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Elizabeth Stuart, Ph.D.nJohns Hopkins Universityn12/01/2015nnMachine learning techniques such as classification and regression trees (CART) have been suggested as promising alternatives to logistic regression for the estimation of propensity scores. The authors examined the performance of various CART-based propensity score models using simulated data. Hypothetical studies of varying sample sizes (n=500, 1000, 2000) with a binary exposure, continuous outcome, and ten covariates were simulated un

PSMG: Improving propensity score weighting using machine learning

⏲ 46 min 20 sec ✓ 08-Dec-2017

TileStats

Covariance and the correlation matrix - with simple examples

⏲ 22 minutes 21 seconds 👁 11.9K

Audiopedia

⏲ 1 minute 56 seconds 👁 6.8K

Biography: nnVirgilio Gómez-Rubio is Associate Professor in the Department of Mathematics at Universidad de CastillaLa Mancha (UCLM) in Spain. Prior to joining UCLM, he was Research Associate in the Department of Epidemiology and Biostatistics, Imperial College London (UK). Dr. Gómez-Rubio has developed and contributed to a number of packages for the R software on spatial data analysis and Bayesian inference. He is also co-author of Springer’s bestselling book Applied Spatial Data Analysis w

Demography Today-Virgilio Gómez Rubio, Disease mapping with R

⏲ 41 min 55 sec ✓ 03-Aug-2017

One Minute Economics

The Covariance Explained in One Minute: Definition, Formula and Examples

⏲ 1 minute 58 seconds 👁 53.6K

The Organic Chemistry Tutor

Correlation Coefficient

⏲ 12 minutes 57 seconds 👁 913.3K

Biography: nnVirgilio Gómez-Rubio is Associate Professor in the Department of Mathematics at Universidad de CastillaLa Mancha (UCLM) in Spain. Prior to joining UCLM, he was Research Associate in the Department of Epidemiology and Biostatistics, Imperial College London (UK). Dr. Gómez-Rubio has developed and contributed to a number of packages for the R software on spatial data analysis and Bayesian inference. He is also co-author of Springer’s bestselling book Applied Spatial Data Analysis w

Demography Today-Virgilio Gómez Rubio, Introduction to spatial data with R: spatial econometrics

⏲ 48 min 45 sec ✓ 28-Jul-2017

ritvikmath

The Covariance Matrix : Data Science Basics

⏲ 11 minutes 👁 109.3K

Biography: nnVirgilio Gómez-Rubio is Associate Professor in the Department of Mathematics at Universidad de CastillaLa Mancha (UCLM) in Spain. Prior to joining UCLM, he was Research Associate in the Department of Epidemiology and Biostatistics, Imperial College London (UK). Dr. Gómez-Rubio has developed and contributed to a number of packages for the R software on spatial data analysis and Bayesian inference. He is also co-author of Springer’s bestselling book Applied Spatial Data Analysis w

Demography Today-Virgilio Gómez Rubio,Introduction to spatial data with R: data classes, visualisation, data import export

⏲ 54 min 80 sec ✓ 28-Jul-2017

N DesainnOne key scientific problem in neuroscience involves assessing how functional connectivity networks in the brain vary across individuals and subject-specific covariates. In this article we introduce a general regression framework for regressing subject-specific connectivity networks on covariates while accounting for inter-edge dependence within the network.

Connectivity Regression

⏲ 1 min 68 sec ✓ 22-Apr-2022

Donald Hedeker, Ph.D.nUniversity of Chicagon03/13/2018nnEcological momentary assessment and/or experience sampling methods are increasingly used in health and psychological studies to study subjective experiences within changing environmental contexts. In these studies, up to 30 or 40 observations are often obtained for each subject, and so these data are also called intensive longitudinal data. Because there are so many measurements per subject, one can characterize a subject’s mean and varia

PSMG: Investigating mood regulation and smoking: Applications of mixed-effects location-scale models for intensive longitudinal

⏲ 50 min 32 sec ✓ 15-Mar-2018

Presented by the ASCPT Family of JournalsnMay, 2023nSpeaker: James A. Rogers, PhDnnThis presentation is intended to foster a few basic intuitions related to causal inference, and to suggest some of the ways that causal inference concepts can ensure adequate planning (prior to analyses) and valid interpretation (after analyses). We will review an example provided in the paper involving interpretation of covariate effects for correlated covariates in a population pharmacokinetic model. Learning ob

An Introduction to Causal Inference for Pharmacometricians

⏲ 34 min 45 sec ✓ 25-May-2023

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