Genetically Informed Regression Analysis: Application to Aggression Prediction by Inattention and Hyperactivity in Children and Adults

We present a procedure to simultaneously fit a genetic covariance structure model and a regression model to multivariate data from mono- and dizygotic twin pairs to test for the prediction of a dependent trait by multiple correlated predictors. We applied the model to aggressive behavior as an outcome trait and investigated the prediction of aggression from inattention (InA) and hyperactivity (HA) in two age groups. Predictions were examined in twins with an average age of 10 years (11,345 pairs), and in adult twins with an average age of 30 years (7433 pairs). All phenotypes were assessed by the same, but age-appropriate, instruments in children and adults. Because of the different genetic architecture of aggression, InA and HA, a model was fitted to these data that specified additive and non-additive genetic factors (A and D) plus common and unique environmental (C and E) influences. Given appropriate identifying constraints, this ADCE model is identified in trivariate data. We obtained different results for the prediction of aggression in children, where HA was the more important predictor, and in adults, where InA was the more important predictor. In children, about 36% of the total aggression variance was explained by the genetic and environmental components of HA and InA. Most of this was explained by the genetic components of HA and InA, i.e., 29.7%, with 22.6% due to the genetic component of HA. In adults, about 21% of the aggression variance was explained. Most was this was again explained by the genetic components of InA and HA (16.2%), with 8.6% due to the genetic component of InA.

The Bayesian Covariance Structure Model for Testlets

Standard item response theory (IRT) models have been extended with testlet effects to account for the nesting of items; these are well known as (Bayesian) testlet models or random effect models for testlets. The testlet modeling framework has several disadvantages. A sufficient number of testlet items are needed to estimate testlet effects, and a sufficient number of individuals are needed to estimate testlet variance. The prior for the testlet variance parameter can only represent a positive association among testlet items. The inclusion of testlet parameters significantly increases the number of model parameters, which can lead to computational problems. To avoid these problems, a Bayesian covariance structure model (BCSM) for testlets is proposed, where standard IRT models are extended with a covariance structure model to account for dependences among testlet items. In the BCSM, the dependence among testlet items is modeled without using testlet effects. This approach does not imply any sample size restrictions and is very efficient in terms of the number of parameters needed to describe testlet dependences. The BCSM is compared to the well-known Bayesian random effects model for testlets using a simulation study. Specifically for testlets with a few items, a small number of test takers, or weak associations among testlet items, the BCSM shows more accurate estimation results than the random effects model.

Endovascular Therapy Demonstrates Benefit over Intravenous Recombinant Tissue Plasminogen Activator Based on Repeatedly Measured National Institutes of Health Stroke Scale

Background and Purpose: The Interventional Management of Stroke (IMS) III trial was a randomized controlled trial designed to compare the effect of endovascular therapy after intravenous recombinant tissue plasminogen activator (i.v. rt-PA) as compared to i.v. rt-PA alone. The primary outcome was modified Rankin Scale at 90 days. Secondary outcomes included National Institutes of Health Stroke Scale (NIHSS), which was assessed repeatedly through 90 days. The objective of this analysis is to evaluate the treatment effect of endovascular therapy over time on NIHSS. Methods: 656 subjects were enrolled in the IMS III trial, including 434 subjects randomized to endovascular therapy and 222 to i.v. rt-PA only. NIHSS scores evaluated at 40 min, 24 h, Day 5, and Day 90 were included in the analysis. A covariance structure model was used to investigate the treatment effect on NIHSS over time, adjusting for relevant covariates including baseline stroke severity. Model assumptions were valid. Results: Based on the covariance structure model, after adjusting for relevant baseline covariates, a significant time-by-treatment interaction effect (p = 0.0137) was observed. Only NIHSS at Day 90 showed a significant treatment effect (p = 0.0473), with subjects in the endovascular arm having a lower NIHSS (less neurologic deficit) compared to the i.v. rt-PA arm. Conclusions: The IMS III trial demonstrated an endovascular treatment effect based on the secondary outcome of NIHSS. However, the magnitude of this treatment effect varied by the time of assessment. It was only at Day 90 that the endovascular arm had a significantly lower NIHSS compared to that in the i.v. rt-PA arm.