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Evaluation of Viable Dynamic Treatment Regimes in a Sequentially Randomized Trial of Advanced Prostate Cancer
Open accessAbstract
We present new statistical analyses of data arising from a clinical trial designed to compare two-stage dynamic treatment regimes (DTRs) for advanced prostate cancer. The trial protocol mandated that patients were to be initially randomized among four chemotherapies, and that those who responded poorly were to be rerandomized to one of the remaining candidate therapies. The primary aim was to compare the DTRs' overall success rates, with success defined by the occurrence of successful responses in each of two consecutive courses of the patient's therapy. Of the one hundred and fifty study participants, forty seven did not complete their therapy per the algorithm. However, thirty five of them did so for reasons that precluded further chemotherapy; i.e. toxicity and/or progressive disease. Consequently, rather than comparing the overall success rates of the DTRs in the unrealistic event that these patients had remained on their assigned chemotherapies, we conducted an analysis that compared viable switch rules defined by the per-protocol rules but with the additional provision that patients who developed toxicity or progressive disease switch to a non-prespecified therapeutic or palliative strategy. This modification involved consideration of bivariate per-course outcomes encoding both efficacy and toxicity. We used numerical scores elicited from the trial's Principal Investigator to quantify the clinical desirability of each bivariate per-course outcome, and defined one endpoint as their average over all courses of treatment. Two other simpler sets of scores as well as log survival time also were used as endpoints. Estimation of each DTR-specific mean score was conducted using inverse probability weighted methods that assumed that missingness in the twelve remaining drop-outs was informative but explainable in that it only depended on past recorded data. We conducted additional worst-best case analyses to evaluate sensitivity of our findings to extreme departures from the explainable drop-out assumption.
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“For example, SMART has been used in oncology to develop medication algorithms to treat prostate cancer [36,52,53].”
From Introduction to SMART designs for the development of adaptive interventions: with application to weight loss research (2014) · cites this paper as [36]
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Cites
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MethodSelf-citationBayesian inference for causal effects: The role of randomization
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“To compare the different switch rules used in the trial, we apply the counterfactual framework for causal inference as originally developed by Rubin (1978) for time-independent treatments and later extended by Robins (1986, 1987) for time-dependent treatments in longitudinal studies.”
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“To compare the different switch rules used in the trial, we apply the counterfactual framework for causal inference as originally developed by Rubin (1978) for time-independent treatments and later extended by Robins (1986, 1987) for time-dependent treatments in longitudinal studies.”
BackgroundMethodInfluentialDoubly robust estimation in missing data and causal inference models
Bang, H., & Robins, J. M. (2005) · Biometrics · doi:10.1111/j.1541-0420.2005.00377.x · Cited by 1,998
Collins, L. M., Murphy, S. A., & Strecher, V. (2007) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2007.01.022 · Cited by 1,082
Optimal Dynamic Treatment Regimes
Murphy, S. A. (2003) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/1467-9868.00389 · Cited by 990 · cited 1×
“…of structural nested models (Robins 1986, 1989, 1993, 1997), some clever variations of g-estimation for optimal treatment regime estimation (Murphy 2003; Robins 2004) and inverse probability weighted estimation of marginal structural models (Murphy, van der Laan, Robins, and CPPRG 2001;…”
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Cited by
Sorted by citations
Almirall, D., Nahum-Shani, I., Sherwood, N. E., & Murphy, S. A. (2014) · Translational Behavioral Medicine · doi:10.1007/s13142-014-0265-0 · Cited by 453
“For example, SMART has been used in oncology to develop medication algorithms to treat prostate cancer [36, 52, 53].”
MethodChakraborty, B., & Murphy, S. A. (2013) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-022513-115553 · Cited by 288
Assessing time-varying causal effect moderation in mobile health
Boruvka, A., Almirall, D., Witkiewitz, K., & Murphy, S. A. (2018) · Journal of the American Statistical Association · doi:10.1080/01621459.2017.1305274 · Cited by 152
“Conditioning on availability is related to the concept of viable or feasible dynamic treatment regimes (Robins 2004;Wang et al. 2012), in which one assesses only the causal effect of treatments that can actually be provided.”
