TY - JOUR
T1 - Evaluation of viable dynamic treatment regimes in a sequentially randomized trial of advanced prostate cancer
AU - Wang, Lu
AU - Rotnitzky, Andrea
AU - Lin, Xihong
AU - Millikan, Randall E.
AU - Thall, Peter F.
N1 - Funding Information:
Lu Wang is Assistant Professor, Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109 (E-mail: luwang@umich.edu). Andrea Rotnitzky is Professor, Department of Economics, Di Tella University, Buenos Aires, 1425, Argentina (E-mail: andrea@utdt.edu) and Adjunct Professor, Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115. Xihong Lin is Professor, Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115 (E-mail: xlin@hsph.harvard.edu). Randall E. Millikan is Associate Professor, Department of Genitourinary Medical Oncology, M.D. Anderson Cancer Center, Houston, TX 77030 (E-mail: rmillika@mdanderson.org). Peter F. Thall is Professor, Department of Biostatistics, M.D. Anderson Cancer Center, Houston, TX 77030 (E-mail: rex@mdanderson.org). Wang and Lin’s research is partially supported by a grant from the National Cancer Institute (R37-CA–76404). Rotnitzky’s research is partially supported by grants R01-GM48704 and R01-AI051164 from the National Institutes of Health. Thall’s research was partially supported by grant 2RO1 CA083932 from the National Institutes of Health.
PY - 2012
Y1 - 2012
N2 - 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 be initially randomized among four chemotherapies, and that those who responded poorly be re-randomized 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 150 study participants, 47 did not complete their therapy as per the algorithm. However, 35 of them did so for reasons that precluded further chemotherapy, that is, 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 were also used as endpoints. Estimation of each DTR-specific mean score was conducted using inverse probability weighted methods that assumed that missingness in the 12 remaining dropouts was informative but explainable in that it only depended on past recorded data.We conducted additional worst-and best-case analyses to evaluate sensitivity of our findings to extreme departures from the explainable dropout assumption.
AB - 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 be initially randomized among four chemotherapies, and that those who responded poorly be re-randomized 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 150 study participants, 47 did not complete their therapy as per the algorithm. However, 35 of them did so for reasons that precluded further chemotherapy, that is, 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 were also used as endpoints. Estimation of each DTR-specific mean score was conducted using inverse probability weighted methods that assumed that missingness in the 12 remaining dropouts was informative but explainable in that it only depended on past recorded data.We conducted additional worst-and best-case analyses to evaluate sensitivity of our findings to extreme departures from the explainable dropout assumption.
KW - Causal inference
KW - Efficiency
KW - Informative dropout
KW - Inverse probability weighting
KW - Marginal structural models
KW - Optimal regime
KW - Simultaneous confidence intervals
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U2 - 10.1080/01621459.2011.641416
DO - 10.1080/01621459.2011.641416
M3 - Article
C2 - 22956855
AN - SCOPUS:84864385401
SN - 0162-1459
VL - 107
SP - 493
EP - 508
JO - Journal of the American Statistical Association
JF - Journal of the American Statistical Association
IS - 498
ER -