Georg Heinze
Biographic Data
| ID | 5537528 |
|---|---|
| NAME | Georg Heinze |
| GIVEN NAMES | Georg |
| FAMILY NAME | Heinze |
| SIGNATURE | HEINZE G |
| AFFILIATIONS | Medical University of Vienna |
| ORCID | 0000-0003-1147-8491 |
| VERIFIED | Yes |
| TOTAL WORKS | 7 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 7 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2002 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 0 |
Cross-Sectoral Comparisons of Process Quality Indicators of Health Care Across Residential Regions Using Restricted Mean Survival Time
BACKGROUND: Practice guidelines recommend patient management based on scientific evidence. Quality indicators gauge adherence to such recommendations and assess health care quality. They are usually defined as adverse event rates, which may not fully capture guideline adherence over time. METHODS: For assessing process indicators where compliance to the recommended treatment can be assessed by evaluating a patient's trace in linked routine databa…
A Training Programme for Developing Social and Personal Resources and Its Effects on the Perceived Stress Level in Adults in Daily Life—Study Protocol for a Prospective Cohort Study
Persistent stress and insufficient coping strategies have negative consequences for physical and mental health. Teaching adults the skills needed to sustainably improve stress-buffering aspects of their character could contribute to the prevention of stress-related diseases. In this non-randomised, observational, prospective cohort study, participants of a training programme for developing social and personal skills, to which they previously self…
Regression with Highly Correlated Predictors
Regression models have been in use for decades to explore and quantify the association between a dependent response and several independent variables in environmental sciences, epidemiology and public health. However, researchers often encounter situations in which some independent variables exhibit high bivariate correlation, or may even be collinear. Improper statistical handling of this situation will most certainly generate models of little o…
Prediction models for diagnosis and prognosis of covid-19
Bring More Data!—A Good Advice? Removing Separation in Logistic Regression by Increasing Sample Size
The parameters of logistic regression models are usually obtained by the method of maximum likelihood (ML). However, in analyses of small data sets or data sets with unbalanced outcomes or exposures, ML parameter estimates may not exist. This situation has been termed 'separation' as the two outcome groups are separated by the values of a covariate or a linear combination of covariates. To overcome the problem of non-existing ML parameter estimat…
Variable selection – A review and recommendations for the practicing statistician
Statistical models support medical research by facilitating individualized outcome prognostication conditional on independent variables or by estimating effects of risk factors adjusted for covariates. Theory of statistical models is well‐established if the set of independent variables to consider is fixed and small. Hence, we can assume that effect estimates are unbiased and the usual methods for confidence interval estimation are valid. In rout…
A solution to the problem of separation in logistic regression
The phenomenon of separation or monotone likelihood is observed in the fitting process of a logistic model if the likelihood converges while at least one parameter estimate diverges to ± infinity. Separation primarily occurs in small samples with several unbalanced and highly predictive risk factors. A procedure by Firth originally developed to reduce the bias of maximum likelihood estimates is shown to provide an ideal solution to separation. It…
No prominent works on this page.
A solution to the problem of separation in logistic regression
The phenomenon of separation or monotone likelihood is observed in the fitting process of a logistic model if the likelihood converges while at least one parameter estimate diverges to ± infinity. Separation primarily occurs in small samples with several unbalanced and highly predictive risk factors. A procedure by Firth originally developed to reduce the bias of maximum likelihood estimates is shown to provide an ideal solution to separation. It…
Variable selection – A review and recommendations for the practicing statistician
Statistical models support medical research by facilitating individualized outcome prognostication conditional on independent variables or by estimating effects of risk factors adjusted for covariates. Theory of statistical models is well‐established if the set of independent variables to consider is fixed and small. Hence, we can assume that effect estimates are unbiased and the usual methods for confidence interval estimation are valid. In rout…
Bring More Data!—A Good Advice? Removing Separation in Logistic Regression by Increasing Sample Size
The parameters of logistic regression models are usually obtained by the method of maximum likelihood (ML). However, in analyses of small data sets or data sets with unbalanced outcomes or exposures, ML parameter estimates may not exist. This situation has been termed 'separation' as the two outcome groups are separated by the values of a covariate or a linear combination of covariates. To overcome the problem of non-existing ML parameter estimat…
Prediction models for diagnosis and prognosis of covid-19
Regression with Highly Correlated Predictors
Regression models have been in use for decades to explore and quantify the association between a dependent response and several independent variables in environmental sciences, epidemiology and public health. However, researchers often encounter situations in which some independent variables exhibit high bivariate correlation, or may even be collinear. Improper statistical handling of this situation will most certainly generate models of little o…
A Training Programme for Developing Social and Personal Resources and Its Effects on the Perceived Stress Level in Adults in Daily Life—Study Protocol for a Prospective Cohort Study
Persistent stress and insufficient coping strategies have negative consequences for physical and mental health. Teaching adults the skills needed to sustainably improve stress-buffering aspects of their character could contribute to the prevention of stress-related diseases. In this non-randomised, observational, prospective cohort study, participants of a training programme for developing social and personal skills, to which they previously self…
Cross-Sectoral Comparisons of Process Quality Indicators of Health Care Across Residential Regions Using Restricted Mean Survival Time
BACKGROUND: Practice guidelines recommend patient management based on scientific evidence. Quality indicators gauge adherence to such recommendations and assess health care quality. They are usually defined as adverse event rates, which may not fully capture guideline adherence over time. METHODS: For assessing process indicators where compliance to the recommended treatment can be assessed by evaluating a patient's trace in linked routine databa…
Econometrics (4 works) · Mathematics (4 works) · Statistics (4 works) · Computer Science (3 works) · Statistical Methods and Bayesian Inference (3 works) · Statistical Methods and Inference (3 works) · Advanced Statistical Methods and Models (2 works) · Covariate (2 works) · Logistic regression (2 works) · Medicine (2 works)