TY - JOUR
T1 - Semiparametric efficiency and its implication on the design and analysis of group-sequential studies
AU - Scharfstein, Daniel O.
AU - Tsiatis, Anastasios A.
AU - Robins, James M.
N1 - Funding Information:
Daniel D. Scharfstein is Assistant Professor of Biostatistics, Johns Hopkins School of Hygiene and Public Health, Baltimore, MD 21205. Anastasios A. Tsiatis is Professor of Statistics, North Carolina State University, Raleigh, NC 27695. James M. Robins is Professor of Epidemiology and Biostatistics, Harvard School of Public Health, Boston, MA 02115. This research was sponsored in part by National Institutes of Health grants ROl-AI-31789 and ROl-AI-32475 from NIAID and ROl-CA-5l962 from NCr. The authors are grateful to Joseph Hogan and Andrea Rotnitzky for their help.
PY - 1997/12/1
Y1 - 1997/12/1
N2 - Authors have shown that the time-sequential joint distributions of many statistics used to analyze data arising from group-sequential time-to-event and longitudinal studies are multivariate normal with an independent increments covariance structure. In Theorem 1 of this article, we demonstrate that this limiting distribution arises naturally when one uses an efficient test statistic to test a single parameter in a semiparametric or parametric model. Because we are able to think of many of the statistics in the literature in this fashion, the limiting distribution under investigation is just a special case of Theorem 1. Using this general structure, we then develop an information-based design and monitoring procedure that can be applied to any type of model for any type of group-sequential study provided that there is a unique parameter of interest that can be efficiently tested.
AB - Authors have shown that the time-sequential joint distributions of many statistics used to analyze data arising from group-sequential time-to-event and longitudinal studies are multivariate normal with an independent increments covariance structure. In Theorem 1 of this article, we demonstrate that this limiting distribution arises naturally when one uses an efficient test statistic to test a single parameter in a semiparametric or parametric model. Because we are able to think of many of the statistics in the literature in this fashion, the limiting distribution under investigation is just a special case of Theorem 1. Using this general structure, we then develop an information-based design and monitoring procedure that can be applied to any type of model for any type of group-sequential study provided that there is a unique parameter of interest that can be efficiently tested.
KW - Independent increment
KW - Information-based design and monitoring
KW - Longitudinal study
KW - Maximum information trial
KW - Time-to-event study
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U2 - 10.1080/01621459.1997.10473655
DO - 10.1080/01621459.1997.10473655
M3 - Article
AN - SCOPUS:0031312687
SN - 0162-1459
VL - 92
SP - 1342
EP - 1350
JO - Journal of the American Statistical Association
JF - Journal of the American Statistical Association
IS - 440
ER -