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Celeus

Factors associated with a survival outcome

Shown in the app as factors_survival. Part of the method reference, under Survival and censored data.

When to use

Which of 2-30 candidate factors are associated with a TIME-TO-EVENT outcome (time + 0/1 event): the full Cox 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

  • Time (follow-up): one numeric column.
  • Event (1 = event, 0 = censored): 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.

Other methods under Survival and censored data:

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.

survival::ovarian

  • Source: Published dataset distributed with an R package
  • Licence: survival (GPL)
  • Reference: Edmonson et al. (1979), Cancer Treatment Reports 63
QuantityCeleusIndependent computationPublished valueAgrees
Estimate1.132941.13294-Yes
p-value0.007772250.00777225-Yes

Validation report

References

  1. Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society, Series B 34: 187-202. doi:10.1111/j.2517-6161.1972.tb00899.x doi:10.1111/j.2517-6161.1972.tb00899.x
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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