Interactive analysis of high-dimensional association structures with graphical models

Graphical chain models are a capable tool for analyzing multivariate data. However, their practical use may still be cumbersome in some respect since fitting the model requires the application of an intensive selection strategy based on the calculation of an enormous number of different regressions. In this paper, we present a computer system especially designed for the calculation of graphical chain models which is not only planned to automatically carry out the model search but also to visualize the corresponding graph at each stage of the model fit on request by the user. It additionally allows to modify the graph and the model fit interactively

A comparative analysis of graphical interaction and logistic regression modelling: self-care and coping with a chronic illness in later life

Quantitative research especially in the social, but also in the biological sciences has been limited by the availability and applicability of analytic techniques that elaborate interactions among behaviours, treatment effects, and mediating variables. This gap has been filled by a newly developed statistical technique, known as graphical interaction modelling. The merit of graphical models for analyzing highly structured data is explored in this paper by an empirical study on coping with a chronic condition as a function of interrelationships between three sets of factors. These include background factors, illness context factors and four self--care practices. Based on a graphical chain model, the direct and indirect dependencies are revealed and discussed in comparison to the results obtained from a simple logistic regression model ignoring possible interaction effects. Both techniques are introduced from a more tutorial point of view instead of going far into technical details

GraphFitI - A computer program for graphical chain models

Fitting a graphical chain model to a multivariate data set consists of different steps some of which being rather tedious. The paper outlines the basic features and overall architecture of the computer program GraphFitI which provides the application of a selection strategy for fitting graphical chain models and for visualising the resulting models as a graph. It additionally supports the user at the different steps of the analysis by an integrated help system

Correcting for bias due to categorisation based on cluster analysis using multiple continuous error-prone exposures

The association between multidimensional exposure patterns and outcomes is commonly investigated by first applying cluster analysis algorithms to derive patterns and then estimating the associations. However, errors in the underlying continuous, possibly skewed, exposure variables lead to misclassified exposure patterns and therefore to biased effect estimates. This is often the case for lifestyle exposures in epidemiology, e.g. for dietary variables measured on daily basis. We introduce three new algorithms for correcting the biased effect estimates, which are based on regression calibration (RC), simulation extrapolation (SIMEX) and multiple imputation (MI). In addition, the naive method ignoring the measurement error structure is considered for comparison. These methods are combined with the k-means cluster algorithm and the Gaussian mixture model to derive exposure patterns. The performance of the correction methods is compared in a simulation study regarding absolute, maximum and relative bias. The simulated data mimic a typical situation in nutritional epidemiology when diet is assessed using repeated 24-hour dietary recalls. Continuous and binary outcomes are considered. Simulation results show, that the correction method based on RC and MI perform better than the naive and the SIMEX-based method. Furthermore, the MI-based approach, which can use outcome information in the error model, is superior to the RC-based approach in most scenarios. Therefore, we recommend using the MI-based approach.Comment: 25 pages, 2 figures; supplementary material attache

Maximum Likelihood Estimation in Graphical Models with Missing Values

In this paper we discuss maximum likelihood estimation when some observations are missing in mixed graphical interaction models assuming a conditional Gaussian distribution as introduced by Lauritzen&Wermuth (1989). For the saturated case ML estimation with missing values via the EM algorithm has been proposed by Little&Schluchter (1985). We expand their results to the special restrictions in graphical models and indicate a more efficient way to compute the E--step. The main purpose of the paper is to show that for certain missing patterns the computational effort can considerably be reduced

Using Genetic Algorithms for Model Selection in Graphical Models

Model selection in graphical models is still not fully investigated. The main difficulty lies in the search space of all possible models which grows more than exponentially with the number of variables involved. Here, genetic algorithms seem to be a reasonable strategy to find good fitting models for a given data set. In this paper, we adapt them to the problem of model search in graphical models and discuss their performance by conducting simulation studies

