COMPARISON OF COX MODEL AND K-NEAREST NEIGHBOR TO ESTIMATION OF SURVIVAL IN KIDNEY TRANSPLANT PATIENTS

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FARADMAL J.; OMIDI T.; POUROLAJAL J.; ROSHANAEI GH.

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Journal: Avicenna Journal of Clinical Medicine Year:2016 | Volume:22 | Issue:4 (SN 78) Page(s): 300-308

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Title

COMPARISON OF COX MODEL AND K-NEAREST NEIGHBOR TO ESTIMATION OF SURVIVAL IN KIDNEY TRANSPLANT PATIENTS

Author(s)

FARADMAL J. | OMIDI T. | POUROLAJAL J. | ROSHANAEI GH. | Issue Writer Certificate

Keywords

COX MODELQ3

KIDNEY TRANSPLANTATIONQ2

KQ2

NEAREST NEIGHBORQ2

TRANSPLANTATION REJECTIONQ2

Abstract

Introduction & Objective: COX MODEL is a common method to estimate survival and validity of the results is dependent on the proportional hazards assumption. K- NEAREST NEIGHBOR is a nonparametric method for survival probability in heterogeneous communities. The purpose of this study was to compare the performance of K- NEAREST NEIGHBOR method (K-NN) with COX MODEL.Materials & Methods: This retrospective cohort study was conducted in Hamadan Province, on 475 patients who had undergone KIDNEY TRANSPLANTATION/fa?page=1&sort=1&ftyp=all&fgrp=all&fyrs=all" target="_blank"> KIDNEY TRANSPLANTATION from 1994 to 2011. Data were extracted from patients’ medical records using a checklist. The duration of the time between KIDNEY TRANSPLANTATION/fa?page=1&sort=1&ftyp=all&fgrp=all&fyrs=all" target="_blank"> KIDNEY TRANSPLANTATION and rejection was considered as the survival time. COX MODEL and Knearest neighbor method were used for Data modeling. The prediction error Brier score was used to compare the performance models.Results: Out of 475 transplantations, 55 episodes of rejection occurred. 5, 10 and 15 year survival rates of transplantation were 91.70%, 84.90% and 74.50%, respectively. The number of neighborhood optimized using cross validation method was 45. Cumulative Brier score of K-NN algorithm for t=5, 10 and 15 years were 0.003, 0.006 and 0.007, respectively. Cumulative Brier ofscore COX MODEL for t=5, 10 and 15 years were 0.036, 0.058 and 0.058, respectively. Prediction error of K-NN algorithm for t=5, 10 and 15 years was less than COX MODEL that shows that the K-NN method outperforms.Conclusions: The results of this study show that the predictions of KNN has higher accuracy than the COX MODEL when sample sizes and the number of predictor variables are high.

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APA: Copy

FARADMAL, J., OMIDI, T., POUROLAJAL, J., & ROSHANAEI, GH.. (2016). COMPARISON OF COX MODEL AND K-NEAREST NEIGHBOR TO ESTIMATION OF SURVIVAL IN KIDNEY TRANSPLANT PATIENTS. AVICENNA JOURNAL OF CLINICAL MEDICINE (SCIENTIFIC JOURNAL OF HAMADAN UNIVERSITY OF MEDICAL SCIENCES AND HEALTH SERVICES), 22(4 (SN 78)), 300-308. SID. https://sid.ir/paper/17816/en

Vancouver: Copy

FARADMAL J., OMIDI T., POUROLAJAL J., ROSHANAEI GH.. COMPARISON OF COX MODEL AND K-NEAREST NEIGHBOR TO ESTIMATION OF SURVIVAL IN KIDNEY TRANSPLANT PATIENTS. AVICENNA JOURNAL OF CLINICAL MEDICINE (SCIENTIFIC JOURNAL OF HAMADAN UNIVERSITY OF MEDICAL SCIENCES AND HEALTH SERVICES)[Internet]. 2016;22(4 (SN 78)):300-308. Available from: https://sid.ir/paper/17816/en

IEEE: Copy

J. FARADMAL, T. OMIDI, J. POUROLAJAL, and GH. ROSHANAEI, “COMPARISON OF COX MODEL AND K-NEAREST NEIGHBOR TO ESTIMATION OF SURVIVAL IN KIDNEY TRANSPLANT PATIENTS,” AVICENNA JOURNAL OF CLINICAL MEDICINE (SCIENTIFIC JOURNAL OF HAMADAN UNIVERSITY OF MEDICAL SCIENCES AND HEALTH SERVICES), vol. 22, no. 4 (SN 78), pp. 300–308, 2016, [Online]. Available: https://sid.ir/paper/17816/en

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