Cancer Patients Missing Pain Score Information:- Application with Imputation Techniques


  • Gajendra Vishwakarma Indian School of Mines, Dhanbad-826004, Jharkhand
  • Atanu Bhattacharjee Malabar Cancer Centre, Thalassery
  • Jesna Jose ICON clinical Research, Bangalore
  • Ramesh Kumar V ICON clinical Research, Bangalore



EM algorithm, Regression method, Imputation, Handling Missing Data


Background: Methods for handling missing data in clinical research have been getting more attentions over last few years. Contemplation of missing data in any study is vital as they may lead to considerable biases and can have an impact on the power of the study.

Objective: This manuscript is dedicated to present different techniques to handle missing observations obtained from repeatedly measured pain score data on palliative cancer.

Methods: This problem caused by subjects drop out before completion of the study. The reason for dropout or withdrawal may be related to study (e.g., adverse event, death, unpleasant study procedures, lack of improvement) or unrelated to the study (e.g., moving away, unrelated disease). The dropout might be very common on studies on palliative cancer patients. The Palliative treatment is designed to relieve symptoms, and improve the quality of life and can be used at any stage of an illness if there are troubling symptoms, such as pain or sickness.

Results: The mean(SD) of observed pain score was 3.638(3.156) whereas the imputed mean values were 3.615(2.980), 3.618(2.954), 3.577(2.892), 3.560(2.999) and 3.627(2.949) respectively for the imputation methods regression, predictive mean matching, propensity score, EM algorithm and MCMC methods for pain score values at visit3. 

Interpretation and Conclusion: The EM algorithm shows the least percentage change from observed values in both visits followed by predictive mean matching method and MCMC methods. The multiple imputation techniques have few advantages; the imputed values are drawsfrom a distribution, so they inherently contain some variation by introducing an additional form of error in the parameter estimates across the imputation







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