A comparison of population average and random‐effect models for the analysis of longitudinal count data with base‐line information

1999 ◽  
Vol 162 (3) ◽  
pp. 331-347 ◽  
Author(s):  
R. Crouchley ◽  
R. B. Davies
Keyword(s):  
Count Data ◽  
Random Effect ◽  
Base Line ◽  
Population Average ◽  
Longitudinal Count Data ◽  
Random Effect Models

scholarly journals Reviewing of Different Methods for Handling Longitudinal Count data

2021 ◽  
Vol 23 (08) ◽  
pp. 195-206
Author(s):  
Amany. M ◽  
◽  
Mousa ◽  
Ahmed. A ◽  
El sheikh ◽  
...  
Keyword(s):  
Method Of Moments ◽  
Count Data ◽  
Generalized Estimating Equations ◽  
Generalized Method Of Moments ◽  
Marginal Models ◽  
Advantages And Disadvantages ◽  
Population Average ◽  
Longitudinal Count Data ◽  
Quadratic Inference Functions ◽  
Primary Focus

In this paper, we will review the methods that used to handle longitudinal data in the case of marginal models when inferences about the population average are the primary focus [1] or when future applications of the results require the expectation of the response as a function of the current covariates [7]. We will review the generalized estimating equations method (GEE), quadratic inference functions (QIF), generalized quasi likelihood (GQL) and the generalized method of moments (GMM). These methods will be reviewed by discussing its advantages and disadvantages in more details.

scholarly journals The effect of data aggregation on dispersion estimates in count data models

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Adam Errington ◽  
Jochen Einbeck ◽  
Jonathan Cumming ◽  
Ute Rössler ◽  
David Endesfelder
Keyword(s):  
Count Data ◽  
Data Aggregation ◽  
Random Effect ◽  
Real Data ◽  
Data Sets ◽  
Count Data Models ◽  
Response Curves ◽  
Poisson Models ◽  
Random Effect Models ◽  
Radiation Biodosimetry

Abstract For the modelling of count data, aggregation of the raw data over certain subgroups or predictor configurations is common practice. This is, for instance, the case for count data biomarkers of radiation exposure. Under the Poisson law, count data can be aggregated without loss of information on the Poisson parameter, which remains true if the Poisson assumption is relaxed towards quasi-Poisson. However, in biodosimetry in particular, but also beyond, the question of how the dispersion estimates for quasi-Poisson models behave under data aggregation have received little attention. Indeed, for real data sets featuring unexplained heterogeneities, dispersion estimates can increase strongly after aggregation, an effect which we will demonstrate and quantify explicitly for some scenarios. The increase in dispersion estimates implies an inflation of the parameter standard errors, which, however, by comparison with random effect models, can be shown to serve a corrective purpose. The phenomena are illustrated by γ-H2AX foci data as used for instance in radiation biodosimetry for the calibration of dose-response curves.

Analyzing discontinuities in longitudinal count data: A multilevel generalized linear mixed model.

2020 ◽  
Author(s):  
James L. Peugh ◽  
Sarah J. Beal ◽  
Meghan E. McGrady ◽  
Michael D. Toland ◽  
Constance Mara
Keyword(s):  
Count Data ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Generalized Linear Mixed Model ◽  
Longitudinal Count Data

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.

scholarly journals LO84: The incidence of fall-related intracranial bleeding in older adults taking anticoagulants, antiplatelets and neither medication: a meta-analysis

CJEM ◽  
2020 ◽  
Vol 22 (S1) ◽  
pp. S38-S38
Author(s):  
K. de Wit ◽  
D. Nishijima ◽  
S. Mason ◽  
R. Jeanmonod ◽  
S. Parpia ◽  
...  
Keyword(s):  
Older Adults ◽  
Meta Analysis ◽  
Grey Literature ◽  
Random Effect ◽  
Intracranial Bleeding ◽  
Inclusion Criteria ◽  
Antiplatelet Medication ◽  
Cochrane Database ◽  
Random Effect Models ◽  
Anticoagulant Medication

