Abstract
GUIDE is a multi-purpose algorithm for classification and regression tree construction with special capabilities for identifying subgroups with differential treatment effects. It is unique among subgroup methods in having all these features: unbiased split variable selection, approximately unbiased estimation of subgroup treatment effects, treatments with two or more levels, allowance for linear effects of prognostic variables within subgroups, and automatic handling of missing predictor variable values without imputation in piecewise-constant models. Predictor variables may be continuous, ordinal, nominal, or cyclical (such as angular measurements, hour of day, day of week, or month of year). Response variables may be univariate, multivariate, longitudinal, or right-censored. This article gives a current account of the main features of the method for subgroup identification and reviews a bootstrap method for conducting post-selection inference on the subgroup treatment effects. A data set pooled from studies of amyotrophic lateral sclerosis is used for illustration.
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Acknowledgements
The authors thank Tao Shen, Yu-Shan Shih and Shijie Tang for their helpful comments. Data used in the preparation of this article were obtained from the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) Database. As such, the following organizations and individuals within the PRO-ACT Consortium contributed to the design and implementation of the PRO-ACT Database and/or provided data, but did not participate in the analysis of the data or the writing of this report: Neurological Clinical Research Institute, MGH Northeast ALS Consortium Novartis Prize4Life Israel Regeneron Pharmaceuticals, Inc., and Sanofi Teva Pharmaceutical Industries, Ltd.
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Loh, WY., Zhou, P. (2020). The GUIDE Approach to Subgroup Identification. In: Ting, N., Cappelleri, J., Ho, S., Chen, (G. (eds) Design and Analysis of Subgroups with Biopharmaceutical Applications. Emerging Topics in Statistics and Biostatistics . Springer, Cham. https://doi.org/10.1007/978-3-030-40105-4_6
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