Association rules of Prognostic Variables for Survival in a Randomized Comparison of Treatments for Prostatic Cancer

Iing Lukman, Emy Khikmawati

Abstract


In this paper, the data was analyzed by data mining techniques of association rules. The data for 506 patients consist of an identification number, stage of tumour, a code for the treatment to which the patient was assigned, the date of randomization, the total months of follow-up since randomization, an indicator for the survival status or cause of death, and the values of twelve pretreatment covariates. The goal of an analysis should be to compare the treatments with respect to survival of the patients. Since this was a randomized study it would ordinarily not be necessary to adjust for the values of the pretreatment covariates. However, in such studies it is advisable to examine the prognostic significance of the covariates and to confirm that they are balanced across treatment groups.  In addition, the analyst should look for important treatment-covariates interactions which might lead to the definition of subsets of patients in which treatment differences were significantly more marked or even reversed.


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References


D. P. Byar and S. B. Green, “The choice of treatment for cancer patients based on covariate information.,” Bull. Cancer, vol. 67, no. 4, pp. 477–90, 1980.

D. P. Byar and D. K. Corle, “Selecting optimal treatment in clinical trials using covariate information,” J. Chronic Dis., vol. 30, no. 7, pp. 445–459, Jul. 1977.

G. W. Chodak, “Independent Prognostic Factors in Patients With Metastatic (Stage D2) Prostate Cancer,” JAMA J. Am. Med. Assoc., vol. 265, no. 5, p. 618, Feb. 1991.

A. David F and H. Agnes M, Data: a collection of problems from many fields for the student and research worker. Springer Science & Business Media, 2012.

A. Jovic, K. Brkic, and N. Bogunovic, “An overview of free software tools for general data mining,” 2014 37th Int. Conv. Inf. Commun. Technol. Electron. Microelectron. MIPRO 2014 - Proc., no. May, pp. 1112–1117, 2014.

A. M. Zamani and B. Amaliah, “Implementasi Algoritma Genetika pada Struktur Backpropagation Neural Network untuk Klasifikasi Kanker Payudara,” J. Tek. POMITS, vol. 1, no. 1, pp. 1–6, 2012.

N. Robert, E. John, and G. Miner, Handbook of statistical analysis and data mining applications. Academic Press, 2009.




DOI: http://dx.doi.org/10.30591/jpit.v4i1.1252

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Creative Commons License
JPIT (Jurnal Informatika: Jurnal Pengembangan IT) is licensed under a Creative Commons Attribution 4.0 International License.