Evaluating Goodness of Fit of Poisson Regression Models in Cohort Studies

1989 ◽  
Vol 43 (3) ◽  
pp. 144 ◽  
Author(s):  
Edward L. Frome ◽  
Max D. Morris
Keyword(s):  
Cohort Studies ◽  
Poisson Regression ◽  
Regression Models ◽  
Goodness Of Fit

scholarly journals Interpreting Poisson Regression Models in Dental Caries Studies

2018 ◽  
Vol 52 (4) ◽  
pp. 339-345 ◽  
Author(s):  
Alex Man Him Chau ◽  
Edward Chin Man Lo ◽  
May Chun Mei Wong ◽  
Chun Hung Chu
Keyword(s):  
Poisson Regression ◽  
Regression Models ◽  
Goodness Of Fit ◽  
Negative Binomial ◽  
Model Fitting ◽  
Population Group ◽  
Basic Knowledge ◽  
Caries Epidemiology ◽  
Oral Epidemiology ◽  
Epidemiology Studies

Oral epidemiology involves studying and investigating the distribution and determinants of dental-related diseases in a specified population group to inform decisions in the management of health problems. In oral epidemiology studies, the hypothesis is typically followed by a cogent study design and data collection. Appropriate statistical analysis is essential to demonstrate the scientific association between the independent factors and the target variable. Analysis also helps to develop and build a statistical model. Poisson regression and its extensions have gained more attention in caries epidemiology than other working models such as logistic regression. This review discusses the fundamental principles and basic knowledge of Poisson regression models. It also introduces the use of a robust variance estimator with a focus on the “robust” interpretation of the model. In addition, extensions of regression models, including the zero-inflated model, hurdle model, and negative binomial model, and their interpretation in caries studies are reviewed. Principles of model fitting, including goodness-of-fit measures, are also discussed. Clinicians and researchers should pay attention to the statistical context of the models used and interpret the models to improve the oral and general health of the communities in which they live.

On goodness‐of‐fit measures for Poisson regression models

2020 ◽  
Vol 62 (3) ◽  
pp. 340-366
Author(s):  
Takeshi Kurosawa ◽  
Francis K.C. Hui ◽  
A.H. Welsh ◽  
Kousuke Shinmura ◽  
Nobuoki Eshima
Keyword(s):  
Poisson Regression ◽  
Regression Models ◽  
Goodness Of Fit

Effects of age, period, and cohort on stroke mortality among the Lithuanian urban population during the 24-year follow-up period

Open Medicine ◽  
2008 ◽  
Vol 3 (3) ◽  
pp. 315-321
Author(s):  
Regina Rėklaitienė ◽  
Marius Noreika ◽  
Abdonas Tamošiūnas ◽  
Dalia Virvičiūtė ◽  
Diana Šopagienė
Keyword(s):  
Poisson Regression ◽  
Regression Models ◽  
Urban Population ◽  
Goodness Of Fit ◽  
Mortality Rates ◽  
Mortality Data ◽  
Period Effect ◽  
Stroke Mortality ◽  
Men And Women ◽  
European Standard Population

AbstractThe main purpose of this paper was to assess the effects of age, period, and cohort on stroke mortality among the urban Lithuanian population. Routine stroke mortality data among the Lithuanian urban population aged 25–64 years (1041 men and 724 women) between 1980 and 2004 were obtained from the official Kaunas region mortality register and classified by codes 430–438 and 160–169 in the 9th and 10th revisions of the International Classifications of Diseases (ICD), respectively. Mortality rates per 100,000 persons for men and women were age-adjusted using the age distribution of the European Standard Population. Goodness of fit of the Poisson regression models was evaluated using the Pearson and Freeman-Tukey residuals. During the study period, mortality rates decreased from 46.8 to 33.0 per 100,000 for men, and from 20.2 to 18.1 per 100,000 for women (average annual decrease of −1.3%, p<0.1 for men, and −1.6%, p<0.03 for women). An age effect was present in both sexes. The definite upward period effect was observed from 1990 to 1994 both among men and women, and was followed by a sharp fall during 2000–2004. Cohort and period effects have contained relevant information that partially explained trends in stroke mortality among 25–64 year-olds in the Lithuanian urban population. The Poisson regression models could be applied for the examination and explanation of the different causes of the population mortality.

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

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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