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Variance reduction in the inverse probability weighted estimators for the average treatment effect using the propensity score

Biometrics, 2021
AbstractThe propensity methodology is widely used in medical research to compare different treatments in designs with a nonrandomized treatment allocation. The inverse probability weighted (IPW) estimators are a primary tool for estimating the average treatment effect but the large variance of these estimators is often a significant concern for their ...
Jiangang Liao, Charles Rohde
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A flexible parametric approach for estimating continuous‐time inverse probability of treatment and censoring weights

Statistics in Medicine, 2016
Marginal structural Cox models are used for quantifying marginal treatment effects on outcome event hazard function. Such models are estimated using inverse probability of treatment and censoring (IPTC) weighting, which properly accounts for the impact of time‐dependent confounders, avoiding conditioning on factors on the causal pathway.
Olli Saarela, Zhihui (Amy) Liu
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Acceptability of suvorexant versus lemborexant: An inverse probability of treatment weighting analysis

Journal of Psychiatric Research
We conducted a retrospective study using medical records from Fujita Health University Hospital to evaluate the treatment discontinuation rates of suvorexant and lemborexant in patients with insomnia and to clarify differences in treatment acceptability.
Kota Hatano   +6 more
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Application of Inverse Probability Weighting to Clinical Trials of Treatments for Opioid Use Disorder

2021
Opioid use disorder (OUD) is a chronic condition that affects three million people in the United States and 16 million people worldwide. Randomized controlled trials (RCTs) are the gold standard for OUD, but there are multiple methodological complexities present in the analysis of randomized controlled OUD trials.
openaire   +1 more source

Exploring large weight deletion and the ability to balance confounders when using inverse probability of treatment weighting in the presence of rare treatment decisions

Pharmacoepidemiology and Drug Safety, 2012
ABSTRACTPurposeWhen medications are modified in response to changing clinical conditions, confounding by indication arises that cannot be controlled using traditional adjustment. Inverse probability of treatment weights (IPTWs) can address this confounding given assumptions of no unmeasured confounders and that all patients have a positive probability ...
Ryan D, Kilpatrick   +5 more
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Research on Stock Market Prediction based on Inverse Probability of Treatment Weighting Method.

Science and Technology of Engineering, Chemistry and Environmental Protection
Although randomized controlled trials (RCTs) are considered the best method to evaluate the impact of interventions, their application in the financial industry is not feasible due to ethical and practical issues. Propensity Score Matching (PSM) minimizes the problem of bias in observational studies.
openaire   +1 more source

ADHD and Sex Differences in School Bullying Victimization and Perpetration Based on Inverse Probability Treatment Weighting

Child Psychiatry & Human Development
Adolescents with attention-deficit/hyperactivity disorder (ADHD) exhibit heightened vulnerability to school bullying, with sex-specific patterns. This study analyzed data from the 2021 Korean National Survey on Mental Health of Youth, comprising 6,689 adolescents (ages 12-18; 50.9% male).
Donghyeon, Kim   +2 more
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Survival analysis using inverse probability of treatment weighted methods based on the generalized propensity score

Pharmaceutical Statistics, 2009
AbstractIn survival analysis, treatment effects are commonly evaluated based on survival curves and hazard ratios as causal treatment effects. In observational studies, these estimates may be biased due to confounding factors. The inverse probability of treatment weighted (IPTW) method based on the propensity score is one of the approaches utilized to ...
openaire   +2 more sources

Active Learning for Regression by Inverse Distance Weighting

Information Sciences, 2023
Alberto Bemporad
exaly  

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