Vahid Nassiri

Unbalanced cluster sizes and rates of convergence in mixed-effects models for clustered data

ABSTRACT Convergence problems often arise when complex linear mixed-effects models are fitted. Previous simulation studies (see, e.g. [Buyse M, Molenberghs G, Burzykowski T, Renard D, Geys H. The validation of surrogate endpoints in meta-analyses of randomized experiments. Biostatistics. 2000;1:49–67, Renard D, Geys H, Molenberghs G, Burzykowski T, Buyse M. Validation of surrogate endpoints in multiple randomized clinical trials with discrete outcomes. Biom J. 2002;44:921–935]) have shown that model convergence rates were higher (i) when the number of available clusters in the data increased, and (ii) when the size of the between-cluster variability increased (relative to the size of the residual variability). The aim of the present simulation study is to further extend these findings by examining the effect of an additional factor that is hypothesized to affect model convergence, i.e. imbalance in cluster size. The results showed that divergence rates were substantially higher for data sets with unbalanced cluster sizes – in particular when the model at hand had a complex hierarchical structure. Furthermore, the use of multiple imputation to restore ‘balance’ in unbalanced data sets reduces model convergence problems.

Non-invasive assessment of cerebral oxygenation: A comparison of retinal and transcranial oximetry

Background To investigate the correlation between cerebral (SO2-transcranial), retinal arterial (SaO2-retinal) and venous (SvO2-retinal) oxygen saturation as measured by near-infrared spectroscopy (NIRS) and retinal oximetry respectively. Methods Paired retinal and cerebral oxygen saturation measurements were performed in healthy volunteers. Arterial and venous retinal oxygen saturation and diameter were measured using a non-invasive spectrophotometric retinal oximeter. Cerebral oxygen saturation was measured using near-infrared spectroscopy. Correlations between SO2-transcranial and retinal oxygen saturation and diameter measurements were assessed using Pearson correlation coefficients. Lin’s concordance correlation coefficient (CCC) and Bland-Altman analysis were performed to evaluate the agreement between SO2-transcranial as measured by NIRS and as estimated using a fixed arterial:venous ratio as 0.3 x SaO2-retinal + 0.7 x SvO2-retinal. The individual relative weight of SaO2-retinal and SvO2-retinal to obtain the measured SO2-transcranial was calculated for all subjects. Results Twenty-one healthy individuals aged 26.4 ± 2.2 years were analyzed. SO2-transcranial was positively correlated with both SaO2-retinal and SvO2-retinal (r = 0.44, p = 0.045 and r = 0.43, p = 0.049 respectively) and negatively correlated with retinal venous diameter (r = -0.51, p = 0.017). Estimated SO2-transcranial based on retinal oximetry showed a tolerance interval of (-13.70 to 14.72) and CCC of 0.46 (95% confidence interval: 0.05 to 0.73) with measured SO2-transcranial. The average relative weights of SaO2-retinal and SvO2-retinal to obtain SO2-transcranial were 0.31 ± 0.11 and 0.69 ± 0.11, respectively. Conclusion This is the first study to show the correlation between retinal and cerebral oxygen saturation, measured by NIRS and retinal oximetry. The average relative weight of arterial and venous retinal oxygen saturation to obtain the measured transcranial oxygen saturation as measured by NIRS, approximates the established arterial:venous ratio of 30:70 closely, but shows substantial inter-individual variation. These findings provide a proof of concept for the role of retinal oximetry in evaluating cerebral oxygenation.

Fast, closed-form, and efficient estimators for hierarchical models with AR(1) covariance and unequal cluster sizes

ABSTRACT This article is concerned with statistically and computationally efficient estimation in a hierarchical data setting with unequal cluster sizes and an AR(1) covariance structure. Maximum likelihood estimation for AR(1) requires numerical iteration when cluster sizes are unequal. A near optimal non-iterative procedure is proposed. Pseudo-likelihood and split-sample methods are used, resulting in computing weights to combine cluster size specific parameter estimates. Results show that the method is statistically nearly as efficient as maximum likelihood, but shows great savings in computation time.

On using multiple imputation for exploratory factor analysis of incomplete data

A simple multiple imputation-based method is proposed to deal with missing data in exploratory factor analysis. Confidence intervals are obtained for the proportion of explained variance. Simulations and real data analysis are used to investigate and illustrate the use and performance of our proposal.

Improved discrimination between normal-tension and primary open-angle glaucoma with advanced vascular examinations - the Leuven Eye Study

Vascular factors have been suggested to influence the development and progression of glaucoma. They are thought to be especially relevant for normal‐tension glaucoma (NTG) patients. We aim to investigate which vascular factors, including advanced vascular examinations, better describe patients with NTG comparing to those with primary open‐angle glaucoma (POAG).

Retinal oxygen saturation as a non-invasive estimate for mixed venous oxygen saturation and cardiac output

To investigate the correlation between retinal vessel oxygen saturation and mixed venous oxygen saturation (SvO2‐mixed) and cardiac output (CO).

Combining Factors from Different Factor Analyses Based on Factor Congruence

While factor analysis is one of the most often used techniques in psychometrics, comparing or combining solutions from different factor analyses can be cumbersome even though it is necessary in several situations. For example, when applying multiple imputation (to account for incompleteness) or multiple outputation (which can be used to deal with clustering in multilevel data) often tens or hundreds of results have to be combined into one final solution. While different solutions have been in use, we propose a simple and easy to implement solution to match factors from different analyses based on factor congruence. To demonstrate this method, the Big Five Inventory data collected under the auspices of the Divorce in Flanders study was analysed combining multiple outputation and factor analysis. This multilevel sample consists of 7533 individuals coming from 4460 families with about 10% of missing values.

Finite information limit variance-covariance structures: Is the entire dataset needed for analysis?

Finite Information Limit (FIL) variance-covariance structures for hierarchical data are introduced and examined: for such data, it is often possible to analyze only a sometimes very small subset, leading to considerable computation time gain, with almost no efficiency loss. A central example is compound-symmetry. A simple approach is proposed to detect this property in a given dataset.