An improved two-stage procedure to compare hazard curves

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.

Benefits of combination therapy with immune checkpoint inhibitors and predictive role of tumour mutation burden in hepatocellular carcinoma: A systematic review and meta-analysis

OBJECTIVES

To investigate the clinical benefits of combination therapy with immune checkpoint inhibitors (ICIs) and best combination regimen for people with advanced hepatocellular carcinoma (HCC) and to explore the predictive performance of tumour mutation burden (TMB).

METHODS

We conducted a systematic literature search to identify clinical trials. Meta-analysis and subgroup analyses were performed to estimate the benefits of combination regimens with PD-1/PD-L1 inhibitors for patients with advanced HCC and compare the effectiveness of PD-1/PD-L1 inhibitors and sorafenib as first-line therapy. Individualized analysis and Kaplan-Meier were used to assess the prognostic value of TMB.

RESULTS

A total of 29 studies with 5396 patients were included. ICIs' combination therapy had higher ORR (26 % vs 15 %) and DCR (73 % vs 55 %), longer PFS (5.5 vs 3.1 months) and OS (15.9 vs 12.6 months) compared to monotherapy. Anti-PD-1/PD-L1 agents provided improved ORR, DCR, PFS and OS compared to sorafenib. The overall ORs of ORR and DCR in subgroup analysis were 3.49 (95 % CI 2.36-5.17, p < 0.01) and 1.60 (95 % CI 1.15-2.21, p < 0.01). The overall HRs of PFS and OS were 0.68 (95 % CI 0.48-0.96, p = 0.03) and 0.73 (95 % CI 0.62-0.85, p < 0.01). PD-1/PD-L1 inhibitors plus anti-VEGF agents had an advantage in DCR (0.80 vs 0.48, meta-regression =  - 0.32, P < 0.001), but an equal ORR (0.29 vs 0.26) compared to dual immune checkpoint inhibitors. The total OS in Dua-ICIs were 16.5 months (95 % CI 14.2-18.7), yet not reached in the major studies of ICI plus anti-VEGF regimen. In individualized analysis, the 1-year OS was superior for patients who had high-TMB (>10, mutations/Mb) than moderate-TMB (1-10, mutations/Mb; 28 % vs 15 %, P = 0.025).

CONCLUSION

Immune checkpoint inhibitors' combination therapy improved clinical outcomes in the management of advanced hepatocellular carcinoma. However, the overall objective response rate still did not exceed 30%. PD-1/PD-L1 inhibitors plus anti-angiogenic agents and dual immunotherapy provided significantly increased survival over sorafenib, which also pose new challenges for future research, and more appropriate and guided control regimens are required. Also, TMB may be a promising prognostic biomarker for immunotherapy in HCC. However, the validation of prospective and large sample studies is needed.

Robust tests for combining p-values under arbitrary dependency structures

Recently Liu and Xie proposed a p-value combination test based on the Cauchy distribution (CCT). They showed that when the significance levels are small, CCT can control type I error rate and the resulting p-value can be simply approximated using a Cauchy distribution. One very special and attractive property of CCT is that it is applicable to situations where the p-values to be combined are dependent. However, in this paper, we show that under some conditions the commonly used MinP test is much more powerful than CCT. In addition, under some other situations, CCT is powerless at all. Therefore, we should use CCT with caution. We also proposed new robust p-value combination tests using a second MinP/CCT to combine the dependent p-values obtained from CCT and MinP applied to the original p-values. We call the new tests MinP-CCT-MinP (MCM) and CCT-MinP-CCT (CMC). We study the performance of the new tests by comparing them with CCT and MinP using comprehensive simulation study. Our study shows that the proposed tests, MCM and CMC, are robust and powerful under many conditions, and can be considered as alternatives of CCT or MinP.

Which test for crossing survival curves? A user’s guideline

Background

The exchange of knowledge between statisticians developing new methodology and clinicians, reviewers or authors applying them is fundamental. This is specifically true for clinical trials with time-to-event endpoints. Thereby, one of the most commonly arising questions is that of equal survival distributions in two-armed trial. The log-rank test is still the gold-standard to infer this question. However, in case of non-proportional hazards, its power can become poor and multiple extensions have been developed to overcome this issue. We aim to facilitate the choice of a test for the detection of survival differences in the case of crossing hazards.

Methods

We restricted the review to the most recent two-armed clinical oncology trials with crossing survival curves. Each data set was reconstructed using a state-of-the-art reconstruction algorithm. To ensure reproduction quality, only publications with published number at risk at multiple time points, sufficient printing quality and a non-informative censoring pattern were included. This article depicts the p-values of the log-rank and Peto-Peto test as references and compares them with nine different tests developed for detection of survival differences in the presence of non-proportional or crossing hazards.

Results

We reviewed 1400 recent phase III clinical oncology trials and selected fifteen studies that met our eligibility criteria for data reconstruction. After including further three individual patient data sets, for nine out of eighteen studies significant differences in survival were found using the investigated tests. An important point that reviewers should pay attention to is that 28% of the studies with published survival curves did not report the number at risk. This makes reconstruction and plausibility checks almost impossible.

Conclusions

The evaluation shows that inference methods constructed to detect differences in survival in presence of non-proportional hazards are beneficial and help to provide guidance in choosing a sensible alternative to the standard log-rank test.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-022-01520-0.

Non-parametric treatment time-lag effect estimation

In general, the change point problem considers inference of a change in distribution for a set of time-ordered observations. This has applications in a large variety of fields, and can also apply to survival data. In survival analysis, most existing methods compare two treatment groups for the entirety of the study period. Some treatments may take a length of time to show effects in subjects. This has been called the time-lag effect in the literature, and in cases where time-lag effect is considerable, such methods may not be appropriate to detect significant differences between two groups. In this paper, we propose a novel non-parametric approach for estimating the point of treatment time-lag effect by using an empirical divergence measure. Theoretical properties of the estimator are studied. The results from the simulated data and the applications to real data examples support our proposed method.

A genetic association test through combining two independent tests

Gene- and pathway-based variant association tests are important tools in finding genetic variants that are associated with phenotypes of interest. Although some methods have been proposed in the literature, powerful and robust statistical tests are still desirable in this area. In this study, we propose a statistical test based on decomposing the genotype data into orthogonal parts from which powerful and robust independent p-value combination approaches can be utilized. Through a comprehensive simulation study, we compare the proposed test with some existing popular ones. Our simulation results show that the new test has great performance in terms of controlling type I error rate and statistical power. Real data applications are also conducted to illustrate the performance and usefulness of the proposed test.

A gene-based test of association through an orthogonal decomposition of genotype scores

The burden test and the sequence kernel association test (SKAT) are two popular methods for detecting association with rare variants. Treated as two different sources of association information, they are adaptively combined to form an optimal SKAT (SKAT-O) method for optimal power. We show that the burden test is part of rather than independent of the SKAT. We introduce a new test statistic that is the sum of the burden statistic and a statistic asymptotically independent of the burden statistic. The performance of this new test statistic is demonstrated through extensive simulation studies and applications to a Genetic Analysis Workshop 17 data set and the Ocular Hypertension Treatment Study data.