Fixed versus Random Effects in Poisson Regression Models for Claim Counts: A Case Study with Motor Insurance

This paper examines the validity of some stylized statements that can be found in the actuarial literature about random effects models. Specifically, the actual meaning of the estimated parameters and the nature of the residual heterogeneity are discussed. A numerical illustration performed on a Belgian motor third party liability portfolio supports this discussion.

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REGRESSION MODELS FOR RECURRENT EVENT DATA: PARAMETRIC RANDOM EFFECTS MODELS WITH MEASUREMENT ERROR

Statistics in Medicine ◽

10.1002/(sici)1097-0258(19970430)16:8<853::aid-sim540>3.0.co;2-n ◽

1997 ◽

Vol 16 (8) ◽

pp. 853-864 ◽

Cited By ~ 10

Author(s):

BRUCE W. TURNBULL ◽

WENXIN JIANG ◽

LARRY C. CLARK

Keyword(s):

Measurement Error ◽

Random Effects ◽

Regression Models ◽

Recurrent Event ◽

Event Data ◽

Recurrent Event Data ◽

Random Effects Models

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Giardiasis in North West England: faecal specimen requesting rates by GP practice

Epidemiology and Infection ◽

10.1017/s0950268814002350 ◽

2014 ◽

Vol 143 (8) ◽

pp. 1692-1701

Author(s):

N. F. REEVE ◽

T. R. FANSHAWE ◽

K. LAMDEN ◽

P. J. DIGGLE ◽

J. CHEESBROUGH ◽

...

Keyword(s):

Spatial Variation ◽

Spatial Correlation ◽

Random Effects ◽

Poisson Regression ◽

Regression Models ◽

Faecal Specimen ◽

North West ◽

Practice Population ◽

Cumulative Rate ◽

The Uk

SUMMARYMany cases of giardiasis in the UK are undiagnosed and among other things, diagnosis is dependent upon the readiness of GPs to request a specimen. The aim of this study is to assess the rate of specimens requested per GP practice in Central Lancashire, to examine the differences between GP practices and to estimate the pattern of unexplained spatial variation in the practice rate of specimens after adjustment for deprivation. To achieve this, we fitted a set of binomial and Poisson regression models, with random effects for GP practice. Our analysis suggests that there were differences in the rate of specimens by GP practices (P < 0·001) for a single year, but no difference in the proportion of positive tests per specimen submitted or in the rate of positive specimens per practice population. There was a difference in the cumulative rate of positive specimens per practice population over a 9-year period (P < 0·001). Neither the specimen rate per practice for a single year nor the cumulative rate of positive specimens over multiple years demonstrated significant spatial correlation. Hence, spatial variation in the incidence of giardiasis is unlikely to be confounded by variation in GP rate of specimens.

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Profiling of county-level foster care placements using random-effects Poisson regression models

Health Services and Outcomes Research Methodology ◽

10.1007/s10742-007-0019-2 ◽

2007 ◽

Vol 7 (3-4) ◽

pp. 97-108 ◽

Cited By ~ 8

Author(s):

Robert D. Gibbons ◽

Kwan Hur ◽

Dulal K. Bhaumik ◽

Carl C. Bell

Keyword(s):

Foster Care ◽

Random Effects ◽

Poisson Regression ◽

Regression Models ◽

County Level

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Modeling Burglar Incidents Data Using Generalized and Quasi Poisson Regression Models: A Case Study of Nairobi City County, Kenya

American Journal of Theoretical and Applied Statistics ◽

10.11648/j.ajtas.20200905.19 ◽

2020 ◽

Vol 9 (5) ◽

pp. 256

Author(s):

Isaac Muchika ◽

Antony Ngunyi ◽

Thomas Mageto

Keyword(s):

Poisson Regression ◽

Regression Models

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Positional Estimation Within a Latent Space Model for Networks

Methodology ◽

10.1027/1614-2241.2.1.24 ◽

2006 ◽

Vol 2 (1) ◽

pp. 24-33 ◽

Cited By ~ 17

Author(s):

Susan Shortreed ◽

Mark S. Handcock ◽

Peter Hoff

Keyword(s):