Sample size calculations for micro‐randomized trials in mHealth
Liao, P., Klasnja, P., Tewari, A., & Murphy, S. A. (2015) · Statistics in Medicine · doi:10.1002/sim.6847 · Cited by 147
“The former conditional aspect of the definition is related to the concept of viable or feasible dynamic treatment regimes [20,21] in which one assesses only the causal effect of treatments that can actually be provided.”
BackgroundThall, P., Fox, P. S., & Wathen, J. K. (2015) · Annals of Oncology · doi:10.1093/annonc/mdv238 · Cited by 124
Self-citationDoubly‐robust dynamic treatment regimen estimation via weighted least squares
Wallace, M. P., & Moodie, E. E. M. (2015) · Biometrics · doi:10.1111/biom.12306 · Cited by 104
Bayesian Nonparametric Estimation for Dynamic Treatment Regimes With Sequential Transition Times
Xu, Y., Müller, P., Wahed, A. S., & Thall, P. F. (2015) · Journal of the American Statistical Association · doi:10.1080/01621459.2015.1086353 · Cited by 81
“In contrast, sequential multi-arm randomized treatment (SMART) designs, wherein patients are re-randomized at stages after the first, have been used in oncology trials (Thall et al., 2000, 2007a,b; Wang et al., 2012), and are being used increasingly in trials to study multi-stage adaptive regimes for behavioral or psy…”
InfluentialSelf-citationAdaptive Interventions in Child and Adolescent Mental Health
Almirall, D., & Chronis-Tuscano, A. M. (2016) · Journal of Clinical Child & Adolescent Psychology · doi:10.1080/15374416.2016.1152555 · Cited by 89
“In oncology research, SMART designs have been used to develop adaptive interventions for prostate cancer (Thall, Wooten, Logothetis, Millikan, & Tannir, 2007; Wang et al., 2012).”
MethodAugmented outcome‐weighted learning for estimating optimal dynamic treatment regimens
Liu, Y., Wang, Y., Kosorok, M. R., Zhao, Y., & Zeng, D. (2018) · Statistics in Medicine · doi:10.1002/sim.7844 · Cited by 79
Interactive model building for Q-learning
Laber, E. B., Linn, K. A., & Stefanski, L. A. (2014) · Biometrika · doi:10.1093/biomet/asu043 · Cited by 74
“…a measure of efficacy, the Quick Inventory of Depression Symptomatology score, as the outcome (Rush et al., 2004); side-effects or other competing outcomes could be accommodated using set-valued treatment regimes (Lizotte et al., 2012; Laber et al., 2013) or composite outcomes (Wang et al., 2012).”
BackgroundSMART designs in cancer research: Past, present, and future
Kidwell, K. (2014) · Clinical Trials · doi:10.1177/1740774514525691 · Cited by 59
“This led to redefining DTRs to include a switch to non-specified therapeutic/palliative care for those who developed toxicity or progressive disease [43].”
