The impact of bias due to exponentiation in the estimation of hazard, risk, and odds ratios: an empirical investigation from 1,495,059 effect sizes from MEDLINE/PubMed abstracts

The impact of bias due to exponentiation in the estimation of hazard, risk, and odds ratios: an empirical investigation from 1,495,059 effect sizes from MEDLINE/PubMed abstracts

Blog Article

Abstract Background Parameter estimation using regression methods plays a vital role in medical research.Often a non-linear transformation of a regression parameter is preferred for its more intuitive interpretation.Important examples in medical research are odds ratios, risk ratios, and hazard ratios, which are obtained by exponentiating the estimated regression coefficients of the logit link binomial generalized click here linear model, log link Poisson generalized linear model or Cox proportional hazards model, respectively.

A lot of attention has been devoted to studying and removing the bias of the estimators on the scale of the regression, but the bias of the transformed parameters is rarely addressed.Methods Two approaches for reducing the bias due to the exponentiation are reviewed and applied to odds ratios, risk ratios, and hazard ratios reported in the abstracts published in the MEDLINE subset of English-language PubMed records.Results We show that correcting for the bias due to the exponentiation may yield substantially different estimates, potentially resulting in a large shrinkage of the reported effect size estimates.

Conclusion Given the wide availability of methods to reduce the bias on the scale of regression, tillman 750m we encourage their routine use to improve estimation.In situations where the consequences of biased estimation are larger at the exponentiated scale than at the scale of regression, as for example in some policy and planning settings, we additionally encourage the removal of the bias due to the exponentiation.