jan 5. PubMed PMID: 31462937; PubMed Central PMCID: PMC6713230
(89) Liao, P., Dempsey, W., Sarker, H., Hossain S.M., al’Absi, M., Klasnja, P., and Murphy, S.A., Just-in-Time But Not Too Much: Determining Treatment Timing in Mobile Health, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., Vol. 2, No. 4, Article 179. December 2018. PMCID: PMC6380673.
(88) Klasnja, P., Smith, S., Seewald, N.J., Lee, A.,Hall, K., Luers, B., Hekler, E.B. and Murphy, S.A. Efficacy of contextually-tailored suggestions for physical activity: A micro-randomized optimization trial of HeartSteps Ann Behav Med. 2019 Jun; 53(6): 573–582. HeartSteps V1 data PubMed PMID: 30192907. PMCID: PMC6401341 Supplement
(87) Bidargaddi N, Almirall D, Murphy, S.A., Nahum-Shani I, Kovalcik M, Pituch T, Maaieh H and Strecher V. (2018). To prompt or not to prompt? A micro-randomized trial of time-varying push notifications to increase proximal engagement with a mobile health application. JMIR mHealth UHealth;6(11):e10123 DOI: 10.2196/10123 Supplement
(86) Rabbi M, Philyaw-Kotov M, Cunningham R, Bonar EE, Nahum-Shani I, Klasnja P, Walton M, Murphy, S., (2018).Towards increasing engagement in substance use data collection: Development of the SARA app and protocol for a micro-randomized trial with adolescents and emerging adults. JMIR Res Protoc 2018;7(7):e166 URL: https://www.researchprotocols.org/2018/7/e166. doi:10.2196/resprot.9850 http://dx.doi.org/10.2196/resprot.9850 PMCID PMC6070723
(85) Walton, A., Nahum‐Shani, I., Crosby, L., Klasnja, P., & Murphy, S. (2018). Optimizing Digital Integrated Care via Micro‐Randomized Trials. Clinical Pharmacology & Therapeutics, 104 (1), 53-58. http://doi.org/10.1002/cpt.1079 NIHMS ID 956009, PMC5995647.
Hall, K., Nahum-Shani, I., August, G., Patrick, M.E., Murphy, S.A., Almirall, D., (2019) Adaptive Intervention Designs in Substance Use Prevention. In Prevention of Substance Use. Pgs 263-280. Z. Sloboda, H. Petras, E. Robertson, R. Hingson, Eds., Springer.
(84) Luers, B., Klasnja P. and Murphy, S.A., Standardized effect sizes for preventive mobile health interventions in micro-randomized trials Prev Sci. 2018 Jan 9. doi: 10.1007/s11121-017-0862-5.PMCID: PMC6037616
(83) Greenewald, K., Tewari A., Klasnja P. and Murphy, S.A., Action Centered Contextual Bandits Adv Neural Inf Process Syst. 2017 Dec; 30: 5973–5981, PMC5719505.
Lei, H., Tewari, A., and Murphy, S.A. An Actor-Critic Contextual Bandit Algorithm for Personalized Mobile Health Interventions. arXiv:1706.09090
Wagner III, B., Liu, E., Shaw, S., Lakovlev, G., Zhou, L., Harrington, C., Abowd, G., Yoon, C., Kumar, S., Murphy, S.A., Spring, B., and Nahum-Shani, I. ewrapper: Operationalizing engagement strategies in mHealth In Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers (UbiComp '17). ACM, New York, NY, USA, 790–798. DOI: 10.1145/3123024.3125612. PMC5776015.
Rabbi, M., Philyaw-Kotov, M., Lee, J., Mansour, A., Dent, L., Wang, X., Cunningham, R., Bonar, E., Nahum-Shani, I., Klasnja, P., Walton, M. and Murphy, S.A. SARA: A Mobile App to Engage Users in Health Data Collection In Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers (UbiComp '17). ACM, New York, NY, USA, 781-789. DOI: https://doi.org/10.1145/3123024.3125611. PMC5831124.
(82) Dempsey, W.H., Moreno, A., Scott, C.K., Dennis, M.L., Gustafson, D.H., Murphy, S.A. and Rehg, J.M., iSurvive: An Interpretable, Event-time Prediction Model for mHealth Proceedings of the 34th International Conference on Machine Learning, Sydney, Australia, PMLR 70, 2017. NIHMS922395. PMC6430609.
