Multistate Survival Analysis: An Application in Breast Cancer

1984 ◽  
Vol 23 (03) ◽  
pp. 157-162 ◽  
Author(s):  
R. Kay

SummaryThe extension of the proportional hazard model of Cox for survival data allows the consideration of transition times between events of interest (e.g., response, relapse, progression, etc.) and of competing risks. This paper applies these new models to a breast cancer study and gives some general remarks for the proceeding.

2016 ◽  
Vol 185 ◽  
pp. 89-96 ◽  
Author(s):  
Elisandra Lurdes Kern ◽  
Jaime Araujo Cobuci ◽  
Claudio Napolis Costa ◽  
Vincent Ducrocq

Blood ◽  
2014 ◽  
Vol 124 (21) ◽  
pp. 3257-3257
Author(s):  
Seong-Ho Kang ◽  
Dae-Soo Moon ◽  
Myung-Hyun Nam ◽  
Soo-Young Yoon ◽  
Ji-Seon Choi

Abstract Background: Trisomy 8 is the most frequent chromosomal abnormality in Korean patients with myelodysplastic syndrome (MDS). MicroRNA (miRNA) deregulation contributes to hematological malignancies, including MDS, and cancer-associated genomic regions are known to encode miRNAs. The aim of the present study was to investigate whether expression of miRNAs encoded by chromosome 8, which is the most frequent abnormal chromosome in MDS in Korean patients, is upregulated. Further, we analyzed the association of the upregulated miRNAs with clinical outcome. Methods: Sixty-five MDS patients and 11 controls were enrolled in the study. miRNAs were extracted from archived unstained bone marrow aspirate slides of the subjects. The TaqMan microRNA assay was used to detect 13 miRNAs encoded by chromosome 8 (miR-30b-5p, miR-30d-5p, miR-124-3p, miR-151a-5p, miR-320a, miR-383-5p, miR-486-5p, miR-596, miR-597-5p, miR-598-5p, miR-599, miR-661, and miR-875-5p) and an endogenous control RNU48. The Ct value of each miRNA and RNU48 was obtained and the 2-deltaCt (deltaCt = CtmiRNA – CtRNU48) for each miRNA was calculated. Clinical data and laboratory data (complete blood cell counts, bone marrow blasts, and karyotyping data) were obtained from patients' medical records. The Mann–Whitney test was used to compare the miRNA expression profile of the patients with that of the controls. Overall survival was analyzed by the Kaplan-Meier Method and Cox's proportional hazard model. Results: The expression of miR-320a and miR-661 was significantly higher in MDS patients than in the controls. Other miRNAs were not significantly upregulated. The expression of miR-320a was 22.30±110.15 (mean±SD) in patients and 4.96±9.56 in controls (P = 0.016). The expression of miR-661 was 0.39±1.72 in patients and 0.05±0.08 in controls (P = 0.021). The patients were divided into 2 groups—patients with high miR-661 expression and patients with low miR-661 expression—using an arbitrary cut-off of miR-661 expression of 0.1. The patients with high miR-661 expression showed significantly decreased overall survival (P = 0.048) (Fig. 1). Blast counts and poor cytogenetics were also significantly associated with the significantly decreased overall survival (all P < 0.001) Hemoglobin and platelet counts showed borderline significance in overall survival (P = 0.068 and P = 0.066, respectively). Multivariate analyses using Cox's proportional hazard model revealed that high miR-661 expression was an independent prognostic variable (P = 0.024) with a hazard ratio of 3.613 (CI; 1.189-11.0). Poor cytogenetics was also found to be an independent prognostic variable (P = 0.047). Conclusion: This is the first report of the association between upregulation of miR-661 and MDS. Although these findings need to be validated by studies on a large number of patients, high expression of miR-661 may have the potential for use as an adverse prognostic marker for Korean patients with MDS. Fig 1. Overall survival analysis of patients with according to miR-661 expression Fig 1. Overall survival analysis of patients with according to miR-661 expression Disclosures No relevant conflicts of interest to declare.


