An alternative approach to the analysis of animal carcinogenicity studies

Non-inferiority trials with time-to-event data: clarifying the impact of censoring

In non-inferiority (NI) trials with time-to-event data, different types and patterns of censoring may occur, but their impact on trial results is not entirely clear. We investigated the influence of informative and non-informative censoring by conducting extensive simulation studies under the assumption that the NI margin is defined as a maximum acceptable hazard ratio and scenarios typically observed in recent NI trials. We found that while non-informative censoring tends to only affect the power, informative censoring can impact the treatment effect estimates, type I error rate, and power. The magnitude of these effects depends on the between-group differences in the failure and informative censoring risks, as well as the correlation between censoring and failure times, among other factors. The adverse impact of informative censoring was generally decreased with larger NI margins.

Non-parametric assessment of non-inferiority with censored data

We suggest non-parametric tests for showing non-inferiority of a new treatment compared to a standard therapy when data are censored. To this end the difference and the odds ratio curves of the entire survivor functions over a certain time period are considered. Two asymptotic approaches for solving these testing problems are investigated, which are based on bootstrap approximations. The performance of the test procedures is investigated in a simulation study, and some guidance on which test to use in specific situations is derived. The proposed methods are applied to a trial in which two thrombolytic agents for the treatment on acute myocardial infarction were compared, and to a study on irradiation therapies for advanced non-small-cell lung cancer. Non-inferiority over a large time period of the study can be shown in both cases.

Methods for Assessing Noninferiority with Censored Data

In this paper we present the existing approaches to the problem of showing noninferiority with randomly right censored data. The main focus is on the choice of the discrepancy measure which is used to define the deviation from the classical null hypothesis, i.e. the noninferiority margin. Most methods are based on certain parametric or semiparametric assumptions. In contrast, a new, completely nonparametric approach is suggested and discussed.

Identifying the maximum safe dose: a multiple testing approach

Identifying the maximum safe dose (MAXSD) is an objective of both randomized clinical dose-finding studies for the safety endpoint and toxicological studies. MAXSD is defined as the highest experimental dose with no significant increased safety effect relative to the placebo or control group. In safety assessment, the primary control of the false-negative error rate is more important than that of the false-positive rate. Therefore, we propose a multiple testing procedure for equivalence in the many-to-one design with a priori ordered contrasts (shifted control vs. dose), where the acceptable risk delta is defined in advance. Tests for shifted and ratio hypotheses are presented and discussed.