Food and Drug Administration, Rockville, MarylandThis paper proposes several statistical approaches for analyzing spontaneous reports of adverse medical device events. The chi-square statistic is used to detect a sudden increase in reports from pairwise comparison of numbers of reports per month. The Cox-Stuart nonparametric trend test is used to detect a gradual, increasing trend in adverse events reports over time. The negative binomial probability model is also used to assess sudden increases by setting threshold values. In this papel; only numerator data (reports of adverse events), not denominator data (device use), are used. The Box-Jenkins ARIMA time series model failed to fit the observed data successfully due to the extremely irregular distributions, including many zeros, of the observed data.
This paper describes several statistical methods for analyzing medical device reports received by the Food and Drug Administration. The nonparametric regressions (polynomial, loess, kernel smooth, and cubic spline smoothing) are used as exploratory tools to evaluate trends in adverse event reports. Several statistical models, including simple Poisson, mixed binornial/Poisson, zero-truncated Poisson, and the negative binomial (compound Poisson), are used to monitor and to determine the upper 95% threshold values for reported adverse events during the study period. The hip implant injury data and the intravenous tube total (sum of death, device malfunction, and injury) data are used to illustrate our model fitting procedures. In this paper; only the numerator data (medical device adverse events), not the denominator data (medical device usage), are available in our statistical analysis. The possible effects of marketing time and other factors, which may be available in adverse drug reactions, are not available and are not considered for medical device adverse events in this pape,:
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