2019 ◽  
Vol 1 (2) ◽  
pp. 88
Author(s):  
Firda Anisa Fajarini ◽  
Mohamat Fatekurohman

<p>Cox proportional hazard model is a regression model that is used to see the factors that cause an event. The survival analysis used in this research is the period of time the client is able to pay the life insurance premium using Cox proportional hazard model with Breslow method.The purpose of this research is to know how sex, age, insured money, job, method of payment of premium, premium, and type of product can influence the level of ability of client to make payment of life insurance premium based on customer data from PT. BRI Life Insurance Branch of Jember in 2007.The result of this research is the final model of Cox proportional hazard obtained from several variables which have significant influence with simultaneous and partial significance test is the variable of insured money (<em>X<sub>3</sub></em>), variable of payment method of premium (<em>X<sub>5</sub></em>), premium variable (<em>X<sub>6</sub></em>) , and insurance product variable (<em>X<sub>7</sub></em>) . The four variables are said to have a significant effect on the model, so that the final model of Cox proportional hazard is obtained that consists of the parameter estimation (<em>β</em>) value of each variable</p><p> </p><p><strong>Keywords</strong><strong> : </strong>survival analysis; cox proportional hazard model; breslow method; life insurance.</p>


2020 ◽  
Vol 9 (4) ◽  
pp. 402-410
Author(s):  
Triastuti Wuryandari ◽  
Sri Haryatmi Kartiko ◽  
Danardono Danardono

Survival data is the length of time until an event occurs. If  the survival  time is affected by other factor, it can be modeled with a regression model. The regression model for survival data is commonly based  on the Cox proportional hazard model. In the Cox proportional hazard model, the covariate effect act  multiplicatively on unknown baseline hazard. Alternative to the multiplicative hazard model is the additive hazard model. One of  the additive hazard models is the semiparametric additive  hazard model  that introduced by Lin Ying in 1994.  The regression coefficient estimates in this model mimic the scoring equation in the Cox model. Score equation of Cox model is the derivative of the Partial Likelihood and methods to maximize partial likelihood with Newton Raphson iterasi. Subject from this paper is describe the multiplicative and additive hazard model that applied to the duration of the birth process. The data is obtained from two different clinics,there are clinic that applies gentlebirth method while the other one no gentlebirth. From the data processing obtained the factors that affect on the duration of the birth process are baby’s weight, baby’s height and  method of birth. Keywords: survival, additive hazard model, cox proportional hazard, partial likelihood, gentlebirth, duration


2020 ◽  
Vol 53 (2) ◽  
pp. 129-146
Author(s):  
AHSAN RAHMAN JAMEE ◽  
WASIMUL BARI

Classical survival regression models may provide misleading results when event of interest occurs due to more than one causes. In this paper, taking all possible causes for the occurrence of event into account, a Truncated Poisson Exponential survival proportional hazard model has been proposed. An extensive simulation study has been conducted to examine the performance of the proposed survival model in the absence and presence of covariates under different percentages of censoring. The simulation results reveal that estimators of the regression parameters are consistent and efficient. To illustrate the model, under–five child survival data extracted from Bangladesh Demographic and Health Survey 2014 have been used.


2021 ◽  
Vol 12 ◽  
pp. 215013272110002
Author(s):  
Gayathri Thiruvengadam ◽  
Marappa Lakshmi ◽  
Ravanan Ramanujam

Background: The objective of the study was to identify the factors that alter the length of hospital stay of COVID-19 patients so we have an estimate of the duration of hospitalization of patients. To achieve this, we used a time to event analysis to arrive at factors that could alter the length of hospital stay, aiding in planning additional beds for any future rise in cases. Methods: Information about COVID-19 patients was collected between June and August 2020. The response variable was the time from admission to discharge of patients. Cox proportional hazard model was used to identify the factors that were associated with the length of hospital stay. Results: A total of 730 COVID-19 patients were included, of which 675 (92.5%) recovered and 55 (7.5%) were considered to be right-censored, that is, the patient died or was discharged against medical advice. The median length of hospital stay of COVID-19 patients who were hospitalized was found to be 7 days by the Kaplan Meier curve. The covariates that prolonged the length of hospital stay were found to be abnormalities in oxygen saturation (HR = 0.446, P < .001), neutrophil-lymphocyte ratio (HR = 0.742, P = .003), levels of D-dimer (HR = 0.60, P = .002), lactate dehydrogenase (HR = 0.717, P = .002), and ferritin (HR = 0.763, P = .037). Also, patients who had more than 2 chronic diseases had a significantly longer length of stay (HR = 0.586, P = .008) compared to those with no comorbidities. Conclusion: Factors that are associated with prolonged length of hospital stay of patients need to be considered in planning bed strength on a contingency basis.


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