Casper J. Albers

A generally applicable validation scheme for the assessment of factors involved in reproducibility and quality of DNA-microarray data

BackgroundIn research laboratories using DNA-microarrays, usually a number of researchers perform experiments, each generating possible sources of error. There is a need for a quick and robust method to assess data quality and sources of errors in DNA-microarray experiments. To this end, a novel and cost-effective validation scheme was devised, implemented, and employed.ResultsA number of validation experiments were performed on Lactococcus lactis IL1403 amplicon-based DNA-microarrays. Using the validation scheme and ANOVA, the factors contributing to the variance in normalized DNA-microarray data were estimated. Day-to-day as well as experimenter-dependent variances were shown to contribute strongly to the variance, while dye and culturing had a relatively modest contribution to the variance.ConclusionEven in cases where 90 % of the data were kept for analysis and the experiments were performed under challenging conditions (e.g. on different days), the CV was at an acceptable 25 %. Clustering experiments showed that trends can be reliably detected also from genes with very low expression levels. The validation scheme thus allows determining conditions that could be improved to yield even higher DNA-microarray data quality.

A novel scheme to assess factors involved in the reproducibility of DNA-microarray data

BackgroundIn research laboratories using DNA-microarrays, usually a number of researchers perform experiments, each generating possible sources of error. There is a need for a quick and robust method to assess data quality and sources of errors in DNA-microarray experiments. To this end, a novel and cost-effective validation scheme was devised, implemented, and employed.ResultsA number of validation experiments were performed on Lactococcus lactis IL1403 amplicon-based DNA-microarrays. Using the validation scheme and ANOVA, the factors contributing to the variance in normalized DNA-microarray data were estimated. Day-to-day as well as experimenter-dependent variances were shown to contribute strongly to the variance, while dye and culturing had a relatively modest contribution to the variance.ConclusionsEven in cases where 90 % of the data were kept for analysis and the experiments were performed under challenging conditions (e.g. on different days), the CV was at an acceptable 25 %. Clustering experiments showed that trends can be reliably detected also from (very) lowly expressed genes. The validation scheme thus allows determining conditions that could be improved to yield even higher DNA-microarray data quality.

Intervention and Causality: Forecasting Traffic Flows Using a Dynamic Bayesian Network

Real-time traffic flow data across entire networks can be used in a traffic management system to monitor current traffic flows so that traffic can be directed and managed efficiently. Reliable short-term forecasting models of traffic flows are crucial for the success of any traffic management system. The model proposed in this article for forecasting traffic flows is a multivariate Bayesian dynamic model called the multiregression dynamic model (MDM). This model is an example of a dynamic Bayesian network and is designed to preserve the conditional independences and causal drive exhibited by the traffic flow series. Sudden changes can occur in traffic flow series in response to such events as traffic accidents or roadworks. A traffic management system is particularly useful at such times of change. To ensure that the associated forecasting model continues to produce reliable forecasts, despite the change, the MDM uses the technique of external intervention. This article will demonstrate how intervention works in the MDM and how it can improve forecast performance at times of change. External intervention has also been used in the context of Bayesian networks to identify causal relationships between variables, and in dynamic Bayesian networks to identify lagged causal relationships between time series. This article goes beyond the identification of lagged causal relationships previously addressed using intervention in dynamic Bayesian networks, to show how intervention in the MDM can be used to identify contemporaneous causal relationships between time series.

Justify your alpha

In response to recommendations to redefine statistical significance to P ≤ 0.005, we propose that researchers should transparently report and justify all choices they make when designing a study, including the alpha level.

Parental rearing and psychopathology in mothers of adolescents with and without borderline personality symptoms

BackgroundA combination of multiple factors, including a strong genetic predisposition and environmental factors, are considered to contribute to the developmental pathways to borderline personality disorder (BPD). However, these factors have mostly been investigated retrospectively, and hardly in adolescents. The current study focuses on maternal factors in BPD features in adolescence.MethodsActual parenting was investigated in a group of referred adolescents with BPD features (N = 101) and a healthy control group (N = 44). Self-reports of perceived concurrent parenting were completed by the adolescents. Questionnaires on parental psychopathology (both Axis I and Axis II disorders) were completed by their mothers.ResultsAdolescents reported significantly less emotional warmth, more rejection and more overprotection from their mothers in the BPD-group than in the control group. Mothers in the BPD group reported significantly more parenting stress compared to mothers in the control group. Also, these mothers showed significantly more general psychopathology and clusters C personality traits than mothers in the control group. Contrary to expectations, mothers of adolescents with BPD features reported the same level of cluster B personality traits, compared to mothers in the control group. Hierarchical logistic regression revealed that parental rearing styles (less emotional warmth, and more overprotection) and general psychopathology of the mother were the strongest factors differentiating between controls and adolescents with BPD symptoms.ConclusionsAdolescents with BPD features experience less emotional warmth and more overprotection from their mothers, while the mothers themselves report more symptoms of anxiety and depression. Addition of family interventions to treatment programs for adolescents might increase the effectiveness of such early interventions, and prevent the adverse outcome that is often seen in adult BPD patients.