BackgroundA Bayesian Machine Learning Approach for Optimizing Dynamic Treatment Regimes
Murray, T. A., Yuan, Y., & Thall, P. F. (2017) · Journal of the American Statistical Association · doi:10.1080/01621459.2017.1340887 · Cited by 57
Self-citationTree-based reinforcement learning for estimating optimal dynamic treatment regimes
Tao, Y., Wang, L., & Almirall, D. (2018) · The Annals of Applied Statistics · doi:10.1214/18-aoas1137 · Cited by 55
Self-citationBayesian Dose-Finding in Two Treatment Cycles Based on the Joint Utility of Efficacy and Toxicity
Lee, J., Thall, P. F., Ji, Y., & Müller, P. (2014) · Journal of the American Statistical Association · doi:10.1080/01621459.2014.926815 · Cited by 52
“While statistical methods for DTRs have seen limited application in actual clinical trials (Rush, Trivedi, and Fava 2003; Thall, et al. 2007; Wang et al. 2012), recently there has been extensive research to develop or optimize DTRs in medicine, including semiparametric methods (Wahed and Tsiatis 2006), reinforcement l…”
BackgroundMethodSelf-citationAdaptive Contrast Weighted Learning for Multi-Stage Multi-Treatment Decision-Making
Tao, Y., & Wang, L. (2016) · Biometrics · doi:10.1111/biom.12539 · Cited by 44
“…have been developed including marginal structural models with inverse probability weighting (IPW) (Robins, 2000; Hern´an, Brumback, and Robins, 2001; Wang et al., 2012), G-estimation of structural nested mean models (Robins, 1986, 1989, 1997), generalized by Murphy (2003) and Robins (2004),…”
Self-citationAlmirall, D., Nahum-Shani, I. B., Wang, L., & Kasari, C. L. (2018) · Statistics for social and behavioral sciences · doi:10.1007/978-3-319-91776-4_4 · Cited by 45
Self-citationInference about the expected performance of a data-driven dynamic treatment regime
Chakraborty, B., Laber, E. B., & Zhao, Y. (2014) · Clinical Trials · doi:10.1177/1740774514537727 · Cited by 43
Optimization of individualized dynamic treatment regimes for recurrent diseases
Huang, X., Ning, J., & Wahed, A. S. (2014) · Statistics in Medicine · doi:10.1002/sim.6104 · Cited by 38
Interpreting Randomized Controlled Trials
Msaouel, P., Lee, J., & Thall, P. F. (2023) · Cancers · doi:10.3390/cancers15194674 · Cited by 38
Self-citationA Causal Framework for Making Individualized Treatment Decisions in Oncology
Msaouel, P., Lee, J., Karam, J. A., & Thall, P. F. (2022) · Cancers · doi:10.3390/cancers14163923 · Cited by 40
Self-citationOptimization of multi‐stage dynamic treatment regimes utilizing accumulated data
Huang, X., Choi, S., Wang, L., & Thall, P. F. (2015) · Statistics in Medicine · doi:10.1002/sim.6558 · Cited by 34
“Recent applications include estimation of survival for dynamic treatment regimes in a sequentially randomized prostate cancer trial [17] and in a partially randomized trial of chemotherapy for acute leukemia [18].”
InfluentialSelf-citationFarmer, R. E., Kounali, D., Walker, A. S., Savović, J., Richards, A., May, M., & Ford, D. (2018) · Trials · doi:10.1186/s13063-017-2381-x · Cited by 34
“ALL: Acute lymphoblastic leukaemia; ART: Antiretroviral therapy; CD4: Cluster of differentiation 4; HIV: Human immunodeficiency virus; IPTW: Inverse probability of treatment weighting; IPW: Inverse probability weighting; ITT: Intention to treat; MSM: Marginal structural model; RCT: Randomised controlled trial; SMART:…”
BackgroundMethodInfluentialRobust Treatment Comparison Based on Utilities of Semi-Competing Risks in Non-Small-Cell Lung Cancer
Murray, T. A., Thall, P. F., Yuan, Y., McAvoy, S., & Gomez, D. R. (2016) · Journal of the American Statistical Association · doi:10.1080/01621459.2016.1176926 · Cited by 31
“An alternative would be a sequential, multiple assignment, randomized trial (SMART) (see Almirall et al. 2014; Murphy 2003; Murphy and McKay 2004) that has an additional randomization for thirdline treatment at disease progression, see, for example, Thall et al. (2000, 2007) or Wang et al. (2012).”
