Using the half normal distribution to quantify covariate balance in cluster-randomized pragmatic trials [PDF]
Trials, 2021 Background Pragmatic trials often consist of cluster-randomized controlled trials (C-RCTs), where staff of existing clinics or sites deliver interventions and randomization occurs at the site level.
Jin Huang, David L. Roth
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Covariate balance-related propensity score weighting in estimating overall hazard ratio with distributed survival data [PDF]
BMC Medical Research Methodology, 2023 Background When data is distributed across multiple sites, sharing information at the individual level among sites may be difficult. In these multi-site studies, propensity score model can be fitted with data within each site or data from all sites when ...
Chen Huang +4 more
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Are Randomized Trials Better? Comparison of Baseline Covariate Balance of a Propensity Score-Balanced Lumbar Spine IDE Trial and Comparable RCTs [PDF]
Global Spine JournalStudy Design Prospective Observational Propensity Score. Objectives Randomization may lead to bias when the treatment is unblinded and there is a strong patient preference for treatment arms (such as in spinal device trials).
Greg Maislin MS, MA +9 more
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An empirical exploration of entropy balancing in estimating treatment effects: Insights from simulation and two applied biomedical studies [PDF]
Songklanakarin Journal of Science and Technology (SJST), 2021 We present entropy balancing – a relatively new technique for estimating treatment effects, which has been under-utilised in the applied biomedical literature.
Lateef Amusa +2 more
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BMC Medical Research Methodology, 2021 Background Cardinality matching (CM), a novel matching technique, finds the largest matched sample meeting prespecified balance criteria thereby overcoming limitations of propensity score matching (PSM) associated with limited covariate overlap, which ...
Stephen P. Fortin +2 more
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Quasi-rerandomization for observational studies
BMC Medical Research Methodology, 2023 Background In the causal analysis of observational studies, covariates should be carefully balanced to approximate a randomized experiment. Numerous covariate balancing methods have been proposed for this purpose.
Hengtao Zhang, Wen Su, Guosheng Yin
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Optimal hyperparameter tuning of random forests for estimating causal treatment effects [PDF]
Songklanakarin Journal of Science and Technology (SJST), 2021 Recent studies have expanded the focus of machine learning methods like random forests beyond prediction. They have found utility in the area of causal inference by using it to estimate propensity scores.
Lateef Amusa +2 more
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Covariate balancing based on kernel density estimates for controlled experiments
Statistical Theory and Related Fields, 2021 Controlled experiments are widely used in many applications to investigate the causal relationship between input factors and experimental outcomes. A completely randomised design is usually used to randomly assign treatment levels to experimental units ...
Yiou Li, Lulu Kang, Xiao Huang
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Evaluating Uses of Deep Learning Methods for Causal Inference
IEEE Access, 2022 Logistic regression (LR) is a popular method that is used for estimating causal effects in observational studies using propensity scores. We examine the use of deep learning models such as the deep neural network (DNN), PropensityNet (PN), convolutional ...
Albert Whata, Charles Chimedza
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Optimal balancing of time-dependent confounders for marginal structural models
Journal of Causal Inference, 2021 Marginal structural models (MSMs) can be used to estimate the causal effect of a potentially time-varying treatment in the presence of time-dependent confounding via weighted regression.
Kallus Nathan, Santacatterina Michele
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