نتایج جستجو برای: survival analysis
تعداد نتایج: 3074356 فیلتر نتایج به سال:
Introduction: Therapies for many of diseases, especially cancer, have been improved significantly in the recent years, resulting in an increase in the number of patients who do not experience mortality. Therefore, the application of cure models is more suitable for survival analysis in this population than the usual survival models are. The aim of this study was to estimate the recurrence-free ...
An important issue in survival data analysis is the identification of risk factors. Some of these factors are identifiable and explainable by presence of some covariates in the Cox proportional hazard model, while the others are unidentifiable or even immeasurable. Spatial correlation of censored survival data is one of these sources that are rarely considered in the literatures. In this paper,...
Temperature is one of the most important environmental factors affecting of fish embryos and the survival and growth of fish larvae. In this study, eyed eggs of rainbow trout were incubated and reared until 30 day after first feeding at three constant temperatures including 7, 11 and 15 °C with four replicates in order to observe their survival, growth, rate of development and some physiologica...
from 1986 to 1996, 1020 renal transplants were performed at our center. the purposes of this study were. 1) to evaluate the patient and graft survival rates and 2) to see if some donors and recipient characteristics such as age, sex and relationship had any effects on graft survivals. 571 transplants were from living related donors (lrd) and 446 from living unrelated donors (lud). 65.9% of reci...
background: breast cancer arising in young patients (≤ 40 years) is being considered as a distinct clinical entity with more aggressive tumor features and poorer survival. our aim was to assess the impact of age on survival among a large group of iranian women diagnosed with breast cancer. methods: in a cross-sectional study, demographic and clinicopathological characteristics of patients with ...
This presentation overviews the applications of survival analysis techniques for marketing. It covers three major areas of applications: customer relationship management (attrition modeling, customer base analytics, customer lifelong value modeling), marketing campaign management (customer selection, marketing campaign evaluation) and trigger event management (evaluation of importance of trigge...
Since survival time is a quantitative variable, why can’t we just use the usual techniques from Table I? Before we explain the main reason why we use survival analysis, let’ us consider a simple example on the survival times (in months) for 25 lung cancer patients who all died; the timings are : 1, 5, 6, 6, 9, 10, 10, 10, 12, 12, 12, 12, 12, 13, 15, 16, 20, 24, 24, 27, 32, 34, 36, 36, 44 months...
Background:The present study compared the differences between survivals of patients with colorectal cancer according to their ethnicity adjusted for other predictors of survival. Methods: In this prospective cohort study patients were followed up from definite diagnosis of colorectal cancer to death. Totally, 2431 person-year follow-ups were undertaken for 1127 colorectal cancer patients on...
The electronic health record (EHR) provides an unprecedented opportunity to build actionable tools to support physicians at the point of care. In this paper, we introduce deep survival analysis, a hierarchical generative approach to survival analysis in the context of the EHR. It departs from previous approaches in two main ways: (1) all observations, including covariates, are modeled jointly c...
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