On the Fleming–Harrington test for late effects in prevention randomized controlled trials

The Inverse Log-Rank Test: A Versatile Procedure for Late Separating Survival Curves

Often in the planning phase of a clinical trial, a researcher will need to choose between a standard versus weighted log-rank test (LRT) for investigating right-censored survival data. While a standard LRT is optimal for analyzing evenly distributed but distinct survival events (proportional hazards), an appropriately weighted LRT test may be better suited for handling non-proportional, delayed treatment effects. The "a priori" misspecification of this alternative may result in a substantial loss of power when determining the effectiveness of an experimental drug. In this paper, the standard unweighted and inverse log-rank tests (iLRTs) are compared with the multiple weight, default Max-Combo procedure for analyzing differential late survival outcomes. Unlike combination LRTs that depend on the arbitrary selection of weights, the iLRT by definition is a single weight test and does not require implicit multiplicity correction. Empirically, both weighted methods have reasonable flexibility for assessing continuous survival curve differences from the onset of a study. However, the iLRT may be preferable for accommodating delayed separating survival curves, especially when one arm finishes first. Using standard large-sample methods, the power and sample size for the iLRT are easily estimated without resorting to complex and timely simulations.

Combination transarterial chemoembolization and microwave ablation vs. microwave ablation monotherapy for hepatocellular carcinomas greater than 3 cm: a comparative study

PURPOSE

To evaluate the efficacy of combination therapy using transarterial chemoembolization with microwave ablation (MWA) therapy vs. MWA monotherapy for hepatocellular carcinomas (HCCs) >3 cm in size.

METHODS

This two-arm retrospective observational study included patients with HCCs >3 cm who underwent either combination therapy (29 patients) or MWA monotherapy (35 patients) between 2014 and 2020. The treatment outcomes related to primary treatment efficacy, local tumor progression (LTP), tumor control rate, and overall survival were compared between each cohort.

RESULTS

The technical success and primary efficacy were 96.56% and 100.00% in the combination therapy cohort, and 91.42% and 100.00% in the MWA cohort, respectively, over a mean follow-up period of 27.6 months. The 1- and 3-year rates of LTP-free survival were 78.57% and 69.56% in the combination therapy cohort, vs. 72.45% and 35.44% in the MWA cohort, respectively (P = 0.001). The overall progression-free survival was longer in the combination therapy cohort compared with the MWA cohort (median: 56.0 vs. 13.0 months; P = 0.017). With the incorporation of additional locoregional therapy, the overall survival rates were not significantly different, with 1- and 3-year overall survival rates of 100.00% and 88.71% in the combination therapy cohort and rates of 90.15% and 82.76% in the MWA cohort, respectively (P = 0.235).

CONCLUSION

The combination therapy provided significantly longer upfront LTP-free survival in HCCs >3 cm when compared with the MWA treatment alone, albeit with similar local tumor control and overall survival rates when accounting for additional locoregional therapies.

Determining the late effect parameter in the Fleming-Harrington test using asymptotic relative efficiency in cancer immunotherapy clinical trials

The delayed treatment effect, which manifests as a separation of survival curves after a change point, has often been observed in immunotherapy clinical trials. A late effect of this kind may violate the proportional hazards assumption, resulting in the non-negligible loss of statistical power of an ordinary log-rank test when comparing survival curves. The Fleming-Harrington (FH) test, a weighted log-rank test, is configured to mitigate the loss of power by incorporating a weight function with two parameters, one each for early and late treatment effects. The two parameters need to be appropriately determined, but no helpful guides have been fully established. Since the late effect is expected in immunotherapy trials, we focus on the late effect parameter in this study. We consider parameterizing the late effect in a readily interpretable fashion and determining the optimal late effect parameter in the FH test to maintain statistical power in reference to the asymptotic relative efficiency (ARE). The optimization is carried out under three lag models (i.e. linear, threshold, and generalized linear lag), where the optimal weights are proportional to the lag functions characterized by the change points. Extensive simulation studies showed that the FH test with the selected late parameter reliably provided sufficient power even when the change points in the lag models were misspecified. This finding suggests that the FH test with the ARE-guided late parameter may be a reasonable and practical choice for the primary analysis in immunotherapy clinical trials.

New Test for the Comparison of Survival Curves to Detect Late Differences

Background. Survival analysis attracted the attention of different scientists from various domains such as engineering, health, and social sciences. It has been widely exploited in clinical trials when comparing different treatments looking at their survival probabilities. Kaplan–Meier curves plotted from the Kaplan–Meier estimates of survival probabilities are used to depict the general image for such situations. Methods. The weighted log-rank test has been dealt with by suggesting different weight functions which give specific strength in specific situations. In this work, we proposed a new weight function comprising all numbers at risk, i.e., the overall number at risk and the separate numbers at risk in the groups under study, to detect late differences between survival curves. Results. The new test has been found to be a good alternative after the FH (0, 1) test in detecting late differences, and it outperformed all tests in case of small samples and heavy censoring rates according to the simulation studies. The new test kept the same strength when applied to real data where it showed itself to be among the powerful ones or even outperforms all other tests under consideration. Conclusion. As the new test stays stronger in the case of small samples and heavy censoring rates, it may be a better choice whenever targeting the detection of late differences between the survival curves.