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Updated: Jan 5, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
An alternative approach for estimating the number needed to treat for survival endpoints
Zhao Yang1, Guosheng Yin1,2
1Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong SAR, China.
Abstract:
To investigate the issues of the NNT based on the absolute risk reduction (ARR), namely NNTARR; and to propose an alternative definition and an estimation procedure based on the restricted mean survival time (RMST), namely NNTRMST, for RCTs. Three recent clinical trials with survival endpoints, representing different scenarios, were selected to compare the performance of the NNTARR and NNTRMST. For each trial, both versions of NNT were estimated using the reconstructed individual-level data, and the average life gain (ALG) was derived to show the differences between the NNTARR and NNTRMST. Four hypothetical scenarios were constructed to further explore the advantages and disadvantages of each definition of the NNT for survival endpoints. For the illustrative trial examples, the NNTARR failed to capture the profile of the treatment effect over time as it is calculated at a specific time point. Sometimes it may even result in misinterpretations of the treatment benefit. In particular, when either the observed event rates are low, the two survival curves cross, or a mixture of survival patterns exist. In contrast, the NNTRMST based on the average survival (or event-free) time can quantify the treatment effect more accurately and its interpretation is more intuitive and clinically meaningful. The NNTRMST can be used as an alternative measure for quantifying treatment effect in RCTs, especially so in the case of the ALG, which helps practitioners to better understand the magnitude of the benefit conferred by treatment.
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