Smith, S.S., Lee, A.J., Hall, K., Seewald, N.J., Boruvka, A., Murphy, S.A. and Klasnja, P., Design Lessons from a Micro-Randomized Pilot Study in Mobile Health, (2017) Mobile Health Sensors, Analytic Methods, and Applications , Springer International Publishing AG 2017, J.M. Rehg et al. (eds.), DOI 10.1007/978-3-319-51394-2_4, pgs. 59-82.
Sarker, H., Hovsepian, K., Chatterjee, S., Nahum-Shani, I. Murphy, S.A. Spring, B., Ertin, E., al'Absi, M., Nakajima, M., and Kumar, S., From Markers to Interventions: The Case of Just-in-Time Stress Intervention, (2017) Mobile Health Sensors, Analytic Methods, and Applications , Springer International Publishing AG 2017, J.M. Rehg et al. (eds.), DOI 10.1007/978-3-319-51394-2_4, pgs 411-433.
Tewari, A. and Murphy, S.A., From Ads to Interventions: Contextual Bandits in Mobile Health, (2017) Mobile Health Sensors, Analytic Methods, and Applications , Springer International Publishing AG 2017, J.M. Rehg et al. (eds.), DOI 10.1007/978-3-319-51394-2_4, pgs 495-518.
(81)Boruvka, A., Almirall, D., Witkiewitz, K., Murphy, S.A. (2018). Assessing Time-Varying Causal Effect Moderation in Mobile Health, Journal of the American Statistical Association. 113:523,1112-112. Accepted author version posted online: 31 Mar 2017 http://dx.doi.org/10.1080/01621459.2017.1305274. PMCID: PMC6241330 NIHMSID909258.
(80) Nahum-Shani, I., Smith, S.N. Spring, B.J., Collins, L.M., Witkiewitz, K., Tewari, A., & Murphy, S.A.. (2018). Just-in-Time Adaptive Interventions (JITAIs) in Mobile Health: Key Components and Design Principles for Ongoing Health Behavior Support. Annals of Behavioral Medicine. May 18;52(6):446-462.doi:10.1007/s12160-016-9830-8, PMCID: PMC5364076
(79) Bekiroglu, K., Lagoa, C., Murphy, S.A. and S. T. Lanza, S.T. (2017). Control Engineering Methods for the Design of Robust Behavioral Treatments, IEEE Transactions on Control Systems Technology.Vol 25(3):979-990. Epub 2016 Jun 28. PubMed PMID: 28344431 PMCID PMC5362168.
(78) Liao,P., Klasnja, P., Tewari, P., Murphy, S.A. Sample Size Calculations for Micro-Randomized Trials in mHealth, Statistics in Medicine. 2016 May 30;35(12):1944-71. [2015 Dec 28 Epub ahead of print] PubMed PMID: 26707831, PMCID PMC4848174.
Dempsey, W., Liao, P., Klasnja, P., Nahum-Shani, I. Murphy, S.A. (2015). Randomized trials for the Fitbit generation, Significance. 12(6):20-23. PMCID: PMC4721268
(77)Klasnja, P., Hekler, E.B., Shiffman, S., Boruvka, A., Almirall, D., Tewari, A. and Murphy, S.A. (2015). Micro-randomized trials: An experimental design for developing just-in-time adaptive interventions, Health Psychology. Vol 34(Suppl):1220-1228. doi: 10.1037/hea0000305. PubMed PMID: 26651463; PubMed Central PMCID: PMC4732571
(76)Lu, X., Lynch, K.G., Oslin, D.W. and Murphy, S.A. Comparing Treatment Policies with Assistance from the Structural Nested Mean Model. Biometrics. 2015 Sep 13. doi: 10.1111/biom.12391. [Epub ahead of print] PubMed PMID: 26363892, PMCID: PMC4789134
(75) Kumar, S., Abowd, G., Abraham, W., al Absi, M., Beck, J.G., Chau, D.H., Condie, T., Conroy, D.E., Ertin, E., Estrin, D., Ganesan, D., Lam, C., Marlin, B., Marsh, C.B., Murphy, S.A., Nahum-Shani, I., Patrick, K., Rehg, J., Sharmin, M., Shetty, V., Sim, I., Spring, B., Srivastava, M., Wetter, D. (2015). Center of Excellence for Mobile Sensor Data-to-Knowledge (MD2K)(in press). Journal of the American Medical Informatics Association. 22(6): 1137-1142 DOI: http://dx.doi.org/10.1093/jamia/ocv056 First published online: 3 July 2015, PMCID: PMC5009911.
