Results 31 to 40 of about 155,620 (242)

Unicompartmental compared with total knee replacement for patients with multimorbidities: a cohort study using propensity score stratification and inverse probability weighting

Health Technology Assessment, 2021
Background: Although routine NHS data potentially include all patients, confounding limits their use for causal inference. Methods to minimise confounding in observational studies of implantable devices are required to enable the evaluation of patients ...
Albert Prats-Uribe, Spyros Kolovos, Klara Berencsi, Andrew Carr, Andrew Judge, Alan Silman, Nigel Arden, Irene Petersen, Ian J Douglas, J Mark Wilkinson, David Murray, Jose M Valderas, David J Beard, Sarah E Lamb, M Sanni Ali, Rafael Pinedo-Villanueva, Victoria Y Strauss, Daniel Prieto-Alhambra   +17 more
doaj   +1 more source

Adjustment for treatment changes in epilepsy trials: A comparison of causal methods for time-to-event outcomes [PDF]

, 2017
BACKGROUND: When trials are subject to departures from randomised treatment, simple statistical methods that aim to estimate treatment efficacy, such as per protocol or as treated analyses, typically introduce selection bias. More appropriate methods to
Dodd, S, White, IR, Williamson, P
core   +1 more source

A note on overadjustment in inverse probability weighted estimation [PDF]

Biometrika, 2010
Standardized means, commonly used in observational studies in epidemiology to adjust for potential confounders, are equal to inverse probability weighted means with inverse weights equal to the empirical propensity scores. More refined standardization corresponds with empirical propensity scores computed under more flexible models.
Andrea Rotnitzky, Lingling Li, Xiaochun Li   +2 more
openaire   +3 more sources

Variance reduction in randomised trials by inverse probability weighting using the propensity score. [PDF]

, 2013
In individually randomised controlled trials, adjustment for baseline characteristics is often undertaken to increase precision of the treatment effect estimate. This is usually performed using covariate adjustment in outcome regression models.
Forbes, Andrew, White, Ian R, Williamson, Elizabeth J   +2 more
core   +1 more source

Sensitivity analysis for causal inference using inverse probability weighting [PDF]

Biometrical Journal, 2011
AbstractEvaluation of impact of potential uncontrolled confounding is an important component for causal inference based on observational studies. In this article, we introduce a general framework of sensitivity analysis that is based on inverse probability weighting.
Shen, Changyu, Li, Xiaochun, Li, Lingling, Were, Martin C.   +3 more
openaire   +2 more sources

Inverse Probability Weights for the Analysis of Polytomous Outcomes [PDF]

American Journal of Epidemiology, 2018
Polytomous outcomes are common in epidemiologic studies. Analyses based on multinomial models employ a likelihood that utilizes the data observed in all outcome categories simultaneously and permits inferences regarding associations across outcome categories.
David B, Richardson, Alan C, Kinlaw, Alexander P, Keil, Ashley I, Naimi, Jay S, Kaufman, Stephen R, Cole   +5 more
openaire   +2 more sources

Effectiveness of fluoroquinolone antimicrobials in addition to tetracyclines for Japanese spotted fever: A retrospective analysis using a national inpatient database

International Journal of Infectious Diseases, 2022
Objectives: This study aim to evaluate the effectiveness of fluoroquinolone (FQ) antimicrobial therapy in combination with tetracyclines (TCs) in patients with Japanese spotted fever (JSF) using a nationwide inpatient database in Japan.Methods: We ...
Satoshi Kutsuna, Hiroyuki Ohbe, Hiroki Matsui, Hideo Yasunaga   +3 more
doaj   +1 more source

Interval-cohort designs and bias in the estimation of per-protocol effects: a simulation study

Trials, 2019
Background Randomized trials are considered the gold standard for making inferences about the causal effects of treatments. However, when protocol deviations occur, the baseline randomization of the trial is no longer sufficient to ensure unbiased ...
Jessica G. Young, Rajet Vatsa, Eleanor J. Murray, Miguel A. Hernán   +3 more
doaj   +1 more source

Adjusting for Confounding in Early Postlaunch Settings: Going beyond Logistic Regression Models [PDF]

, 2016
Copyright © 2015 Wolters Kluwer Health, Inc. All rights reserved. Background: Postlaunch data on medical treatments can be analyzed to explore adverse events or relative effectiveness in real-life settings.
Groenwold, RHH, Klungel, OH, Schmidt, AF
core   +1 more source

Comparison of Dynamic Treatment Regimes via Inverse Probability Weighting [PDF]

Basic & Clinical Pharmacology & Toxicology, 2006
Abstract: Appropriate analysis of observational data is our best chance to obtain answers to many questions that involve dynamic treatment regimes. This paper describes a simple method to compare dynamic treatment regimes by artificially censoring subjects and then using inverse probability weighting (IPW) to adjust for any selection bias introduced by
Miguel A, Hernán, Emilie, Lanoy, Dominique, Costagliola, James M, Robins   +3 more
openaire   +2 more sources