Consent and confidentiality in the light of recent demands for data sharing

Many attempts have been made to formalize ethical requirements for research. Among the most prominent mechanisms are informed consent requirements and data protection regimes. These mechanisms, however, sometimes appear as obstacles to research. In this opinion paper, we critically discuss conventional approaches to research ethics that emphasize consent and data protection. Several recent debates have highlighted other important ethical issues and underlined the need for greater openness in order to uphold the integrity of health-related research. Some of these measures, such as the sharing of individual-level data, pose problems for standard understandings of consent and privacy. Here, we argue that these interpretations tend to be overdemanding: They do not really protect research subjects and they hinder the research process. Accordingly, we suggest another way of framing these requirements. Individual consent must be situated alongside the wider distribution of knowledge created when the actions, commitments, and procedures of researchers and their institutions are opened to scrutiny. And instead of simply emphasizing privacy or data protection, we should understand confidentiality as a principle that facilitates the sharing of information while upholding important safeguards. Consent and confidentiality belong to a broader set of safeguards and procedures to uphold the integrity of the research process

Consistency of the Bootstrap Procedure in Individual Bioequivalence

Recently, new concepts have been proposed for assessing bioequivalence of two drug formulations, namely the so-called population and individual bioequivalence. Using moment-based and probability-based measures for evaluating the proposed bioequivalence concepts, criteria have been formulated to decide whether two formulations should be regarded as bioequivalent or not. This decision has of course to be based on an adequate statistical method where the Food and Drug Administration (FDA) guidance (1997) recommends the use of a bootstrap percentile interval. In this paper, we discuss theoretical properties such as consistency and accuracy of the recommended bootstrap intervals. We focus our investigations on the concept of individual bioequivalence and here especially on the scaled versions of the moment-based as well as the probability-based measures as recommended by the FDA. As estimates for the former, we consider those obtained from an according analysis of variance and restricted maximum likelihood estimators under mixed effect models, where an unbiased estimator of the latter can be derived from the corresponding relative frequencies

Primary prevention from the epidemiology perspective: three examples from the practice

Background: Primary prevention programmes are of increasing importance to reduce the impact of chronic diseases on the individual, institutional and societal level. However, most initiatives that develop and implement primary prevention programmes are not evaluated with scientific rigor. On the basis of three different projects we discuss necessary steps on the road to evidence-based primary prevention. Discussion: We first discuss how to identify suitable target groups exploiting sophisticated statistical methods. This is illustrated using data from a health survey conducted in a federal state of Germany. A literature review is the more typical approach to identify target groups that is demonstrated using a European project on the prevention of childhood obesity. In the next step, modifiable risk factors and realistic targets of the intervention have to be specified. These determine the outcome measures that in turn are used for effect evaluation. Both, the target groups and the outcome measures, lay the ground for the study design and the definition of comparison groups as can be seen in our European project. This project also illustrates the development and implementation of a prevention programme. These may require active involvement of participants which can be achieved by participatory approaches taking into account the socio-cultural and living environment. Evaluation is of utmost importance for any intervention to assess structure, process and outcome according to rigid scientific criteria. Different approaches used for this are discussed and illustrated by a methodological project developed within a health promotion programme in a deprived area. Eventually the challenge of transferring an evidence-based intervention into practice and to achieve its sustainability is addressed. Summary: This article describes a general roadmap to primary prevention comprising (1) the identification of target groups and settings, (2) the identification of modifiable risk factors and endpoints, (3) the development and implementation of an intervention programme, (4) the evaluation of structure, process and outcome and (5) the transfer of an evidence-based intervention into practice

A graphical chain model derived from a model selection strategy for the sociologists graduates study

This paper objects to the arising problems due to fitting graphical chain models to multidimensional data sets. This multivariate statistical tool is used to cope with complex research questions concerning not only direct, but also indirect associations between the variables of interest. Due to this high complexity sensible strategies for fitting such models are required. Here, a data--driven selection strategy is discussed. Its application is illustrated for an empirical data example in detail