BackgroundSelf-citationAdaptive Interventions and SMART Designs
Kidwell, K., & Hyde, L. W. (2015) · American Journal of Evaluation · doi:10.1177/1098214015617013 · Cited by 30
Reinforcement Learning in Modern Biostatistics: Constructing Optimal Adaptive Interventions
Deliu, N., Williams, J. J., & Chakraborty, B. (2024) · International Statistical Review · doi:10.1111/insr.12583 · Cited by 32
“In contrast, IPW-based strategies [107, 86, 135] seek optimal policies by directly looking for the policy (within a pre-specified class of policies) that maximizes an objective function (e.g., the expected return), without postulating an outcome model [143]; they are regarded as direct meth-ods .”
BackgroundSMARTer Discontinuation Trial Designs for Developing an Adaptive Treatment Strategy
Almirall, D., Compton, S. N., Rynn, M. A., Walkup, J. T., & Murphy, S. A. (2012) · Journal of Child and Adolescent Psychopharmacology · doi:10.1089/cap.2011.0073 · Cited by 22
Li, X., Young, J. G., & Toh, S. (2017) · Current Epidemiology Reports · doi:10.1007/s40471-017-0124-x · Cited by 21
Lorenzoni, G., Petracci, E., Scarpi, E., Baldi, I., Gregori, D., & Nanni, O. (2022) · British Journal of Cancer · doi:10.1038/s41416-022-02110-z · Cited by 20
“[13], the study presenting the first analysis of the data was included in this review [12], whereas for the one by Petracci et al.”
BackgroundMethodResultInfluentialSherry, A. D., Msaouel, P., Lin, T. A., Jaoude, J. A., Kouzy, R., Beck, E. J., Miller, A. M., Passy, A. H., Kupferman, G. S., Koay, E. J., et al. (2024) · BMJ Oncology · doi:10.1136/bmjonc-2024-000322 · Cited by 21
Almirall, D., Lizotte, D. J., & Murphy, S. A. (2012) · Journal of the American Statistical Association · doi:10.1080/01621459.2012.665615 · Cited by 19
Stochastic Tree Search for Estimating Optimal Dynamic Treatment Regimes
Sun, Y., & Wang, L. (2020) · Journal of the American Statistical Association · doi:10.1080/01621459.2020.1819294 · Cited by 18
“Various methods for estimating optimal DTRs have been proposed; some examples include marginal structural models (e.g., Murphy et al. 2001; Wang et al. 2012), G-estimation of structural nested mean models (e.g., Robins 2004), likelihood-based approaches (e.g., Thall et al. 2007), Q-learning (e.g.,…”
MethodSelf-citationInnovative designs of point-of-care comparative effectiveness trials
Shih, M., Turakhia, M. P., & Lai, T. L. (2015) · Contemporary Clinical Trials · doi:10.1016/j.cct.2015.06.014 · Cited by 19
“Additional care is needed to analyze the data and [21, 22] describe methods to do this.”
MethodChow, J. C., & Hampton, L. H. (2018) · Remedial and Special Education · doi:10.1177/0741932518759422 · Cited by 19
Micro-Randomized Trials in mHealthPreprint
Liao, P., Klasnja, P., Tewari, A., & Murphy, S. A. (2015) · arXiv (Cornell University) · Cited by 17
Dynamic Question Ordering in Online Surveys
Early, K., Mankoff, J. C., & Fienberg, S. E. (2017) · Journal of Official Statistics · doi:10.1515/jos-2017-0030 · Cited by 16
Shen, J., Wang, L., & Taylor, J. M. G. (2016) · Biometrics · doi:10.1111/biom.12621 · Cited by 16
“Careful formulation and assumptions are required to make valid causal inference, especially on how the observational data may restrict the set of DTRs that can be assessed, which are called the feasible (Robins, 1994) or viable (Wang et al., 2012) DTRs.”
