Regression for skewed biomarker outcomes subject to pooling

Semiparametric regression models for a right-skewed outcome subject to pooling.

Pooling specimens prior to performing laboratory assays has various benefits. Pooling can help to reduce cost, preserve irreplaceable specimens, meet minimal volume requirements for certain lab tests, and even reduce information loss when a limit of detection is present. Regardless of the motivation for pooling, appropriate analytical techniques must be applied in order to obtain valid inferenc...

Logistic regression analysis of biomarker data subject to pooling and dichotomization.

There is growing interest in pooling specimens across subjects in epidemiologic studies, especially those involving biomarkers. This paper is concerned with regression analysis of epidemiologic data where a binary exposure is subject to pooling and the pooled measurement is dichotomized to indicate either that no subjects in the pool are exposed or that some are exposed, without revealing furth...

Multiple Imputation and Quantile Regression Methods for Biomarker Data Subject to Detection Limits

Nonparametric Multivariate Regression Subject to Constraint

We review Hildreth's algorithm for computing the least squares regression subject to inequality constraints and Dykstra's generalization. We provide a geometric proof of convergence and several ehancements to the algorithm and generalize the application of the algorithm from convex cones to convex sets.

Generalized modeling approaches to risk adjustment of skewed outcomes data.

There are two broad classes of models used to address the econometric problems caused by skewness in data commonly encountered in health care applications: (1) transformation to deal with skewness (e.g., ordinary least square (OLS) on ln(y)); and (2) alternative weighting approaches based on exponential conditional models (ECM) and generalized linear model (GLM) approaches. In this paper, we en...