Carel F.W. Peeters

Ridge estimation of inverse covariance matrices from high-dimensional data

The ridge estimation of the precision matrix is investigated in the setting where the number of variables is large relative to the sample size. First, two archetypal ridge estimators are reviewed and it is noted that their penalties do not coincide with common quadratic ridge penalties. Subsequently, starting from a proper ź 2 -penalty, analytic expressions are derived for two alternative ridge estimators of the precision matrix. The alternative estimators are compared to the archetypes with regard to eigenvalue shrinkage and risk. The alternatives are also compared to the graphical lasso within the context of graphical modeling. The comparisons may give reason to prefer the proposed alternative estimators.

A note on a simple and practical randomized response framework for eliciting sensitive dichotomous and quantitative information.

Many issues of interest to social scientists and policy makers are of a sensitive nature in the sense that they are intrusive, stigmatizing, or incriminating to the respondent. This results in refusals to cooperate or evasive cooperation in studies using self-reports. In a seminal article, Warner (1965) proposed to curb this problem by generating an artificial variability in responses to inoculate the individual meaning of answers to sensitive questions. This procedure was further developed and extended and came to be known as the randomized response (RR) technique. Here, the authors propose a unified treatment for eliciting sensitive binary as well as quantitative information with RR based on a model where the inoculating elements are provided for by the randomization device. The procedure is simple and the authors will argue that its implementation in a computer-assisted setting may have superior practical capabilities.

Rotational Uniqueness Conditions Under Oblique Factor Correlation Metric

In an addendum to his seminal 1969 article Jöreskog stated two sets of conditions for rotational identification of the oblique factor solution under utilization of fixed zero elements in the factor loadings matrix (Jöreskog in Advances in factor analysis and structural equation models, pp. 40–43, 1979). These condition sets, formulated under factor correlation and factor covariance metrics, respectively, were claimed to be equivalent and to lead to global rotational uniqueness of the factor solution. It is shown here that the conditions for the oblique factor correlation structure need to be amended for global rotational uniqueness, and, hence, that the condition sets are not equivalent in terms of unicity of the solution.

Detecting functional decline from normal aging to dementia: Development and validation of a short version of the Amsterdam IADL Questionnaire

Detecting functional decline from normal aging to dementia is relevant for diagnostic and prognostic purposes. Therefore, the Amsterdam IADL Questionnaire (A‐IADL‐Q) was developed: a 70‐item proxy‐based tool with good psychometric properties. We aimed to design a short version while preserving its psychometric quality.

Pathophysiological domains underlying the metabolic syndrome: an alternative factor analytic strategy

PURPOSE Factor analysis (FA) has become part and parcel in metabolic syndrome (MBS) research. Both exploration- and confirmation-driven factor analyzes are rampant. However, factor analytic results on MBS differ widely. A situation that is at least in part attributable to misapplication of FA. Here, our purpose was (i) to review factor analytic efforts in the study of MBS with emphasis on misusage of the FA model and (ii) to propose an alternative factor analytic strategy. METHODS The proposed factor analytic strategy consists of four steps and confronts weaknesses in application of the FA model. At its heart lies the explicit separation of dimensionality and pattern selection and the direct evaluation of competing inequality-constrained loading patterns. A high-profile MBS data set with anthropometric measurements on overweight children and adolescents is reanalyzed using this strategy. RESULTS The reanalysis implied a more parsimonious constellation of pathophysiological domains underlying phenotypic expressions of MBS than the original analysis (and many other analyses). The results emphasize correlated factors of impaired glucose metabolism and impaired lipid metabolism. CONCLUSIONS Pathophysiological domains underlying phenotypic expressions of MBS included in the analysis are driven by multiple interrelated metabolic impairments. These findings indirectly point to the possible existence of a multifactorial etiology.

Identification and Validation of a 3-Gene Methylation Classifier for HPV-Based Cervical Screening on Self-Samples

Purpose: Offering self-sampling of cervico-vaginal material for high-risk human papillomavirus (hrHPV) testing is an effective method to increase the coverage in cervical screening programs. Molecular triage directly on hrHPV-positive self-samples for colposcopy referral opens the way to full molecular cervical screening. Here, we set out to identify a DNA methylation classifier for detection of cervical precancer (CIN3) and cancer, applicable to lavage and brush self-samples. Experimental Design: We determined genome-wide DNA methylation profiles of 72 hrHPV-positive self-samples, using the Infinium Methylation 450K Array. The selected DNA methylation markers were evaluated by multiplex quantitative methylation-specific PCR (qMSP) in both hrHPV-positive lavage (n = 245) and brush (n = 246) self-samples from screening cohorts. Subsequently, logistic regression analysis was performed to build a DNA methylation classifier for CIN3 detection applicable to self-samples of both devices. For validation, an independent set of hrHPV-positive lavage (n = 199) and brush (n = 287) self-samples was analyzed. Results: Genome-wide DNA methylation profiling revealed 12 DNA methylation markers for CIN3 detection. Multiplex qMSP analysis of these markers in large series of lavage and brush self-samples yielded a 3-gene methylation classifier (ASCL1, LHX8, and ST6GALNAC5). This classifier showed a very good clinical performance for CIN3 detection in both lavage (AUC = 0.88; sensitivity = 74%; specificity = 79%) and brush (AUC = 0.90; sensitivity = 88%; specificity = 81%) self-samples in the validation set. Importantly, all self-samples from women with cervical cancer scored DNA methylation–positive. Conclusions: By genome-wide DNA methylation profiling on self-samples, we identified a highly effective 3-gene methylation classifier for direct triage on hrHPV-positive self-samples, which is superior to currently available methods. Clin Cancer Res; 24(14); 3456–64. ©2018 AACR.

Blood-based metabolic signatures in Alzheimer's disease.

Identification of blood‐based metabolic changes might provide early and easy‐to‐obtain biomarkers.

PROFILING PERIPHERAL METABOLIC DYSREGULATION IN ALZHEIMER’S DISEASE: THE ADDED VALUE OF MULTIPLE SIGNATURES

actual mechanism in AD pathogenesis is poorly understood. Methods: In this cross-sectional comparison, we examined the plasma LDL lipoprotein profiles of clinically classified healthy control and Alzheimer’s affected individuals from the Australian Imaging, Biomarker and Lifestyle (AIBL) study. These individuals had also undergone several other characterisations including PiBPET imaging for determination of brain amyloid load. Results: The data indicate that the APOE ε4 participants tended to have higher median levels of larger plasma LDL species while also being associated with higher amyloid load. These individuals also showed high variations in large LDL levels, indicating altered regulation of LDL metabolism. We also identified a possible association with LDL levels in non-ε4 participants and neocortical amyloid load, suggesting that plasma LDL is involved with AD pathogenesis. Conclusions:APOE ε4 influences the levels of plasma LDL species and is associated with higher amyloid load in the brain. The larger LDL species show positive association with higher brain amyloid load, supporting a metabolic link between brain and periphery.

Application of a New Ridge Estimator of the Inverse Covariance Matrix to the Reconstruction of Gene-Gene Interaction Networks

A proper ridge estimator of the inverse covariance matrix is presented. We study the properties of this estimator in relation to other ridge-type estimators. In the context of Gaussian graphical modeling, we compare the proposed estimator to the graphical lasso. This work is a brief expose of the technical developments in [1], focussing on applications in gene-gene interaction network reconstruction.