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PreprintCC BY-NC-ND 4.0arXiv

Data generation for the logistic regression model under controlled conditions

  • Kabiru Abubakari
  • Silvia Liverani
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Abstract

Generating data with desired properties for a logistic regression model under controlled conditions is challenging. We propose an integrated iterative bisection algorithm that determines values of the intercept and regression coefficients capable of generating data with a specified prevalence and predictive accuracy. The performance of the proposed method is demonstrated through simulation studies. We further show that the choice of parameter values, or, in a Bayesian setting, the choice of priors for these parameters, induces a particular level of predictive accuracy. This relationship should therefore be taken into account when generating data for model evaluation under controlled conditions.

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Cite

BibTeXRIS
BibTeX
@misc{abubakari2026data,
  title = {{Data generation for the logistic regression model under controlled conditions}},
  author = {Kabiru Abubakari and Silvia Liverani},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2610.12381},
  eprint = {2610.12381},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2610.12381},
  note = {CC BY-NC-ND 4.0},
}
RIS
TY  - UNPB
TI  - Data generation for the logistic regression model under controlled conditions
AU  - Kabiru Abubakari
AU  - Silvia Liverani
PY  - 2026
DA  - 2026/10/08
PB  - arXiv
DO  - 10.48550/arxiv.2610.12381
UR  - https://arxiv.org/abs/2610.12381
AB  - Generating data with desired properties for a logistic regression model under controlled conditions is challenging. We propose an integrated iterative bisection algorithm that determines values of the intercept and regression coefficients capable of generating data with a specified prevalence and predictive accuracy. The performance of the proposed method is demonstrated through simulation studies. We further show that the choice of parameter values, or, in a Bayesian setting, the choice of priors for these parameters, induces a particular level of predictive accuracy. This relationship should therefore be taken into account when generating data for model evaluation under controlled conditions.
ER  -