Developments in Post-marketing Comparative Effectiveness Research

Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
Clinical Pharmacology &#38 Therapeutics (Impact Factor: 7.9). 09/2007; 82(2):143-56. DOI: 10.1038/sj.clpt.6100249
Source: PubMed


Physicians and insurers need to weigh the effectiveness of new drugs against existing therapeutics in routine care to make decisions about treatment and formularies. Because Food and Drug Administration (FDA) approval of most new drugs requires demonstrating efficacy and safety against placebo, there is limited interest by manufacturers in conducting such head-to-head trials. Comparative effectiveness research seeks to provide head-to-head comparisons of treatment outcomes in routine care. Health-care utilization databases record drug use and selected health outcomes for large populations in a timely way and reflect routine care, and therefore may be the preferred data source for comparative effectiveness research. Confounding caused by selective prescribing based on indication, severity, and prognosis threatens the validity of non-randomized database studies that often have limited details on clinical information. Several recent developments may bring the field closer to acceptable validity, including approaches that exploit the concepts of proxy variables using high-dimensional propensity scores, within-patient variation of drug exposure using crossover designs, and between-provider variation in prescribing preference using instrumental variable (IV) analyses.

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    • "The major strength of our study is that the study is population-based , increasing the validity of the results. Moreover, the propensity score methodology is frequently used in studies with administrative database to control for confounding (Schneeweiss, 2007), therefore increasing the comparability of cohorts. "
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    ABSTRACT: We aimed to evaluate the frequency of hypoglycemia and its impact on the length of stay and all-cause in-hospital mortality in hospitalized patients with diabetes. We used data from the Basic Minimum Data Set of the Spanish National Health System. Hypoglycemia was defined as having an ICD-9-CM code 250.8, 251.0, 251.1, and 251.2, and categorized as primary if it was the main cause of admission and secondary if it occurred during the hospital stay. The association between hypoglycemia and the study outcomes was evaluated in two cohorts - with and without secondary hypoglycemia - matched by propensity scores and using multivariate models. Among the 5,447,725 discharges with a diagnosis of diabetes recorded from January 1997 to December 2010, there were 92,591 (1.7%) discharges with primary hypoglycemia and 154,510 (2.8%) with secondary hypoglycemia. The prevalence of secondary hypoglycemia increased from 1.1% in 1997 to a peak of 3.8% in 2007, while the prevalence of primary hypoglycemia remained fairly stable. Primary hypoglycemia was associated with reduced in-hospital mortality (Odds ratio [OR] 0.06; 95% Confidence interval [CI], 0.03-0.10) and a significant decrease in time to discharge (Hazard ratio [HR] 2.53; 95% CI, 2.30-2.76), while secondary hypoglycemia was associated with an increased likelihood of in-hospital mortality (OR 1.12; 95% CI, 1.09-1.15) and a significant increase in time to discharge (HR 0.80; 95% CI, 0.79-0.80). In conclusion, the prevalence of secondary hypoglycemia is increasing in patients with diabetes and is associated with an increased likelihood of in-hospital mortality and a longer hospital stay. Copyright © 2015. Published by Elsevier Inc.
    Journal of diabetes and its complications 07/2015; 29(8). DOI:10.1016/j.jdiacomp.2015.07.018 · 3.01 Impact Factor
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    • "The recent emphasis on comparative effectiveness research has promoted interdisciplinary sharing of methods and perspectives [4], including those from econometrics, statistics , health services research, and epidemiology [5]. However , researchers may be unfamiliar with the appropriate assumptions and estimands produced by a particular analytic technique. "
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    ABSTRACT: To compare the assumptions and estimands across three approaches to estimate the effect of erythropoietin-stimulating agents (ESAs) on mortality. Using data from the Renal Management Information System, we conducted two analyses using a change to bundled payment that, we hypothesized, mimicked random assignment to ESA (pre-post, difference-in-difference, and instrumental variable analyses). A third analysis was based on multiply imputing potential outcomes using propensity scores. There were 311,087 recipients of ESAs and 13,095 non-recipients. In the pre-post comparison, we identified no clear relationship between bundled payment (measured by calendar time) and the incidence of death within 6 months (risk difference -1.5%; 95% confidence interval [CI] -7.0%, 4.0%). In the instrumental variable analysis, the risk of mortality was similar among ESA recipients (risk difference -0.9%; 95% CI -2.1, 0.3). In the multiple imputation analysis, we observed a 4.2% (95% CI 3.4%, 4.9%) absolute reduction in mortality risk with the use of ESAs, but closer to the null for patients with baseline hematocrit level >36%. Methods emanating from different disciplines often rely on different assumptions but can be informative about a similar causal contrast. The implications of these distinct approaches are discussed.
    Journal of clinical epidemiology 08/2013; 66(8 Suppl):S42-50. DOI:10.1016/j.jclinepi.2013.02.014 · 3.42 Impact Factor
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    ABSTRACT: Risk management plans aim to facilitate a proactive approach to potential safety concerns by both the marketing authorisation holder and the competent authorities. Within this hospital pharmacists can play an important role in the pharmacovigilance of biopharmaceuticals.
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