Covariates In Linear Models Research Articles

The associations between glial biomarkers and cognitive changes in individuals at risk of Alzheimer’s disease

AbstractBackgroundIn preclinical Alzheimer’s disease (AD), previous evidence suggests that glial reactivity may affect cognitive performance. This study aimed to test whether glial markers cerebrospinal fluid (CSF) GFAP, YKL‐40, sTREM2, and plasma GFAP are associated with cognitive change in cognitively unimpaired individuals at risk of AD. We hypothesized that higher levels of glial markers may be associated with better cognitive outcomes.MethodWe included 353 individuals from the ALFA+ cohort with available baseline (BL) glial biomarkers and cognition at follow‐up (FU) 3 years later (Table 1). CSF Aβ42/40, pTau181, YKL‐40, GFAP, and sTREM2, were analyzed using the exploratory Roche NeuroToolKit, a panel of automated robust prototype immunoassays. Plasma GFAP was determined with the Simoa HD‐X (Quanterix) platform using the commercial Neurology 4‐Plex E assay (N4PE). Amyloid pathology was studied with Centiloids derived from [18F]flutemetamol PET. Rate of change ([FU‐BL]/time) in the Preclinical Alzheimer’s Cognitive Composite (PACC) and domain‐specific cognitive composites was used as the dependent variable, glial biomarkers at BL as predictors, and age, sex and education as covariates in linear models. Interaction terms between each glial biomarker and different AD pathology were also explored. Aβ+ was defined as CSF Aβ42/40 <0.071.ResultHigher baseline CSF sTREM2 was associated with a positive PACC rate of change (p = 0.002) (Table 2). These results remained significant after controlling for CSF Aβ42/40, pTau181 and Centiloid levels (all p < 0.01). Moreover, CSF sTREM2 at baseline was associated with a better memory outcome (p = 0.012), independently of amyloid and tau (all p < 0.02), and better executive functioning (p = 0.046), independently of tau pathology (p = 0.011). These positive associations remained in the Aβ‐ group regardless of AD pathology, but not in the Aβ+ group, in which some negative associations were observed when adjusting for Centiloids (YKL‐40, p = 0.024) (Figure 1).ConclusionHigher TREM2‐mediated microglial response in individuals at risk of AD is associated with better cognitive performance. These associations were driven by the Aβ‐ group. These results may have important implications for the design of clinical interventions targeting TREM2 in this early stage of AD.

Efficient estimation with missing data and endogeneity

I study the problem of missing values in the outcome and endogenous covariates in linear models. I propose an estimator that improves efficiency relative to a complete cases 2SLS. Unlike traditional imputation, my estimator is consistent even if the model contains nonlinear functions – like squares and interactions – of the endogenous covariates. It can also be used to combine data sets with missing outcome, missing endogenous covariates, and no missing variables. It includes the well-known “Two-Sample 2SLS” as a special case under weaker assumptions than the corresponding literature.

Deep Learning image segmentation for extraction of fish body measurements and prediction of body weight and carcass traits in Nile tilapia

Individual measurement of traits of interest is extremely important in aquaculture, both for production systems and for breeding programs. Most of the current methods are based on manual measurements, which are laborious and stressful to the animals. Therefore, the development of fast, precise and indirect measurement methods for traits such as body weight (BW) and carcass weight (CW) is of great interest. An appealing way to take noninvasive measurements is through computer vision. Hence, the objectives in the current work were to: (1) devise a computer vision system (CVS) for autonomous measurement of Nile tilapia body area (A), length, height, and eccentricity, and (2) develop linear models for prediction of fish BW, CW, and carcass yield (CY). Images from 1653 fish were taken at the same time as their BW and CW were measured. A set of 822 images had pixels labeled into three classes: background, fish fins, and A. This labeled dataset was then used for training of Deep Learning Networks for automatic segmentation of the images into those pixel classes. In a subsequent step, the segmentations obtained from the best network were used for extraction of A, length, height, and eccentricity. These variables were then used as covariates in linear models for prediction of BW, CW, and CY. A network with an input image of 0.2 times the original size and four encoder/decoder layers achieved the best results for intersection over union on the test set of 99, 90 and 64 percent for background, fish body and fin areas, respectively. The overall best predictive model included A and its square as predictor variables and achieved R2 of 0.96 and 0.95 for fish BW and CW, respectively. Overall, the devised CVS was able to correctly differentiate fish body from background and fins, and the extracted area of the fish body could be successfully used for prediction of body and carcass weights.

New Litter Trap Devices Outperform Pitfall Traps for Studying Arthropod Activity.

Soil fauna play a key role in nutrient cycling and decomposition, and in recent years, researchers have become more and more interested in this compartment of terrestrial ecosystems. In addition, soil fauna can act as ecosystem engineers by creating, modifying, and maintaining the habitat for other organisms. Ecologists usually utilize live catches in pitfalls traps as a standard method to study the activity of epigeic fauna in addition to relative abundance. Counts in pitfall traps can be used as estimates of relative activity to compare among experimental treatments. This requires taking independent estimates of abundance (e.g., by sifting soil litter, mark–recapture), which can then be used as covariates in linear models to compare the levels of fauna activity (trap catches) among treatments. However, many studies show that the use of pitfall traps is not the most adequate method to estimate soil fauna relative abundances, and these concerns may be extensible to estimating activity. Here, we present two new types of traps devised to study activity in litter fauna, and which we call “cul-de-sac” and “basket traps”, respectively. We experimentally show that, at least for litter dwellers, these new traps are more appropriate to estimate fauna activity than pitfall traps because: (1) pitfall traps contain 3.5× more moisture than the surrounding environment, potentially attracting animals towards them when environmental conditions are relatively dry; (2) cul-de-sac and basket traps catch ca. 4× more of both meso- and macrofauna than pitfall traps, suggesting that pitfall traps are underestimating activity; and (3) pitfall traps show a bias towards collecting 1.5× higher amounts of predators, which suggests that predation rates are higher within pitfall traps. We end with a protocol and recommendations for how to use these new traps in ecological experiments and surveys aiming at estimating soil arthropod activity.

