Sample Size in Cancer Prognostic Studies and Clinical Staging of the Disease

Music City Center 

In classical approaches for cancer staging studies, sample size is computed based on differentials in risk of death between different stages of the disease. Risk can be expressed as risk-differences, risk-ratios, hazard ratios or other similar measures and sample size is derived accordingly (e.g. based on HR between adjacent stages in terms of survival). This approach has several drawbacks, as (i) in its simplest formulations, it assumes independence among stages, (ii) it requires most often proportionality in hazards between stages, with difficulties in managing crossing of curves, (iii) it does not incorporate information on side-variables, like biomarkers, omics and other relevant or even latent factors. The latter aspects are managed by alternative approaches (e.g. Riley 2019, 2021) which concentrate on the overall model precision. However, there's no a-priori guarantee that all models converge toward a unique indication w.r.t. sample size needed. Our study compares the different approaches using Monte Carlo simulations, based on Lung Cancer Staging data as derived from published literature. In particular, the loss of power and the limitations of detecting reasonable number of relevant covariates is evaluated. A concrete example on lung cancer staging system using a composite approach is presented.

Sample Size

Time-to-event outcome

External validation

Monte Carlo simulation 

WNAR