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Patients with chronic diseases, such as cancer or epilepsy, are often followed through multiple stages of clinical interventions. Dynamic treatment regimes (DTRs) are sequences of decision rules that assign treatments at each stage based on measured covariates for each patient. A DTR is said to be optimal if the expectation of the desirable clinical benefit reaches a maximum when applied to a population. When there are three or more options for treatments at each decision point and the clinical outcome of interest is a time-to-event variable, estimating an optimal DTR can be complicated. We propose a doubly robust method to estimate optimal DTRs with multicategory treatments and survival outcomes. A novel blip function is defined to measure the difference in expected outcomes among treatments, and a doubly robust weighted least squares algorithm is designed for parameter estimation. Simulations using various weight functions and scenarios support the advantages of the proposed method in estimating optimal DTRs over existing approaches. We further illustrate the practical value of our method by applying it to data from the Standard and New Antiepileptic Drugs study. In this analysis, the proposed method supports the use of the new drug lamotrigine over the standard option carbamazepine. When the actual treatments match the estimated optimal treatments, survival outcomes tend to be better. The newly developed method provides a practical approach for clinicians that is not limited to cases of binary treatment options. [ABSTRACT FROM AUTHOR]

In this article, we introduce the L1 regularized support vector machine (L1-SVM) as an effective feature selection technique into the modeling of financial early warning system (EWS), for the purpose of establishing compact financial EWSs for Chinese-listed companies. By introducing LASSO penalty into the SVM framework, the L1-SVM is a capable methodology to select causative features in classification problem. We evaluate the feature selection performance of L1-SVM under different circumstances through numerical simulations and find it suitable for selecting features for financial EWS. In the real study, we establish four financial EWSs with features selected by L1-SVM and compare them with those trained with full features. The empirical result illustrates that our EWSs, with only minority of features, outperform significantly than full ones in the respect of generalization performance, which indicates the feasibility of L1-SVM in real applications. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]