Bias and sensitivity analysis when estimating treatment effects from the cox model with omitted covariates.
Bottom Line: It is shown that the bias converges to fixed limits as the effect of the omitted covariate increases, irrespective of the degree of confounding.The bias formulae are used as the basis for developing a new method of sensitivity analysis to assess the impact of omitted covariates on estimates of treatment or exposure effects.In simulation studies, the proposed method gave unbiased treatment estimates and confidence intervals with good coverage when the true sensitivity parameters were known.
Affiliation: Institute of Health Research, University of Exeter Medical School, Exeter, U.K.; Centre for Health and Environmental Statistics, University of Plymouth, Plymouth, U.K.Show MeSH
Related in: MedlinePlus
Mentions: We explored a general framework for assessing bias in treatment estimates from the Cox model with omitted covariates. Bias formulae based on asymptotic properties of the likelihood estimator were presented and validated in simulation experiments. The results showed that the confounding biases for censored survival data are typically complicated. However, the proposed approach made it possible to describe the influence of three different sources of bias: omission of a balanced covariate, data censoring and unmeasured confounding. Figure 5 characterises the sources of bias:
Affiliation: Institute of Health Research, University of Exeter Medical School, Exeter, U.K.; Centre for Health and Environmental Statistics, University of Plymouth, Plymouth, U.K.