The Imprecise Logit-Normal Model and its Application to Estimating Hazard Functions

2009 ◽  
Vol 3 (1) ◽  
pp. 183-195 ◽  
Author(s):  
Miķelis Bickis
Keyword(s):  
Normal Model ◽  
Hazard Functions

scholarly journals Competing risks joint models using R-INLA

2021 ◽  
Vol 21 (1-2) ◽  
pp. 56-71
Author(s):  
Janet van Niekerk ◽  
Haakon Bakka ◽  
Håvard Rue
Keyword(s):  
Competing Risks ◽  
Count Data ◽  
Joint Model ◽  
Point Of View ◽  
Spatial Structures ◽  
Joint Models ◽  
Hazard Functions ◽  
Longitudinal Count Data ◽  
Non Gaussian ◽  
Made In

The methodological advancements made in the field of joint models are numerous. None the less, the case of competing risks joint models has largely been neglected, especially from a practitioner's point of view. In the relevant works on competing risks joint models, the assumptions of a Gaussian linear longitudinal series and proportional cause-specific hazard functions, amongst others, have remained unchallenged. In this article, we provide a framework based on R-INLA to apply competing risks joint models in a unifying way such that non-Gaussian longitudinal data, spatial structures, times-dependent splines and various latent association structures, to mention a few, are all embraced in our approach. Our motivation stems from the SANAD trial which exhibits non-linear longitudinal trajectories and competing risks for failure of treatment. We also present a discrete competing risks joint model for longitudinal count data as well as a spatial competing risks joint model as specific examples.

On Proportional Hazard Functions

Technometrics ◽  
1970 ◽  
Vol 12 (2) ◽  
pp. 413-416 ◽  
Author(s):  
Arthur NáDas
Keyword(s):  
Proportional Hazard ◽  
Hazard Functions

scholarly journals Mixture of Inverse Weibull and Lognormal Distributions: Properties, Estimation, and Illustration

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
K. S. Sultan ◽  
A. S. Al-Moisheer
Keyword(s):  
Information Matrix ◽  
Real Data ◽  
Likelihood Method ◽  
Model Parameters ◽  
Component Mixture ◽  
Data Set ◽  
Hazard Functions ◽  
Lognormal Distributions ◽  
Proposed Model ◽  
Estimation Of Model Parameters

We discuss the two-component mixture of the inverse Weibull and lognormal distributions (MIWLND) as a lifetime model. First, we discuss the properties of the proposed model including the reliability and hazard functions. Next, we discuss the estimation of model parameters by using the maximum likelihood method (MLEs). We also derive expressions for the elements of the Fisher information matrix. Next, we demonstrate the usefulness of the proposed model by fitting it to a real data set. Finally, we draw some concluding remarks.

Incidence and mortality rates of intracranial hemorrhage in hemophilia: a systematic review and meta-analysis

Blood ◽  
2021 ◽  
Author(s):  
Anne-Fleur Zwagemaker ◽  
Samantha C Gouw ◽  
Julie J Jansen ◽  
Caroline Vuong ◽  
Michiel Coppens ◽  
...  
Keyword(s):  
Systematic Review ◽  
Young Adults ◽  
Intracranial Hemorrhage ◽  
Meta Analysis ◽  
Age Groups ◽  
Mortality Rates ◽  
Normal Model ◽  
Live Births ◽  
Incidence And Mortality ◽  
Children And Young Adults

Intracranial hemorrhage (ICH) is a severe complication that is relatively common among hemophilia patients. This systematic review aimed to obtain more precise estimates of ICH incidence and mortality in hemophilia, which may be important for patients, caregivers, researchers and health policy-makers. PubMed and EMBASE were systematically searched using terms related to "hemophilia" and "intracranial hemorrhage" or "mortality". Studies that allowed calculation of ICH incidence or mortality rates in a hemophilia population of at least 50 patients were included. We summarized evidence on ICH incidence and calculated pooled ICH incidence and mortality in three age groups: (1) persons of all ages with hemophilia, (2) children and young adults below 25 years of age with hemophilia and (3) neonates with hemophilia. Incidence and mortality were pooled with a Poisson-Normal model or a Binomial-Normal model. We included 45 studies that represented 54 470 patients, 809 151 person-years and 5326 live births of hemophilia patients. In persons of all ages, the pooled ICH incidence and mortality rates were 2.3 (95% CI 1.2-4.8) and 0.8 (95% CI 0.5-1.2) per 1000 person-years, respectively. In children and young adults, the pooled ICH incidence and mortality rates were 7.4 (95% CI 4.9-11.1) and 0.5 (95% CI 0.3-0.9) per 1000 person-years, respectively. In neonates, the pooled cumulative ICH incidence was 2.1% (95% CI 1.5-2.8) per 100 live births. ICH was classified as spontaneous in 35-58% of cases. Our findings suggest that ICH is an important problem in hemophilia that occurs among all ages, requiring adequate preventive strategies.

scholarly journals Weibull-like Model of Cancer Development in Aging

2010 ◽  
Vol 9 ◽  
pp. CIN.S5460 ◽  
Author(s):  
Tengiz Mdzinarishvili ◽  
Simon Sherman
Keyword(s):  
Mathematical Modeling ◽  
Hazard Function ◽  
White Men ◽  
Cancer Registries ◽  
Incidence Rates ◽  
Linear Functions ◽  
Cancer Development ◽  
Additional Parameter ◽  
Men And Women ◽  
Hazard Functions

Mathematical modeling of cancer development is aimed at assessing the risk factors leading to cancer. Aging is a common risk factor for all adult cancers. The risk of getting cancer in aging is presented by a hazard function that can be estimated from the observed incidence rates collected in cancer registries. Recent analyses of the SEER database show that the cancer hazard function initially increases with the age, and then it turns over and falls at the end of the lifetime. Such behavior of the hazard function is poorly modeled by the exponential or compound exponential-linear functions mainly utilized for the modeling. In this work, for mathematical modeling of cancer hazards, we proposed to use the Weibull-like function, derived from the Armitage-Doll multistage concept of carcinogenesis and an assumption that number of clones at age t developed from mutated cells follows the Poisson distribution. This function is characterized by three parameters, two of which ( r and λ) are the conventional parameters of the Weibull probability distribution function, and an additional parameter ( C0) that adjusts the model to the observational data. Biological meanings of these parameters are: r—the number of stages in carcinogenesis, λ—an average number of clones developed from the mutated cells during the first year of carcinogenesis, and C0—a data adjustment parameter that characterizes a fraction of the age-specific population that will get this cancer in their lifetime. To test the validity of the proposed model, the nonlinear regression analysis was performed for the lung cancer (LC) data, collected in the SEER 9 database for white men and women during 1975–2004. Obtained results suggest that: (i) modeling can be improved by the use of another parameter A- the age at the beginning of carcinogenesis; and (ii) in white men and women, the processes of LC carcinogenesis vary by A and C0, while the corresponding values of r and λ are nearly the same. Overall, the proposed Weibull-like model provides an excellent fit of the estimates of the LC hazard function in aging. It is expected that the Weibull-like model can be applicable to fit estimates of hazard functions of other adult cancers as well.

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