BACKGROUND: Epidemiological studies of rare events, which are common in the medical literature, often involve modeling sparse data sets. Assessing the fit of these models may be complicated by the large numbers of observed zeros in the data set. METHODS: Poisson models, fitted as generalized linear models, were used to investigate the referral patterns of patients suffering from end-stage renal failure in south west Wales. The usual method for assessing the goodness of fit is to compare the deviance with a chi 2 distribution with appropriate degrees of freedom. However, this test may be invalid when the data set is sparse, as the deviance values may be unusually low compared to the degrees of freedom. This would suggest that there is a problem with underdispersion when, in fact, the large numbers of zeros in the data set make the comparison with the chi 2 distribution unreliable. A simulation approach is advocated as an alternative method of assessing model fit in these situations. RESULTS: Three models are considered in detail here. The first modelled the total referrals in each of the 245 wards in the study area and included two explanatory variables. These observations were not unusually sparse and both the chi 2 goodness of fit test and the simulation methodology outlined here suggested that the model did not fit. The second model included the population 'at risk' as an offset and the model improved considerably. Both the chi 2 test and the simulation approach suggested that this model did fit. Finally, the data were disaggregated into five age groups providing 1225 observations and a very sparse data set. According to the chi 2 goodness of fit test, the deviance was very low suggesting that the model was underdispersed. Using simulated data, it was shown that the deviance was not unusually low and that the model fitted the data reasonably well. CONCLUSION: In cases where the data set being modelled is sparse, it is useful to test the goodness of fit of a Poisson model using a simulation approach, rather than relying on the chi 2 test.
BACKGROUND: Epidemiological studies of rare events, which are common in the medical literature, often involve modeling sparse data sets. Assessing the fit of these models may be complicated by the large numbers of observed zeros in the data set. METHODS: Poisson models, fitted as generalized linear models, were used to investigate the referral patterns of patients suffering from end-stage renal failure in south west Wales. The usual method for assessing the goodness of fit is to compare the deviance with a chi 2 distribution with appropriate degrees of freedom. However, this test may be invalid when the data set is sparse, as the deviance values may be unusually low compared to the degrees of freedom. This would suggest that there is a problem with underdispersion when, in fact, the large numbers of zeros in the data set make the comparison with the chi 2 distribution unreliable. A simulation approach is advocated as an alternative method of assessing model fit in these situations. RESULTS: Three models are considered in detail here. The first modelled the total referrals in each of the 245 wards in the study area and included two explanatory variables. These observations were not unusually sparse and both the chi 2 goodness of fit test and the simulation methodology outlined here suggested that the model did not fit. The second model included the population 'at risk' as an offset and the model improved considerably. Both the chi 2 test and the simulation approach suggested that this model did fit. Finally, the data were disaggregated into five age groups providing 1225 observations and a very sparse data set. According to the chi 2 goodness of fit test, the deviance was very low suggesting that the model was underdispersed. Using simulated data, it was shown that the deviance was not unusually low and that the model fitted the data reasonably well. CONCLUSION: In cases where the data set being modelled is sparse, it is useful to test the goodness of fit of a Poisson model using a simulation approach, rather than relying on the chi 2 test.
Authors: John Darrell Van Horn; Andrei Irimia; Carinna M Torgerson; Avnish Bhattrai; Zachary Jacokes; Paul M Vespa Journal: J Neurosci Res Date: 2017-05-20 Impact factor: 4.164