The DOI matches its Crossref record; 60 of the 62 references it cites were found in a registry or an index.
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
Docetaxel plus Prednisone or Mitoxantrone plus Prednisone for Advanced Prostate Cancer
Tannock, I. F., de Wit, R., Berry, W. R., Horti, J., Pluzanska, A., Chi, K. N., Oudard, S., Théodore, C., James, N. D., Turesson, I., et al. (2004) · New England Journal of Medicine · doi:10.1056/nejmoa040720 · Cited by 5,735 · cited 1×
“…and van der Laan 2007) and comparison of the regimens used in the trial to a particular combination chemotherapy reported in the medical literature while the trial was still ongoing (Tannock et al. 2004; Armstrong and Eisenberger 2006; Armstrong et al. 2007; Millikan, Logothetis, and Thall 2008).”
ResultStatistics for experimenters. An introduction to design, data analysis, and model building.
Box, G. E. P., Hunter, W. G., & Hunter, J. S. (1978) · Cited by 4,021
Scandinavian Journal of Statistics
Rosén, B. G. (2005) · Wiley eBooks · doi:10.1002/0471667196.ess2351.pub2 · Cited by 3,654
Estimation of Regression Coefficients When Some Regressors Are Not Always Observed
Robins, J. M., Rotnitzky, A. G., & Zhao, L. P. (1994) · Journal of the American Statistical Association · doi:10.1080/01621459.1994.10476818 · Cited by 2,943 · cited 1×
“In IPTW estimation, efficiency can be improved by replacing the known treatment probabilities that form the factors in the weights ω by maximum likelihood estimates under correctly specified models (e.g., Robins, Rotnitzky, and Zhao 1994).”
MethodSelf-citationBayesian inference for causal effects: The role of randomization
Rubin, D. B. (1978) · The Annals of Statistics · doi:10.1214/aos/1176344064 · Cited by 2,503 · cited 1×
“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.”
MethodRobins, J. (1986) · Mathematical Modelling · doi:10.1016/0270-0255(86)90088-6 · Cited by 2,451 · cited 8×
“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;…”
BackgroundSteyerberg, E. W., Eijkemans, M. J. C., Harrell, F. E., & Habbema, J. D. F. (2000) · Statistics in Medicine · doi:10.1002/(sici)1097-0258(20000430)19:8 · Cited by 789
Self-citation
Research works printed but not linked: 4
- Orellana L., 2010, International Journal of Biostatistics, 6
- Robins J. M., 1989, Health Service Research Methodology: A Focus on AIDS, 113
- Robins J. M., 1993, Methodological issues of AIDS Mental Health Research., 213
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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-citation
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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