scholarly journals Introduction to Regression Analysis for Epidemiological Data (1)

2020 ◽  
Vol 24 (1) ◽  
pp. 29-35
Author(s):  
Arata HIDANO
2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
John Carlin ◽  
Margarita Moreno-Betancur

Abstract Focus of Presentation Multivariable regression models are widely used in epidemiological data analysis. Traditional teaching often focusses on technical aspects with insufficient attention paid to the purposes for which regression methods are used. Findings We have addressed these issues in a new short course that provides an introduction to regression analysis in the context of learning about causal inference, beginning from the standpoint that the majority of research questions in epidemiology are causal in nature. This approach leads naturally to using regression models in two different ways, firstly for direct estimation of a causal effect, under an assumption of constancy of the effect across strata of confounders, and secondly for prediction of outcomes, as a necessary step in the estimation of causal effects via g-computation. Conclusions/Implications Approaching the teaching of regression methods within a causal inference framework helps to dispel confusion created by traditional statistical approaches that imply the existence of “true models” and encourage the building of models in a way that is unclear about the purpose for which they will be used, for example seeking to identify “risk factors” in an exploratory manner. Key messages The teaching and practice of regression methods in epidemiology can be enhanced by emphasising the key differences between three distinct analytic purposes: description, prediction, causal. Regression models may play a role in all three but the way in which models are developed and interpreted differs between them.


Author(s):  
Poonam Chauhan ◽  
Ashok Kumar ◽  
Pooja Jamdagni

AbstractLinear and polynomial regression model has been used to investigate the COVID-19 outbreak in India and its different states using time series epidemiological data up to 26th May 2020. The data driven analysis shows that the case fatality rate (CFR) for India (3.14% with 95% confidence interval of 3.12% to 3.16%) is half of the global fatality rate, while higher than the CFR of the immediate neighbors i.e. Bangladesh, Pakistan and Sri Lanka. Among Indian states, CFR of West Bengal (8.70%, CI: 8.21–9.18%) and Gujrat (6.05%, CI: 4.90–7.19%) is estimated to be higher than national rate, whereas CFR of Bihar, Odisha and Tamil Nadu is less than 1%. The polynomial regression model for India and its different states is trained with data from 21st March 2020 to 19th May 2020 (60 days). The performance of the model is estimated using test data of 7 days from 20th May 2020 to 26th May 2020 by calculating RMSE and % error. The model is then used to predict number of patients in India and its different states up to 16th June 2020 (21 days). Based on the polynomial regression analysis, Maharashtra, Gujrat, Delhi and Tamil Nadu are continue to remain most affected states in India.


Author(s):  
Ahmad Abubakar Suleiman ◽  
Aminu Suleiman ◽  
Usman Aliyu Abdullahi ◽  
Suleiman Abubakar Suleiman

2017 ◽  
Vol 22 (1) ◽  
pp. 11-16
Author(s):  
Joel Weddington ◽  
Charles N. Brooks ◽  
Mark Melhorn ◽  
Christopher R. Brigham

Abstract In most cases of shoulder injury at work, causation analysis is not clear-cut and requires detailed, thoughtful, and time-consuming causation analysis; traditionally, physicians have approached this in a cursory manner, often presenting their findings as an opinion. An established method of causation analysis using six steps is outlined in the American College of Occupational and Environmental Medicine Guidelines and in the AMA Guides to the Evaluation of Disease and Injury Causation, Second Edition, as follows: 1) collect evidence of disease; 2) collect epidemiological data; 3) collect evidence of exposure; 4) collect other relevant factors; 5) evaluate the validity of the evidence; and 6) write a report with evaluation and conclusions. Evaluators also should recognize that thresholds for causation vary by state and are based on specific statutes or case law. Three cases illustrate evidence-based causation analysis using the six steps and illustrate how examiners can form well-founded opinions about whether a given condition is work related, nonoccupational, or some combination of these. An evaluator's causal conclusions should be rational, should be consistent with the facts of the individual case and medical literature, and should cite pertinent references. The opinion should be stated “to a reasonable degree of medical probability,” on a “more-probable-than-not” basis, or using a suitable phrase that meets the legal threshold in the applicable jurisdiction.


Author(s):  
A. Colin Cameron ◽  
Pravin K. Trivedi

Optimization ◽  
1972 ◽  
Vol 3 (5) ◽  
pp. 373-388
Author(s):  
Hilmar Drygas

GeroPsych ◽  
2020 ◽  
Vol 33 (4) ◽  
pp. 246-251
Author(s):  
Gozde Cetinkol ◽  
Gulbahar Bastug ◽  
E. Tugba Ozel Kizil

Abstract. Depression in older adults can be explained by Erikson’s theory on the conflict of ego integrity versus hopelessness. The study investigated the relationship between past acceptance, hopelessness, death anxiety, and depressive symptoms in 100 older (≥50 years) adults. The total Beck Hopelessness (BHS), Geriatric Depression (GDS), and Accepting the Past (ACPAST) subscale scores of the depressed group were higher, while the total Death Anxiety (DAS) and Reminiscing the Past (REM) subscale scores of both groups were similar. A regression analysis revealed that the BHS, DAS, and ACPAST predicted the GDS. Past acceptance seems to be important for ego integrity in older adults.


2011 ◽  
Vol 25 (4) ◽  
pp. 164-173 ◽  
Author(s):  
Brian Healy ◽  
Aaron Treadwell ◽  
Mandy Reagan

The current study was an attempt to determine the degree to which the suppression of respiratory sinus arrhythmia (RSA) and attentional control were influential in the ability to engage various executive processes under high and low levels of negative affect. Ninety-four college students completed the Stroop Test while heart rate was being recorded. Estimates of the suppression of RSA were calculated from each participant in response to this test. The participants then completed self-ratings of attentional control, negative affect, and executive functioning. Regression analysis indicated that individual differences in estimates of the suppression of RSA, and ratings of attentional control were associated with the ability to employ executive processes but only when self-ratings of negative affect were low. An increase in negative affect compromised the ability to employ these strategies in the majority of participants. The data also suggest that high attentional control in conjunction with attenuated estimates of RSA suppression may increase the ability to use executive processes as negative affect increases.


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