Title: Semiparametric regression models for indirectly observed outcomes
Authors: Jan De Neve - Ghent University (Belgium) [presenting]
Abstract: In several applications the outcome of interest is not measured directly, but instead a proxy is used. Examples include the body mass index as a proxy for body fat percentage, fluorescence intensity as a proxy for gene expression and the proportion of words correctly recalled as a proxy for the information stored in the memory. We illustrate by examples that the relationship between the outcome of interest and the proxy can be non-linear. The majority of the available methods, however, typically assume that this relationship is linear. Via simulations we illustrate how deviations from linearity can have a substantial impact on the validity of these procedures. We therefore present a semiparametric regression strategy to quantify the effect of covariates on a summary measure of the unobserved outcome, this without assuming linearity. We use the probabilistic index as a summary measure, i.e. the probability that the outcome of one subject exceeds the outcome of another subject, conditional on covariates. Since this effect measure is invariant under monotone transformations, we do not need to model the relationship between the unobserved outcome and the proxy. The estimation strategy makes use of semiparametric linear transformation models which enables us to use existing software packages for data analysis.