Results 21 to 30 of about 1,389,910 (274)

SPsimSeq : semi-parametric simulation of bulk and single-cell RNA-sequencing data [PDF]

, 2020
SPsimSeq is a semi-parametric simulation method to generate bulk and single-cell RNA-sequencing data. It is designed to simulate gene expression data with maximal retention of the characteristics of real data.
Assefa, Alemu Takele, Thas, Olivier, Vandesompele, Jo   +2 more
core   +1 more source

Study Designs and Statistical Analyses for Biomarker Research

Sensors, 2012
Biomarkers are becoming increasingly important for streamlining drug discovery and development. In addition, biomarkers are widely expected to be used as a tool for disease diagnosis, personalized medication, and surrogate endpoints in clinical research.
Yasunori Sato, Kengo Nagashima, Masahiko Gosho   +2 more
doaj   +1 more source

Need for and practical interpretations of the person-year construct in neuropsychiatric research

Indian Journal of Psychological Medicine, 2019
In observational studies, groups of interest may be carved out of predictors of interest. Thus, for example, if cardiovascular (CVS) health at age 50 years is the predictor of interest for dementia as the long-term outcome, groups of interest could ...
Chittaranjan Andrade
doaj   +1 more source

Genetic ancestry influences gene-environment interactions with sociocultural factors: Results from the Hispanic Community Health Study/Study of Latinos

HGG Advances
Summary: Often, studies will aggregate all participants identified as Hispanic/Latino, despite genetic and environmental substructures, preventing the meaningful interrogation of the roles of genetics and environment in human health.
Jayati Sharma, Cristin E. McArdle, Mariaelisa Graff, Christina Cordero, Martha Daviglus, Linda C. Gallo, Carmen R. Isasi, Tanika N. Kelly, Krista M. Perreira, Gregory A. Talavera, Jianwen Cai, Kari E. North, Lindsay Fernández-Rhodes, Genevieve L. Wojcik   +13 more
doaj   +1 more source

The Deductive Approach to Causal Inference

Journal of Causal Inference, 2014
This paper reviews concepts, principles, and tools that have led to a coherent mathematical theory that unifies the graphical, structural, and potential outcome approaches to causal inference. The theory provides solutions to a number of pending problems
Pearl Judea
doaj   +1 more source

Confounding and Statistical Significance of Indirect Effects: Childhood Adversity, Education, Smoking, and Anxious and Depressive Symptomatology

Frontiers in Psychology, 2017
The life course perspective, the risky families model, and stress-and-coping models provide the rationale for assessing the role of smoking as a mediator in the association between childhood adversity and anxious and depressive symptomatology (ADS) in ...
Mashhood Ahmed Sheikh
doaj   +1 more source

Targeted Maximum Likelihood Estimation for Dynamic and Static Longitudinal Marginal Structural Working Models

Journal of Causal Inference, 2014
This paper describes a targeted maximum likelihood estimator (TMLE) for the parameters of longitudinal static and dynamic marginal structural models. We consider a longitudinal data structure consisting of baseline covariates, time-dependent intervention
Petersen Maya, Schwab Joshua, Gruber Susan, Blaser Nello, Schomaker Michael, van der Laan Mark   +5 more
doaj   +1 more source

Third Variable Effects in Management Studies

Problemy Zarządzania, 2016
The article’s aim is to explain the third variable effects in management studies –mediation, suppression, and confounding. Examples of these three types of the third variable effects are based on the European Social Survey (2012) data.
Anna Olga Kuźmińska
doaj   +1 more source

Causal inference taking into account unobserved confounding

, 2017
Causal inference with observational data can be performed under an assumption of no unobserved confounders (unconfoundedness assumption). There is, however, seldom clear subject-matter or empirical evidence for such an assumption.
de Luna, Xavier, Genbäck, Minna
core   +1 more source

DFW: a novel weighting scheme for covariate balancing and treatment effect estimation

Frontiers in Applied Mathematics and Statistics
Estimating causal effects from observational data is challenging due to selection bias, which leads to imbalanced covariate distributions across treatment groups.
Ahmad Saeed Khan, Erik Schaffernicht, Johannes Andreas Stork   +2 more
doaj   +1 more source