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Performance of logistic regression modeling: beyond the number of events per variable, the role of data structure |
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Published in | Journal of clinical epidemiology. 2011, vol. 64, no. 9, p. 993-1000 | |
Abstract | Logistic regression is commonly used in health research, and it is important to be sure that the parameter estimates can be trusted. A common problem occurs when the outcome has few events; in such a case, parameter estimates may be biased or unreliable. This study examined the relation between correctness of estimation and several data characteristics: number of events per variable (EPV), number of predictors, percentage of predictors that are highly correlated, percentage of predictors that were non-null, size of regression coefficients, and size of correlations. | |
Keywords | Bias (Epidemiology) — Computer Simulation — Humans — Logistic Models — Research Design — Statistics as Topic/methods | |
Identifiers | PMID: 21411281 | |
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Research group | Epidémiologie clinique (115) | |
Citation (ISO format) | COURVOISIER, Delphine et al. Performance of logistic regression modeling: beyond the number of events per variable, the role of data structure. In: Journal of clinical epidemiology, 2011, vol. 64, n° 9, p. 993-1000. doi: 10.1016/j.jclinepi.2010.11.012 https://archive-ouverte.unige.ch/unige:25409 |