TY - JOUR
T1 - A computationally simple bivariate survival estimator for efficacy and safety
AU - Scholtens, Denise
AU - Betensky, Rebecca A.
PY - 2006/9
Y1 - 2006/9
N2 - Both treatment efficacy and safety are typically the primary endpoints in Phase II, and even in some Phase III, clinical trials. Efficacy is frequently measured by time to response, death, or some other milestone event and thus is a continuous, possibly censored, outcome. Safety, however, is frequently measured on a discrete scale; in Eastern Cooperative Oncology Group clinical trial E2290, it was measured as the number of weekly rounds of chemotherapy that were tolerable to colorectal cancer patients. For the joint analysis of efficacy and safety, we propose a non-parametric, computationally simple estimator for the bivariate survival function when one time-to-event is continuous, one is discrete, and both are subject to right-censoring. The bivariate censoring times may depend on each other, but they are assumed to be independent of both event times. We derive a closed-form covariance estimator for the survivor function which allows for inference to be based on any of several possible statistics of interest. In addition, we derive its covariance with respect to calendar time of analysis, allowing for its use in sequential studies.
AB - Both treatment efficacy and safety are typically the primary endpoints in Phase II, and even in some Phase III, clinical trials. Efficacy is frequently measured by time to response, death, or some other milestone event and thus is a continuous, possibly censored, outcome. Safety, however, is frequently measured on a discrete scale; in Eastern Cooperative Oncology Group clinical trial E2290, it was measured as the number of weekly rounds of chemotherapy that were tolerable to colorectal cancer patients. For the joint analysis of efficacy and safety, we propose a non-parametric, computationally simple estimator for the bivariate survival function when one time-to-event is continuous, one is discrete, and both are subject to right-censoring. The bivariate censoring times may depend on each other, but they are assumed to be independent of both event times. We derive a closed-form covariance estimator for the survivor function which allows for inference to be based on any of several possible statistics of interest. In addition, we derive its covariance with respect to calendar time of analysis, allowing for its use in sequential studies.
KW - Bivariate survival
KW - Efficacy and safety
KW - Group sequential analysis
UR - http://www.scopus.com/inward/record.url?scp=33749361964&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33749361964&partnerID=8YFLogxK
U2 - 10.1007/s10985-006-9011-3
DO - 10.1007/s10985-006-9011-3
M3 - Article
C2 - 16917735
AN - SCOPUS:33749361964
SN - 1380-7870
VL - 12
SP - 365
EP - 387
JO - Lifetime Data Analysis
JF - Lifetime Data Analysis
IS - 3
ER -