Single-Variable Poisson Regression: A Goodness-of-Fit Test and the Comparison of Regression Coefficients

1979 ◽  
Vol 74 (366) ◽  
pp. 489 ◽  
Author(s):  
David A. Pyne
Keyword(s):  
Poisson Regression ◽  
Goodness Of Fit ◽  
Regression Coefficients ◽  
Single Variable ◽  
Goodness Of Fit Test

Validation of the CASUS score in Tunisian patients: a quality management tool in intensive care units

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
T Besbes ◽  
S Mleyhi ◽  
J Sahli ◽  
M Messai ◽  
J Ziadi ◽  
...  
Keyword(s):  
Cardiac Surgery ◽  
Roc Curve ◽  
Goodness Of Fit ◽  
Cardiovascular Surgery ◽  
Management Tool ◽  
Medical Decision ◽  
Tunisian Population ◽  
Correct Classification ◽  
Goodness Of Fit Test ◽  
A Value

Abstract Background Early prediction of patients at highest risk of a poor outcome after cardiovascular surgery, including death can aid medical decision making, and adapt health care management in order to improve prognosis. In this context, we conducted this study to validate the CASUS severity score after cardiac surgery in the Tunisian population. Methods This is a retrospective cohort study conducted among patients who underwent cardiac surgery under extracorporeal circulation during the year 2018 at the Cardiovascular Surgery Department of La Rabta University Hospital in Tunisia. Data were collected from the patients hospitalization records. The discrimination of the score was assessed using the ROC curve and the calibration using the Hosmer-Lemeshow goodness of fit test and then by constructing the calibration curve. Overall correct classification was also obtained. Results In our study, the observed mortality rate was 10.52% among the 95 included patients. The discriminating power of the CASUS score was estimated by the area under the ROC curve (AUC), this scoring system had a good discrimination with AUC greater than 0.9 from postoperative Day 0 to Day 5.From postoperative day 0 to day 5, the Hosmer-Lemeshow's test gave a value of chi square test statistic ranging from 1.474 to 8.42 and a value of level of significance ranging from 0.39 to 0.99 indicating a good calibration. The overall correct classification rate from postoperative day 0 to day 5 ranged from 84.4% to 92.4%. Conclusions Despite the differences in the profile of the risk factors between the Tunisian population and the population constituting the database used to develop the CASUS score, we can say that this risk model presents acceptable performances in our population, attested by adequate discrimination and calibration. Prospective and especially multicentre studies on larger samples are needed before definitively conclude on the performance of this model in our country. Key messages The casus score seems to be valid to predict mortality among patients undergoing cardiac surgery. Multicenter study on larger sample is needed to derive and validate models able to predict in-hospitals mortality.

Goodness-of-fit test with a robustness feature

Test ◽  
2021 ◽  
Author(s):  
Jiming Jiang ◽  
Mahmoud Torabi
Keyword(s):  
Goodness Of Fit ◽  
Goodness Of Fit Test

scholarly journals Joint Modeling of Precipitation and Temperature Using Copula Theory for Current and Future Prediction under Climate Change Scenarios in Arid Lands (Case Study, Kerman Province, Iran)

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
T. Mesbahzadeh ◽  
M. M. Miglietta ◽  
M. Mirakbari ◽  
F. Soleimani Sardoo ◽  
M. Abdolhoseini
Keyword(s):  
Climate Change ◽  
Goodness Of Fit ◽  
Joint Modeling ◽  
Copula Function ◽  
Gaussian Copula ◽  
Climate Change Scenarios ◽  
Climatic Parameters ◽  
Goodness Of Fit Test ◽  
Copula Theory ◽  
Precipitation And Temperature

