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USING DATA FROM SYSTEMATIC REVIEWS OR MULTIPLE SOURCES IN DECISION ANALYSES IMPACTS CONCLUSIONS

Tuesday, October 25, 2011
Grand Ballroom AB (Hyatt Regency Chicago)
Poster Board # 38
(ESP) Applied Health Economics, Services, and Policy Research

Rahul Mhaskar, PhD1, Helen Georgiev, MPH2, Hesborn Wao, PhD2, Ambuj Kumar, MD, MPH3 and Benjamin Djulbegovic, MD, PhD4, (1)University of South Florida,, Tampa, FL, (2)University of South Florida, Tampa, FL, (3)Center for Evidence Based Medicine, University of South Florids, Tampa, FL, (4)University of South Florida & H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL

Purpose: To compare the conclusions of decision analyses (DA) and matching systematic reviews (SR) of randomized controlled trials (RCTs).

Methods: We searched PubMed up to 2008 for DAs comparing at least two interventions followed by SRs that matched the DAs based on patient population, intervention, control, and outcome criteria (PICO). From each DA and SR, we extracted data on PICO, conclusion, and impact of sensitivity analyses on the conclusion. We also collected data on the DA design characteristics such as: whether DA conducted primary data collection, derived data from published literature, used data from meta-analysis, incorporated expert opinion, and used single or multiple data sources. Agreement between DA and SR was based on matching of respective conclusions. We examined association of DA design characteristics with agreement by Pearson Chi-square or the Fisher’s exact test.

Results: From 42,704 retrieved DA citations, we found matching SR for 38 comparisons (Figure 1). Infection (11/38) and cancer (10/38) were the most frequently studied diseases. There was a 74% (28/38) agreement between the conclusions of the DAs and the SRs. Twenty-six percent (10/38) of the SR conclusions disagreed with the conclusions of the DA. The sensitivity analyses conducted in either DA or SR did not impact the agreement. Two DA design characteristics were significantly associated with agreement: use of single versus multiple data source (p=0.048) and use of meta-analysis data (p=0.040).

Conclusions: This first study quantifying the correlation between the results of DA and SR of RCTs suggests a high level of agreement. These findings emphasize on the need for incorporation of data from systematic reviews or multiple sources, when possible, to inform the decision analyses. However, the findings are limited by small sample size (N=38). Nevertheless, it appears that use of meta-analysis data and use of multiple sources of data impact the predictive value of DA.

Figure 1

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