Multivariate multidistance tests for high-dimensional low sample size case-control studies

Use of multivariate distance measures for high-dimensional data in tests for difference, superiority, equivalence and non-inferiority

Tests based on pairwise distance measures for multivariate sample vectors are common in ecological studies but are usually restricted to two-sided tests for differences. In this paper, we investigate extensions to tests for superiority, equivalence and non-inferiority.

Small sample sizes: A big data problem in high-dimensional data analysis

In many experiments and especially in translational and preclinical research, sample sizes are (very) small. In addition, data designs are often high dimensional, i.e. more dependent than independent replications of the trial are observed. The present paper discusses the applicability of max t-test-type statistics (multiple contrast tests) in high-dimensional designs (repeated measures or multivariate) with small sample sizes. A randomization-based approach is developed to approximate the distribution of the maximum statistic. Extensive simulation studies confirm that the new method is particularly suitable for analyzing data sets with small sample sizes. A real data set illustrates the application of the methods.

Systemic Chemotherapy for Metastatic Colitis-Associated Cancer Has a Worse Outcome Than Sporadic Colorectal Cancer: Matched Case Cohort Analysis

BACKGROUND

Colitis-associated cancers (CAC) are a catastrophic complication of inflammatory bowel disease; at diagnosis, CAC is frequently at an advanced stage. Although the genomic alterations (GA) in CAC are different from sporadic colorectal cancer (CRC), the same systemic therapies are used. We compared clinically relevant outcomes using standard care systemic chemotherapy of stage IV CAC versus a matched patient control cohort of stage IV CRC patients.

PATIENTS AND METHODS

A retrospective matched cohort design was used. Eighteen cases of stage IV CAC (7 ulcerative colitis, 11 Crohn disease) and 18 CRC were identified. GA analysis was available for all patients. Outcome endpoints included response rate and response duration, progression-free survival, and OS.

RESULTS

Although the response rates were similar (CAC 35.7% vs. CRC 57.1%, P = .45), the median duration of response for CAC was significantly shorter (1.4 months, vs. CRC 11.8 months, P = .006). There was no difference in dose density of first-line therapy between cohorts, suggesting that shorter response duration was due to more rapid development of chemotherapy resistance. Median OS was significantly shorter for CAC patients (13 vs. 27.6 months, P = .034). As expected, there was a difference in the spectrum of GA between CAC and CRC cohorts. However, GA associated with poor prognosis (eg, B-Raf) were no more frequent in the CAC cohort.

CONCLUSION

Clinically meaningful outcomes of duration of response and OS are worse for CAC versus sporadic CRC patients treated with FOLFOX or FOLFIRI as first therapy for metastatic disease.

Using simulation studies to evaluate statistical methods

Simulation studies are computer experiments that involve creating data by pseudo-random sampling. A key strength of simulation studies is the ability to understand the behavior of statistical methods because some "truth" (usually some parameter/s of interest) is known from the process of generating the data. This allows us to consider properties of methods, such as bias. While widely used, simulation studies are often poorly designed, analyzed, and reported. This tutorial outlines the rationale for using simulation studies and offers guidance for design, execution, analysis, reporting, and presentation. In particular, this tutorial provides a structured approach for planning and reporting simulation studies, which involves defining aims, data-generating mechanisms, estimands, methods, and performance measures ("ADEMP"); coherent terminology for simulation studies; guidance on coding simulation studies; a critical discussion of key performance measures and their estimation; guidance on structuring tabular and graphical presentation of results; and new graphical presentations. With a view to describing recent practice, we review 100 articles taken from Volume 34 of Statistics in Medicine, which included at least one simulation study and identify areas for improvement.

AGMT3-D: A software for 3-D landmarks-based geometric morphometric shape analysis of archaeological artifacts

We present here a newly developed software package named Artifact GeoMorph Toolbox 3-D (AGMT3-D). It is intended to provide archaeologists with a simple and easy-to-use tool for performing 3-D landmarks-based geometric morphometric shape analysis on 3-D digital models of archaeological artifacts. It requires no prior knowledge of programming or proficiency in statistics. AGMT3-D consists of a data-acquisition procedure for automatically positioning 3-D models in space and fitting them with grids of 3-D semi-landmarks. It also provides a number of analytical tools and procedures that allow the processing and statistical analysis of the data, including generalized Procrustes analysis, principal component analysis, a warp tool, automatic calculation of shape variabilities and statistical tests. It provides an output of quantitative, objective and reproducible results in numerical, textual and graphic formats. These can be used to answer archaeologically significant questions relating to morphologies and morphological variabilities in artifact assemblages. Following the presentation of the software and its functions, we apply it to a case study addressing the effects of different types of raw material on the morphologies and morphological variabilities present in an experimentally produced Acheulian handaxe assemblage. The results show that there are statistically significant differences between the mean shapes and shape variabilities of handaxes produced on flint and those produced on basalt. With AGMT3-D, users can analyze artifact assemblages and address questions that are deducible from the morphologies and morphological variabilities of material culture assemblages. These questions can relate to issues of, among others, relative chronology, cultural affinities, tool function and production technology. AGMT3-D is aimed at making 3-D landmarks-based geometric morphometric shape analysis more accessible to archaeologists, in the hope that this method will become a tool commonly used by archaeologists.

Tests for comparison of multiple endpoints with application to omics data

In biomedical research, multiple endpoints are commonly analyzed in "omics" fields like genomics, proteomics and metabolomics. Traditional methods designed for low-dimensional data either perform poorly or are not applicable when analyzing high-dimensional data whose dimension is generally similar to, or even much larger than, the number of subjects. The complex biochemical interplay between hundreds (or thousands) of endpoints is reflected by complex dependence relations. The aim of the paper is to propose tests that are very suitable for analyzing omics data because they do not require the normality assumption, are powerful also for small sample sizes, in the presence of complex dependence relations among endpoints, and when the number of endpoints is much larger than the number of subjects. Unbiasedness and consistency of the tests are proved and their size and power are assessed numerically. It is shown that the proposed approach based on the nonparametric combination of dependent interpoint distance tests is very effective. Applications to genomics and metabolomics are discussed.

A simulation based method for assessing the statistical significance of logistic regression models after common variable selection procedures

Classification models can demonstrate apparent prediction accuracy even when there is no underlying relationship between the predictors and the response. Variable selection procedures can lead to false positive variable selections and overestimation of true model performance. A simulation study was conducted using logistic regression with forward stepwise, best subsets, and LASSO variable selection methods with varying total sample sizes (20, 50, 100, 200) and numbers of random noise predictor variables (3, 5, 10, 15, 20, 50). Using our critical values can help reduce needless follow-up on variables having no true association with the outcome.

Does bad inference drive out good?

The (mis)use of statistics in practice is widely debated, and a field where the debate is particularly active is medicine. Many scholars emphasize that a large proportion of published medical research contains statistical errors. It has been noted that top class journals like Nature Medicine and The New England Journal of Medicine publish a considerable proportion of papers that contain statistical errors and poorly document the application of statistical methods. This paper joins the debate on the (mis)use of statistics in the medical literature. Even though the validation process of a statistical result may be quite elusive, a careful assessment of underlying assumptions is central in medicine as well as in other fields where a statistical method is applied. Unfortunately, a careful assessment of underlying assumptions is missing in many papers, including those published in top class journals. In this paper, it is shown that nonparametric methods are good alternatives to parametric methods when the assumptions for the latter ones are not satisfied. A key point to solve the problem of the misuse of statistics in the medical literature is that all journals have their own statisticians to review the statistical method/analysis section in each submitted paper.