(74) Gunlicks-Stoessel, M., Mufson , L., Westervelt, A., Almirall, D., Murphy, S.A. (2015). A Pilot SMART for Developing an Adaptive Treatment Strategy for Adolescent Depression. Journal of Clinical Child and Adolescent Psychology. Mar 18:1-15. [Epub ahead of print] PMID: 25785788, PMCID: PMC4575229.
(73) Kilbourne, A. M., Almirall, D., Eisenberg, D., Waxmonsky, J., Goodrich, D. E., Fortney, J. C., Kirchner, J. E., Solberg, L. I., Main, D., Bauer, M.S., Kyle, J., Murphy, S.A., Nord, K.M., and M. R. Thomas (2014). 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. Implementation Science. Sep 30;9:132. PMCID: PMC4189548
Laber, E.B., S.A. Murphy, Adaptive inference after model selection. submitted. To obtain a copy, send email to Eric Laber
(72) Laber,E., D. Lizotte, M. Qian, W.E. Pelham and S.A. Murphy (2014). Dynamic treatment regimes: technical challenges and applications. Electronic Journal of Statistics, with discussion. Vol. 8, No. 0, 1225-1272. PMCID: PMC4209714
(71) Shortreed, S.M., E. Laber, T.S. Stroup, J. Pineau, & Murphy S.,(2014). A multiple imputation strategy for sequential multiple assignment randomized trials. Statistics in Medicine Oct 30;33(24):4202-14. PMCID: PMC4184954
(70) Almirall D., Nahum-Shani, I., Sherwood, N.E., Murphy S.,(2014). Introduction to SMART Designs for the Development of Adaptive Interventions: With Application to Weight Loss Research. Translational Behavioral Medicine: Practice, Policy and Research. Sep; 4(3): 260-274. PMCID: PMC4167891
(69) Kasari C., Kaiser A., Goods K., Nietfeld J., Mathy P., Landa R., Murphy S.,Almirall D. (2014) Communication Interventions for Minimally Verbal Children with Autism: Sequential Multiple Assignment Randomized Trial. Journal of the American Academy of Child and Adolescent Psychiatry 53(6):635-646. PMCID: PMC4030683
(68) Lagoa, C.M., Bekiroglu, K., Lanza, S.T. & S.A. Murphy(2014) Designing Adaptive Intensive Interventions Using Methods from Engineering. Journal of Consulting and Clinical Psychology; 82(5), 868-78. PMCID PMC4176810
Bekiroglu, K., Lagoa, C., Murphy S.& Lanza, S. T.(2013). A robust MPC approach to the design of treatments. Proceedings of the 2013 52nd IEEE Conference on Decision and Control. Dec:3505 - 3510. DOI:10.1109/CDC.2013.6760421
Chakraborty, B. and S.A. Murphy,(2013) Dynamic Treatment Regimes. Annual Review of Statistics and Its Application Vol. 1: 447-464 (Volume publication date January 2014) First published online as a Review in Advance on August 26, 2013 DOI: 10.1146/annurev-statistics-022513-115553 PMCID PMC4231831
(67) Kumar, S., W.J. Nilsen, A. Abernethy, A. Atienza, K. Patrick, M. Pavel, W.T. Riley, A. Shar, B. Spring, D. Spruijt-Metz, D. Hedeker, V. Honavar, R. Kravitz, R. Craig Lefebvre, D.C. Mohr, S.A. Murphy,(2013) C. Quinn, V. Shusterman, D. Swendeman, Mobile Health Technology Evaluation, The mHealth Evidence Workshop. Am J Prev Med;45(2):228-236. PMCID: PMC3803146
M. Qian, I. Nahum-Shani, S.A. Murphy,(2013) Dynamic Treatment Regimes, Book chapter in Modern Clinical Trial Analysis , edited by X. Tu and W. Tang, Springer Science, pgs. 127-148. PMC Exempt-invited review.
