Biomedical subjects

Lluís Jover

Publications and source records attributed to Lluís Jover.

5 recordsLinked to original sources

PubMed2005

The structural error-in-equation model to evaluate individual bioequivalence.

Individual bioequivalence is assessed using an extension of the classical structural equation model, known as the error-in-equation model. This procedure estimates the relationship between individual means, as well as the variance-covariance parameters, of the bioavailabilities measurement model, by considering individual means related through a straight line with a random term, whereas the classical structural equation considers a deterministic linear relationship. We discuss the implications of this approach in terms of the bioavailabilities measurement model and how to test the overall hypothesis of individual bioequivalence. Both models are compared in a simulation study and a case example is presented.

Analysis of Variance↗

PubMed2004

[Graphics in scientific communication and reasoning: tools or ornaments?].

Whenever relationships among variables are complex, or time processes play an essential part, or random components mask the process under study, graphical display becomes an indispensable tool. Biomedicine, in a broad sense, from research to medical care or management activities, is a field with these features, and good use of graphics can facilitate a new and valuable approximation to the available information. The aim of the paper is twofold: to explain to the reader that graphical display is not limited to be some way to attract information quickly, but means and instrument in the process of knowledge acquisition, and to stress that graphical language needs some maturity, and perhaps some guidelines, so that quantitative information will be revealed through high-quality graphics, avoiding worthless and mechanical displays.

Communication↗

PubMed2004

[Statistical approaches to evaluate agreement].

Reliability and agreement of measurement methods is a fundamental issue in health sciences which is not usually borne in mind. In this document the connotations of using measurement methods with error and the switchability among measurements from methods which disagree are highlighted. These implications are illustrated through examples showing up the confounding effect that measurement error can produce. Throughout the document several procedures to assess agreement and to identify the error sources are suggested. These procedures are classified according to the sort of data, quantitative or qualitative data, as well as the way of agreement is assessed, in an aggregate way by means a values or in a disaggregate way analysing separately the error sources. By means of these procedures is showed that frequently used approaches to assess agreement as the averages comparison, the correlation coefficient or the regression model appear as insufficient or inadequate approaches.

Bias↗

PubMed2003

Assessing individual bioequivalence using the structural equation model.

The structural equation model (SEM) is introduced as a useful approach for assessing individual bio-equivalence. SEM parameters are estimated using a partial likelihood analysis and the hypotheses of individual bioequivalence is evaluated in a disaggregate way, testing separately the hypothesis concerning SEM parameters, and assessing the overall hypothesis of individual bioequivalence using the intersection-union principle. Limits of bioequivalence for SEM parameters are proposed and a power analysis is carried out.

Biological Availability↗

PubMed2003

Estimating the generalized concordance correlation coefficient through variance components.

The intraclass correlation coefficient (ICC) and the concordance correlation coefficient (CCC) are two of the most popular measures of agreement for variables measured on a continuous scale. Here, we demonstrate that ICC and CCC are the same measure of agreement estimated in two ways: by the variance components procedure and by the moment method. We propose estimating the CCC using variance components of a mixed effects model, instead of the common method of moments. With the variance components approach, the CCC can easily be extended to more than two observers, and adjusted using confounding covariates, by incorporating them in the mixed model. A simulation study is carried out to compare the variance components approach with the moment method. The importance of adjusting by confounding covariates is illustrated with a case example.

Analysis of Variance↗