scholarly journals An additive–multiplicative mean model for panel count data with dependent observation and dropout processes

2018 ◽  
Vol 46 (2) ◽  
pp. 414-431 ◽  
Author(s):  
Guanglei Yu ◽  
Yang Li ◽  
Liang Zhu ◽  
Hui Zhao ◽  
Jianguo Sun ◽  
...  
Keyword(s):  
Count Data ◽  
Panel Count Data ◽  
Dependent Observation ◽  
Multiplicative Mean

Regression Analysis of Panel Count Data with Dependent Observation Times

Biometrics ◽  
2007 ◽  
Vol 63 (4) ◽  
pp. 1053-1059 ◽  
Author(s):  
Jianguo Sun ◽  
Xingwei Tong ◽  
Xin He
Keyword(s):  
Regression Analysis ◽  
Count Data ◽  
Panel Count Data ◽  
Observation Times ◽  
Dependent Observation

scholarly journals Beta-binomial models for meta-analysis with binary outcomes: Variations, extensions, and additional insights from econometrics

2021 ◽  
pp. 263208432199622
Author(s):  
Tim Mathes ◽  
Oliver Kuss
Keyword(s):  
Simulation Study ◽  
Count Data ◽  
Negative Binomial ◽  
Meta Analysis ◽  
Negative Binomial Regression ◽  
Binary Outcomes ◽  
Small Scale ◽  
Panel Count Data ◽  
Count Data Models ◽  
Meta Analyses

Background Meta-analysis of systematically reviewed studies on interventions is the cornerstone of evidence based medicine. In the following, we will introduce the common-beta beta-binomial (BB) model for meta-analysis with binary outcomes and elucidate its equivalence to panel count data models. Methods We present a variation of the standard “common-rho” BB (BBST model) for meta-analysis, namely a “common-beta” BB model. This model has an interesting connection to fixed-effect negative binomial regression models (FE-NegBin) for panel count data. Using this equivalence, it is possible to estimate an extension of the FE-NegBin with an additional multiplicative overdispersion term (RE-NegBin), while preserving a closed form likelihood. An advantage due to the connection to econometric models is, that the models can be easily implemented because “standard” statistical software for panel count data can be used. We illustrate the methods with two real-world example datasets. Furthermore, we show the results of a small-scale simulation study that compares the new models to the BBST. The input parameters of the simulation were informed by actually performed meta-analysis. Results In both example data sets, the NegBin, in particular the RE-NegBin showed a smaller effect and had narrower 95%-confidence intervals. In our simulation study, median bias was negligible for all methods, but the upper quartile for median bias suggested that BBST is most affected by positive bias. Regarding coverage probability, BBST and the RE-NegBin model outperformed the FE-NegBin model. Conclusion For meta-analyses with binary outcomes, the considered common-beta BB models may be valuable extensions to the family of BB models.

Sign in / Sign up

Export Citation Format

Share Document