Constructing Treatment Decision Rules Based on Scalar and Functional Predictors when Moderators of Treatment Effect are Unknown
Tóm tắt
Treatment response heterogeneity poses serious challenges for selecting treatment for many diseases. To understand this heterogeneity better and to help in determining the best patient-specific treatments for a given disease, many clinical trials are collecting large amounts of patient level data before administering treatment in the hope that some of these data can be used to identify moderators of treatment effect. These data can range from simple scalar values to complex functional data such as curves or images. Combining these various types of baseline data to discover ‘biosignatures’ of treatment response is crucial for advancing precision medicine. Motivated by the problem of selecting optimal treatment for subjects with depression based on clinical and neuroimaging data, we present an approach that both identifies covariates associated with differential treatment effect and estimates a treatment decision rule based on these covariates. We focus on settings where there is a potentially large collection of candidate biomarkers consisting of both scalar and functional data. The validity of the approach proposed is justified via extensive simulation experiments and illustrated by using data from a placebo-controlled clinical trial investigating antidepressant treatment response in subjects with depression.
Từ khóa
Tài liệu tham khảo
Ciarleglio, 2015, Treatment decisions based on scalar and functional baseline covariates, Biometrics, 71, 884, 10.1111/biom.12346
Ciarleglio, 2016, Flexible functional regression methods for estimating indivdualized treatment rules, Stat, 5, 185, 10.1002/sta4.114
Fan, 2001, Variable selection via nonconcave penalized likelihood and its oracle properties, J. Am. Statist. Ass., 96, 1348, 10.1198/016214501753382273
Friedman, 2010, Regularization paths for generalized linear models via coordinate descent, J. Statist. Softwr., 33, 1
Gertheiss, 2013, Variable selection in generalized functional linear models, Stat, 2, 86, 10.1002/sta4.20
Gueorguieva, 2011, Trajectories of depression severity in clinical trials of duloxetine: insights into antidepressant and placebo responses, Arch. Gen. Psychiatr., 68, 1227, 10.1001/archgenpsychiatry.2011.132
Hardin, 2013, Understanding heterogeneity in response to antidiabetes treatment: a post hoc analysis using sides, a subgroup identification algorithm, J. Diab. Sci. Technol., 7, 420, 10.1177/193229681300700219
Herrmann, 2014, lokern: kernel regression smoothing with local or global plug-in bandwidth
Laber, 2017, Functional feature construction for individualized treatment regimes, J. Am. Statist. Ass.
Liu, 2017, Augmented multistage outcome weighted learning
Lu, 2013, Variable selection for optimal treatment decision, Statist. Meth. Med. Res., 22, 493, 10.1177/0962280211428383
Masi, 2017, An overview of autism spectrum disorder, heterogeneity and treatment options, Neursci. Bull., 33, 183, 10.1007/s12264-017-0100-y
McGrath, 2013, Toward a neuroimaging treatment selection biomarker for major depressive disorder, J. Am. Med. Ass. Psychiatr., 70, 821
McKeague, 2014, Estimation of treatment policies based on functional predictors, Statist. Sin., 24, 1461
Meier, 2015, grplasso: fitting user specified models with Group Lasso penalty
Murphy, 2003, Optimal dynamic treatment regimes (with discussion), J. R. Statist. Soc., 65, 331, 10.1111/1467-9868.00389
Oliva, 2014, Fusso: functional shrinkage and selection operator, J. Mach. Learn. Res. Wrkshp Conf. Proc., 33, 715
Qian, 2011, Performance guarantees for individualized treatment rules, Ann. Statist., 39, 1180, 10.1214/10-AOS864
Rubin, 1978, Bayesian inference for causal effects: the role of randomization, Ann. Statist., 6, 34, 10.1214/aos/1176344064
Stone, 1974, Cross-validatory choice and assessment of statistical predictions (with discussion), J. R. Statist. Soc., 36, 111, 10.1111/j.2517-6161.1974.tb00994.x
Therneau, 2015, rpart: recursive partitioning and regression trees
Tian, 2014, A simple method for estimating interactions between a treatment and a large number of covariates, J. Am. Statist. Ass., 109, 1517, 10.1080/01621459.2014.951443
Tibshirani, 1996, Regression shrinkage and selection via the lasso, J. R. Statist. Soc., 58, 267, 10.1111/j.2517-6161.1996.tb02080.x
Wood, 2011, Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models, J. R. Statist. Soc., 73, 3, 10.1111/j.1467-9868.2010.00749.x
Zhang, 2012, Estimating optimal treatment regimes from a classification perspective, Stat, 1, 103, 10.1002/sta.411
Zhang, 2012, A robust method for estimating optimal treatment regimes, Biometrics, 68, 1010, 10.1111/j.1541-0420.2012.01763.x
Zhao, 2012, Estimating individualized treatment rules using outcome weighted learning, J. Am. Statist. Ass., 107, 1106, 10.1080/01621459.2012.695674
Zhou, 2015, Residual weighted learning for estimating individualized treatment rules, J. Am. Statist. Ass., 112, 169, 10.1080/01621459.2015.1093947
Zou, 2006, The adaptive lasso and its oracle properties, J. Am. Statist. Ass., 101, 1418, 10.1198/016214506000000735