proportional hazard models การใช้

• The hazard function for the Cox proportional hazard model has the form
• The Cox proportional hazards model is sometimes called a " semiparametric model " by contrast.
• Such models are generally classed semiparametric proportional hazards model, and the exponential, Gompertz and Weibull parametric models.
• However, this usage is potentially ambiguous since the Cox proportional hazards model can itself be described as a regression model.
• The generic term " parametric proportional hazards models " can be used to describe proportional hazards models in which the hazard function is specified.
• The generic term " parametric proportional hazards models " can be used to describe proportional hazards models in which the hazard function is specified.
• For example, assuming the hazard function to be the " Weibull " hazard function gives the " Weibull proportional hazards model ".
• Compensation law of mortality is a paradoxical empirical observation, and it represents a challenge for methods of survival analysis based on proportionality assumption ( proportional hazard models ).
• However, this does not mean that the hazard function \ lambda ( t | \ theta ) is always twice as high-that would be the proportional hazards model.
• Some authors use the term " Cox proportional hazards model " even when specifying the underlying hazard function, to acknowledge the debt of the entire field to David Cox.
• He has made pioneering and important contributions to numerous areas of statistics and applied probability, of which the best known is perhaps the proportional hazards model, which is widely used in the analysis of survival data.
• The Weibull distribution ( including the exponential distribution as a special case ) can be parameterised as either a proportional hazards model or an AFT model, and is the only family of distributions to have this property.
• Lasso regularization can be extended to a wide variety of objective functions such as those for generalized linear models, generalized estimating equations, proportional hazards models, and M-estimators in general, in the obvious way.
• Though originally defined for least squares, lasso regularization is easily extended to a wide variety of statistical models including generalized linear models, generalized estimating equations, proportional hazards models, and M-estimators, in a straightforward fashion.
• In statistics, the "'one in ten rule "'is a rule of thumb for how many predictors can be derived from data when doing regression analysis ( in particular proportional hazards models and logistic regression ) without risk of overfitting.
• A few examples are multiple linear regression, logistic regression, Cox regression, proportional hazards models, multi-stage survival model, structural equation modeling, generalized linear model, and epidemic model, which are now widely used by epidemiologists and other medical researchers in China.
• A 2006 European study on occasional smoking published findings that the risk of the major smoking-related cancers for occasional smokers was 1.24 times that of those who have never smoked at all but the result was not statistically significant . ( For a confidence interval of 95 %, this data showed an incidence rate ratio of 0.80 to 1.94 . ) ( Data reduction used Cox proportional hazard model, stratified by gender and country . ) This compares to studies showing that habitual heavy smokers have greater than 50 times the incidence of smoking-related cancers.