Information theory-based surrogate marker evaluation from several randomized clinical trials with binary endpoints, using SAS

One of the paradigms for surrogate marker evaluation in clinical trials is based on employing data from several clinical trials: the meta-analytic approach. It was originally developed for continuous outcomes by means of the linear mixed model, but other situations are of interest. One such situation is when both outcomes are binary. Although joint models have been proposed for this setting, they are cumbersome in the sense of computationally complex and of producing validation measures that are, unlike in the Gaussian case, not of an R(2) type (Burzykowski et al., 2005). A way to put these problems to rest is by employing information theory, already applied in the continuous case (Alonso and Molenberghs, 2007). In this paper, the information-theoretic approach is applied to the case of binary surrogate and true endpoints. Its use is illustrated using a case study in acute migraine and its performance, relative to existing methods, assessed by means of a simulation study. Because the usefulness of a method critically depends, among others, on the availability of software, a SAS implementation accompanies the methodological work.status: publishe

Estimating negative variance components from Gaussian and non-Gaussian data: A mixed models approach

The occurrence of negative variance components is a reasonably well understood phenomenon in the case of linear models for hierarchical data, such as variance-component models in designed experiments or linear mixed models for longitudinal data. In many cases, such negative variance components can be translated as negative within-unit correlations. It is shown that negative variance components, with corresponding negative associations, can occur in hierarchical models for non-Gaussian outcomes as well, such as repeated binary data or counts. While this feature poses no problem for marginal models, in which the mean and correlation functions are modeled directly and separately, the issue is more complicated in, for example, generalized linear mixed models. This owes in part to the non-linear nature of the link function, non-constant residual variance stemming from the mean-variance link, and the resulting lack of closed-form expressions for the marginal correlations. It is established that such negative variance components in generalized linear mixed models can occur in practice and that they can be estimated using standard statistical software. Marginal-correlation functions are derived. Important implications for interpretation and model choice are discussed. Simulations and the analysis of data from a developmental toxicity experiment underscore these results.Gaussian and Non-Gaussian data Generalized linear mixed model Linear mixed model Marginal model Negative variance component Random-effects model

A unified framework for the evaluation of surrogate endpoints in mental-health clinical trials

For a number of reasons, surrogate endpoints are considered instead of the so-called true endpoint in clinical studies, especially when such endpoints can be measured earlier, and/or with less burden for patient and experimenter. Surrogate endpoints may occur more frequently than their standard counterparts. For these reasons, it is not surprising that the use of surrogate endpoints in clinical practice is increasing. Building on the seminal work of Prentice(1) and Freedman et al.,(2) Buyse et al. (3) framed the evaluation exercise within a meta-analytic setting, in an effort to overcome difficulties that necessarily surround evaluation efforts based on a single trial. In this article, we review the meta-analytic approach for continuous outcomes, discuss extensions to non-normal and longitudinal settings, as well as proposals to unify the somewhat disparate collection of validation measures currently on the market. Implications for design and for predicting the effect of treatment in a new trial, based on the surrogate, are discussed. A case study in schizophrenia is analysed

Estimating precision, repeatability, and reproducibility from Gaussian and non- Gaussian data: a mixed models approach

Quality control relies heavily on the use of formal assessment metrics. In this paper, for the context of veterinary epidemiology, we review the main proposals, precision, repeatability, reproducibility, and intermediate precision, in agreement with ISO (international Organization for Standardization) practice, generalize these by placing them within the linear mixed model framework, which we then extend to the generalized linear mixed model setting, so that both Gaussian as well as non-Gaussian data can be employed. Similarities and differences are discussed between the classical ANOVA (analysis of variance) approach and the proposed mixed model settings, on the one hand, and between the Gaussian and non-Gaussian cases, on the other hand. The new proposals are applied to five studies in three diseases: Aujeszky's disease, enzootic bovine leucosis (EBL) and bovine brucellosis. The mixed-models proposals are also discussed in the light of their computational requirements.accuracy, analysis of variance, Aujeszky's disease, bias, bovine brucellosis, enzootic bovine leucosis, generalized linear mixed models, linear mixed models, quality control,

Use of statistics as another factor leading to an overestimation of chlorhexidine's role in skin antisepsis

10.1086/671282Infection Control and Hospital Epidemiology348872-873ICEP

Flexible Surrogate Marker Evaluation from Several Randomized Clinical Trials with Continuous Endpoints, Using R and SAS

The evaluation of surrogate endpoints is thought to be first studied by Prentice (1989), who presented a definition of a surrogate as well as a set of criteria. Freedman et al (2001) supplemented these criteria with the so-called proportion explained after notifying some drawbacks in Prentice’s approach. Buyse et al (2000) framed the evaluation exercise within a meta-analytic setting, thereby overcoming difficulties that necessarily surround evaluation efforts based on a single trial. In this paper, we briefly review the meta-analytic approach for continuous outcomes. Advantages and problems are highlighted by means of two case studies, one in schizophrenia and one in ophthalmology, and a simulation study. One of the critical issues for the broad adoption of methodology like the one presented here is the availability of flexible implementations in standard statistical software. We have developed generically applicable SAS macros and R functions, at the reader’s disposal