Results 261 to 270 of about 19,276,699 (283)
Identifying systemic vulnerability phenotypes associated with programmed regimens in frozen embryo transfer: a secondary analysis of a multicentre randomized clinical trial. [PDF]
Front Med (Lausanne)Chen J, Li H, Wang M, Cai B, Qu J, Li Y.
europepmc +1 more source
Constructing an External Control Arm Using the French REALYSA Cohort to Replicate Outcomes of a Randomized Trial Arm in Advanced Hodgkin Lymphoma. [PDF]
Clin Pharmacol TherFebvey-Combes O +8 more
europepmc +1 more source
The average treatment effect and average partial effect in nonlinear models [PDF]
, 2007 In the literature on program impact evaluation, the popular impact parameters can the average treatment effect, the average treatment effect on the treated, the average partial effect, and the average partial effect on the treated. In empirical studies, these parameters are not always presented and estimated clearly. In addition, when outcome functions
Nguyen Viet, Cuong
core +3 more sources
Some of the next articles are maybe not open access.
Related searches:
Related searches:
Estimating average treatment effect by model averaging
Economics Letters, 2015zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yichen Gao, Wei Long, Zhengwei Wang
openaire +2 more sources
On estimating average effects for multiple treatment groups
Statistics in Medicine, 2012We propose to estimate average exposure (or treatment) effects from observational data for multiple exposure groups by fitting an approximation of the marginal sample distribution of the response variable in each exposure group to the data. The marginal sample distribution is a function of the true distribution of the response variable in the ...
Landsman, V., Pfeiffer, R. M.
openaire +2 more sources
Model averaging for treatment effect estimation in subgroups
Pharmaceutical Statistics, 2016AbstractIn many clinical trials, biological, pharmacological, or clinical information is used to define candidate subgroups of patients that might have a differential treatment effect. Once the trial results are available, interest will focus on subgroups with an increased treatment effect.
Björn, Bornkamp +3 more
openaire +2 more sources
Subclassification estimation of the weighted average treatment effect
Biometrical Journal, 2021AbstractWeighting and subclassification are popular approaches using propensity scores (PSs) for estimation of causal effects. Weighting is appealing in that it gives consistent estimators for various causal estimands if appropriate weights are well defined and the PS model is correctly specified.
openaire +3 more sources
Model averaging for estimating treatment effects
Annals of the Institute of Statistical Mathematics, 2023zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Zhao, Zhihao +4 more
openaire +1 more source
The Sample Average Treatment Effect
2018In cluster randomized trials (CRTs), the study units usually are not a simple random sample from some clearly defined target population. Instead, the target population tends to be hypothetical or ill-defined, and the selection of study units tends to be systematic, driven by logistical and practical considerations.
Laura B. Balzer +2 more
openaire +1 more source
Biometrical Journal, 2017
Propensity score based statistical methods, such as matching, regression, stratification, inverse probability weighting (IPW), and doubly robust (DR) estimating equations, have become popular in estimating average treatment effect (ATE) and average treatment effect among treated (ATT) in observational studies.
Abdia, Younathan +4 more
openaire +2 more sources
Propensity score based statistical methods, such as matching, regression, stratification, inverse probability weighting (IPW), and doubly robust (DR) estimating equations, have become popular in estimating average treatment effect (ATE) and average treatment effect among treated (ATT) in observational studies.
Abdia, Younathan +4 more
openaire +2 more sources