Survival models
Point process survival models for epilepsy data
... From the fitted values analysis under the point process survival models, we propose in Chapter 4 a zero-truncation of the seizure count density due to the inherent nature of epilepsy. Given that a patient ...
207
Model-based Recursive Partitioning for Survival of Iranian Female Breast Cancer Patients: Comparing with Parametric Survival Models
... proper survival models under the assumptions of four statistical distributions as the most frequently used distributions for time to event ...the survival time of Iranian women with breast ...
9
Incorporating unobserved heterogeneity in Weibull survival models : a Bayesian approach
... standard survival models through random effects, using an arbitrary (parametric) mixing ...These models can be represented as an infinite mixture of survival distributions with ...
34
spatsurv:an R package for Bayesian inference with spatial survival models
... spatial survival models, where n is the number of observations and (ii) simultaneously solves the spatial prediction problem on a fine regular grid at a cost of O(m log m), where m is the number of ...
33
Partitioning of excess mortality in population-based cancer patient survival studies using flexible parametric survival models
... parametric survival models and later adapted for relative survival by Nelson et ...latter models are fitted on the log cumulative excess hazard scale using restricted cubic splines [12] for the ...
15
Design considerations for complex survival models
... complex survival models, such as joint models of survival and longitudinal data and multivariate frailty models, have gained popularity in recent years because these models can ...
97
Comparing multilevel and Bayesian spatial random effects survival models to assess geographical inequalities in colorectal cancer survival: a case study
... discrete-time survival analysis requires an initial re- structuring to the person-period format so that standard binary response regression can be carried out ...analyzing survival outcomes over the entire ...
14
Parametric dynamic survival models
... In Chapter 6, the methods are applied to some real data applications: including a set of breast cancer survival times, generating an interval censored data set Finkelstein, 1986; a group[r] ...
263
Dynamic analysis of survival models and related processes
... C=HARD DISK INPUT TITLE FOR DATA FILE : DEM04 INPUT DATASET DESCRIPTION : xxxxx INPUT NAME OF TIME UNITS : MONTHS INPUT NUMBER OF OBSERVATIONS 4 INPUT NUMBER OF COVARIATES : 1 NAME OF CO[r] ...
194
A review of nest survival models
... Nests which succeed are used to estimate the proportions of nests being found (encountered) in each time interval. Assuming that these encounter probabilities also apply to the nests whi[r] ...
26
Survival models for censored point processes
... Hougaard 1987 gives a good overview of the analysis of multivariate survival data, and also discusses some aspects of recurrent event data in the form of counts, and Poisson mixture mode[r] ...
239
Sensitivity analysis for correlated survival models
... If we assume that Data A are t.he pot.ential observed data then Data B are the "true" data, "t.rue" in the sense that we are able to observe the exact death times of the informatively ce[r] ...
167
Factors influencing survival time of hemodialysis patients; time to event analysis using parametric models: a cohort study
... the survival models are parametric models, in which the distribution of the survival time is specified ...parametric survival models as an alternative to the Cox-PH ...of ...
9
Download Download PDF
... data, survival analysis methods were used. Univariate and multivariate survival analysis was carried out on the interval between marriage and the first ...test, survival functions of the interval ...
11
Survival Analysis of Patients with End Stage Renal Disease the Case of Adama Hospital, Ethiopia
... of survival data in the presence of covariates or prognostic ...parametric survival models when the assumptions of Cox- PH should not ...Parametric survival models are statistically ...
8
Generalized linear models for flexible parametric modeling of the hazard function
... parametric models may be used to extrapolate model outcomes to a lifetime and hence obtain estimates of mean TTE (such as mean ...2-parameter models are available, including the exponential, Weibull, ...
13
Generalised linear models for flexible parametric modelling of the hazard function
... flexible models provide lower AIC values than the lognormal, although in general differences between values are small, and cannot be tested for statistical ...the models provide a good fit to the observed ...
22
Learning Transformation Models for Ranking and Survival Analysis
... transformation models is a powerful tool to model data arising from survival studies for different ...) models, see, for example, Dabrowska and Doksum, 1988, Koenker and Geling, 2001, Cheng, Wei, and ...
44
nihms-1005525.pdf
... suppression versus those without could be concerning; however, trend findings were the same in analyses using 1 random VL test per person per year rather than all VL tests. We used inverse probability weights to provide ...
25
Multistate recursively imputed survival trees for time-to-event data analysis: an application to AIDS and mortality post-HIV infection data
... random survival forests (RSF) as its extension to sur- vival data analysis ...imputed survival trees (RIST), the conditional failure times of the censored obser- vations are incorporated into the model ...
12