Using Data Reduction Methods To Predict Quality Of Life In Brest Cancer Patients

Abstract

Background: Quality of life study has an important role in health care especially in chronic diseases, in clinical judgment and in medical resources supplying. Statistical tools like linear regression are widely used to assess the predictors of quality of life. But usually existed a lot of factor cause difficulty for fitting the models and predicting. In statistical method there are different methods of data reduction that recommended. Methods: A cross-sectional study conducted on 119 breast cancer patients that admitted and treated in chemotherapy ward of Namazi hospital in Shiraz. QLQ-C30 questionnaire was used to assessment quality of life in these patients. Principal component analyzing and factor analyzing are tow statistical method of data reduction was used for reducing the number of predictors. Results: The mean score for the global health status for breast cancer patients was 64.92±11.42. univariate Linear regression showed that only role function, social function and diarrhea were not significant. Principal component analyzing and factor analyzing, consider all of 14 factors to 7 component and 7 factors. According to adjusted R square model fitting with reducing predictors were better than model fitting with
initial predictors. Conclusion: when there are a lot of factors existed in a model, use different method of data reduction causing better and easier model fitting and predicting