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

AUTHORS

Mohammad Reza Baneshi 1 , * , AR Talei 2

1 Health School, Kerman University of Medical Sciences, Deptartment of Biostatistics and Epidemiology, Kerman, Iran

2 Shahid Faghihi Hospital, Shiraz University of Medical Sciences, Shiraz, Iran

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.

ARTICLE INFORMATION

International Journal of Cancer Management: 3 (3); e80700
Published Online: September 30, 2010
Article Type: Research Article
Received: January 12, 2010
Accepted: June 21, 2010

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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.

Keywords

Missing data Multiple imputation Breast neoplasm Overall survival Iran

© 2010, Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/) which permits copy and redistribute the material just in noncommercial usages, provided the original work is properly cited.

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