Cluster randomized trials (CRTs) are often prone to selection bias despite randomization. Using a simulation study, we investigated the use of propensity score (PS) based methods in estimating treatment effects in CRTs with selection bias when the outcome is quantitative. Of four PS-based methods (adjustment on PS, inverse weighting, stratification, and optimal full matching method), three successfully corrected the bias, as did an approach using classical multivariable regression. However, they showed poorer statistical efficiency than classical methods, with higher standard error for the treatment effect, and type I error much smaller than the 5% nominal level
International audienceBACKGROUND: Despite randomization, baseline imbalance and confounding bias may...
Abstract Background Despite randomization, baseline imbalance and confounding bias may occur in clus...
This paper considers causal inference and sample selection bias in non-experimental settings in whic...
Despite randomization, selection bias may occur in cluster randomized trials. Classical multivariabl...
Propensity Score Matching (PSM) is a useful method to reduce the impact of Treatment-Selection Bias ...
Propensity score matching (PSM) has become a popular approach for research studies when randomizatio...
This article focuses on the implementation of propensity score matching for clustered data. Differen...
Propensity score analysis has been used to minimize the selection bias in observational studies to i...
Real-world data are increasingly available to investigate real-world' safety and efficacy. However, ...
In epidemiological research, the association between treatment and outcome may vary across values of...
Educational researchers frequently study the impact of treatments or interventions on educational ou...
Randomized clinical trials (RCT) are accepted as the gold-standard approaches to measure effects of ...
Background: Randomized controlled trials are considered the best scientific proof of effectiveness. ...
Background: Randomized controlled trials are considered the best scientific proof of effectiveness. ...
Background: Nursing intervention studies often suffer from a selection bias introduced by failure of...
International audienceBACKGROUND: Despite randomization, baseline imbalance and confounding bias may...
Abstract Background Despite randomization, baseline imbalance and confounding bias may occur in clus...
This paper considers causal inference and sample selection bias in non-experimental settings in whic...
Despite randomization, selection bias may occur in cluster randomized trials. Classical multivariabl...
Propensity Score Matching (PSM) is a useful method to reduce the impact of Treatment-Selection Bias ...
Propensity score matching (PSM) has become a popular approach for research studies when randomizatio...
This article focuses on the implementation of propensity score matching for clustered data. Differen...
Propensity score analysis has been used to minimize the selection bias in observational studies to i...
Real-world data are increasingly available to investigate real-world' safety and efficacy. However, ...
In epidemiological research, the association between treatment and outcome may vary across values of...
Educational researchers frequently study the impact of treatments or interventions on educational ou...
Randomized clinical trials (RCT) are accepted as the gold-standard approaches to measure effects of ...
Background: Randomized controlled trials are considered the best scientific proof of effectiveness. ...
Background: Randomized controlled trials are considered the best scientific proof of effectiveness. ...
Background: Nursing intervention studies often suffer from a selection bias introduced by failure of...
International audienceBACKGROUND: Despite randomization, baseline imbalance and confounding bias may...
Abstract Background Despite randomization, baseline imbalance and confounding bias may occur in clus...
This paper considers causal inference and sample selection bias in non-experimental settings in whic...