Random Effects ◽

Network Data ◽

Random Effects Models ◽

Space Model ◽

Network Analyses ◽

Recent Advances ◽

Latent Space ◽

Latent Space Model ◽

Modeling Data

Recent advances in latent space and related random effects models hold much promise for representing network data. The inherent dependency between ties in a network makes modeling data of this type difficult. In this article we consider a recently developed latent space model that is particularly appropriate for the visualization of networks. We suggest a new estimator of the latent positions and perform two network analyses, comparing four alternative estimators. We demonstrate a method of checking the validity of the positional estimates. These estimators are implemented via a package in the freeware statistical language R. The package allows researchers to efficiently fit the latent space model to data and to visualize the results.

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Dyslipidaemia and mortality in COVID-19 patients - a meta-analysis

QJM ◽

10.1093/qjmed/hcab071 ◽

2021 ◽

Author(s):

Marco Zuin ◽

Gianluca Rigatelli ◽

Claudio Bilato ◽

Carlo Cervellati ◽

Giovanni Zuliani ◽

...

Keyword(s):

Random Effects ◽

Mortality Risk ◽

Meta Analysis ◽

Random Effect ◽

Final Analysis ◽

Short Term ◽

Random Effects Models ◽

Pooled Prevalence ◽

Statistical Heterogeneity ◽

Trim And Fill

Abstract Objective The prevalence and prognostic implications of pre-existing dyslipidaemia in patients infected by the SARS-CoV-2 remain unclear. To perform a systematic review and meta-analysis of prevalence and mortality risk in COVID-19 patients with pre-existing dyslipidaemia. Methods Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed in abstracting data and assessing validity. We searched MEDLINE and Scopus to locate all the articles published up to January 31, 2021, reporting data on dyslipidaemia among COVID-19 survivors and non-survivors. The pooled prevalence of dyslipidaemia was calculated using a random effects model and presenting the related 95% confidence interval (CI), while the mortality risk was estimated using the Mantel-Haenszel random effects models with odds ratio (OR) and related 95% CI. Statistical heterogeneity was measured using the Higgins I2 statistic. Results Eighteen studies, enrolling 74.132 COVID-19 patients [mean age 70.6 years], met the inclusion criteria and were included in the final analysis. The pooled prevalence of dyslipidaemia was 17.5% of cases (95% CI: 12.3-24.3%, p < 0.0001), with high heterogeneity (I2=98.7%). Pre-existing dyslipidaemia was significantly associated with higher risk of short-term death (OR: 1.69, 95% CI: 1.19-2.41, p = 0.003), with high heterogeneity (I2=88.7%). Due to publication bias, according to the Trim-and-Fill method, the corrected random-effect ORs resulted 1.61, 95% CI 1.13-2.28, p < 0.0001 (one studies trimmed). Conclusions Dyslipidaemia represents a major comorbidity in about 18% of COVID-19 patients but it is associated with a 60% increase of short-term mortality risk.

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Anticipated help-seeking for cancer symptoms before and after the coronavirus pandemic: results from the Onco-barometer population survey in Spain

British Journal of Cancer ◽

10.1038/s41416-021-01382-1 ◽

2021 ◽

Author(s):

Dafina Petrova ◽

Marina Pollán ◽

Miguel Rodriguez-Barranco ◽

Dunia Garrido ◽

Josep M. Borrás ◽

...

Keyword(s):

