scholarly journals Analyzing sickness absence with statistical models for survival data

2007 ◽  
Vol 33 (3) ◽  
pp. 233-239 ◽  
Author(s):  
Karl B Christensen ◽  
Per Kragh Andersen ◽  
Lars Smith-Hansen ◽  
Martin L Nielsen ◽  
Tage S Kristensen
Keyword(s):  
Sickness Absence ◽  
Survival Data ◽  
Statistical Models

Estimating Housing Mortality with Standard Loss Curves

1986 ◽  
Vol 18 (11) ◽  
pp. 1521-1530 ◽  
Author(s):  
M E Gleeson
Keyword(s):  
Survival Data ◽  
Statistical Models ◽  
Life Tables ◽  
Time To Failure ◽  
Truncated Data ◽  
Mobile Homes ◽  
Analytical Work ◽  
Tests Of Fit

Tests of fit using one set of data on mobile homes and another on conventional housing indicate that standard loss curves, such as the Pearl-Reed and Weibull curves, can be used to approximate housing survivorship functions. This finding opens up the possibility of analytical work using standard curves and the application of time-to-failure statistical models that are based on such curves. Tests of fit of standard curves to the two housing survivorship functions using truncated data are also encouraging, suggesting means of estimating housing mortality and computing life tables with incomplete cohort survival data.

scholarly journals Predictive abilities of different statistical models for analysis of survival data in dairy cattle

2009 ◽  
Vol 92 (11) ◽  
pp. 5730-5738 ◽  
Author(s):  
M. Holtsmark ◽  
B. Heringstad ◽  
J. Ødegård
Keyword(s):  
Dairy Cattle ◽  
Survival Data ◽  
Statistical Models

scholarly journals Mixture Network Regularized Generalized Linear Model with Feature Selection

2019 ◽  
Author(s):  
Kaiqiao Li ◽  
Xuefeng Wang ◽  
Pei Fen Kuan
Keyword(s):  
Network Structure ◽  
Survival Data ◽  
Statistical Models ◽  
Gene Networks ◽  
Prediction Models ◽  
Statistical Prediction ◽  
High Dimensional ◽  
Operating Characteristics ◽  
Proposed Model ◽  
Response To Chemotherapy

AbstractHigh dimensional genomics data in biomedical sciences is an invaluable resource for constructing statistical prediction models. With the increasing knowledge of gene networks and pathways, this information can be utilized in the statistical models to improve prediction accuracy and enhance model interpretability. However, in some scenarios the network structure may only be partially known or inaccurately specified. Thus, the performance of statistical models incorporating such network structure may be compromised. In this paper, we proposed a weighted sparse network learning method by optimally combining a data driven network with sparsity property to a known or partially known prior network to address this issue. We showed that our proposed model attained the oracle property which aims to improve the accuracy of parameter estimation and achieved a parsimonious model in high dimensional setting for different outcomes including continuous, binary and survival data in extensive simulations studies. Case studies on ovarian cancer proteomics and melanoma gene expression further demonstrated that our proposed model achieved good operating characteristics in predicting response to chemotherapy and survival risk. An R package glmaag implemented our method is available on the Comprehensive R Archive Network (CRAN).

An Actuarial Survey of Statistical Models for Decrement and Transition Data, II. Competing Risks, Non-Parametric and Regression Models

1996 ◽  
Vol 2 (2) ◽  
pp. 429-448 ◽  
Author(s):  
A.S. Macdonald
Keyword(s):  
Competing Risks ◽  
Survival Data ◽  
Statistical Models ◽  
Regression Models ◽  
Cox Model ◽  
Likelihood Estimation ◽  
Partial Likelihood ◽  
Kaplan Meier ◽  
Non Parametric

ABSTRACTThis paper surveys some statistical models of survival data. Competing risks models are described; the unidentifiability of net decrements suggests a sceptical approach to the use of underlying single decrement tables. Approaches based on observations of complete lifetimes (with censoring) are surveyed including the Kaplan-Meier and Nelson-Aalen estimates. Regression models for lifetimes depending on covariates are discussed, in particular the Cox model and partial likelihood estimation.

An Actuarial Survey of Statistical Models for Decrement and Transition Data - I: Multiple State, Poisson and Binomial Models

1996 ◽  
Vol 2 (1) ◽  
pp. 129-155 ◽  
Author(s):  
A.S. Macdonald
Keyword(s):  
Survival Data ◽  
Statistical Models ◽  
Poisson Model ◽  
Basic Model ◽  
Multiple State ◽  
Binomial Type ◽  
State Models ◽  
Two State Model ◽  
Binomial Models ◽  
Flexible Models

ABSTRACTThis paper surveys some statistical models of survival data. A basic model of a random lifetime is defined, and censoring is introduced. Methods based on observations of small segments of lifetimes are compared. Markov and semi-Markov (multiple state) models are recommended as well-understood and flexible models well suited to actuarial data. A Poisson model is discussed as an approximation to a two state model, while traditional Binomial-type models are shown to be more restricted and less tractable than multiple state models.

The Effect of Partly Missing Covariates on Statistical Power in Randomized Controlled Trials With Discrete-Time Survival Endpoints

Methodology ◽  
2017 ◽  
Vol 13 (2) ◽  
pp. 41-60
Author(s):  
Shahab Jolani ◽  
Maryam Safarkhani
Keyword(s):  
Randomized Controlled Trials ◽  
Discrete Time ◽  
Treatment Effect ◽  
Survival Data ◽  
Controlled Trials ◽  
Missing Covariates ◽  
Indicator Method ◽  
Imputation Methods ◽  
Randomized Controlled ◽  
Baseline Covariates

Abstract. In randomized controlled trials (RCTs), a common strategy to increase power to detect a treatment effect is adjustment for baseline covariates. However, adjustment with partly missing covariates, where complete cases are only used, is inefficient. We consider different alternatives in trials with discrete-time survival data, where subjects are measured in discrete-time intervals while they may experience an event at any point in time. The results of a Monte Carlo simulation study, as well as a case study of randomized trials in smokers with attention deficit hyperactivity disorder (ADHD), indicated that single and multiple imputation methods outperform the other methods and increase precision in estimating the treatment effect. Missing indicator method, which uses a dummy variable in the statistical model to indicate whether the value for that variable is missing and sets the same value to all missing values, is comparable to imputation methods. Nevertheless, the power level to detect the treatment effect based on missing indicator method is marginally lower than the imputation methods, particularly when the missingness depends on the outcome. In conclusion, it appears that imputation of partly missing (baseline) covariates should be preferred in the analysis of discrete-time survival data.

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