Hana Šinkovec
Biographic Data
| ID | 5537526 |
|---|---|
| NAME | Hana Šinkovec |
| GIVEN NAMES | Hana |
| FAMILY NAME | Šinkovec |
| SIGNATURE | ŠINKOVEC H |
| AFFILIATIONS | Medical University of Vienna |
| ORCID | 0000-0001-9508-8364 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2019 |
| 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…
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…
No prominent works on this page.
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…
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…
Advanced Causal Inference Techniques (1 work) · Causal inference (1 work) · Censoring (clinical trials) (1 work) · Chronic Disease Management Strategies (1 work) · Comparability (1 work) · Computer Science (1 work) · Confounding (1 work) · Covariate (1 work) · Data set (1 work) · Econometrics (1 work)