Markov chain Monte Carlo with the Integrated Nested Laplace Approximation

The Integrated Nested Laplace Approximation (INLA) has established itself as a widely used method for approximate inference on Bayesian hierarchical models which can be represented as a latent Gaussian model (LGM). INLA is based on producing an accurate approximation to the posterior marginal distributions of the parameters in the model and some other quantities of interest by using repeated approximations to intermediate distributions and integrals that appear in the computation of the posterior marginals. INLA focuses on models whose latent effects are a Gaussian Markov random field. For this reason, we have explored alternative ways of expanding the number of possible models that can be fitted using the INLA methodology. In this paper, we present a novel approach that combines INLA and Markov chain Monte Carlo (MCMC). The aim is to consider a wider range of models that can be fitted with INLA only when some of the parameters of the model have been fixed. We show how new values of these parameters can be drawn from their posterior by using conditional models fitted with INLA and standard MCMC algorithms, such as Metropolis–Hastings. Hence, this will extend the use of INLA to fit models that can be expressed as a conditional LGM. Also, this new approach can be used to build simpler MCMC samplers for complex models as it allows sampling only on a limited number of parameters in the model. We will demonstrate how our approach can extend the class of models that could benefit from INLA, and how the R-INLA package will ease its implementation. We will go through simple examples of this new approach before we discuss more advanced applications with datasets taken from the relevant literature. In particular, INLA within MCMC will be used to fit models with Laplace priors in a Bayesian Lasso model, imputation of missing covariates in linear models, fitting spatial econometrics models with complex nonlinear terms in the linear predictor and classification of data with mixture models. Furthermore, in some of the examples we could exploit INLA within MCMC to make joint inference on an ensemble of model parameters.

Identification of biomarkers for tuberculosis susceptibility via integrated analysis of gene expression and longitudinal clinical data.

Tuberculosis (TB) is an infectious disease caused by the bacteria Mycobacterium tuberculosis (Mtb) that affects millions of people worldwide. The majority of individuals who are exposed to Mtb develop latent infections, in which an immunological response to Mtb antigens is present but there is no clinical evidence of disease. Because currently available tests cannot differentiate latent individuals who are at low risk from those who are highly susceptible to developing active disease, there is considerable interest in the identification of diagnostic biomarkers that can predict reactivation of latent TB. We present results from our analysis of a controlled longitudinal experiment in which a group of rhesus macaques were exposed to a low dose of Mtb to study their progression to latent infection or active disease. Subsets of the animals were then euthanized at scheduled time points, and granulomas taken from their lungs were assayed for gene expression using microarrays. The clinical profiles associated with the animals following Mtb exposure revealed considerable variability, and we developed models for the disease trajectory for each subject using a Bayesian hierarchical B-spline approach. Disease severity estimates were derived from these fitted curves and included as covariates in linear models to identify genes significantly associated with disease progression. Our results demonstrate that the incorporation of clinical data increases the value of information extracted from the expression profiles and contributes to the identification of predictive biomarkers for TB susceptibility.

Model selection strategies for identifying most relevant covariates in homoscedastic linear models

A new method in two variations for the identification of most relevant covariates in linear models with homoscedastic errors is proposed. In contrast to many known selection criteria, the method is based on an interpretable scaled quantity. This quantity measures a maximal relative error one makes by selecting covariates from a given set of all available covariates. The proposed model selection procedures rely on asymptotic normality of test statistics, and therefore normality of the errors in the regression model is not required. In a simulation study the performance of the suggested methods along with the performance of the standard model selection criteria AIC, BIC, Lasso and relaxed Lasso is examined. The simulation study illustrates the favorable performance of the proposed method as compared to the above reference criteria, especially when regression effects possess influence of several orders in magnitude. The accuracy of the normal approximation to the test statistics is also investigated; it has been already satisfactory for sample sizes 50 and 100. As an illustration the US college spending data from 1994 is analyzed.

Progressive Statistics for Studies in Sports Medicine and Exercise Science

Statistical guidelines and expert statements are now available to assist in the analysis and reporting of studies in some biomedical disciplines. We present here a more progressive resource for sample-based studies, meta-analyses, and case studies in sports medicine and exercise science. We offer forthright advice on the following controversial or novel issues: using precision of estimation for inferences about population effects in preference to null-hypothesis testing, which is inadequate for assessing clinical or practical importance; justifying sample size via acceptable precision or confidence for clinical decisions rather than via adequate power for statistical significance; showing SD rather than SEM, to better communicate the magnitude of differences in means and nonuniformity of error; avoiding purely nonparametric analyses, which cannot provide inferences about magnitude and are unnecessary; using regression statistics in validity studies, in preference to the impractical and biased limits of agreement; making greater use of qualitative methods to enrich sample-based quantitative projects; and seeking ethics approval for public access to the depersonalized raw data of a study, to address the need for more scrutiny of research and better meta-analyses. Advice on less contentious issues includes the following: using covariates in linear models to adjust for confounders, to account for individual differences, and to identify potential mechanisms of an effect; using log transformation to deal with nonuniformity of effects and error; identifying and deleting outliers; presenting descriptive, effect, and inferential statistics in appropriate formats; and contending with bias arising from problems with sampling, assignment, blinding, measurement error, and researchers' prejudices. This article should advance the field by stimulating debate, promoting innovative approaches, and serving as a useful checklist for authors, reviewers, and editors.