کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
5121966 1486846 2016 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Propensity score model overfitting led to inflated variance of estimated odds ratios
ترجمه فارسی عنوان
بیش از حد مدل تساوی مدل منجر به واریانس فشرده نسبت شانس تخمینی شد
کلمات کلیدی
نمره گرایش، رگرسیون لجستیک، بیش از حد، تنظیم مقابله، نسبت شانس، مقیاس احتمال معکوس،
موضوعات مرتبط
علوم پزشکی و سلامت پزشکی و دندانپزشکی سیاست های بهداشت و سلامت عمومی
چکیده انگلیسی

ObjectiveSimulation studies suggest that the ratio of the number of events to the number of estimated parameters in a logistic regression model should be not less than 10 or 20 to 1 to achieve reliable effect estimates. Applications of propensity score approaches for confounding control in practice, however, do often not consider these recommendations.Study Design and SettingWe conducted extensive Monte Carlo and plasmode simulation studies to investigate the impact of propensity score model overfitting on the performance in estimating conditional and marginal odds ratios using different established propensity score inference approaches. We assessed estimate accuracy and precision as well as associated type I error and type II error rates in testing the null hypothesis of no exposure effect.ResultsFor all inference approaches considered, our simulation study revealed considerably inflated standard errors of effect estimates when using overfitted propensity score models. Overfitting did not considerably affect type I error rates for most inference approaches. However, because of residual confounding, estimation performance and type I error probabilities were unsatisfactory when using propensity score quintile adjustment.ConclusionOverfitting of propensity score models should be avoided to obtain reliable estimates of treatment or exposure effects in individual studies.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Clinical Epidemiology - Volume 80, December 2016, Pages 97-106
نویسندگان
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