23 papers · ranked by Valyu relevance
Peter Craig, Srinivasa Vittal Katikireddi, Alastair Leyland, Frank Popham
'Frank Popham'] Population health interventions are essential to reduce health inequalities and tackle other public health priorities, but they are not always amenable to experimental manipulation. Natural experiment (NE) approaches are attracting growing interest as a way of providing evidence in such circumstances.…
M Sanni Ali, Daniel Prieto-Alhambra, Luciane Cruz Lopes, Dandara Ramos + 7 more
'Dandara Ramos' 'Nivea Bispo' 'Maria Y. Ichihara' 'Julia M. Pescarini' 'Elizabeth Williamson' 'Rosemeire L. Fiaccone' 'Mauricio L. Barreto' 'Liam Smeeth'] Randomized clinical trials (RCT) are accepted as the gold-standard approaches to measure effects of intervention or treatment on outcomes. They are also the designs…
Stephanie L. Mayne, Brian K. Lee, Amy H. Auchincloss, Marc A Adams
After deriving each participant’s propensity score, the scores were used to match or weight treated and control individuals in preparation for the final outcome model. Four different methods were used. Matching was implemented using the MatchIt package in R (Methods 1 and 2) and Matchit with the Optmatch package…
David Kaplan
This paper reviews recent research on causal inference with large-scale assessments in education from a Bayesian perspective. I begin by adopting the potential outcomes model of Rubin (J Educ Psychol 66:688-701, 1974) as a framework for causal inference that I argue is appropriate with large-scale educational…
Felix Thoemmes
Felix Thoemmes, Center for Educational Science and Psychology, University of Tübingen, Europastr. 6, 72072 Tübingen, Germany, felix.thoemmes@gmail.com. Starting January 2012 Felix Thoemmes will be at the Department of Human Development, Cornell University, Ithaca, NY. The author would like to thank Philip Parker for…
Ailish Nimmo, Nicholas Latimer, Gabriel C. Oniscu, Rommel Ravanan + 2 more
'Dominic M. Taylor' 'James Fotheringham'] Inferring causality from observational studies is difficult due to inherent differences in patient characteristics between treated and untreated groups. The randomised controlled trial is the gold standard study design as the random allocation of individuals to treatment and…
Emily Granger, Tim Watkins, Jamie C. Sergeant, Mark Lunt
Background Propensity scores are widely used to deal with confounding bias in medical research. An incorrectly specified propensity score model may lead to residual confounding bias; therefore it is essential to use diagnostics to assess propensity scores in a propensity score analysis. The current use of propensity…
Alberto Arletti, Maria Letizia Tanturri, Omar Paccagnella
Online data has the potential to transform how researchers and companies produce election forecasts. Social media surveys, online panels and even comments scraped from the internet can offer valuable insights into political preferences. However, such data is often affected by significant selection bias, as online…
Evan T. R. Rosenman, Art B. Owen, Michael Baiocchi, Hailey R. Banack
| 1 | Introduction | | 2 | | --- | --- | --- | --- | | 2 | | Notation, assumptions and estimators | 4 | | 2.1 | Model | | 4 | | 2.2 | Stratification | | 5 | | 2.3 | | Estimators | 7 | | 3 | Delta method results | | 8 | | 3.1 | | Population quantities | 9 | | 3.2 | | Main theorem | 10 | | 3.3 | | Delta method means and…
Elizabeth J Williamson, Andrew Forbes, Ian R White
In individually randomised controlled trials, adjustment for baseline characteristics is often undertaken to increase precision of the treatment effect estimate. This is usually performed using covariate adjustment in outcome regression models. An alternative method of adjustment is to use inverse…
Corey Shanbrom, Michelle Norris, Caitlin Esgana, Matthew Krauel + 2 more
The Peer Assisted Learning program at Sacramento State University (PAL) was established in 2012 with one section supporting introductory chemistry. It now serves 17 gatekeeper courses in Biology, Chemistry, Mathematics, Physics, and Statistics, enrolling approximately 1,400 students annually. Adapting the Peer-Led Team…
Olivia Ross, Katherine Siegel, Kathy Baylis, Sara Goeking + 1 more
