Ordinal scale responses have always been popular in the biomedical, educational, and social science fields of study, but more recently the use of statistical methods tailored to the characteristics of ordinal responses have begun to gain in popularity. While many models have been proposed which allow the use of the ordering without treating the data as quantitative, little has been written abou...

Word knowledge is often partial, rather than all-or-none. In this paper, we describe a method for estimating partial word knowledge on a trial-by-trial basis. Users generate a free-form synonym for a newly learned word. We then apply a probabilistic regression model that combines features based on Latent Semantic Analysis (LSA) with features derived from a large-scale, multi-relation word graph...

A didactic on latent growth curve modeling for ordinal outcomes is presented. The conceptual aspects of modeling growth with ordinal variables and the notion of threshold invariance are illustrated graphically using a hypothetical example. The ordinal growth model is described in terms of 3 nested models: (a) multivariate normality of the underlying continuous latent variables (yt) and its rela...

Advancing technology has enabled us to study the molecular configuration of single cells or whole tissue samples. Molecular biology produces vast amounts of high-dimensional omics data at continually decreasing costs, so that molecular screens are increasingly often used in clinical applications. Personalized diagnosis or prediction of clinical treatment outcome based on high-throughput omics d...

This paper considers the problem of parameter estimation in a model for a continuous response variable y when an important ordinal explanatory variable x is missing for a large proportion of the sample. Nonmissingness of x, or sample selection, is correlated with the response variable and/or with the unobserved values the ordinal explanatory variable takes when missing. We suggest solving the e...

©FSRH J Fam Plann Reprod Health Care 2008: 34(3) What is it? When a response variable has only two possible values (e.g. recurrence/not), binary logistic regression is commonly used to test or model the association between that response and a number of potential explanatory variables, with each association estimated in terms of an odds ratio (OR). Multinomial logistic regression is an extension...

The co-twin control design has been widely used in studying the effects of environmental factors on the development of diseases. For binary outcomes that arise from co-twin control studies, the conditional likelihood method is commonly used. This approach, however, does not readily extend to ordinal response data because the standard conditional likelihood does not exist for cumulative logit or...

High-throughput genomic assays are performed using tissue samples with the goal of classifying the samples as normal < pre-malignant < malignant or by stage of cancer using a small set of molecular features. In such cases, molecular features monotonically associated with the ordinal response may be important to disease development; that is, an increase in the phenotypic level (stage of cancer) ...

The article proposes a unified least squares method to estimate the receiver operating characteristic (ROC) parameters for continuous and ordinal diagnostic tests, such as cancer biomarkers. The method is based on a linear model framework using the empirically estimated sensitivities and specificities as input "data." It gives consistent estimates for regression and accuracy parameters when the...