title: Managing the evolving complexity of pharmacologic treatment: comparative effectiveness research, pharmacoeconomic data analyses, and other decision support tools.
abstract: Eleven classes of antidiabetic medicines are now available to help the 25.8 million Americans with type 2 diabetes control their blood sugar levels when diet and lifestyle modifications are not sufficient. Although patients benefit from the myriad of treatment options, there are little comparative data to effectively differentiate the products and predict their relative utility. In the absence of true comparative outcomes data, comparative effectiveness research (CER) provides a valuable tool to compare the safety and efficacy of agents and applies the results to heterogeneous patient populations, including patients ordinarily excluded from randomized controlled trials. Thus, CER provides more generalizable results that better reflect real-world situations faced by practitioners and patients. In addition to traditional CER approaches such as systematic reviews, meta-analyses, and retrospective claims analyses, Markov modeling and Bayesian analysis can be applied to predict patient outcomes in scenarios in which clinical trials are not feasible. CER may be the best way to consolidate and interpret data on the many agents involved and thereby guide rational treatment decisions.
authors: Harvard Pilgrim Health Care, Inc, Wellesley, MA, USA. jim_kenney@hphc.org
authors: James T
authors: JT
authors: Kenney
mesh_terms: D057186:Comparative Effectiveness Research; D003661:Decision Support Techniques; D003924:Diabetes Mellitus, Type 2 / Q000188:drug therapy*; D017850:Economics, Pharmaceutical / Q000706:statistics & numerical data; D006801:Humans; D054539:Medication Therapy Management; D015233:Models, Statistical; D014481:United States