TY - JOUR
T1 - Generation of "virtual" control groups for single arm prostate cancer adjuvant trials
AU - Jia, Zhenyu
AU - Lilly, Michael B.
AU - Koziol, James A.
AU - Chen, Xin
AU - Xia, Xiao Qin
AU - Wang, Yipeng
AU - Skarecky, Douglas
AU - Sutton, Manuel
AU - Sawyers, Anne
AU - Ruckle, Herbert
AU - Carpenter, Philip M.
AU - Wang-Rodriguez, Jessica
AU - Jiang, Jun
AU - Deng, Mingsen
AU - Pan, Cong
AU - Zhu, Jian Guo
AU - McLaren, Christine E.
AU - Gurley, Michael J.
AU - Lee, Chung
AU - McClelland, Michael
AU - Ahlering, Thomas
AU - Kattan, Michael W.
AU - Mercola, Dan
PY - 2014/1/21
Y1 - 2014/1/21
N2 - It is difficult to construct a control group for trials of adjuvant therapy (Rx) of prostate cancer after radical prostatectomy (RP) due to ethical issues and patient acceptance. We utilized 8 curve-fitting models to estimate the time to 60%, 65%, ... 95% chance of progression free survival (PFS) based on the data derived from Kattan post-RP nomogram. The 8 models were systematically applied to a training set of 153 post-RP cases without adjuvant Rx to develop 8 subsets of cases (reference case sets) whose observed PFS times were most accurately predicted by each model. To prepare a virtual control group for a single-arm adjuvant Rx trial, we first select the optimal model for the trial cases based on the minimum weighted Euclidean distance between the trial case set and the reference case set in terms of clinical features, and then compare the virtual PFS times calculated by the optimum model with the observed PFSs of the trial cases by the logrank test. The method was validated using an independent dataset of 155 post-RP patients without adjuvant Rx. We then applied the method to patients on a Phase II trial of adjuvant chemo-hormonal Rx post RP, which indicated that the adjuvant Rx is highly effective in prolonging PFS after RP in patients at high risk for prostate cancer recurrence. The method can accurately generate control groups for single-arm, post-RP adjuvant Rx trials for prostate cancer, facilitating development of new therapeutic strategies.
AB - It is difficult to construct a control group for trials of adjuvant therapy (Rx) of prostate cancer after radical prostatectomy (RP) due to ethical issues and patient acceptance. We utilized 8 curve-fitting models to estimate the time to 60%, 65%, ... 95% chance of progression free survival (PFS) based on the data derived from Kattan post-RP nomogram. The 8 models were systematically applied to a training set of 153 post-RP cases without adjuvant Rx to develop 8 subsets of cases (reference case sets) whose observed PFS times were most accurately predicted by each model. To prepare a virtual control group for a single-arm adjuvant Rx trial, we first select the optimal model for the trial cases based on the minimum weighted Euclidean distance between the trial case set and the reference case set in terms of clinical features, and then compare the virtual PFS times calculated by the optimum model with the observed PFSs of the trial cases by the logrank test. The method was validated using an independent dataset of 155 post-RP patients without adjuvant Rx. We then applied the method to patients on a Phase II trial of adjuvant chemo-hormonal Rx post RP, which indicated that the adjuvant Rx is highly effective in prolonging PFS after RP in patients at high risk for prostate cancer recurrence. The method can accurately generate control groups for single-arm, post-RP adjuvant Rx trials for prostate cancer, facilitating development of new therapeutic strategies.
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U2 - 10.1371/journal.pone.0085010
DO - 10.1371/journal.pone.0085010
M3 - Article
C2 - 24465467
AN - SCOPUS:84898621899
SN - 1932-6203
VL - 9
JO - PloS one
JF - PloS one
IS - 1
M1 - e85010
ER -
