Incompletely Observed Nonparametric Factorial Designs With Repeated Measurements: A Wild Bootstrap Approach.

In many life science experiments or medical studies, subjects are repeatedly observed and measurements are collected in factorial designs with multivariate data. The analysis of such multivariate data is typically based on multivariate analysis of variance (MANOVA) or mixed models, requiring complete data, and certain assumption on the underlying parametric distribution such as continuity or a specific covariance structure, for example, compound symmetry. However, these methods are usually not applicable when discrete data or even ordered categorical data are present.

Vision-related quality of life among patients with keratoconus: a cross sectional study.

Purpose: The present study aimed to evaluate the quality of life of keratoconus patients residing in Nablus city, Palestine. Furthermore, we investigated the possible correlation between Pentacam topographic indices and the quality of life of patients with keratoconus.

Methods: This cross-sectional study evaluated keratoconus patients at An-Najah University Hospital in 2019, diagnosed through clinical examination and corneal tomography.

Asymptotic-based bootstrap approach for matched pairs with missingness in a single arm.

The issue of missing values is an arising difficulty when dealing with paired data. Several test procedures are developed in the literature to tackle this problem. Some of them are even robust under deviations and control type-I error quite accurately.

A cautionary tale on using imputation methods for inference in matched-pairs design.

Motivation: Imputation procedures in biomedical fields have turned into statistical practice, since further analyses can be conducted ignoring the former presence of missing values. In particular, non-parametric imputation schemes like the random forest have shown favorable imputation performance compared to the more traditionally used MICE procedure. However, their effect on valid statistical inference has not been analyzed so far.

Multiplication-combination tests for incomplete paired data.

We consider statistical procedures for hypothesis testing of real valued functionals of matched pairs with missing values. In order to improve the accuracy of existing methods, we propose a novel multiplication combination procedure. Dividing the observed data into dependent (completely observed) pairs and independent (incompletely observed) components, it is based on combining separate results of adequate tests for the two sub data sets.