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- W2031103619 abstract "BACKGROUND: Decisions are often based on relative risk and their asymptotic properties, which is not reliable when the number of events is small. Moreover, clinical decision-making primarily depends on individual risk of adverse outcome rather than relative risk. Bayesian decision analysis predicts individual outcome, is valid for small samples and can include decision-maker's prior knowledge into the analysis. METHOD: We analysed data about gastrointestinal adverse events of medium and high doses of ibuprofen in a population of 46,249 patients. We used a Bayesian method based on expected utility with a utility function EFF − q L(No. Events). Where EFF is the efficiency, L is a quadratic function representing the risk of Adverse Outcome and q represents relative importance of the risk. Bayesian value of information (VOI) of additional observations of a particular subgroup was calculated. Markov Chain Monte Carlo procedure and software BUGS were used to fit a Poisson regression model to adjust for confounders. RESULT: There were 1 and 5 G.I. events in high and medium dose groups (relative risks, RR 5.26 and 2.36 respectively). The Bayesian mean log-RR between high and medium was 0.41 (95%CI −2.72, 2.58). Assuming that the higher dose had 20% higher efficiency, we found that medium dose is preferable when q is larger than 15. VOI of additional observations was calculated for a range of q and showed that additional observations of the higher dose would be most valuable. For example, when q = 50 the VOI of an additional subgroup of 1000 person-years exposure was 15% for high doses but only 3% for medium doses. CONCLUSION: In comparison with the classical approach for drug safety or other outcome studies, Bayesian methods provides much more information to assist decision-making, especially when the number of events is small." @default.
- W2031103619 created "2016-06-24" @default.
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- W2031103619 date "2001-09-01" @default.
- W2031103619 modified "2023-09-28" @default.
- W2031103619 title "PMA14: BAYESIAN DECISION ANALYSIS IN OUTCOME STUDIES WITH SMALL NUMBERS OF EVENTS: A SIMULATION BASED PREDICTION APPROACH" @default.
- W2031103619 doi "https://doi.org/10.1046/j.1524-4733.2001.40202-308.x" @default.
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