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Bias and sensitivity analysis when estimating treatment effects from the cox model with omitted covariates.

Lin NX, Logan S, Henley WE - Biometrics (2013)

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.

View Article: PubMed Central - PubMed

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.

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An illustration of the influence of the different sources of bias when estimating binary treatment effects from the Cox proportional hazards model with an omitted binary covariate. (a) solid: no missing data, no bias; dashed: bias due to omitting a balanced covariate. (b) solid: bias due to omitting a balanced covariate; dashed: bias due to omitting a balanced covariate and censoring. (c) solid: bias due to omitting a balanced covariate and censoring; dashed: bias due to omitting a confounder and censoring.
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fig05: An illustration of the influence of the different sources of bias when estimating binary treatment effects from the Cox proportional hazards model with an omitted binary covariate. (a) solid: no missing data, no bias; dashed: bias due to omitting a balanced covariate. (b) solid: bias due to omitting a balanced covariate; dashed: bias due to omitting a balanced covariate and censoring. (c) solid: bias due to omitting a balanced covariate and censoring; dashed: bias due to omitting a confounder and censoring.

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:


Bias and sensitivity analysis when estimating treatment effects from the cox model with omitted covariates.

Lin NX, Logan S, Henley WE - Biometrics (2013)

An illustration of the influence of the different sources of bias when estimating binary treatment effects from the Cox proportional hazards model with an omitted binary covariate. (a) solid: no missing data, no bias; dashed: bias due to omitting a balanced covariate. (b) solid: bias due to omitting a balanced covariate; dashed: bias due to omitting a balanced covariate and censoring. (c) solid: bias due to omitting a balanced covariate and censoring; dashed: bias due to omitting a confounder and censoring.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4230475&req=5

fig05: An illustration of the influence of the different sources of bias when estimating binary treatment effects from the Cox proportional hazards model with an omitted binary covariate. (a) solid: no missing data, no bias; dashed: bias due to omitting a balanced covariate. (b) solid: bias due to omitting a balanced covariate; dashed: bias due to omitting a balanced covariate and censoring. (c) solid: bias due to omitting a balanced covariate and censoring; dashed: bias due to omitting a confounder and censoring.
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:

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.

View Article: PubMed Central - PubMed

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