scholarly journals Nonproportional hazards and unobserved heterogeneity in clustered survival data: When can we tell the difference?

2019 ◽  
Vol 38 (18) ◽  
pp. 3405-3420
Author(s):  
Theodor Adrian Balan ◽  
Hein Putter
Keyword(s):  
Survival Data ◽  
Unobserved Heterogeneity ◽  
Nonproportional Hazards ◽  
Clustered Survival Data ◽  
The Difference

IFRS Adoption and Audit Delay: The Role of Shareholders in the Audit Committee

2018 ◽  
Vol 8 (1) ◽  
pp. 325
Author(s):  
Ishaq Ahmed Mohammed ◽  
Ayoib Che-Ahmad ◽  
Mazrah Malek
Keyword(s):  
Audit Committee ◽  
Unobserved Heterogeneity ◽  
Value Relevance ◽  
Stock Exchange ◽  
Delay Model ◽  
Corporate Reporting ◽  
Ifrs Adoption ◽  
The Difference ◽  
The Relationship

This study examined the relationship between audit delay after IFRS adoption and the role of shareholders in the audit committee as well as testing the difference of pre-and post IFRS adoption periods. A sample of 101 firms with 505 firm-year observations over five year period for firms listed on the Nigeria Stock Exchange was employed for the study, utilizing data from the annual report and accounts of the sample firms. Generalized Methods of Moment (GMM) estimation was used to check the effects of unobserved heterogeneity in audit delay model, while the test of difference in R2 value for pre-and post-adoption periods was determined using Cramer’s Z-statistics. Findings indicate that audit report lag is faster with shareholders in the audit committee. The study proved that brand named auditors such as Big4 can significantly perform faster audit task than non-Big4 firms in IFRS regime. The importance of the study’s findings demonstrates statistical inference on value relevance increase based on the unique IFRS adoption in Nigeria. Thus, regulators should consider increasing the tenure of shareholders in the audit committee to enable them to become more familiar with the corporate reporting under IFRS regime.

A Method for Sequential Analysis of Survival Data with Nonproportional Hazards

Biometrics ◽  
1998 ◽  
Vol 54 (3) ◽  
pp. 1072 ◽  
Author(s):  
M. R. Sooriyarachchi ◽  
John Whitehead
Keyword(s):  
Survival Data ◽  
Sequential Analysis ◽  
Nonproportional Hazards

scholarly journals Additive Hazard Regression for the Analysis of Clustered Survival Data from Case-Cohort Studies

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
June Liu ◽  
Yi Zhang
Keyword(s):  
Cohort Studies ◽  
Survival Data ◽  
Failure Time ◽  
Weight Functions ◽  
Disease Rate ◽  
Failure Time Data ◽  
Finite Sample ◽  
Time Data ◽  
Cohort Design ◽  
Clustered Survival Data

The case-cohort design is an effective and economical method in large cohort studies, especially when the disease rate is low. Case-cohort design in most of the existing literature is mainly used to analyze the univariate failure time data. But in practice, multivariate failure time data are commonly encountered in biomedical research. In this paper, we will propose methods based on estimating equation method for case-cohort designs for clustered survival data. By introducing the event failure rate, three different weight functions are constructed. Then, three estimating equations and parameter estimators are presented. Furthermore, consistency and asymptotic normality of the proposed estimators are established. Finally, the simulation results show that the proposed estimation procedure has reasonable finite sample behaviors.

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