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"Survival data consist of a single event for each population unit, namely, end of life, which is modeled with a life distribution. In contrast, many applications involve repeated-events data, where a unit may accumulate any number of events over time. Examples include the number and cost of repairs of products, the number and treatment costs of recurrent disease episodes in patients, and the number of childbirths to statisticians. This applied book provides practitioners with basic nonparametric methods for such data, particularly the plot of the estimate of the population mean cumulative function (MCF), which yields most of the information sought.Recurrent Events Data Analysis for Product Repairs, Disease Recurrences, and Other Applications is the first book to present a simple, unified theory that includes data on costs or other "values" of discrete events, not just the number of events. It surveys computer programs that calculate and plot the MCF estimate with confidence limits, shows their output, and explains how to interpret such plots. Many such calculations can be easily done with a pocket calculator or spreadsheet program. Also, the book introduces basic Poisson and Cox regression models and parametric models, including homogeneous and nonhomogeneous Poisson processes and renewal processes."

"Analisis survival telah banyak digunakan dalam berbagai bidang, termasuk aktuaria. Analisis survival hendak diketahui distribusi dari waktu hingga terjadi suatu kejadian yang diamati. salah satu parameter yang dapat menjelaskan distribusidari waktu hingga terjadi kejadian tersebut ..."

"This greatly expanded third edition of Survival analysis : a self-learning text provides a highly readable description of state-of-the-art methods of analysis of survival/event-history data. The third edition continues to use the unique "lecture-book" format of the first two editions with one new chapter, additional sections and clarifications to several chapters, and a revised computer appendix. The Computer Appendix, with step-by-step instructions for using the computer packages STATA, SAS, and SPSS, is expanded this third edition to include the software package R."

"Model prediksi kesintasan kanker prostat metastasis tulang sudah pernah dilakukan sebelumnya. Namun, model prediksi kesintasan kanker prostat metastasis tulang pra-terapi belum pernah dialukan sebelumnya. Tujuan penelitian ini adalah untuk menganalisis faktor-faktor klinis yang mempengaruhi ketahanan hidup (survival) pada kanker prostat dengan metastasis tulang serta mengembangkan nomogram prognostik ketahanan hidup pada pasien dengan kondisi tersebut. Terdapat 392 subyek dengan kanker prostat dengan metastasis tulang yang mendapat terapi Androgen Deprivation Therapy (ADT) dalam penelitian ini. Parameter pra-perawatan dianalisis menggunakan model cox-proportional untuk mengidentifikasi prediktor ketahanan hidup secara keseluruhan. Kovariat yang menunjukkan nilai signifikansi secara statistik pada analisis multivariat akan dipakai untuk membentuk nomogram. Model prediktor linier digunakan untuk mengembangkan nomogram. Nilai median ketahanan hidup keseluruhan adalah 40,3 bulan (95% CI: 32.2 - 48.5). Analisis univariat menunjukkan bahwa T-stage, Gleason Score, nilai antigen spesifik prostat inisial, dan jumlah lesi metastasis merupakan faktor-faktor prognostik independen terhadap angka ketahanan hidup keseluruhan. Semua prediktor ini tetap menunjukkan hasil yang signifikan secara statistik sebagai faktor prognostik independen pada analisis model multivariat cox-regression. Nomogram yang terbentuk dari faktor-faktor prediktor tersebut menunjukkan diskriminasi yan baik dalam memprediksi ketahanan hidup dalam 5 tahun dengan area under the curve (AUC) sebesar 0.69. Kesepakatan yang diterima dari probabilitas yang diamati dan diprediksi telah dinilai dalam plot kalibrasi. Nilai median ketahanan hidup keseluruhan adalah 40,3 bulan. Prediksi nomogram ini dapat berguna sebagai alat untuk memprediksi angka ketahanan hidup keseluruhan pada sebelum terapi kanker prostat metastasis, secara spesifik pada populasi Indonesia. Penelitian lebih lanjut dibutuhkan untuk memberikan validasi eksternal untuk mendukung penggunaan nomogram ini.

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A survival prognostic model of prostate cancer with bone metastasis had been done before. However, a prognostic model of pre-treatment prostate cancer with bone metastasis had not yet done. This study aims to analyze the clinical factors among bone-metastatic prostate cancer and their relationships with survival as well as to develop a prognostic nomogram for overall survival in patients with this condition. This study included 392 patients with bone metastatic prostate caner treated with androgen deprivation therapy. Pre-treatment parameters were analyzed using cox-proportional hazard model to identify the predictors of overall survival. Covariates, which showed statistical significance on multivariate analysis, were used to develop a nomogram. Linear predictor model was utilized to develop the nomogram. Median overall survival was 40.3 months (95% CI: 32.2 to 48.5). Univariate analysis showed that clinical T-stage, Gleason Score, initial prostate specific antigen value, and number of metastatic lesion were independent prognostic factors for OS. These predictors still remained significant as independent prognostic factors for overall survival following analysis using multivariate cox-regression model. The nomogram constructed from those prognostic factors showed good discriminaton for predicting the 5-year OS with an Area Under the Curve of 0.69. Acceptable agreement of the observed and predicted probabilites was observed in the calibration plot. The median overall survival of patient with bone metastatic prostate cancer was 40.3 months. The prediction nomogram might be a useful tool for predicting overall survival in pre-treatment bone metastatic prostate cancer, specifically among Indonesian patients. Further studies are needed to provide external validation to support the utilization of this nomogram."