scholarly journals One-way random effects ANOVA with random sample sizes: An application to a Brazilian database on cancer registries

2015 ◽  
Author(s):  
Gilberto Capistrano ◽  
Célia Nunes ◽  
Dário Ferreira ◽  
Sandra S. Ferreira ◽  
João T. Mexia
Keyword(s):  
Random Sample ◽  
Random Effects ◽  
Cancer Registries ◽  
Sample Sizes

scholarly journals ANOVA with random sample sizes: An application to a Brazilian database on cancer registries

2013 ◽  
Author(s):  
Célia Nunes ◽  
Gilberto Capistrano ◽  
Dário Ferreira ◽  
Sandra S. Ferreira
Keyword(s):  
Random Sample ◽  
Cancer Registries ◽  
Sample Sizes

A unified approach for assessing heterogeneity in age–period–cohort model parameters using random effects

2017 ◽  
Vol 28 (1) ◽  
pp. 20-34 ◽  
Author(s):  
Pavel Chernyavskiy ◽  
Mark P Little ◽  
Philip S Rosenberg
Keyword(s):  
Random Effects ◽  
Cancer Incidence ◽  
Population Level ◽  
Cancer Registries ◽  
Population Based ◽  
Model Parameters ◽  
Cohort Model ◽  
Annual Percent Change ◽  
Period Effects ◽  
Incidence And Mortality

Age–period–cohort models are a popular tool for studying population-level rates; for example, trends in cancer incidence and mortality. Age–period–cohort models decompose observed trends into age effects that correlate with natural history, period effects that reveal factors impacting all ages simultaneously (e.g. innovations in screening), and birth cohort effects that reflect differential risk exposures that vary across birth years. Methodology for the analysis of multiple population strata (e.g. ethnicity, cancer registry) within the age–period–cohort framework has not been thoroughly investigated. Here, we outline a general model for characterizing differences in age–period–cohort model parameters for a potentially large number of strata. Our model incorporates stratum-specific random effects for the intercept, the longitudinal age trend, and the model-based estimate of annual percent change (net drift), thereby enabling a comprehensive analysis of heterogeneity. We also extend the standard model to include quadratic terms for age, period, and cohort, along with the corresponding random effects, which quantify possible stratum-specific departures from global curvature. We illustrate the utility of our model with an application to metastatic prostate cancer incidence (2004–2013) in non-Hispanic white and black men, using 17 population-based cancer registries in the Surveillance, Epidemiology, and End Results Program.

Random sample sizes in orthogonal mixed models with stability

2019 ◽  
Vol 1 (5) ◽  
Author(s):  
Célia Nunes ◽  
Anacleto Mário ◽  
Dário Ferreira ◽  
Sandra S. Ferreira ◽  
João T. Mexia
Keyword(s):  
Random Sample ◽  
Mixed Models ◽  
Sample Sizes

Bivariate Limit Theorems for Record Values Based on Random Sample Sizes

Sankhya A ◽  
2019 ◽  
Vol 82 (1) ◽  
pp. 50-67
Author(s):  
M. A. Abd Elgawad ◽  
H. M. Barakat ◽  
Ting Yan
Keyword(s):  
Random Sample ◽  
Limit Theorems ◽  
Record Values ◽  
Sample Sizes
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