国产bbaaaaa片,成年美女黄网站色视频免费,成年黄大片,а天堂中文最新一区二区三区,成人精品视频一区二区三区尤物

首頁> 外文學(xué)位 >Bayesian propensity score analysis for observational data.
【24h】

Bayesian propensity score analysis for observational data.

機(jī)譯：貝葉斯傾向得分分析用于觀測數(shù)據(jù)。

獲取原文
獲取原文并翻譯 | 示例
  • 摘要
  • 著錄項
  • 相似文獻(xiàn)
  • 相關(guān)主題

摘要

Propensity scores analysis (PSA) involves regression adjustment for the estimated propensity scores, and the method can be used for estimating causal effects from observational data. However, confidence intervals for the treatment effect may be falsely precise because PSA ignores uncertainty in the estimated propensity scores. We propose Bayesian propensity score analysis (BPSA) for observational studies with a binary treatment, binary outcome and measured confounders. The method uses logistic regression models with the propensity score as a latent variable. The first regression models the relationship between the outcome, treatment and propensity score, while the second regression models the relationship between the propensity score and measured confounders. Markov chain Monte Carlo is used to study the posterior distribution of the exposure effect. We demonstrate BPSA in an observational study of the effect of statin therapy on all-cause mortality in patients discharged from Ontario hospitals following acute myocardial infarction. The results illustrate that BPSA and PSA may give different inferences despite the large sample size. We study performance using Monte Carlo simulations. Synthetic datasets are generated using competing models for the outcome variable and various fixed parameter values. The results indicate that if the outcome regression model is correctly specified, in the sense that the outcome risk within treatment groups is a smooth function of the propensity score, then BPSA permits more efficient estimation of the propensity scores compared to PSA. BPSA exploits prior information about the relationship between the outcome variable and the propensity score. This information is ignored by PSA. Conversely, when the model for the outcome variable is misspecified, then BPSA generally performs worse than PSA.
機(jī)譯：傾向得分分析（PSA）涉及對估計的傾向得分進(jìn)行回歸調(diào)整，該方法可用于從觀測數(shù)據(jù)中估計因果效應(yīng)。但是，由于PSA忽略了估計的傾向評分中的不確定性，因此治療效果的置信區(qū)間可能是錯誤的。我們建議對貝葉斯傾向得分分析（BPSA）進(jìn)行具有二元治療，二元結(jié)果和測量的混雜因素的觀察性研究。該方法使用傾向得分作為潛在變量的邏輯回歸模型。第一次回歸模型模擬結(jié)果，治療和傾向評分之間的關(guān)??系，而第二次回歸模型模擬傾向評分和測量的混雜因素之間的關(guān)系。馬爾可夫鏈蒙特卡羅用于研究曝光效果的后驗分布。我們在對急性心肌梗死后從安大略省醫(yī)院出院的患者中，他汀類藥物治療對全因死亡率的影響的觀察性研究中證明了BPSA。結(jié)果表明，盡管樣本量很大，但BPSA和PSA可能會給出不同的推論。我們使用蒙特卡洛模擬研究性能。使用結(jié)果變量和各種固定參數(shù)值的競爭模型來生成綜合數(shù)據(jù)集。結(jié)果表明，如果正確指定了結(jié)果回歸模型，就治療組內(nèi)的結(jié)果風(fēng)險是傾向評分的平穩(wěn)函數(shù)而言，則BPSA與PSA相比可以更有效地估計傾向評分。 BPSA利用有關(guān)結(jié)果變量和傾向得分之間關(guān)系的先驗信息。 PSA將忽略此信息。相反，當(dāng)錯誤指定結(jié)果變量的模型時，BPSA通常會比PSA表現(xiàn)差。

著錄項

  • 作者單位

    The University of British Columbia (Canada).;

  • 授予單位 The University of British Columbia (Canada).;
  • 學(xué)科 Statistics.
  • 學(xué)位 Ph.D.
  • 年度 2007
  • 頁碼 129 p.
  • 總頁數(shù) 129
  • 原文格式 PDF
  • 正文語種 eng
  • 中圖分類
  • 關(guān)鍵詞

相似文獻(xiàn)

  • 外文文獻(xiàn)
  • 中文文獻(xiàn)
  • 專利
獲取原文

客服郵箱：kefu@zhangqiaokeyan.com

京公網(wǎng)安備：11010802029741號 ICP備案號：京ICP備15016152號-6 六維聯(lián)合信息科技 (北京) 有限公司?版權(quán)所有

  • 客服微信

  • 服務(wù)號