scholarly journals The non-stationary bivariate INAR under an unconstrained inter correlation structure

2018 ◽  
Vol 52 (1) ◽  
pp. 19-41
Author(s):  
YUVRAJ SUNECHER ◽  
NAUSHAD MAMODE KHAN ◽  
VANDNA JOWAHEER
Keyword(s):  
Time Series ◽  
Method Of Moments ◽  
Cross Correlation ◽  
Conditional Likelihood ◽  
Time Varying ◽  
Correlation Effects ◽  
Estimation Of Parameters ◽  
Simulation Experiments ◽  
Regression Effects ◽  
Time Varying Covariates

It is commonly observed in medical and financial studies that large volume of time series of count data are collected for several variates. The modelling of such time series and the estimation of parameters under such processes are rather challenging since these high dimensional time series are influenced by time-varying covariates that eventually render the data non-stationary. This paper considers the modelling of a bivariate integer-valued autoregressive (BINAR(1)) process where the innovation terms are distributed under non- stationary Poisson moments. Since the full and conditional likelihood approaches are cumbersome in this situation, a Generalized Quasi-likelihood (GQL) approach is proposed to estimate the regression effects while the serial and time-dependent cross correlation effects are handled by method of moments. This new technique is assessed over several simulation experiments and the results demonstrate that GQL yields consistent estimates and is computationally stable since few non-convergent simulations are reported.

scholarly journals A competing risks model with binary time varying covariates for estimation of breast cancer risks in BRCA1 families

2021 ◽  
pp. 096228022110089
Author(s):  
Yun-Hee Choi ◽  
Hae Jung ◽  
Saundra Buys ◽  
Mary Daly ◽  
Esther M John ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Ovarian Cancer ◽  
Competing Risks ◽  
Brca1 Mutation ◽  
Prophylactic Surgery ◽  
Time Varying ◽  
Cancer Risks ◽  
Competing Risks Model ◽  
Time Varying Covariates ◽  
Risk Reducing

Mammographic screening and prophylactic surgery such as risk-reducing salpingo oophorectomy can potentially reduce breast cancer risks among mutation carriers of BRCA families. The evaluation of these interventions is usually complicated by the fact that their effects on breast cancer may change over time and by the presence of competing risks. We introduce a correlated competing risks model to model breast and ovarian cancer risks within BRCA1 families that accounts for time-varying covariates. Different parametric forms for the effects of time-varying covariates are proposed for more flexibility and a correlated gamma frailty model is specified to account for the correlated competing events.We also introduce a new ascertainment correction approach that accounts for the selection of families through probands affected with either breast or ovarian cancer, or unaffected. Our simulation studies demonstrate the good performances of our proposed approach in terms of bias and precision of the estimators of model parameters and cause-specific penetrances over different levels of familial correlations. We applied our new approach to 498 BRCA1 mutation carrier families recruited through the Breast Cancer Family Registry. Our results demonstrate the importance of the functional form of the time-varying covariate effect when assessing the role of risk-reducing salpingo oophorectomy on breast cancer. In particular, under the best fitting time-varying covariate model, the overall effect of risk-reducing salpingo oophorectomy on breast cancer risk was statistically significant in women with BRCA1 mutation.

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