Evaluating potential sources of bias in linear growth rate analysis of renal tumors: case study in Von Hippel–Lindau disease

Abstract Accurate tumor growth rate estimation is vital for linking disease progression to patient characteristics and outcomes, especially in rare diseases where limited data increase bias risk. We evaluated 2 potential sources of bias in linear growth rate (LGR) estimation using data from a prior natural history (NH) study of von Hippel–Lindau (VHL) renal cell carcinoma (RCC), a rare hereditary disorder. A quantitative bias analysis assessed selection bias from differential follow-up, while a Bayesian analysis (BA) examined shrinkage bias from a frequentist linear mixed-effects approach. In the NH cohort (244 patients; 601 target tumors), the original median tumor-level LGR was 1.6 mm/year. Quantitative bias analysis produced a bias-corrected LGR of 1.7 mm/year, suggesting minimal impact of selection bias. Bayesian modeling produced an LGR estimate of 3.93 mm/year, aligning more closely with external reports and suggesting that surgically removed tumors had substantially higher growth rates than the mixed cohort implied by frequentist methods. Quantitative bias analysis showed limited bias from differential follow-up, while BA provided a complementary estimate that was less influenced by shrinkage bias inherent to mixed-effects modeling. These findings underscore the importance of appropriate study design and method selection to diagnose and adjust for biases when characterizing tumor growth in VHL RCC.