A comparative simulation study of AR(1) estimators in short time series

Various estimators of the autoregressive model exist. We compare their performance in estimating the autocorrelation in short time series. In Study 1, under correct model specification, we compare the frequentist r1 estimator, C-statistic, ordinary least squares estimator (OLS) and maximum likelihood estimator (MLE), and a Bayesian method, considering flat (Bf) and symmetrized reference (Bsr) priors. In a completely crossed experimental design we vary lengths of time series (i.e., T = 10, 25, 40, 50 and 100) and autocorrelation (from −0.90 to 0.90 with steps of 0.10). The results show a lowest bias for the Bsr, and a lowest variability for r1. The power in different conditions is highest for Bsr and OLS. For T = 10, the absolute performance of all measurements is poor, as expected. In Study 2, we study robustness of the methods through misspecification by generating the data according to an ARMA(1,1) model, but still analysing the data with an AR(1) model. We use the two methods with the lowest bias for this study, i.e., Bsr and MLE. The bias gets larger when the non-modelled moving average parameter becomes larger. Both the variability and power show dependency on the non-modelled parameter. The differences between the two estimation methods are negligible for all measurements.

Dutch research funding, gender bias, and Simpson’s paradox

Based on, among other criteria, three consecutive years of grant applications to the “Veni programme” of the Netherlands Organization for Scientific Research (NWO), van der Lee and Ellemers (1) conclude that these data “provide compelling evidence of gender bias in personal grant applications to obtain research funding.” One of the main results this claim is based upon is that of the 1,635 applications by males, 17.7% were successful, whereas of the 1,188 applications by females, only 14.4% were successful. When applying the χ2 test to the data, the authors found a P value of 0.045 (1). This conclusion is based on the application of an inappropriate statistical procedure, and is therefore questionable, due to the so-called “Simpson’s paradox.”

Trying to resolve the two-envelope problem

After explaining the well-known two-envelope ‘paradox’ by indicating the fallacy involved, we consider the two-envelope ‘problem’ of evaluating the ‘factual’ information provided to us in the form of the value contained by the envelope chosen first. We try to provide a synthesis of contributions from economy, psychology, logic, probability theory (in the form of Bayesian statistics), mathematical statistics (in the form of a decision-theoretic approach) and game theory. We conclude that the two-envelope problem does not allow a satisfactory solution. An interpretation is made for statistical science at large.

Comparison of Estimation Procedures for Multilevel AR(1) Models

To estimate a time series model for multiple individuals, a multilevel model may be used. In this paper we compare two estimation methods for the autocorrelation in Multilevel AR(1) models, namely Maximum Likelihood Estimation (MLE) and Bayesian Markov Chain Monte Carlo. Furthermore, we examine the difference between modeling fixed and random individual parameters. To this end, we perform a simulation study with a fully crossed design, in which we vary the length of the time series (10 or 25), the number of individuals per sample (10 or 25), the mean of the autocorrelation (−0.6 to 0.6 inclusive, in steps of 0.3) and the standard deviation of the autocorrelation (0.25 or 0.40). We found that the random estimators of the population autocorrelation show less bias and higher power, compared to the fixed estimators. As expected, the random estimators profit strongly from a higher number of individuals, while this effect is small for the fixed estimators. The fixed estimators profit slightly more from a higher number of time points than the random estimators. When possible, random estimation is preferred to fixed estimation. The difference between MLE and Bayesian estimation is nearly negligible. The Bayesian estimation shows a smaller bias, but MLE shows a smaller variability (i.e., standard deviation of the parameter estimates). Finally, better results are found for a higher number of individuals and time points, and for a lower individual variability of the autocorrelation. The effect of the size of the autocorrelation differs between outcome measures.

Severity of borderline personality symptoms in adolescence: relationship with maternal parenting stress, maternal psychopathology, and rearing styles

The development of borderline personality disorder (BPD) has been associated with parenting styles and parental psychopathology. Only a few studies have examined current parental rearing styles and parental psychopathology in relationship to BPD symptoms in adolescents. Moreover, parenting stress has not been examined in this group. The current study examined 101 adolescents (14-19 years old) with BPD symptoms and their mothers. Assessments were made on severity of BPD symptoms, youth-perceived maternal rearing styles, and psychopathology and parenting stress in mothers. Multiple regression analyses were used to examine potential predictors of borderline severity. No correlation was found between severity of BPD symptoms in adolescents and parenting stress. Only youth-perceived maternal overprotection was significantly related to BPD severity. The combination of perceived maternal rejection with cluster B traits in mothers was significantly related to BPD severity in adolescents. This study provides a contribution to the disentanglement of the developmental pathways that lead to BPD.