Nahum-Shani, I., Xi, L., Henderson, M.M., S.A. Murphy, (2013). An innovative experimental design for addressing the "assistance dilemma" using interactive learning environments. Book chapter in Advances in Help Seeking Research and Applications: The role of emerging technologies. S.A. Karabenick and M. Puustinen (Eds). pp. 227-264. Information Age Publishing. PMC Exempt-invited review.
(66) Almirall D, Griffin BA, McCaffrey DF, Ramchand R, Yuen RA and Murphy S.A.,(2014). "Time-varying effect moderation using the structural nested mean model: estimation using inverse-weighted regression with residuals." Statistics in Medicine. Sep 10;33(20):3466-87 PMCID: PMC4008726
(65) Fonteneau, R., Murphy S.A.,Wehenkel, L., D. Ernst, (2013). Batch Mode Reinforcement Learning based on the Synthesis of Artificial Trajectories. Annals of Operations Research. 208(1), 383-416. PMCID: PMC3773886
(64) Lizotte D.J., Bowling M., S.A. Murphy, Linear Fitted-Q Iteration with Multiple Reward Functions. Journal of Machine Learning Research. 13(Nov):3253-3295, 2012. PMCID: PMC3670261
(63) Almirall D., Compton S.N., Rynn M.A., Walkup J.T., S.A. Murphy, (2012). SMARTer Discontinuation Trials: With Application to the Treatment of Anxious Youth. Journal of Child and Adolescent Psychopharmacology. 22(5): 364-374. PMCID: PMC3482379 doi: 10.1089/cap.2011.0073
(62) Little RJ, D Agostino R, Cohen ML, Dickersin K, Emerson SS, Farrar JT, Frangakis C, Hogan JW, Molenberghs G, S.A. Murphy, Neaton JD, Rotnitzky A, Scharfstein D, Shih W, Siegel JP and H Stern. (2012). The Prevention and Treatment of Missing Data in Clinical Trials. New England Journal of Medicine. vol. 367:1355-1360. PMID: 23034025 PMCID: PMC3771340
(61)I. Nahum-Shani, M. Qian, D. Almiral, W.. Pelham, B. Gnagy, G. Fabiano, J. Waxmonsky, J. Yu and S.A. Murphy (2012). Experimental Design and Primary Data Analysis Methods for Comparing Adaptive Interventions. SAS Code Psychological Methods 17(4), 457-477. doi: 10.1037/a0029372. Epub 2012 Oct 1 PMCID: PMC3825557
(60)I. Nahum-Shani, M. Qian, D. Almiral, W.. Pelham, B. Gnagy, G. Fabiano, J. Waxmonsky, J. Yu and S.A. Murphy (2012). Q-Learning: A Data Analysis Method for Constructing Adaptive Interventions. R Code Psychological Methods 17(4):478-94. doi: 10.1037/a0029373. Epub 2012 Oct 1. PMID: PMC23025434
D. Almirall, D. Lizotte and S.A. Murphy (2012). SMART Design Issues and the Consideration of Opposing Outcomes, Discussion of the paper, Evaluation of Viable Dynamic Treatment Regimes in a Sequentially Randomized Trial of Advanced Prostate Cancer by Wang et al. JASA 107(498), 509-512. PMID: 23543940 [PubMed] PMCID: PMC3607391
H. Lei, I. Nahum-Shani, K. Lynch, D. Oslin and S.A. Murphy (2012). A SMART Design for Building Individualized Treatment Sequences, The Annual Review of Clinical Psychology, Review in Advance first posted online on December 12, 2011 Vol. 8: 21-48. PMCID: PMC3887122
(59) D. Almirall, S. N. Compton, M. Gunlicks-Stoessel, N. Duan, S.A. Murphy (2012). Designing a Pilot Sequential Multiple Assignment Randomized Trial for Developing an Adaptive Treatment Strategy. Statistics in Medicine 31(17):1887-1902. PMCID: PMC3399974
L. Gunter, J. Zhu, and S.A. Murphy (2011). Variable Selection for Qualitative Interactions in Personalized Medicine while Controlling the Family-wise Error Rate. Journal of Biopharmaceutical Statistics. 21 (6), 1063-1078. PMCID: PMC3396291
(58)K. Deng, J. Pineau and S.A. Murphy (2011). Active Learning for Developing Personalized Treatment. Proceedings of the Twenty-Seventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-11) (These papers are externally reviewed; 34\% acceptance rate.) AUAI Press 161-8.
