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370 Statistics in a new erative effectiveness trials. Section 33.3 proceeds further to discuss innovativedesigns to improve the efficiency of clinical trials in the development of newtreatments.In Section 33.4, after reviewing the flaws in credit risk modeling and managementthat led to the 2007–08 financial crisis, we discuss how these flawscan be addressed by using better statistical methods. Section 33.5 continuesthis discussion on the role of statistics in financial and risk modeling in thenew era after the Dodd–Frank Act. Some concluding remarks are given inSection 33.6.33.2 Comparative effectiveness research clinical studiesOne approach to CER is to use observational studies, including analysis ofclaims or registry data; see, e.g., Stukel et al. (2007). As pointed out by Shihand Lavori (2013), such an approach involves “confounding by indication,”the tendency for clinicians and patients to choose treatments with their anticipatedeffects in mind. This leads to bias in estimating the effectiveness,which has to be handled by statistical adjustments and modeling techniques,or instrumental variables methods, or some combination. An obvious way toremove confounding is a randomized trial. However, conventional randomizedtrial designs are not only too costly but also ineffective in changing medicalpractice. An example is the Antihypertensive and Lipid Lowering Treatmentto Prevent Heart Attack Trial (ALLHAT), which was a randomized,double-blind, multi-center clinical trial designed to recruit 40,000 hypertensivepatients to be randomized to a diuretic treatment (chlorthalidone) andthree alternative antihypertensive pharmacologic treatments. Patients werefollowed every three months for the first year and every four months thereafterfor an average of six years of follow-up. This landmark CER trial costover $100 million. The results showed no difference in the prevention of heartattack and the superiority of chlorthalidone in preventing one or more forms ofcardiovascular disease (ALLHAT Collaborative Research Group, 2002). Yet, afew years later, the impact of the trial was found to be disappointing becauseof difficulty in pursuading doctors to change, scientific disagreement aboutthe interpretation of the results, and heavy marketing by the pharmaceuticalcompanies of their own drugs; see Lai and Lavori (2011).Section 4 of Lai and Lavori (2011) describes some innovative approachesthat are promising to meet the challenges of designing CER clinical trials. Oneis sequential multiple-assignment randomization for dynamic treatment strategiesin the management of patients with chronic diseases, as in Thall et al.(2007) who describe a two-stage randomized trial of twelve different strategiesof first-line and second-line treatments of androgen-independent prostate cancer.Another is equipoise-stratified randomization used by the STAR*D trial