Multiple Imputation in Survival Models: Applied on Breast Cancer Data
نویسندگان
1 Department of Surgery, Shiraz University of Medical Sciences, Shiraz, Iran
2 Department of Biostatistics and Epidemiology, Kerman University of Medical Sciences, Kerman,
doi
چکیده
Background: Missing data is a common problem in cancer research. While simple methods such as completecase(C-C) analysis are commonly employed for handling this problem, several studies have shown that thesemethods led to biased estimates. We aim to address the methodological issues in development of a prognosticmodel with missing data.Methods: Three hundred and ten breast cancer patients were enrolled. At first, patients with missing data on anyof four candidate variables were omitted. Secondly, missing data were imputed 10 times. Cox regression modelwas fitted to the C-C and imputed data. Results were compared in terms of variables retained in the model,discrimination ability, and goodness of fit.Results: Some variables lost their effect in complete-case analysis, due to loss in power, but reached significancelevel after imputation of missing data. Discrimination ability and goodness of fit of imputed data sets model washigher than that of complete-case model (C-index 76% versus 72%; Likelihood Ratio Test 51.19 versus 32.44).Conclusion: Our findings showed inappropriateness of ad hoc complete-case analysis. This approach led toloss in power and imprecise estimates. Application of multiple imputation techniques to avid such problems isrecommended.
