Article
Biology
P. R. Rosenbaum, D. B. Rubin
Summary: This article emphasizes the importance of design activities prior to examining outcome variables in experimental or observational studies. Balancing the propensity scores is an aspect of the design of observational studies, which can be achieved through matching or balancing on the propensity score. Controlling for observed covariates is a crucial step from association to causation.
Article
Mathematical & Computational Biology
Marie Salditt, Steffen Nestler
Summary: There has been limited research on nonparametric propensity score estimation in clustered data settings. This article extends existing research by proposing a general algorithm for incorporating random effects into a machine learning model for clustered IPW. The results showed that nonparametric approaches performed well in the absence of unmeasured confounding, and fixed and random effects models reduced bias compared to single-level models in marginal IPW.
STATISTICS IN MEDICINE
(2023)
Review
Mathematical & Computational Biology
Ting-Hsuan Chang, Elizabeth A. Stuart
Summary: Propensity score methods are commonly used to estimate causal effects in observational studies, but their application in clustered data is relatively new. This article presents a framework for estimating causal effects using propensity scores when study units are nested within clusters and are nonrandomly assigned to treatment conditions.
STATISTICS IN MEDICINE
(2022)
Article
Mathematical & Computational Biology
Ting-Hsuan Chang, Trang Quynh Nguyen, Youjin Lee, John W. Jackson, Elizabeth A. Stuart
Summary: This study examines the performance of nonparametric propensity score estimation methods in settings with clustering of individuals and unmeasured cluster-level confounding. The results suggest that nonparametric methods may provide better results when the sample and cluster sizes are large, but they may be more vulnerable to unmeasured cluster-level confounding when the sizes are small, making multilevel logistic regression a potentially better alternative.
STATISTICS IN MEDICINE
(2022)
Article
Oncology
Debashis Ghosh, Arya Amini, Bernard L. Jones, Sana D. Karam
Summary: The exclusion of unmatched observations in propensity score matching affects the generalizability of causal effects. Machine learning methods can help identify the differences between the study population and the unmatched subpopulation.
FRONTIERS IN ONCOLOGY
(2022)
Article
Engineering, Civil
Farman Ali, Bing-Zhao Li, Zulfiqar Ali
Summary: The study discusses the procedure of improving time series data of meteorological indicators for analyzing drought and proposes a new drought index method. Experimental results show a high significant correlation between the STWMSDI and SPI, indicating improved data provides better results in terms of drought indices. The STWMSDI method is a good candidate for accurate drought monitoring.
WATER RESOURCES MANAGEMENT
(2021)
Article
Mathematics, Applied
Sergio Martinez, Maria del Mar Rueda, Maria Dolores Illescas
Summary: This work deals with the problem of selecting the calibration auxiliary vector that minimizes the asymptotic variance of the calibration estimator of distribution function. The optimal dimension of the optimal auxiliary vector is reduced considerably with respect to previous studies so that with a smaller set of points, the minimum of the asymptotic variance can be reached, which in turn allows to improve the efficiency of the estimates.
MATHEMATICAL METHODS IN THE APPLIED SCIENCES
(2022)
Article
Health Care Sciences & Services
Nicolas H. Thurin, Jeremy Jove, Regis Lassalle, Magali Rouyer, Stephanie Lamarque, Pauline Bosco-Levy, Corentin Segalas, Sebastian Schneeweiss, Patrick Blin, Cecile Droz-Perroteau
Summary: This study examines how specific medical procedures may affect treatment effect estimation in propensity score-adjusted comparative studies and proposes a solution. The analysis shows that excluding the immediate pre-exposure time can reduce the risk of including potential instrumental variables and bias in the study.
JOURNAL OF CLINICAL EPIDEMIOLOGY
(2023)
Article
Health Care Sciences & Services
J. Fernando Vera, Carmen Cecilia Sanchez Zuleta, Maria del Mar Rueda
Summary: Survey calibration is a widely used method in medical research to estimate population mean or total score. Traditional calibration techniques may not work when qualitative variables are involved. This article proposes the use of linear calibration with multidimensional scaling-based set of continuous auxiliary variables, avoiding computational problems.
STATISTICAL METHODS IN MEDICAL RESEARCH
(2023)
Editorial Material
Anesthesiology
Benjamin Y. Andrew, M. Alan Brookhart, Rupert Pearse, Karthik Raghunathan, Vijay Krishnamoorthy
Summary: Causal inference in observational research requires careful adjustment for confounding, and one approach is the use of propensity score analyses. This editorial focuses on the role of propensity score-based methods in estimating causal effects from non-randomised observational data. It highlights the details, assumptions, and limitations of these methods and provides guidelines for authors to conduct and report propensity score analyses.
BRITISH JOURNAL OF ANAESTHESIA
(2023)
Review
Clinical Neurology
Tomas Kalincik, Izanne Roos, Sifat Sharmin
Summary: This review discusses the methodological aspects of observational data research in clinical neurology, including data sources, limitations of observational data, and statistical approaches. It focuses on leading clinical themes studied with observational data, such as comparative treatment effectiveness, development of diagnostic criteria, and clinical outcome definitions. The review provides key points to help clinical audience critically evaluate the design and analysis of studies using observational data.
Review
Rheumatology
Ibrahim Almaghlouth, Eleanor Pullenayegum, Dafna D. Gladman, Murray B. Urowitz, Sindhu R. Johnson
Summary: Observational studies provide insights into rheumatic conditions, risk factors, and treatment effects, but may have confounding bias. Propensity score methods can help achieve study group balance and reduce confounding effects in rare disease research.
JOURNAL OF RHEUMATOLOGY
(2021)
Article
Mathematics, Applied
Luis Castro-Martin, Mara del Mar Rueda, Ramon Ferri-Garcia
Summary: The convenience of online surveys has made them increasingly popular for data collection. However, this method often suffers from biased samples. To address this issue, methods like Statistical Matching and Propensity Score Adjustment (PSA) have been proposed. This study suggests combining both techniques and evaluates their performance using real datasets.
JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS
(2022)
Article
Statistics & Probability
Honghe Zhao, Xiaofei Zhang, Shu Yang
Summary: This article proposes a double score matching (DSM) estimator for comparing the effects of multi-level treatment in observational studies. The DSM estimator not only maintains the advantages of matching methods, but also alleviates the model dependence problem through its double robustness.
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
(2022)
Article
Statistics & Probability
Raphael Jauslin, Yves Tille
Summary: Statistical matching aims to integrate two statistical sources, which can be two samples or a sample and the entire population. This paper proposes an efficient method for matching two samples with a weighting scheme, creating a directly usable file that integrates data from both sources.
JOURNAL OF STATISTICAL PLANNING AND INFERENCE
(2023)
