K-sample omnibus non-proportional hazards tests based on right-censored data

  title={K-sample omnibus non-proportional hazards tests based on right-censored data},
  author={Malka Gorfine and Matan Schlesinger and Li Hsu},
  journal={Statistical Methods in Medical Research},
  pages={2830 - 2850}
This work presents novel and powerful tests for comparing non-proportional hazard functions, based on sample–space partitions. Right censoring introduces two major difficulties, which make the existing sample–space partition tests for uncensored data non-applicable: (i) the actual event times of censored observations are unknown and (ii) the standard permutation procedure is invalid in case the censoring distributions of the groups are unequal. We overcome these two obstacles, introduce… 

Inferring median survival differences in general factorial designs via permutation tests

Factorial survival designs with right-censored observations are commonly inferred by Cox regression and explained by means of hazard ratios. However, in case of non-proportional hazards, their

Permutation inference in factorial survival designs with the CASANOVA

We propose inference procedures for general nonparametric factorial survival designs with possibly right-censored data. Similar to additive Aalen models, null hypotheses are formulated in terms of

A Multiple kernel testing procedure for non-proportional hazards in factorial designs

In this paper we propose a Multiple kernel testing procedure to infer survival data when several factors (e.g. different treatment groups, gender, medical history) and their interaction are of

CauchyCP: A powerful test under non-proportional hazards using Cauchy combination of change-point Cox regressions

This work proposes CauchyCP, an omnibus test of change-point Cox regression models, to overcome both challenges while detecting signals of non-proportional hazards patterns and is more computationally efficient than popular methods like MaxCombo for large-scale data analysis.

A sensitivity analysis approach for the causal hazard ratio in randomized and observational studies

The Hazard Ratio (HR) is often reported as the main causal effect when studying survival data. Despite its popularity, the HR suffers from an unclear causal interpretation. As already pointed out in

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

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.

The Cauchy Combination Test under Arbitrary Dependence Structures

This test is revisited to show that (i) the tail probability of the CCT can be approximated just as well when more relaxed assumptions are imposed on individual p-values compared to those of the original test statistics; and (ii) such assumptions are satisfied by six popular copula distributions.

Cauchy Combination Test for Sparse Signals

Aggregating multiple effects is often encountered in large-scale data analysis where the fraction of significant effects is generally small. Many existing methods cannot handle it effectively because



Testing and interval estimation for two-sample survival comparisons with small sample sizes and unequal censoring.

2 methods for testing and interval estimation, for use with small samples and possibly unequal censoring, based on first imputing survival and censoring times and then applying permutation methods are developed.

Weighted Kaplan-Meier statistics: a class of distance tests for censored survival data.

Results from small-sample simulation studies indicate that these statistics compare favorably with the log-rank procedure even under the proportional hazards alternative, and may perform better than it under the crossing hazards alternative.

A versatile test for equality of two survival functions based on weighted differences of Kaplan–Meier curves

The results from extensive numerical studies demonstrate that the new procedure performs well under various general alternatives with a caution of a minor inflation of the type I error rate when the sample size is small or the number of observed events is small.

Checking the Cox model with cumulative sums of martingale-based residuals

SUMMARY This paper presents a new class of graphical and numerical methods for checking the adequacy of the Cox regression model. The procedures are derived from cumulative sums of martingale-based

Alternative Analysis Methods for Time to Event Endpoints Under Nonproportional Hazards: A Comparative Analysis

In the absence of prior knowledge regarding the underlying or non-PH patterns, the MaxCombo test is relatively robust across patterns, and multiple measures of the treatment effect should be prespecified as sensitivity analyses to describe the totality of the data.

Weighted Kaplan‐Meier Statistics: Large Sample and Optimality Considerations

SUMMARY We propose a cumulative weighted difference in the Kaplan-Meier estimates as a test statistic for equality of distributions in the two-sample censored data survival analysis problem. For

An extension of the Anderson–Darling k-sample test to arbitrary sample space partition sizes

In this paper we first show that the k-sample Anderson–Darling test is basically an average of Pearson statistics in 2 × k contingency tables that are induced by observation-based partitions of the

K-Sample Anderson–Darling Tests

Abstract Two k-sample versions of an Anderson–Darling rank statistic are proposed for testing the homogeneity of samples. Their asymptotic null distributions are derived for the continuous as well as

Interim monitoring using the adaptively weighted log‐rank test in clinical trials for survival outcomes

  • Song Yang
  • Mathematics
    Statistics in medicine
  • 2019
Simulation studies show that the new method improves the log-rank test for a wide range of treatment effect patterns, and that the resampling approach yields better control of the type 1 error rate than the originally proposed approach in the work of Yang and Prentice.