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Analysis of exacerbation rates in asthma and chronic obstructive pulmonary disease: example from the TRISTAN study
Author(s) -
Keene Oliver N.,
Jones Mark R. K.,
Lane Peter W.,
Anderson Julie
Publication year - 2007
Publication title -
pharmaceutical statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.421
H-Index - 38
eISSN - 1539-1612
pISSN - 1539-1604
DOI - 10.1002/pst.250
Subject(s) - exacerbation , copd , medicine , asthma , negative binomial distribution , poisson distribution , clinical trial , poisson regression , statistics , binomial distribution , intensive care medicine , event (particle physics) , pulmonary disease , parametric statistics , econometrics , mathematics , population , physics , environmental health , quantum mechanics
Recurrent events in clinical trials have typically been analysed using either a multiple time‐to‐event method or a direct approach based on the distribution of the number of events. An area of application for these methods is exacerbation data from respiratory clinical trials. The different approaches to the analysis and the issues involved are illustrated for a large trial (n = 1465) in chronic obstructive pulmonary disease (COPD). For exacerbation rates, clinical interest centres on a direct comparison of rates for each treatment which favours the distribution‐based analysis, rather than a time‐to‐event approach. Poisson regression has often been employed and has recently been recommended as the appropriate method of analysis for COPD exacerbations but the key assumptions often appear unreasonable for this analysis. By contrast use of a negative binomial model which corresponds to assuming a separate Poisson parameter for each subject offers a more appealing approach. Non‐parametric methods avoid some of the assumptions required by these models, but do not provide appropriate estimates of treatment effects because of the discrete and bounded nature of the data. Copyright © 2007 John Wiley & Sons, Ltd.