Partitioned log-rank tests for the overall homogeneity of hazard rate functions

Validation of quantitative prognostic prediction using ADV score for resection of hepatocellular carcinoma: A Korea-Japan collaborative study with 9200 patients

BACKGROUND

A score derived from the concentrations of α-fetoprotein (AFP) and des-γ-carboxy prothrombin (DCP) and tumor volume (TV), called ADV score, has been shown to be prognostic of hepatocellular carcinoma (HCC) recurrence following hepatic resection (HR) or liver transplantation.

METHODS

This multicenter, multinational validation study included 9200 patients who underwent HR from 2010 to 2017 at 10 Korean and 73 Japanese centers, and were followed up until 2020.

RESULTS

AFP, DCP, and TV showed weak correlations (ρ ≤ .463, r ≤ .189, p < .001). Disease-free survival (DFS), overall survival (OS), and post-recurrence survival rates were dependent on 1.0 log and 2.0 log intervals of ADV scores (p < .001). Receiver operating characteristic (ROC) curve analysis showed that ADV score cutoffs of 5.0 log for DFS and OS yielded the areas under the curve ≥ .577, with both being significantly prognostic of tumor recurrence and patient mortality at 3 years. ADV score cutoffs of ADV 4.0 log and 8.0 log, derived through K-adaptive partitioning method, showed higher prognostic contrasts in DFS and OS. ROC curve analysis showed that an ADV score cutoff of 4.2 log was suggestive of microvascular invasion, with both microvascular invasion and an ADV score cutoff of 4.2 log showing similar DFS rates.

CONCLUSIONS

This international validation study demonstrated that ADV score is an integrated surrogate biomarker for post-resection prognosis of HCC. Prognostic prediction using ADV score can provide reliable information that can assist in planning treatment of patients with different stages of HCC and guide individualized post-resection follow-up based on the relative risk of HCC recurrence.

CASANOVA: Permutation inference in factorial survival designs

We propose inference procedures for general factorial designs with time-to-event endpoints. Similar to additive Aalen models, null hypotheses are formulated in terms of cumulative hazards. Deviations are measured in terms of quadratic forms in Nelson-Aalen-type integrals. Different from existing approaches, this allows to work without restrictive model assumptions as proportional hazards. In particular, crossing survival or hazard curves can be detected without a significant loss of power. For a distribution-free application of the method, a permutation strategy is suggested. The resulting procedures' asymptotic validity is proven and small sample performances are analyzed in extensive simulations. The analysis of a data set on asthma illustrates the applicability.

A simulation-free group sequential design with max-combo tests in the presence of non-proportional hazards

Non-proportional hazards (NPH) have been observed in many immuno-oncology clinical trials. Weighted log-rank tests (WLRT) with suitable weights can be used to improve the power of detecting the difference between survival curves in the presence of NPH. However, it is not easy to choose a proper WLRT in practice. A versatile max-combo test was proposed to achieve the balance of robustness and efficiency, and has received increasing attention recently. Survival trials often warrant interim analyses due to their high cost and long durations. The integration and implementation of max-combo tests in interim analyses often require extensive simulation studies. In this report, we propose a simulation-free approach for group sequential designs with the max-combo test in survival trials. The simulation results support that the proposed method can successfully control the type I error rate and offer excellent accuracy and flexibility in estimating sample sizes, with light computation burden. Notably, our method displays strong robustness towards various model misspecifications and has been implemented in an R package.