BackgroundSelf-citationTreatment Evaluation With Multiple Outcome Periods Under Endogeneity and Attrition
Frölich, M., & Huber, M. (2014) · Journal of the American Statistical Association · doi:10.1080/01621459.2014.896804 · Cited by 15
Schnell, P., Tang, Q., Müller, P., & Carlin, B. P. (2017) · The Annals of Applied Statistics · doi:10.1214/17-aoas1024 · Cited by 15
Multiobjective tree-based reinforcement learning for estimating tolerant dynamic treatment regimes
Song, Y., & Wang, L. (2024) · Biometrics · doi:10.1093/biomtc/ujad017 · Cited by 13
Self-citationStatistical design and analysis in trials of proportionate interventions: a systematic review
Candlish, J., Teare, M. D., Cohen, J., & Bywater, T. J. (2019) · Trials · doi:10.1186/s13063-019-3206-x · Cited by 14
“Wang [34] 2012 Oncology United States 7 150”
BackgroundMethodInfluentialRuppert, A. S., Yin, J., Davidian, M., Tsiatis, A. A., Byrd, J. C., Woyach, J. A., & Mandrekar, S. J. (2019) · Annals of Oncology · doi:10.1093/annonc/mdz053 · Cited by 12
“Data from this trial were re-analyzed, using all patients and with statistical methods for comparing embedded treatment regimens, with fairly consistent results [20, 22].”
MethodMultiply robust estimators of causal effects for survival outcomes
Wen, L., Hernán, M. Á., & Robins, J. M. (2021) · Scandinavian Journal of Statistics · doi:10.1111/sjos.12561 · Cited by 11
“This may be the case, for example, in a sequentially randomized trial where the propensity scores are known in advance by design (Benkeser et al., 2020; Wang et al., 2012).”
BackgroundAlshamsi, M. H. A., Mehta, J., & Nibali, L. (2021) · Journal Of Clinical Periodontology · doi:10.1111/jcpe.13443 · Cited by 12
“Although these types of studies are becoming increasingly popular in medicine, particularly for cancer (Wang et al. 2012; Kidwell, 2014), their application to periodontology is still limited (Xu et al. 2020).”
BackgroundNollen, N. L., Ahluwalia, J. S., Mayo, M. S., Ellerbeck, E. F., Leavens, E. L. S., Salzman, G. A., Shanks, D., Woodward, J. M., Greiner, K. A., & Cox, L. S. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.17895 · Cited by 10
Li, Z. (2016) · Statistics in Medicine · doi:10.1002/sim.7136 · Cited by 12
Causal Multi-Agent Reinforcement Learning: Review and Open ProblemsPreprint
Grimbly, S. J., Shock, J. P., & Pretorius, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.06721 · Cited by 29
“For example, remission due to a multiple-course chemotherapy treatment plan could be dependent on initial treatment (Wang et al., 2012).”
BackgroundRudolph, K. E., Shulman, M., Fishman, M. J., Diaz, I. M., Rotrosen, J., & Nunes, E. V. (2021) · Addiction · doi:10.1111/add.15654 · Cited by 9
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Chaffee, P. H., & van der Laan, M. J. (2012) · The International Journal of Biostatistics · doi:10.1515/1557-4679.1406 · Cited by 8
Interim Monitoring of Sequential Multiple Assignment Randomized Trials Using Partial Information
Manschot, C., Laber, E. B., & Davidian, M. (2023) · Biometrics · doi:10.1111/biom.13854 · Cited by 7
“…increased dramatically; SMARTs have been conducted in a range of disease and disorder areas, including cancer (Kelleher et al., 2017; Thall, 2015; Wang et al., 2012), behavioral sciences (Almirall et al., 2014; Kidwell & Hyde, 2016), and mental health (Manschreck & Boshes, 2007; Sinyor et al.,…”
BackgroundBerg, S. A., Rosa, S. L., Zhang, T., Pierorazio, P. M., Albiges, L. K., Beckermann, K. E., Campbell, M. T., Carlo, M. I., Coleman, K., George, D. J., et al. (2024) · Urologic Oncology Seminars and Original Investigations · doi:10.1016/j.urolonc.2024.10.022 · Cited by 8
Self-citationSample Size Calculations for SMARTsPreprint
Rose, E. J., Laber, E. B., Davidian, M., Tsiatis, A. A., Zhao, Y., & Kosorok, M. R. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.06646 · Cited by 8
“, 2017), cancer (Thall et al., 2000; Zhao et al., 2011; Wang et al., 2012; Zhang et al., 2012, 2015; Murray et al., 2017), cystic fibrosis (Zhou et al.”
