Logistic regression and latent class models for estimating positivities in diagnostic assays with poor resolution |
| |
Authors: | Tom Smith Penelope Vounatsou |
| |
Affiliation: | 1. Dept. Public Health &2. Epidemiology , Swiss Tropical Institute Postfach , Basel, CH, 4002, Switzerland |
| |
Abstract: | In biomedical research and diagnostic practice it is common to classify objects dichotomously based on continuous observations (x) measuring some form of biological activity, where some proportion of the objects have a level of activity above background. In this paper, we consider the problem of estimating the proportion of positive objects for a typical assay where:(i) the distribution of x for positive objects is unknown. although (ii) the risk of positivity is known to be a monotonic function of x:and (iii) x has been measured for a set of negative control objects. Monte Carlo simulations evaluating four alternative estimators of the positivity, including novel non-parametric mixture decompositions, indicate that where the positives and negatives have distributions of x with a moderate degree of overlap, a non-parametric decomposition using a latent class model provides precise and close to unbiased estimates. The methods are illustrated using data from an autoradiography assay used in cell biology. |
| |
Keywords: | attributable fraction finite mixture distribution EM algorithm non-parametric regression Monte Carlo simulation bootstrap |
|
|