(57)K. Deng, J. Pineau and S.A. Murphy (2011). Active Learning for Personalizing Treatment. Adaptive Dynamic Programming And Reinforcement Learning (ADPRL), 2011 IEEE Symposium on (These papers are externally reviewed.) 11-15 April 2011. pgs 32-39.
(56) Z. Li and S.A. Murphy Sample Size Formulae for Two-Stage Randomized Trials with Survival Outcomes. (2011) Simulation Code Supplementary Material Biometrika 98(3):503-518 PMCID: PMC3254237
(55)D. Almirall, D.F. McCaffrey, R. Ramchand and S.A. Murphy (2011). Subgroups Analysis when Treatment and Moderators are Time-varying. Prevention Science Published Online First 22 March 2011 PMCID: PMC3135740
A.I. Oetting, J.A. Levy, R.D. Weiss, S.A. Murphy(2011), Statistical Methodology for a SMART Design in the Development of Adaptive Treatment Strategies,, Causality and Psychopathology: Finding the Determinants of Disorders and their Cures, (P.E. Shrout, K.M. Keyes, K. Ornstein, Eds.) Arlington VA: American Psychiatric Publishing, Inc, pgs. 179-205 Associated Technical Report..
(54)R. Fonteneau, S.A. Murphy, L. Wehenkel and D. Ernst (2011). Towards min max generalization in reinforcement learning. In Agents and Artificial Intelligence: International Conference, ICAART 2010, Valencia, Spain, January 2010, Revised Selected Papers, Series: Communications in Computer and Information Science (CCIS), Volume 129, J. Filipe, A. Fred, and B. Sharp (Editors), pp. 61-77. Springer, Heidelberg. , To obtain a copy, send email to R Fonteneau
(53) M. Qian and S.A. Murphy (2011). Performance Guarantees for Individualized Treatment Rules. Annals of Statistics, Supplement. Apr 1;39(2):1180-1210. PMC3110016
(52)S. M. Shortreed, E. Laber, D. J. Lizotte, T. S. Stroup, J Pineau and S.A. Murphy (2010). Informing sequential clinical decision-making through reinforcement learning: an empirical study. . Machine Learning, Published Online 22 December 2010, 84(1)109-136. PMCID: PMC3143507.
(51)E. Laber and S.A. Murphy (2011). Adaptive Confidence Intervals for the Test Error in Classification. Journal of the American Statistical Association, with discussion Online supplement Posted online on 30 Mar 2011, vol. 106:904-913. PMCID: PMC3285493
(50)D. Lizotte, M. Bowling and S.A. Murphy, Efficient Reinforcement Learning with Multiple Reward Functions for Randomized Controlled Trial Analysis. Proceedings of the 27th International Conference on Machine Learning (ICML 2010), pgs. 695-702.
R. Fonteneau, S.A. Murphy, L. Wehenkel and D. Ernst. Generating informative trajectories by using bounds on the return of control policies ". In Proceedings of the Workshop on Active Learning and Experimental Design 2010 (in conjunction with AISTATS 2010), 2-page highlight paper,Chia Laguna, Sardinia, Italy, May 16 2010.
(49)Fonteneau, R., S.A. Murphy , L.Wehenkel and D. Ernst (2010). Model-Free Monte Carlo-like Policy Evaluation. In Proceedings of The Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2010), JMLR W&CP Volume 9, pages 217-224, Chia Laguna, Sardinia, Italy, 13-15 May 2010. This paper was also published in the proceedings of CAp 2010.
(48) H. McGowan, R.L. Nix, S.A. Murphy, K.L. Bierman and CPPRG (2010). Investigating the Effects of Selection Bias in Dose-Response Analyses of Preventive Interventions . Text file with simulation code can be found here. Prevention Science, vol: 11, pgs 239-251. PMC3044506
(47)Fonteneau, R., S.A. Murphy , L.Wehenkel and D. Ernst (2010). A Cautious Approach to Generalization in Reinforcement Learning. Joaquim Filipe, Ana L. N. Fred, Bernadette Sharp (Eds.): ICAART 2010 - Proceedings of the International Conference on Agents and Artificial Intelligence, Volume 1 - Artificial Intelligence, Valencia, Spain, January 22-24, 2010. INSTICC Press 2010, 64-73. Best student paper award (Certificate).
