Impact of Imputation of Missing Data on Estimation of Survival Rates: An Example in Breast Cancer

authors:

avatar Mohammad Reza Baneshi 1 , * , avatar AR Talei 2

Health School, Kerman University of Medical Sciences, Deptartment of Biostatistics and Epidemiology, Kerman, Iran
Shahid Faghihi Hospital, Shiraz University of Medical Sciences, Shiraz, Iran
International Journal of Cancer Management: Vol.3, issue 3; e80700
published online: September 30, 2010
article type: Research Article
received: January 12, 2010
accepted: June 21, 2010

how to cite: Baneshi M R , Talei A. Impact of Imputation of Missing Data on Estimation of Survival Rates: An Example in Breast Cancer. Int J Cancer Manag. 2010;3(3):e80700. 

Abstract

Background: Multifactorial regression models are frequently used in medicine to estimate survival rate of patients across risk groups. However, their results are not generalisable, if in the development of models assumptions required are not satisfied. Missing data is a common problem in pathology. The aim of this paper is to address the danger of exclusion of cases with missing data, and to highlight the importance of imputation of missing data before development of multifactorial models.
Methods: This study was performed on 310 breast cancer patients diagnosed in Shiraz (Southern Iran). Performing a complete-case Cox regression model, a prognostic index was calculated so as to categorise the patients into 3 risk groups. Then, applying the Multivariate Imputation via Chained Equations (MICE) method, missing data were imputed 10 times. Using imputed data sets, modelling was performed to assign patients into risk groups. Estimated actuarial Overal Survival (OS) rates corresponding to analysis of complete-case and imputed data sets were compared.
Results: Cases with at least one missing datum experienced a significantly better survival curve. Estimates derived analysing complete-case data, relative to imputed data sets, underestimated the OS rate in all risk groups. In addition confidence intervals were wider indicating loss in precision due to attrition in sample size and power.
Conclusion: Results obtained highlighted the danger of exclusion of missing data. Imputation of missing data avoids biased estimates, increases the precision of estimates, and improves genralisability of results to other similar populations.

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