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From |
Ronan Conroy <rconroy@rcsi.ie> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Binomial Regression |

Date |
Wed, 8 Aug 2007 17:31:00 +0100 |

On 8 Aug 2007, at 14:33, Jay Kaufman wrote:

Journal of Clinical Epidemiology

Volume 60, Issue 9, September 2007, Pages 874-882

Relative risks and confidence intervals were easily computed indirectly from multivariable logistic regression

Interestingly, they sound the same notes of caution around Poisson regression with robust variance estimation as around binomial regression -

(Extracted from the discussion)

Unlike other recent articles on this subject [references omitted], we are less optimistic about the use of log-linear models, either Poisson or log binomial, to estimate relative risks directly. Log- binomial regression, as others and we [31] have found, will not converge routinely. Failure of the log-binomial model to converge should not, as has been suggested, point to Poisson regression as an alternative. Rather, it should signal a fundamental shortcoming of any log-link model—the failure to fit the data and to bound estimates of risk by the interval [0,1]. The chances of exceeding these bounds are especially high when outcomes are common, the situation that prompted this and other articles. Log-link models by definition assume a constant relative risk across different patterns of covariates. But this assumption can fail when the reference risk (of outcome in the unexposed) is high and relative risk is not small. In addition, unlike logit models for which recoding the 0/1 outcomes to 1/0 merely inverts the resulting ORs, log-linear models with recoded outcomes will not generate inverted relative risks.

P Before printing, think about the environment

=================================

Ronan Conroy

rconroy@rcsi.ie

Royal College of Surgeons in Ireland

120 St Stephen's Green, Dublin 2, Ireland

+353 (0)1 402 2431

+353 (0)87 799 97 95

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**References**:**st: Binomial Regression***From:*Jay Kaufman <Jay_Kaufman@unc.edu>

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