Poisson Regression ◽

Help Seeking ◽

Regression Models ◽

Population Survey ◽

Waiting Times ◽

Perceived Barriers ◽

Older Individuals ◽

Cancer Symptoms ◽

Before And After ◽

Diagnostic Delays

Abstract Background The patient interval—the time patients wait before consulting their physician after noticing cancer symptoms—contributes to diagnostic delays. We compared anticipated help-seeking times for cancer symptoms and perceived barriers to help-seeking before and after the coronavirus pandemic. Methods Two waves (pre-Coronavirus: February 2020, N = 3269; and post-Coronavirus: August 2020, N = 1500) of the Spanish Onco-barometer population survey were compared. The international ABC instrument was administered. Pre–post comparisons were performed using multiple logistic and Poisson regression models. Results There was a consistent and significant increase in anticipated times to help-seeking for 12 of 13 cancer symptoms, with the largest increases for breast changes (OR = 1.54, 95% CI 1.22–1–96) and unexplained bleeding (OR = 1.50, 1.26–1.79). Respondents were more likely to report barriers to help-seeking in the post wave, most notably worry about what the doctor may find (OR = 1.58, 1.35–1.84) and worry about wasting the doctor’s time (OR = 1.48, 1.25–1.74). Women and older individuals were the most affected. Conclusions Participants reported longer waiting times to help-seeking for cancer symptoms after the pandemic. There is an urgent need for public interventions encouraging people to consult their physicians with symptoms suggestive of cancer and counteracting the main barriers perceived during the pandemic situation.

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Regression Models for Soil Water Storage Estimation Using the ESA CCI Satellite Soil Moisture Product: A Case Study in Northeast Portugal

Water ◽

10.3390/w13010037 ◽

2020 ◽

Vol 13 (1) ◽

pp. 37

Author(s):

Tomás de Figueiredo ◽

Ana Caroline Royer ◽

Felícia Fonseca ◽

Fabiana Costa de Araújo Schütz ◽

Zulimar Hernández

Keyword(s):

Soil Moisture ◽

Soil Water ◽

Regression Models ◽

Meteorological Data ◽

Water Storage ◽

Model Performance ◽

Soil Water Balance ◽

Soil Water Storage ◽

The Relationship

The European Space Agency Climate Change Initiative Soil Moisture (ESA CCI SM) product provides soil moisture estimates from radar satellite data with a daily temporal resolution. Despite validation exercises with ground data that have been performed since the product’s launch, SM has not yet been consistently related to soil water storage, which is a key step for its application for prediction purposes. This study aimed to analyse the relationship between soil water storage (S), which was obtained from soil water balance computations with ground meteorological data, and soil moisture, which was obtained from radar data, as affected by soil water storage capacity (Smax). As a case study, a 14-year monthly series of soil water storage, produced via soil water balance computations using ground meteorological data from northeast Portugal and Smax from 25 mm to 150 mm, were matched with the corresponding monthly averaged SM product. Linear (I) and logistic (II) regression models relating S with SM were compared. Model performance (r2 in the 0.8–0.9 range) varied non-monotonically with Smax, with it being the highest at an Smax of 50 mm. The logistic model (II) performed better than the linear model (I) in the lower range of Smax. Improvements in model performance obtained with segregation of the data series in two subsets, representing soil water recharge and depletion phases throughout the year, outlined the hysteresis in the relationship between S and SM.

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Association of bisphenol A with puberty timing: a meta-analysis

Reviews on Environmental Health ◽

10.1515/reveh-2020-0091 ◽

2020 ◽

Vol 0 (0) ◽

Author(s):

Hui Meng ◽

Yunping Zhou ◽

Yunxia Jiang

Keyword(s):

Bisphenol A ◽

Confidence Intervals ◽

Random Effects ◽

Strong Correlation ◽

Fixed Effects ◽

Meta Analysis ◽

Random Effects Models ◽

Study Heterogeneity ◽

The Relationship ◽

Prevalence Ratios

AbstractObjectivesThe results of existing studies on bisphenol A (BPA) and puberty timing did not reach a consensus. Thereby we performed this meta-analytic study to explore the association between BPA exposure in urine and puberty timing.MethodsMeta-analysis of the pooled odds ratios (OR), prevalence ratios (PR) or hazards ratios (HR) with 95% confidence intervals (CI) were calculated and estimated using fixed-effects or random-effects models based on between-study heterogeneity.ResultsA total of 10 studies involving 5621 subjects were finally included. The meta-analysis showed that BPA exposure was weakly associated with thelarche (PR: 0.96, 95% CI: 0.93–0.99), while no association was found between BPA exposure and menarche (HR: 0.99, 95% CI: 0.89–1.12; OR: 1.02, 95% CI: 0.73–1.43), and pubarche (OR: 1.00, 95% CI: 0.79–1.26; PR: 1.00, 95% CI: 0.95–1.05).ConclusionsThere was no strong correlation between BPA exposure and puberty timing. Further studies with large sample sizes are needed to verify the relationship between BPA and puberty timing.

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