Biomedical subjects

Tony Vangeneugden

Publications and source records attributed to Tony Vangeneugden.

7 recordsLinked to original sources

PubMed2006

A unifying approach for surrogate marker validation based on Prentice's criteria.

Part of the recent literature on the evaluation of surrogate endpoints starts from a multi-trial approach which leads to a definition of validity in terms of the quality of both trial-level and individual-level association between a potential surrogate and a true endpoint, Buyse et al. These authors proposed their methodology based on the simplest cross-sectional case in which both the surrogate and the true endpoint are continuous and normally distributed. Different variations to this theme have been implemented for binary responses, times to event, combinations of binary and continuous endpoints, etc. However, a drawback of this methodology is that different settings have led to different definitions to quantify the association at the individual-level. In the longitudinal setting; Alonso et al. defined a class of canonical correlation functions that can be used to study surrogacy at the trial and individual-level. In the present work, we propose a new approach to evaluate surrogacy in the repeated measurements framework, we also show the connection between this proposal and the previous ones reported in the literature. Finally, we extend this concept to the non-normal case using the so-called 'likelihood reduction factor' (LRF) a new validation measure based on some of the Prentice's criteria. We apply the previous methodology using data from two clinical studies in psychiatry and ophthalmology.

Antipsychotic Agents↗

PubMed2006

Generalized reliability estimation using repeated measurements.

Reliability can be studied in a generalized way using repeated measurements. Linear mixed models are used to derive generalized test-retest reliability measures. The method allows for repeated measures with a different mean structure due to correction for covariate effects. Furthermore, different variance-covariance structures between measurements can be implemented. When the variance structure reduces to a random intercept (compound symmetry), classical methods are recovered. With more complex variance structures (e.g. including random slopes of time and/or serial correlation), time-dependent reliability functions are obtained. The effect of time lag between measurements on reliability estimates can be evaluated. The methodology is applied to a psychiatric scale for schizophrenia.

Humans↗

PubMed2005

Applying concepts of generalizability theory on clinical trial data to investigate sources of variation and their impact on reliability.

This work aims at applying concepts of generalizability theory to data resulting from clinical trials. The focus is to study the sources of variance and their impact on the reliability and generalizability of a psychiatric measurement scale. The goal is to identify, measure, and thereby potentially find strategies to reduce the influence of these sources on the measurement in question for future trials. This approach was originally devised by Cronbach and his associates and is known as generalizability theory. This work shows how full modeling power in mixed models can be used to study generalizability using data from five double-blind randomized clinical trials, comparing the effects of risperidone to conventional antipsychotic agents for the treatment of chronic schizophrenia.

Antipsychotic Agents↗

PubMed2004

Applying linear mixed models to estimate reliability in clinical trial data with repeated measurements.

Repeated measures are exploited to study reliability in the context of psychiatric health sciences. It is shown how test-retest reliability can be derived using linear mixed models when the scale is continuous or quasi-continuous. The advantage of this approach is that the full modeling power of mixed models can be used. Repeated measures with a different mean structure can be used to usefully study reliability, correction for covariate effects is possible, and a complicated variance-covariance structure between measurements is allowed. In case the variance structure reduces to a random intercept (compound symmetry), classical methods are recovered. With more complex variance structures (e.g., including random slopes of time and/or serial correlation), time-dependent reliability functions are obtained. The methodology is motivated by and applied to data from five double-blind randomized clinical trials comparing the effects of risperidone to conventional antipsychotic agents for the treatment of chronic schizophrenia. Model assumptions are investigated through residual plots and by investigating the effect of influential observations.

Analysis of Variance↗

PubMed2004

Validation of surrogate markers in multiple randomized clinical trials with repeated measurements: canonical correlation approach.

Part of the recent literature on the evaluation of biomarkers as surrogate endpoints starts from a multitrial context, which leads to a definition of validity in terms of the quality of both trial-level and individual-level association between the surrogate and true endpoints (Buyse et al., 2000, Biostatistics1, 49-67). These authors concentrated on cross-sectional continuous responses. However, in many randomized clinical studies, repeated measurements are encountered on either or both endpoints. A challenge in this setting is the formulation of a simple and meaningful concept of "surrogacy."Alonso et al. (2003, Biometrical Journal45, 931-945) proposed the variance reduction factor (VRF) to evaluate surrogacy at the individual level. They also showed how and when this concept should be extended to study surrogacy at the trial level. Here, we approach the problem from the natural canonical correlation perspective. We define a class of canonical correlation functions that can be used to study surrogacy at the trial and individual level. We show that the VRF and the R2 measure defined by Buyse et al. (2000) follow as special cases. Simulations are conducted to evaluate the performance of different members of this family. The methodology is illustrated on data from a meta-analysis of five clinical trials comparing antipsychotic agents for the treatment of chronic schizophrenia.

Antipsychotic Agents↗

PubMed2002

Selection models and pattern-mixture models to analyse longitudinal quality of life data subject to drop-out.

Longitudinally observed quality of life data with large amounts of drop-out are analysed. First we used the selection modelling framework, frequently used with incomplete studies. An alternative method consists of using pattern-mixture models. These are also straightforward to implement, but result in a different set of parameters for the measurement and drop-out mechanisms. Since selection models and pattern-mixture models are based upon different factorizations of the joint distribution of measurement and drop-out mechanisms, comparing both models concerning, for example, treatment effect, is a useful form of a sensitivity analysis.

Aged↗

PubMed2002

Investigating the criterion validity of psychiatric symptom scales using surrogate marker validation methodology.

This work investigates whether techniques that are generally used for the validation of surrogate markers in clinical trials can be applied in the validation of psychiatric health measurements (often scales) and more generally to investigate relationships between treatment effects on different measurements. However, the categorical nature of some scales makes these techniques inapplicable in the way they were originally defined. In this work, we show a possible extension of this methodology to the setting in which one of the scales is an ordinal categorical variable. When psychiatric health measurements are either developed or used in a new population, reliability and validity must be investigated. Reliability, more specifically internal consistency, test-retest reliability, and inter-rater reliability, is focused on the reproducibility of the measurement. Validity is defined as the degree to which the scale measures what it purports to measure. This can be performed through the analysis of content, construct, and criterion validity. We argue that recent methodology, in particular developed to study surrogate endpoints, can be used to examine criterion validity, concurrent validity, and predictive validity. In concurrent validity, we correlate the measurement with a criterion measure, both of which are given at the same time. In predictive validity, the criterion will not be available to some point in time in the future. The surrogate methods were applied on pooled data from five trials in schizophrenia.

Clinical Trials as Topic↗