(46)D. Almirall, T. Ten Have, S.A. Murphy (2010), Structural Nested Mean Models for Assessing Time-Varying Effect Moderation, Biometrics, 66(1), 131-139. PMC 2875310
(45) L. Gunter, J. Zhu, S.A. Murphy (2011). Variable Selection for Qualititative Interactions. available online: 20-MAY-2009 DOI: 10.1016/j.stamet.2009.05.003 Statistical Methodology 8(1):42-55. PMC 3003934
(44)Almirall D, Coffman CJ, Yancy, Jr. WS, S.A. Murphy. 2010. Structural Nested Models. In Analysis of Observational Health-Care Data Using SAS (eds D. Faries, A. Leon, JM Haro & RL Obenchain) Cary NC: SAS Institute, pgs. 231-262. This was externally reviewed.
(43)B. Chakraborty, S.A. Murphy and V. Strecher (2009). Inference for Nonregular Parameters in Optimal Dynamic Treatment Regimes . available online: 16-July-2009 DOI: 10.1177/0962280209105013 Statistical Methods in Medical Research Vol 19, Issue 3, pgs 317-343. PMCID: PMC2891316
(42)B. Chakraborty, L. Collins, V. Strecher, S.A. Murphy(2009), Developing Multicomponent Interventions using Fractional Factorial Designs , Statistics in Medicine . Published Online: Jul 3 2009 4:41AM DOI: 10.1002/sim.3643 September 20; 28(21): 2687-2708. PMC2746448
S.A. Murphy and D. Almirall, (2009) Dynamic Treatment Regimes, (Kattan MW, ed.) Encyclopedia of Medical Decision Making. Thousand Oaks, CA: Sage Publications, Vol. 1, pgs. 419-422.
(41) S.A. Murphy and D. Bingham (2009). Screening Experiments for Developing Dynamic Treatment Regimes. JASA. 184:391-408. PMCID: PMC2892819
(40) R. Fonteneau, S.A. Murphy, L.Wehenkel and D. Ernst (2008). Inferring bounds on the performance of a control policy from a sample of trajectories. accepted for presentation at the 2009 IEEE International Symposium on Adaptive Dynamic Programming and Reinforcement Learning and for publication in the conference proceedings published annually by IEEE, To obtain a copy, send email to R. Fonteneau
D. Lizotte, L.Gunter, E. Laber and S.A. Murphy (2008). Missing Data and Uncertainty in Batch Reinforcement Learning . Selected for a poster presentation at the NIPS-08 Workshop on Model Uncertainty and Risk in Reinforcement Learning.
B. Chakraborty and S.A. Murphy (2008). Bias Correction and Confidence Intervals for Fitted Q-iteration . Selected for a verbal presentation at the NIPS-08 Workshop on Model Uncertainty and Risk in Reinforcement Learning.
(39)L.M. Collins, B. Chakraborty, S.A. Murphy, V. Strecher (2009), Comparison of a phased experimental approach and a single randomized clinical trial for developing multicomponent behavioral interventions. Clinical Trials, Vol 6: 5-15. PMC2711350
(38)E. Laber, S.A. Murphy (2008), Small Sample Inference for Generalization Error in Classification Using the CUD Bound, Proceedings of the 2008 Uncertainty in Artificial Intelligence Conference} AUAI Press, 357:365 PMC2876736
(37)V. Nair, V. Strecher, A. Fagerlin, P. Ubel, K. Resnicow, S. Murphy, R. Little, B. Chakraborty, A. Zhang (2008), Screening Experiments and Fractional Factorial Designs in Behavioral Intervention Research, The American Journal of Public Health, Vol 98, No. 8: 1354-1359. PMC2446451
(36) L. Gunter, J. Zhu, S.A. Murphy (2007). Variable Selection for Optimal Decision Making(Short Version). Variable Selection for Optimal Decision Making(Long Version). Poceedings of the 11th Conference on Artificial Intelligence in Medicine, AIME 2007, LNCS/LNAI 4594, 149-154.
(35) J. Pineau, M.G. Bellemare, A. J. Rush, A. Ghizaru, S.A. Murphy (2007). Constructing evidence-based treatment strategies using methods from computer science. Drug and Alcohol Dependence, 88, Supplement 2:S52-S60. PMC1934348
(34)L.M. Collins, S.A. Murphy, V. Strecher (2007). The Multiphase Optimization Strategy (MOST) and the Sequential Multiple Assignment Randomized Trial (SMART): New Methods for More Potent e-Health Interventions. American Journal of Preventive Medicine , 32(5S):S112-118.