BackgroundTang, M., Wang, L., Gorin, M. A., & Taylor, J. M. G. (2021) · Statistics in Medicine · doi:10.1002/sim.9177 · Cited by 6
“ds1∈s1 E[Ỹ∗s1|Ds1 = ds1,Hs1]I{gs1(Hs1) = ds1}] (3)”
MethodSelf-citationLess is More? First Impressions From COSMIC-313
Msaouel, P. (2022) · Cancer Investigation · doi:10.1080/07357907.2022.2136681 · Cited by 6
Zhou, N., Brook, R. D., Dinov, I. D., & Wang, L. (2022) · Biometrical Journal · doi:10.1002/bimj.202100077 · Cited by 6
“…and semiparametric methods include marginal structural models with inverse probability weighting (Hernán et al., 2001; Murphy et al., 2001; Wang et al., 2012), G-estimation of structural nested mean models (Robins, 1994, 1997, 2004), and targeted maximum likelihood estimations (Laan &…”
MethodSelf-citationZhou, N., Wang, L., & Almirall, D. (2022) · Biometrics · doi:10.1111/biom.13754 · Cited by 6
“Tao et al. (2018) generalized the method with augmented AIPW for use with multistage multitreatment observational data to estimate an optimal tree-based DTR (ie, multistage treatment regime).”
InfluentialSelf-citationArtificial Intelligence-Based Decision Support Systems for Precision and Digital Health
Deliu, N., & Chakraborty, B. (2025) · Springer eBooks · doi:10.1007/978-981-96-0742-6_1 · Cited by 15
Dynamic Treatment Regimes for Optimizing Healthcare
Deliu, N., & Chakraborty, B. (2022) · Springer series in supply chain management · doi:10.1007/978-3-031-01926-5_15 · Cited by 5
Statistical Data Mining of Clinical Data
Lipkovich, I. A., Ratitch, B., & Ivanescu, C. (2020) · Springer eBooks · doi:10.1007/978-3-030-48555-9_6 · Cited by 4
A novel approach to assess dynamic treatment regimes embedded in a SMART with an ordinal outcome
Ghosh, P., Yan, X., & Chakraborty, B. (2023) · Statistics in Medicine · doi:10.1002/sim.9659 · Cited by 3
“For small N, use of (7) is preferable.”
MethodA generalized outcome-adaptive sequential multiple assignment randomized trial design
Yang, X., Cheng, Y., Thall, P. F., & Wahed, A. S. (2024) · Biometrics · doi:10.1093/biomtc/ujae073 · Cited by 3
Self-citationWu, D., Goldfeld, K. S., Petkova, E., & Park, H. (2024) · BMC Medical Research Methodology · doi:10.1186/s12874-024-02333-z · Cited by 3
Bayesian Sample Size Calculations for Comparing Two Strategies in SMART Studies
Turchetta, A., Moodie, E. E. M., Stephens, D. A., & Lambert, S. (2022) · Biometrics · doi:10.1111/biom.13813 · Cited by 3
“So far, SMARTs have been deployed to estimate optimal strategies in a wide range of fields, such as weight loss (Almirall et al., 2014), substance abuse (Murphy et al., 2007), and cancer (Kidwell, 2014; Sikorskii et al., 2017), with particular emphasis on prostate cancer (Wang et al., 2012).”
