AIMC Topic: Propensity Score

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Using machine learning to evaluate treatment effects in multiple-group interrupted time series analysis.

Journal of evaluation in clinical practice
RATIONALE, AIMS, AND OBJECTIVES: Interrupted time series analysis (ITSA) is a popular evaluation methodology in which a single treatment unit's outcome is studied over time, and the intervention is expected to "interrupt" the level and/or trend of th...

Understanding and diagnosing the potential for bias when using machine learning methods with doubly robust causal estimators.

Statistical methods in medical research
Data-adaptive methods have been proposed to estimate nuisance parameters when using doubly robust semiparametric methods for estimating marginal causal effects. However, in the presence of near practical positivity violations, these methods can produ...

Impact of the off-clamp endoscopic robot-assisted simple enucleation (ERASE) of clinical T1 renal tumors on the postoperative renal function: Results from a matched-pair comparison.

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
PURPOSE: To evaluate the surgical and functional outcomes of a matched-paired series of on-clamp vs off-clamp endoscopic robot-assisted simple enucleation (ERASE) and standardized renorraphy in a tertiary referral institution, to search for predictor...

Risk Modeling to Optimize Patient Selection for Management of the Descending Thoracic Aortic Aneurysm.

The Annals of thoracic surgery
BACKGROUND: A single-institutional study comparing early and long-term outcomes of thoracic endovascular aortic repair (TEVAR) and open surgical repair (OSR) was performed to determine the appropriate treatment option for descending thoracic aortic a...

Estimating causal effects for survival (time-to-event) outcomes by combining classification tree analysis and propensity score weighting.

Journal of evaluation in clinical practice
RATIONALE, AIMS AND OBJECTIVES: A common approach to assessing treatment effects in nonrandomized studies with time-to-event outcomes is to estimate propensity scores and compute weights using logistic regression, test for covariate balance, and then...

Estimating Local Costs Associated With Clostridium difficile Infection Using Machine Learning and Electronic Medical Records.

Infection control and hospital epidemiology
BACKGROUND Reported per-patient costs of Clostridium difficile infection (CDI) vary by 2 orders of magnitude among different hospitals, implying that infection control officers need precise, local analyses to guide rational decision making between in...

Collaborative targeted learning using regression shrinkage.

Statistics in medicine
Causal inference practitioners are routinely presented with the challenge of model selection and, in particular, reducing the size of the covariate set with the goal of improving estimation efficiency. Collaborative targeted minimum loss-based estima...