Precipitation and temperature are very important climatic parameters as their changes may affect life conditions. Therefore, predicting temporal trends of precipitation and temperature is very useful for societal and urban planning. In this research, in order to study the future trends in precipitation and temperature, we have applied scenarios of the fifth assessment report of IPCC. The results suggest that both parameters will be increasing in the studied area (Iran) in future. Since there is interdependence between these two climatic parameters, the independent analysis of the two fields will generate errors in the interpretation of model simulations. Therefore, in this study, copula theory was used for joint modeling of precipitation and temperature under climate change scenarios. By the joint distribution, we can find the structure of interdependence of precipitation and temperature in current and future under climate change conditions, which can assist in the risk assessment of extreme hydrological and meteorological events. Based on the results of goodness of fit test, the Frank copula function was selected for modeling of recorded and constructed data under RCP2.6 scenario and the Gaussian copula function was used for joint modeling of the constructed data under the RCP4.5 and RCP8.5 scenarios.

scholarly journals Risk factors for peripheral venous catheter failure: A prospective cohort study of 5345 patients

2021 ◽  
pp. 112972982110150
Author(s):  
Ya-mei Chen ◽  
Xiao-wen Fan ◽  
Ming-hong Liu ◽  
Jie Wang ◽  
Yi-qun Yang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Cohort Study ◽  
Goodness Of Fit ◽  
Insertion Site ◽  
Healthcare Providers ◽  
Venous Catheter ◽  
Goodness Of Fit Test ◽  
Peripheral Venous Catheter ◽  
Independent Risk Factors

Purpose: The objective of this study was to determine the independent risk factors associated with peripheral venous catheter (PVC) failure and develop a model that can predict PVC failure. Methods: This prospective, multicenter cohort study was carried out in nine tertiary hospitals in Suzhou, China between December 2017 and February 2018. Adult patients undergoing first-time insertion of a PVC were observed from catheter insertion to removal. Logistic regression was used to identify the independent risk factors predicting PVC failure. Results: This study included 5345 patients. The PVC failure rate was 54.05% ( n = 2889/5345), and the most common causes of PVC failure were phlebitis (16.3%) and infiltration/extravasation (13.8%). On multivariate analysis, age (45–59 years: OR, 1.295; 95% CI, 1.074–1.561; 60–74 years: OR, 1.375; 95% CI, 1.143–1.654; ⩾75 years: OR, 1.676; 95% CI, 1.355–2.073); department (surgery OR, 1.229; 95% CI, 1.062–1.423; emergency internal/surgical ward OR, 1.451; 95% CI, 1.082–1.945); history of venous puncture in the last week (OR, 1.298, 95% CI 1.130–1.491); insertion site, number of puncture attempts, irritant fluid infusion, daily infusion time, daily infusion volume, and type of sealing liquid were independent predictors of PVC failure. Receiver operating characteristic curve analysis indicated that a logistic regression model constructed using these variables had moderate accuracy for the prediction of PVC failure (area under the curve, 0.781). The Hosmer-Lemeshow goodness of fit test demonstrated that the model was correctly specified (χ2 = 2.514, p = 0.961). Conclusion: This study should raise awareness among healthcare providers of the risk factors for PVC failure. We recommend that healthcare providers use vascular access device selection tools to select a clinically appropriate device and for the timely detection of complications, and have a list of drugs classified as irritants or vesicants so they can monitor patients receiving fluid infusions containing these drugs more frequently.

Using the dependent wild bootstrap for the nonparametric goodness-of-fit test for density functions

Statistics ◽  
2016 ◽  
Vol 50 (4) ◽  
pp. 750-774
Author(s):  
Taeyoon Kim ◽  
Cheolyong Park ◽  
Jeongcheol Ha ◽  
Zhi-Ming Luo ◽  
Sun Young Hwang
Keyword(s):  
Goodness Of Fit ◽  
Density Functions ◽  
Wild Bootstrap ◽  
Goodness Of Fit Test

Goodness-of-fit test for homogeneous Markov processes

2013 ◽  
Vol 351 (3-4) ◽  
pp. 149-154
Author(s):  
Vilijandas Bagdonavičius ◽  
Mikhail Nikulin
Keyword(s):  
Markov Processes ◽  
Goodness Of Fit ◽  
Goodness Of Fit Test
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