MethodIncorporating participants’ welfare into sequential multiple assignment randomized trials
Wang, X., Deliu, N., Narita, Y., & Chakraborty, B. (2024) · Biometrics · doi:10.1093/biomtc/ujad004 · Cited by 3
“From the analysis perspective, there have been some works (Wang et al., 2012; Johnson et al., 2021; Xu et al., 2022) to construct composite outcomes accounting for both treatment efficacy and other factors, e.g., toxicity and quality of life, to evaluate DTRs.”
BackgroundAssessing Time-Varying Causal Effect Moderation in Mobile HealthPreprint
Boruvka, A., Almirall, D., Witkiewitz, K. A., & Murphy, S. A. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1601.00237 · Cited by 3
“Conditioning on availability is related to the concept of viable or feasible dynamic treatment regimes (Robins 2004;Wang et al. 2012), in which one assesses only the causal effect of treatments that can actually be provided.”
Simultaneous Confidence Regions and Weighted Hypotheses on Parameter Arrays
Ma, Y., Yeh, A. B., & Chen, J. T. (2023) · Methodology And Computing In Applied Probability · doi:10.1007/s11009-023-10030-5 · Cited by 2
“(2007) or Wang et al. (2012)).”
Jin, W., Ni, Y., O’Halloran, J. A., Spence, A. B., Rubin, L. H., & Xu, Y. (2023) · The Annals of Applied Statistics · doi:10.1214/23-aoas1750 · Cited by 2
Chang, J.-Y. A., Chilcott, J. B., & Latimer, N. R. (2024) · PharmacoEconomics · doi:10.1007/s40273-024-01363-1 · Cited by 4
“Others predominantly comprised clinical studies, researching the clinical impacts of sequential treatment strategies and treatment switching, without an emphasis on HTA [44–51].”
Chuang‐Stein, C., Follman, D., & Chappell, R. J. (2014) · Clinical Trials · doi:10.1177/1740774514538552 · Cited by 2
Simultaneous confidence bands for restricted logistic regression models
Kerns, L. X., & Chen, J. T. (2016) · Journal of Applied Statistics · doi:10.1080/02664763.2016.1238056 · Cited by 2
Sequential Multiple Assignment Randomized Treatment (SMART)
Bowden, C. L., & Singh, V. K. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-405170-6.00007-5 · Cited by 2
Dong, L., Laber, E. B., Goldberg, Y., Song, R., & Yang, S. (2020) · Statistics in Medicine · doi:10.1002/sim.8678 · Cited by 2
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Novel Flexible Statistical Methods for Missing Data Problems and Personalized Health Care
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Metadata
- Authors
- Wang, L., Rotnitzky, A., Lin, X., Millikan, R. E., & Thall, P. F.
- Affiliations
- University of Michigan (US)
- Harvard University (US)
- The University of Texas MD Anderson Cancer Center (US)
- Corresponding author
- Thall, Peter F.
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 2012-06
- Volume
- 107
- Issue
- 498
- Pages
- 493-508
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- PMC
- PMC3433243
- OpenAlex
- W2167371237
- Cited by
- 108
- References
- 62
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 17 · 2023: 7 · 2022: 8 · 2021: 6 · 2020: 8 · 2019: 5 · 2018: 7 · 2017: 8 · 2016: 10 · 2015: 10 · 2014: 9 · 2013: 3 · 2012: 5
- FWCI
- 8.25
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of the American Statistical Association (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- National Cancer Institute (P50CA140388, P01CA049639, R37CA076404, R01CA083932, P30CA016672, R35CA197449)
- National Institute of General Medical Sciences (R01GM048704)
- National Institute of Allergy and Infectious Diseases (R01AI051164)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Keywords
- dynamic treatment regimes, advanced prostate cancer, chemotherapy, treatment switching, dropout, inverse probability weighting, efficacy-toxicity trade-off
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