Measuring effectiveness of drugs in observational databanks: promises and perils.
Observational databanks have inherent strengths and shortcomings. As in randomized controlled trials, poor design of these databanks can either exaggerate or reduce estimates of drug effectiveness and can limit generalizability. This commentary highlights selected aspects of study design, data collection and statistical analysis that can help overcome many of these inadequacies. An international metaRegister and a formal mechanism for standardizing and sharing drug data could help improve the utility of databanks. Medical journals have a vital role in enforcing a quality checklist that improves reporting.
Krishnan
Eswar
E
Division of Immunology, Department of Medicine, Stanford University, Palo Alto, CA, USA. eswar_krishnan@hotmail.com
Fries
James F
JF
D018501:Antirheumatic Agents / Q000627:therapeutic use; D001172:Arthritis, Rheumatoid / Q000188:drug therapy; D015982:Bias; D015986:Confounding Factors, Epidemiologic; D016208:Databases, Factual / Q000639:trends*; D004341:Drug Evaluation / Q000639:trends*; D006801:Humans; D016032:Randomized Controlled Trials as Topic
D018501:Antirheumatic Agents