Department of Pharmacy, University of Pisa Via Bonanno 6 56126 Pisa Italy giulia.bononi@farm.unipi.it valentina.citi@unipi.it alma.martelli@unipi.it giulio.poli@unipi.it tiziano.tuccinardi@unipi.it carlotta.granchi@unipi.it lara.testai@unipi.it vincenzo.calderone@unipi.it filippo.minutolo@unipi.it.
Department of Pharmacy, University of Pisa Via Bonanno 6 56126 Pisa Italy giulia.bononi@farm.unipi.it valentina.citi@unipi.it alma.martelli@unipi.it giulio.poli@unipi.it tiziano.tuccinardi@unipi.it carlotta.granchi@unipi.it lara.testai@unipi.it vincenzo.calderone@unipi.it filippo.minutolo@unipi.it.
Department of Pharmacy, University of Pisa Via Bonanno 6 56126 Pisa Italy giulia.bononi@farm.unipi.it valentina.citi@unipi.it alma.martelli@unipi.it giulio.poli@unipi.it tiziano.tuccinardi@unipi.it carlotta.granchi@unipi.it lara.testai@unipi.it vincenzo.calderone@unipi.it filippo.minutolo@unipi.it.
Center for Instrument Sharing of the University of Pisa (CISUP) Lungarno Pacinotti 43 56126 Pisa Italy.
Department of Pharmacy, University of Pisa Via Bonanno 6 56126 Pisa Italy giulia.bononi@farm.unipi.it valentina.citi@unipi.it alma.martelli@unipi.it giulio.poli@unipi.it tiziano.tuccinardi@unipi.it carlotta.granchi@unipi.it lara.testai@unipi.it vincenzo.calderone@unipi.it filippo.minutolo@unipi.it.
Department of Pharmacy, University of Pisa Via Bonanno 6 56126 Pisa Italy giulia.bononi@farm.unipi.it valentina.citi@unipi.it alma.martelli@unipi.it giulio.poli@unipi.it tiziano.tuccinardi@unipi.it carlotta.granchi@unipi.it lara.testai@unipi.it vincenzo.calderone@unipi.it filippo.minutolo@unipi.it.
Center for Instrument Sharing of the University of Pisa (CISUP) Lungarno Pacinotti 43 56126 Pisa Italy.
Department of Pharmacy, University of Pisa Via Bonanno 6 56126 Pisa Italy giulia.bononi@farm.unipi.it valentina.citi@unipi.it alma.martelli@unipi.it giulio.poli@unipi.it tiziano.tuccinardi@unipi.it carlotta.granchi@unipi.it lara.testai@unipi.it vincenzo.calderone@unipi.it filippo.minutolo@unipi.it.
Center for Instrument Sharing of the University of Pisa (CISUP) Lungarno Pacinotti 43 56126 Pisa Italy.
Department of Pharmacy, University of Pisa Via Bonanno 6 56126 Pisa Italy giulia.bononi@farm.unipi.it valentina.citi@unipi.it alma.martelli@unipi.it giulio.poli@unipi.it tiziano.tuccinardi@unipi.it carlotta.granchi@unipi.it lara.testai@unipi.it vincenzo.calderone@unipi.it filippo.minutolo@unipi.it.
Center for Instrument Sharing of the University of Pisa (CISUP) Lungarno Pacinotti 43 56126 Pisa Italy.
Department of Pharmacy, University of Pisa Via Bonanno 6 56126 Pisa Italy giulia.bononi@farm.unipi.it valentina.citi@unipi.it alma.martelli@unipi.it giulio.poli@unipi.it tiziano.tuccinardi@unipi.it carlotta.granchi@unipi.it lara.testai@unipi.it vincenzo.calderone@unipi.it filippo.minutolo@unipi.it.
Center for Instrument Sharing of the University of Pisa (CISUP) Lungarno Pacinotti 43 56126 Pisa Italy.
