We thank Yan and Liu for their comment regarding the multivariate analysis in our study.1
We acknowledge that this analysis does not entirely comply with the ten events-per-variable (EBV) rule, a rule of thumb that is commonly used to guide model complexity in small datasets.2
While we recognize the limitations of our analysis due to the small sample size of 28 patients, we limited our model to four covariables (not 7 as suggested), and used Firth’s bias-reduced logistic regression to address the small sample size.3 Soluble Fms-like tyrosine kinase 3 ligand (sFL) kinetics was identified as significant in univariate analysis, and we considered relevant to evaluate whether this positive effect persisted when taking into account covariables that are classically associated with prognosis in myelodysplastic syndrome.
It is important to note that this study is exploratory in nature, and as such aims to identify trends and generate hypotheses, rather than draw definitive conclusions. Despite these limitations, our results suggest that the sFL profile may be an interesting marker in high-risk myelodysplastic syndrome and chronic myelomonocytic leukemia, and de-
serves further investigation in larger, adequately powered cohorts.
Footnotes
- Received January 13, 2025
- Accepted January 20, 2025
Correspondence
References
- Peterlin P, Gaschet J, Turlure P. FLT3 ligand kinetic profile predicts response to treatment in patients with high-risk myelodysplastic syndrome/chronic myelomonocytic leukemia receiving CPX-351: a study from the Groupe Francophone des Myélodysplasies. Haematologica. 2025; 110(4):980-984. Google Scholar
- Pavlou M, Ambler G, Seaman SR. How to develop a more accurate risk prediction model when there are few events. BMJ. 2015; 351:h3868. Google Scholar
- Heinze G, Schemper M. A solution to the problem of separation in logistic regression. Stat Med. 2002; 21(16):2409-2419. Google Scholar
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