Limits...
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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The effect of overall censoring and confounding on bias: (a) biases of omitting a balanced covariate where  data are censored; (b) biases under different strengths of confounding,  and  when  data are censored.
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fig02: The effect of overall censoring and confounding on bias: (a) biases of omitting a balanced covariate where data are censored; (b) biases under different strengths of confounding, and when data are censored.

Mentions: Figure 2a shows the effect of censoring on the bias of omitting a balanced covariate. The event times were generated from (1) with and . The possible censoring times were simulated from uniform with .


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

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

The effect of overall censoring and confounding on bias: (a) biases of omitting a balanced covariate where  data are censored; (b) biases under different strengths of confounding,  and  when  data are censored.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig02: The effect of overall censoring and confounding on bias: (a) biases of omitting a balanced covariate where data are censored; (b) biases under different strengths of confounding, and when data are censored.
Mentions: Figure 2a shows the effect of censoring on the bias of omitting a balanced covariate. The event times were generated from (1) with and . The possible censoring times were simulated from uniform with .

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