Factors associated with a binary outcome
Shown in the app as factors_binary. Part of the method reference, under Regression.
When to use
Which of 2-30 candidate factors are associated with a BINARY outcome: the full multivariable logistic model with every candidate is the primary result, with per-factor likelihood-ratio tests (Benjamini-Hochberg adjusted), correlations and collinearity, and an exploratory LASSO layer reporting how often each factor is selected across 200 bootstrap resamples.
What you provide
- Outcome: one two-level column.
- Predictors: one or more columns.
Options
Missing data
- Complete case (listwise deletion)
Multiple comparisons
- Not adjustable for this method. Where it makes post-hoc comparisons, the procedure applies its own correction.
Related methods
Other methods under Regression:
- Linear regression
- Logistic regression
- Firth penalised logistic regression
- Poisson regression
- Negative-binomial regression
- Ordinal logistic regression
- Multinomial logistic regression
- Factors associated with outcome (continuous outcome)
Worked example
Celeus's result on a published or illustrative dataset, next to an independent computation and, where one exists, a published value. The data themselves are not reproduced here.
MASS::birthwt (race as a factor)
- Source: Published dataset distributed with an R package
- Licence: MASS (GPL)
- Reference: Hosmer & Lemeshow (1989), Applied Logistic Regression
| Quantity | Celeus | Independent computation | Published value | Agrees |
|---|---|---|---|---|
| Estimate | -0.029549 | -0.029549 | - | Yes |
| p-value | 0.424895 | 0.424895 | - | Yes |
References
- Nelder, J. A., Wedderburn, R. W. M. (1972). Generalized linear models. Journal of the Royal Statistical Society, Series A 135: 370-384. doi:10.2307/2344614 doi:10.2307/2344614
- Heinze, G., Wallisch, C., Dunkler, D. (2018). Variable selection - a review and recommendations for the practicing statistician. Biometrical Journal 60: 431-449. doi:10.1002/bimj.201700067 doi:10.1002/bimj.201700067
- Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society, Series B 58: 267-288. doi:10.1111/j.2517-6161.1996.tb02080.x doi:10.1111/j.2517-6161.1996.tb02080.x
- Meinshausen, N., Buhlmann, P. (2010). Stability selection. Journal of the Royal Statistical Society, Series B 72: 417-473. doi:10.1111/j.1467-9868.2010.00740.x doi:10.1111/j.1467-9868.2010.00740.x
- Harrell, F. E., Lee, K. L., Mark, D. B. (1996). Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Statistics in Medicine 15: 361-387. doi:10.1002/(SICI)1097-0258(19960229)15:4<361::AID-SIM168>3.0.CO;2-4 doi:10.1002/(SICI)1097-0258(19960229)15:4<361::AID-SIM168>3.0.CO;2-4
- Benjamini, Y., Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society, Series B 57: 289-300. doi:10.1111/j.2517-6161.1995.tb02031.x doi:10.1111/j.2517-6161.1995.tb02031.x