Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).
Testing for sufficient follow-up in survival data with immunes
Tsz Pang Yuen, Eni Musta
University of Amsterdam, Netherlands, The
In order to estimate the proportion of 'immune' or 'cured' subjects who will never experience failure, a sufficiently long follow-up period is required. Several statistical tests have been proposed in the literature for assessing the assumption of sufficient follow-up. However, they have not been satisfactory for practical purposes due to their conservative behaviour or underlying parametric assumptions. A novel method is proposed for testing sufficient follow-up under general nonparametric assumptions. This approach differs from existing methods. The hypotheses are formulated in a broader context, eliminating the requirement for event times of interest to have compact support. Instead, the notion of sufficient follow-up is characterized by the quantiles of the distribution. The underlying assumption for the proposed method is that the event times have a non-increasing density function, which can also be relaxed to an unimodal density. The test is based on a shape-constrained density estimator such as the Grenander or the kernel-smoothed Grenander estimator. The performance of the test is investigated through a simulation study, and the method is illustrated on data from cancer clinical trials.
9:25am - 9:50am
Combining profile likelihood with Bayesian estimation for Crow-AMSAA process
Marek Skarupski1,2
1Eindhoven University of Technology, The Netherlands; 2Wrocław University of Science and Technology, Poland
The shape parameter in the Crow-AMSAA model is, in practice, more interpretable than the scale parameter. Hence, incorporating initial knowledge in the estimation seems more necessary for it than for the scale parameter. In this talk we will show the application of the profile likelihood estimation method to Crow-AMSAA and the combination of this method with Bayesian estimation. We show the possibility of using double-truncated gamma distribution. As part of the numerical investigation, we present an analysis of the sensitivity of posterior inference to the incorrect selection of the hyperparameters of the prior distributions.
9:50am - 10:15am
Optimizing the allocation of trials to sub-regions in multi-environment crop variety testing for the case of correlated genotype effects
Maryna Prus
University of Hohenheim, Germany
New crop varieties are extensively tested in multi-environment trials in order to obtain a solid basis for recommendations to farmers. When the target population of environments is large, a division into sub-regions is often advantageous. If the same set of genotypes is tested in each of the sub-regions, a linear mixed model (LMM) may be fitted with random genotype-within-sub-region effects.
The first analytical results to optimizing allocation of trials to sub-regions have been obtained in Prus and Piepho (2021). In that paper the genotype effects are assumed to be uncorrelated. However, this assumption is not always suitable for practical situations. In praxis, genetic markers are often used in plant breeding for determining genetic relationships of genotypes, which helps to model their correlation.
In this work a more general LMM with correlated genotype effects is considered. An analytical solution and a computational approach are proposed for optimal allocation of trials.
Prus, M. and Piepho, H.-P. (2021). Optimizing the allocation of trials to sub-regions in multi-environment crop variety testing. Journal of Agricultural, Biological and Environmental Statistics, 26, 267–288.