Field ecologists often rely on observational data to understand the impact of environmental stressors and management interventions on natural systems. Natural and anthropogenic events (e.g. wildfires, protected areas, nutrient deposition) do not occur randomly in space, however, which can introduce bias into…
R. Michael Alvarez, Inés Levin
We compare the performance of standard nearest-neighbor propensity score matching with that of an analogous Bayesian propensity score matching procedure. We show that the Bayesian approach makes better use of available information, as it makes less arbitrary decisions about which observations to drop and which ones to…
Hlynur Davíð Hlynsson
Doubly robust learning offers a robust framework for causal inference from observational data by integrating propensity score and outcome modeling. Despite its theoretical appeal, practical adoption remains limited due to perceived complexity and inaccessible software. This tutorial aims to demystify doubly robust…
Tenglong Li, Jordan Lawson
The inverse probability of treatment weighting (IPTW) approach is commonly used in propensity score analysis to infer causal effects in regression models. Due to oversized IPTW weights and errors associated with propensity score estimation, the IPTW approach can underestimate the standard error of causal effect. To…
F Balazard, S Le Fur, S Valtat, AJ Valleron + 1 more
The incidence of childhood type 1 diabetes (T1D) incidence is rising in many countries, supposedly because of changing environmental factors, which are yet largely unknown. To unravel environmental markers associated with T1D. Methods: Cases were children with T1D from the French Isis-Diab cohort. Controls were…
Eric S. Davis, Wancen Mu, Stuart Lee, Mikhail G. Dozmorov + 2 more
Deriving biological insights from genomic data commonly requires comparing attributes of selected genomic loci to a null set of loci. The selection of this null set is non trivial, as it requires careful consideration of potential covariates, a problem that is exacerbated by the non-uniform distribution of genomic…
Yilin Chen, Pengfei Li, Changbao Wu
We establish a general framework for statistical inferences with non-probability survey samples when relevant auxiliary information is available from a probability survey sample. We develop a rigorous procedure for estimating the propensity scores for units in the non-probability sample, and construct doubly robust…
Lin Li, Mohammadreza Bayat, Timothy B. Hayes, Wesley K. Thompson + 2 more
This paper addresses the challenges of managing missing values within expansive longitudinal neu-roimaging datasets, using the specific example of data derived from the Adolescent Brain and Cog-nitive Development (ABCD^®^) study. The conventional listwise deletion method, while widely used, is not recommended due to…
Xinlei Mi, Patrick Tighe, Fei Zou, Baiming Zou
Deep Treatment Learning (deepTL), a robust yet efficient deep learning-based semiparametric regression approach, is proposed to adjust the complex confounding structures in comparative effectiveness analysis of observational data, e.g. electronic health record (EHR) data, in which complex confounding structures are…
Jin-Hong Du, Maya Shen, Hansruedi Mathys, Kathryn Roeder
Advances in single-cell sequencing and CRISPR technologies have enabled detailed case-control comparisons and experimental perturbations at single-cell resolution. However, uncovering causal relationships in observational genomic data remains challenging due to selection bias and inadequate adjustment for unmeasured…
Joshua Hesse, Davide Boldini, Stephan Sieber
In the rapidly evolving field of drug discovery, High Throughput Screening (HTS) is a pivotal technique for identifying promising compounds. Despite its wide usage, the primary challenge remains in efficiently sifting through vast chemical libraries to discern true bioactive compounds from false positives. This study…
Authors not listed
Solubility is critical in drug discovery and development, as it significantly influences a medication's bioavailability and therapeutic efficacy. Understanding solubility at the early stages of drug discovery is essential for minimizing resource consumption and enhancing the likelihood of clinical success via…