ArticleInternational journal of environmental research and public health2019
Bring More Data!-A Good Advice? Removing Separation in Logistic Regression by Increasing Sample Size.
Article in International journal of environmental research and public health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed.
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- Effect of fresh-frozen plasma transfusion on intraventricular hemorrhage risk in preterm infants with prolonged APTT: a target trial emulation with causal forest analysis.Frontiers in neuroscience · 2026Article
- Changes in Health Risks, Daily Activities, and Antipsychotic Use After Humanitude Care for People With Dementia: A Retrospective Study.Journal of clinical nursing · 2025Observational
- Sleep and circadian rhythm disturbances as risk and progression factors for multiple chronic overlapping pain conditions: a protocol for a longitudinal study.Pain reports · 2024Article
- Influence of various factors on the legal outcome of cases of child abuse-experiences gathered at an interdisciplinary forensic examination center in Vienna, Austria.International journal of legal medicine · 2024Article
- Seroepidemiology of Lassa virus in pregnant women in Southern Nigeria: A prospective hospital-based cohort study.PLoS neglected tropical diseases · 2023Article
- An exact regression-based approach for the estimation of natural direct and indirect effects with a binary outcome and a continuous mediator.Statistics in medicine · 2023Article
- Aging through the time of COVID-19: a survey of self-reported healthcare access.BMC health services research · 2021Article
- To tune or not to tune, a case study of ridge logistic regression in small or sparse datasets.BMC medical research methodology · 2021Article
- Modelling hospital outcome: problems with endogeneity.BMC medical research methodology · 2021Article
- Statistical Advances in Epidemiology and Public Health.International journal of environmental research and public health · 2021Article
- Elucidating variations in outcomes among older end-stage renal disease patients on hemodialysis in Fukuoka Prefecture, Japan.PloS one · 2021Article
- Reasons Behind Generation Z Nursing Students' Intentions to Leave their Profession: A Cross-Sectional Study.Inquiry : a journal of medical care organization, provision and financingArticle
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3 authors.
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Abstract
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 estimates, applying Firth's correction (FC) was proposed. In practice, however, a principal investigator might be advised to 'bring more data' in order to solve a separation issue. We illustrate the problem by means of examples from colorectal cancer screening and ornithology. It is unclear if such an increasing sample size (ISS) strategy that keeps sampling new observations until separation is removed improves estimation compared to applying FC to the original data set. We performed an extensive simulation study where the main focus was to estimate the cost-adjusted relative efficiency of ML combined with ISS compared to FC. FC yielded reasonably small root mean squared errors and proved to be the more efficient estimator. Given our findings, we propose not to adapt the sample size when separation is encountered but to use FC as the default method of analysis whenever the number of observations or outcome events is critically low.
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