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Open Access Research article

Imputation of missing values of tumour stage in population-based cancer registration

Nora Eisemann1*, Annika Waldmann2 and Alexander Katalinic12

Author Affiliations

1 Institute of Cancer Epidemiology, University Luebeck, Ratzeburger Allee 160 (Haus 50), 23562 Luebeck, Germany

2 Institute of Clinical Epidemiology, University hospital Schleswig-Holstein, Campus Luebeck, Ratzeburger Allee 160 (Haus 50), 23562 Luebeck, Germany

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BMC Medical Research Methodology 2011, 11:129  doi:10.1186/1471-2288-11-129

Published: 19 September 2011

Abstract

Background

Missing data on tumour stage information is a common problem in population-based cancer registries. Statistical analyses on the level of tumour stage may be biased, if no adequate method for handling of missing data is applied. In order to determine a useful way to treat missing data on tumour stage, we examined different imputation models for multiple imputation with chained equations for analysing the stage-specific numbers of cases of malignant melanoma and female breast cancer.

Methods

This analysis was based on the malignant melanoma data set and the female breast cancer data set of the cancer registry Schleswig-Holstein, Germany. The cases with complete tumour stage information were extracted and their stage information partly removed according to a MAR missingness-pattern, resulting in five simulated data sets for each cancer entity. The missing tumour stage values were then treated with multiple imputation with chained equations, using polytomous regression, predictive mean matching, random forests and proportional sampling as imputation models. The estimated tumour stages, stage-specific numbers of cases and survival curves after multiple imputation were compared to the observed ones.

Results

The amount of missing values for malignant melanoma was too high to estimate a reasonable number of cases for each UICC stage. However, multiple imputation of missing stage values led to stage-specific numbers of cases of T-stage for malignant melanoma as well as T- and UICC-stage for breast cancer close to the observed numbers of cases. The observed tumour stages on the individual level, the stage-specific numbers of cases and the observed survival curves were best met with polytomous regression or predictive mean matching but not with random forest or proportional sampling as imputation models.

Conclusions

This limited simulation study indicates that multiple imputation with chained equations is an appropriate technique for dealing with missing information on tumour stage in population-based cancer registries, if the amount of unstaged cases is on a reasonable level.