S.A. Murphy, L.M. Collins, A.J. Rush (2007). Customizing Treatment to the Patient: Adaptive Treatment Strategies (Editorial). Drug and Alcohol Dependence, Drug and Alcohol Dependence. 2007;88(2):S1-S72. PMC1924645
(33) S.A. Murphy, K.G. Lynch, J.R. McKay, D. Oslin, T. TenHave (2007). Developing Adaptive Treatment Strategies in Substance Abuse Research. Drug and Alcohol Dependence, 88(2):S24-S30 PMC1922034
Y. Xie, S. Murphy (2007). Discussion of Principal Stratification Designs to Estimate Input Data Missing due to Death, by Frangakis, Rubin, An, and MaKenzie, Biometrics, 63:655-658. To obtain a copy send email to Yu Xie
(32)B. Bray, D. Almirall, R. Zimmerman, D. Lynam & S.A. Murphy (2006). Assessing the Total Effect of Time-varying Predictors in Prevention Research. Prevention Science 7(1):1-17. PMC1479302
(31) S.A. Murphy, D. Oslin, A.J.Rush, J. Zhu for MCATS (2006). Methodological Challenges in Constructing Effective Treatment Sequences for Chronic Disorders, Neuropsychopharmacology, 32(2):257-62. advance online publication, November 8 2006, doi: 10.1038/sj.npp.1301241 Supplementary Information, PMID: PMC17091129
L.M. Collins, J.R. McKay, D.W. Oslin & S.A. Murphy(2005). The Intervention Science in the Service of Clinical Art: The Sequential Multiple Assignment Randomized Trial (SMART),
(30) K.A. Bierman, R.L. Nix, J.J. Maples, S.A. Murphy and CPPRG (2006), Examining Clinical Judgment in an Adaptive Intervention Design: The Fast Track Program To obtain a copy, send email to kb2@psu.edu Journal of Consulting and Clinical Psychology Jun;74(3):468-81. 2006 PMC2753970
(29) S.A. Murphy, (2005). A Generalization Error for Q-Learning, Journal of Machine Learning Research. 6(Jul):1073--1097. PMC1475741
(28)L.M. Collins, S.A. Murphy, V. Nair & V. Strecher (2005), A Strategy for Optimizing and Evaluating Behavioral Interventions , Annals of Behavioral Medicine. 30:65-73. PMID16097907
(27) S.A. Murphy (2005), An Experimental Design for the Development of Adaptive Treatment Strategies., Statistics in Medicine. 24:1455-1481. PMID: PMC15586395
D. Blatt, S.A. Murphy, J. Zhu (2004). A-learning for Approximate Planning Technical Report 04-63, The Methodology Center, Pennsylvania State University.
S.A. Murphy & J.R. McKay (2004), Adaptive Treatment Strategies: an Emerging Approach for Improving Treatment Effectiveness. Clinical Science (Newsletter of the American Psychological Association Division 12, section III: The Society for the Science of Clinical Psychology) Winter 2003/Spring 2004. See (Clinical Science Website).
(26)J.S. Barber, S.A. Murphy, N. Verbitsky (2004),
Adjusting for Time-Varying Confounding in Survival Analysis (long version) , (short version) . Sociological Methodology. 34:163-192.
(25) L.M Collins, S.A. Murphy and K.A. Bierman (2004), A Conceptual Framework for Adaptive Preventive Interventions, Prevention Science 5:185-196. PMID: PMC3544191
(24) S.A. Murphy (2003), Optimal Dynamic Treatment Regimes, Journal of the Royal Statistical Society, Series B (with discussion) 65(2), 331-366.
(23)Maples, J.J., Murphy, S.A. and W.G. Axinn (2002), Two Level Proportional Hazards Models, Biometrics, 58(4), 180-188.
(22) S.A. Murphy, M.J. van der Laan, J. Robins and CPPRG (2001),
Marginal Mean Models for Dynamic Regimes, (dynamic.pdf) (dynamic_tables.pdf) , JASA, 96, 1410-1423.
(21)Barber, J.S., S.A. Murphy, W.G. Axinn and J. Maples, Discrete Time Multilevel Survival Analysis., (paper.pdf) , (table.pdf) , Sociological Methodology, 2000. 30, 201-235.