Department of Pharmacy, University of Pisa Via Bonanno 6 56126 Pisa Italy giulia.bononi@farm.unipi.it valentina.citi@unipi.it alma.martelli@unipi.it giulio.poli@unipi.it tiziano.tuccinardi@unipi.it carlotta.granchi@unipi.it lara.testai@unipi.it vincenzo.calderone@unipi.it filippo.minutolo@unipi.it.
Center for Instrument Sharing of the University of Pisa (CISUP) Lungarno Pacinotti 43 56126 Pisa Italy.
Department of Animal Sciences, The Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot 761001, Israel.
School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China.
State Key Laboratory of Chemical Biology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China.
Most propensity score (PS) analysis methods rely on a correctly specified parametric PS model, which may result in biased estimation of the average treatment effect (ATE) when the model is misspecifie...
Propensity scores (PS) are typically evaluated using balance metrics that focus on covariate balance, often without considering their predictive power for the outcome. This approach may not always res...
We conduct a series of simulations alongside an analysis of empirical observational data to compare the performances of weighted treatment effect estimates using the proposed and existing approaches. ...
The simulation and empirical data analysis reveal that our proposed model-averaging approaches for PS estimation consistently yield lower bias and less variability in treatment effect estimates across...
The prognostic score-based model averaging approach for estimating PS can outperform existing model averaging methods. In particular, the estimator using the model averaging prognostic score as a bala...
Using the prognostic score as the balance metric for the PS model averaging enhances the performance of the treatment effect estimator, which can be recommended for a wide variety of situations. When ...
Propensity score matching is commonly used in observational studies to control for confounding and estimate the causal effects of a treatment or exposure. Frequently, in observational studies data are...
Trauma represents the leading cause of nonobstetrical maternal death. How in-hospital outcomes of acutely injured pregnant patients (PP) compares to that of similarly aged nonpregnant control groups (...
The American College of Surgeons Trauma Quality Improvement Program (TQIP) was used to identify traumatically injured females between 2017 and 2019. Propensity score matching on age, race, injury seve...
A total of 1078 traumatically injured pregnant females were identified. Propensity score matching resulted in 990 patients in the PP and CG cohorts. After matching, PPs were more likely to be assault ...
After acute trauma, PPs did not have increased mortality or complications when compared to matched controls, although they were more likely to be victims of assault, directly proceed to the OR, requir...
There has been growing interest in using nonparametric machine learning approaches for propensity score estimation in order to foster robustness against misspecification of the propensity score model....
Spouse bereavement is one of life's greatest stresses and has been suggested to trigger or accelerate cognitive decline and dementia. However, little information is available about the potential brain...
A total of 319 ever-married older adults between the ages of 61 and 90 years underwent comprehensive clinical assessments and multimodal brain imaging including [...
Spouse bereavement was significantly associated with higher cerebral white matter hyperintensity (WMH) volume compared with no spouse bereavement. Interaction and subsequent subgroup analyses showed t...
The findings suggest that the spouse bereavement may contribute to dementia or cognitive decline by increasing cerebrovascular injury, particularly in older individuals and those with no- or low-skill...
Propensity score weighting is a useful tool to make causal or unconfounded comparisons between groups. According to the definition by the Institute of Medicine (IOM), estimates of health care disparit...
The present study introduces a deweighting method that uses two types of propensity scores. One is a function of all covariates of health status and SES variables and is used to weight study subjects ...
The procedure of deweighting is illustrated using a dataset from a right heart catheterization (RHC) study, where it was used to examine whether there was a disparity between black and white patients ...
Deweighting is a promising tool for implementing the IOM-definition of health care disparities. The method is expected to be broadly applied to quantitative research on health care disparities....
Liver resection (LR) is the only recommended effective curative treatment for patients with intrahepatic cholangiocarcinoma (ICC), but the prognosis of patients with ICC is still poor even after curat...
Many spatial phenomena exhibit interference, where exposures at one location may affect the response at other locations. Because interference violates the stable unit treatment value assumption, stand...
Propensity score methods are a popular approach to mitigating confounding bias when estimating causal effects in observational studies. When study units are clustered (eg, patients nested within healt...