The methodology for handling missing data during development of predictive model

The methodology for handling missing data during development of predictive model
Xiao-Ou Ping1, Ja-Der Liang3, Yi-Ju Tseng2, Pei-Ming Yang3,
Guan-Tarn Huang3, Feipei Lai1, 2
1. Department of Computer Science and Information Engineering,
National Taiwan University
2. Graduate Institute of Biomedical Electronic and Bioinformatics,
National Taiwan University
3. Department of Internal Medicine,
National Taiwan University Hospital and
National Taiwan University College of Medicine

Introduction
In medical research, the problem of missing data occurs frequently
According to a study of reviewing 100 articles among seven cancer journals
81 of these articles have evidences of missing data
In our study of developing liver cancer recurrence predictive model, there have still missing data
The adoption of methods for dealing with missing data is necessary
The aims of this study are as follows
To evaluate the imputed performance of imputation methods in the stability and accuracy
To present the impact of different missing data handling methods in the predictive results of recurrence predictive model
To discover whether if the clinical features with missing values still have the potential for building the more accurate predictive model
2

Materials and methods
To develop predictive models based on incomplete clinical data using the missing data handling methods
Complete case (CC) analysis, complete variable (CV) analysis, and imputation method (IM)
The 92 liver cancer patients were included in the study
7.6% missing values
Analyzed features contain age, gender, laboratory tests, tumor size, tumor number, and cancer staging, etc
In the simulation experiment
The observed values are randomly masked as missing values and the IMs are employed for imputing these missing entries
After the process of data imputation, the masked true valuesand the imputed valued estimated by IMscan be compared
The normalized root mean squared errors (NRMSEs) can be used for evaluating the imputation accuracy of IMs
The summation of first quartile, third quartile, and the median is regarded as the IM selection criterion for comparing the imputation performance in the stability and accuracy
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Evaluation of missing data handling methods
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Complete case (CC) analysis: analyzing only the data of patients without missing data
Complete variable (CV) analysis: dropping the variables with missing data and analyzing only the variables without missing data
Imputation method (IM): estimating the missing values (MVs) based on different methods
8 IMs are compared
in this study:
6 single MIs
1. “SVDImpute”
2. “LLSImpute”
3. “PPCA”
4. “BPCA”
5. “NLPCA”
6. “Nipals PCA”
2 multiple MIs
1. “MICE”
2. “mi”

Results and Conclusion
The sensitivity and specificity of CC are 100% and 83%
The best sensitivity and specificity of CV are 86% and 79%
In the study, we designed the score of Imputation Method (IM) selection criterionfor selecting the more appropriate IMs
The best three IMs can achieve better predictive accuracy (i.e. the same sensitivity, 86%, and the better specificity, 88%) than CV
The best IM, “BPCA”, can use just four features
Included the feature with missing values
IMs for data with missing values still show the compatible benefit for the recurrence predictive model
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The methodology for handling missing data during development of predictive model

by pingxiaoou on Oct 05, 2011

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