Introduction: It is unclear whether anticoagulant or antiplatelet medications increase the risk for intracranial bleeding in older adults after a fall. Our aim was to report the incidence of intracranial bleeding among older adults presenting to the emergency department (ED) with a fall, among patients taking anticoagulants, antiplatelet medications, both medications and neither medication. Methods: This was a systematic review and meta-analysis, PROSPERO reference CRD42019122626. Medline, EMBASE (via OVID 1946 - July 2019), Cochrane, Database of Abstracts of Reviews of Effects databases and the grey literature were searched for studies reporting on older adults who were evaluated after a fall. We included prospective studies conducted in the ED where more than 80% of the cohort were 65 years or older and had fallen. We contacted study authors for aggregate data on intracranial bleeding in patients prescribed anticoagulant medication, antiplatelet medication and neither medication. Incidences of intracranial bleeding were pooled using random effect models, and I2 index was used to assess heterogeneity. Results: From 7,240 publication titles, 10 studies met inclusion criteria. The authors of 8 of these 10 studies provided data (on 9,489 patients). All studies scored low or moderate risk of bias. The pooled incidence of intracranial bleeding among patients taking an anticoagulant medication was 5.1% (n = 5,016, 95% Confidence Interval (CI): 4.1 to 6.3%) I2 = 42%, a single antiplatelet 6.4% (n = 2,148, 95% CI: 5.4 to 7.6%) I2 = 75%, both anticoagulant and antiplatelet medications 5.9% (n = 212, 95% CI: 1.3 to 13.5%) I2 = 72%, and neither of these medications 4.8% (n = 1,927, 95% CI: 3.5 to 6.2%) I2 = 50%. A sensitivity analysis restricted to patients who had a head CT in the ED reported incidences of 6.1% (n = 3,561, 95% CI: 3 to 8.3%), 8.4% (n = 1,781, 95% CI: 5.5 to 11.8%), 6.7% (n = 206, 95% CI 1.5 to 15.2%) and 6.6% (n = 1,310, 95% CI: 5.0 to 8.4%) respectively. Conclusion: The incidence of fall-related intracranial bleeding in older ED patients was similar among patients who take anticoagulant medication, antiplatelet medication, both and neither medication, although there was heterogeneity between study findings.

Correlation between Methylenetetrahydrofolate Reductase Polymorphisms and Hepatocellular Carcinoma: A Meta-Analysis

2019 ◽  
Vol 74 (3) ◽  
pp. 251-256 ◽  
Author(s):  
Hailong Su ◽  
Guo Zhang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Gene Polymorphisms ◽  
Methylenetetrahydrofolate Reductase ◽  
Meta Analysis ◽  
Random Effect ◽  
Recessive Model ◽  
Mthfr Gene ◽  
Systematic Research ◽  
The Relationship ◽  
Random Effect Models

Background: The correlation between methylenetetrahydrofolate reductase (MTHFR) gene polymorphisms and hepatocellular carcinoma (HCC) remains controversial. Objectives: We performed this study to better assess the relationship between MTHFR gene polymorphisms and the likelihood of HCC. Methods: A systematic research of PubMed, Medline, and Embase was performed to retrieve relevant articles. ORs and 95% CIs were calculated. Results: A total of 15 studies with 8,378 participants were analyzed. In overall analyses, a significant association with the likelihood of HCC was detected for the rs1801131 polymorphism with fixed-effect models (FEMs) in recessive comparison (p = 0.002, OR 0.62, 95% CI 0.43–0.82). However, no positive results were detected for the rs1801133 polymorphism in any comparison. Further subgroup analyses revealed that the rs1801131 polymorphism was significantly associated with the likelihood of HCC in Asians with both FEMs (recessive model: p < 0.0001, OR 0.42, 95% CI 0.29–0.62; allele model: p = 0.004, OR 1.20, 95% CI 1.06–1.35) and random-effect models (recessive model: p = 0.002, OR 0.47, 95% CI 0.29–0.75). Nevertheless, we failed to detect any significant correlation between the rs1801133 polymorphism and HCC. Conclusions: Our findings indicated that the rs1801131 polymorphism may serve as a genetic biomarker of HCC in Asians.

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