(20) Murphy, S.A. and A.W. van der Vaart (2000), On Profile Likelihood , JASA (with discussion), 95, 449-485.

(19) Murphy, S.A. and A.W. van der Vaart (1999), Observed Information in Semiparametric Models, Bernoulli, 5, 381-412.
(18) Murphy, S.A., A.W. van der Vaart and J.A. Wellner (1999), Current Status Regression, Mathematical Methods of Statistics, 8, 407-425.
(17) J.M. Bacik, S.A. Murphy and J.C. Anthony (1998),
Drug Use Prevention Data, Missing Assessments and Survival Analysis . (multimp.pdf) , Multivariate Behavioral Research, 33, 573-588.
(16) Murphy, S.A. and A.W. van der Vaart (1997), Semiparametric Mixtures in Case-Control Studies, (CaseControl.pdf) ,
To appear in Journal of Multivariate Analysis.
(15) Murphy, S.A. and A.W. van der Vaart (1997), Semiparametric Likelihood Ratio Inference , Annals of Statistics , 25, 1471-1509.
(14) Murphy, S.A., A.J. Rossini and A.W. van der Vaart (1997), MLE in the Proportional Odds Model , JASA. , 92, 968-976.
Tipa, E.M., S.A. Murphy and D.K. McLaughlin (1997), Ignorable Dropout in Longitudinal Studies ,
Malacane, L.A., , S.A. Murphy and Linda Collins(1996),
Determining the Risk Set in Event History Analysis ,
(13) Murphy, S.A. and A.W. van der Vaart (1996),
Likelihood Inference in the Errors-in-Variables Model, J. of Multivariate Analysis , 59, 81-108.
(12) Akritas, M., S. Murphy , M. LaValley and E. Feigelson (1995),
The Theil-Sen Estimator with Doubly Censored Data and Applications to Astronomy, JASA,90 170-177.
(11) Murphy, S., M.A. O'Hanesian and G. Bentley (1995),
An Analysis for Menstrual Data with Time-Varing Covariates , Stat. in Med ,14 1843-1857.
(10) Murphy, S.A. (1995)
Asymptotic Theory for the Frailty Model, Annals of Statistics ,23 182-198.
(9) Murphy, S.A.(1995),
A Central Limit Theorem for Local Martingales with Applications to the Analysis of Longitudinal Data , Scand. J. of Stat., 22 279-294.
(8) Murphy, S.A.(1995), Likelihood Ratio-Based Confidence Intervals in Survival Analysis . JASA ,90 1299-1405.
(7) Murphy, S.A. and B. Li (1995),
Projected Partial Likelihood and its Application to Longitudinal Data , Biometrika,82 399-406.
(6) Murphy, S.A. (1994),
Consistency in a Proportional Hazards Model Incorporating a Random Effect, Annals of Statistics, 22 712-731.
(5) Murphy, S. , R. Tice, M. Smit and B. Margolin(1992),
Contributions to the Design and Statistical Analysis of in vivo SCE Experiments. Mutation Research,271 39-48.
(4) Murphy, S.A.(1991),
Testing for a Time Dependent Coefficient in Cox's Regression Model, Scandinavian Journal of Statistics,20 25-50.
(3) Murphy, S.A. and P.K. Sen (1991),
Time Dependent Coefficients in a Cox-type Regression Model, Stochastic Processes and Their Applications ,39 153-180.
(2) Jauhar, P., P. Henika , J. MacGregor, C. Wehr, M. Shelby, S. Murphy and B. Margolin (1988),
1,3- Butadiene: Induction Micronucleated Erythrocytes in the Peripheral Blood of BbC3F₁ Mice Exposed by Inhalation for Thirteen Weeks. Mutation Research , 209 171-176.
(1) Murphy, S., W. Caspary and B. Margolin (1988),
A Statistical Analysis for the Mouse Lymphoma Cell Forward Mutation Assay, Mutation Research 203 145-154.

Statistics Seminars (click here and then on desired files)

Drug Abuse and Mental Health Seminars

Susan A. Murphy,
Harvard University
Science Center 400 Suite
One Oxford Street
Cambridge, MA 02138-2901
Admin: Jess Jackson (617) 495 5497
email: samurphy@fas.harvard.edu

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