首页    期刊浏览 2024年11月29日 星期五
登录注册

文章基本信息

  • 标题:ltmle: An R Package Implementing Targeted Minimum Loss-Based Estimation for Longitudinal Data
  • 本地全文:下载
  • 作者:Samuel D. Lendle ; Joshua Schwab ; Maya L. Petersen
  • 期刊名称:Journal of Statistical Software
  • 印刷版ISSN:1548-7660
  • 电子版ISSN:1548-7660
  • 出版年度:2017
  • 卷号:81
  • 期号:1
  • 页码:1-21
  • DOI:10.18637/jss.v081.i01
  • 语种:English
  • 出版社:University of California, Los Angeles
  • 摘要:In recent years, targeted minimum loss-based estimation methodology has been used to develop estimators of parameters in longitudinal data structures (Gruber and van der Laan 2012; Petersen, Schwab, Gruber, Blaser, Schomaker, and van der Laan 2014; Schnitzer, Moodie, van der Laan, Platt, and Klein 2013). These methods are implemented in the ltmle package for R. The ltmle package provides methods to estimate intervention-specific means and measures of association including the average treatment effect, causal odds ratio and causal risk ratio and parameters of a longitudinal working marginal structural model. The package allows for multiple time point treatments, time-varying covariates and right censoring of the outcome. In this paper we described the usage of the ltmle package and provide examples.
  • 关键词:targeted minimum loss-based estimation;longitudinal data;causal inference;estimation;R
国家哲学社会科学文献中心版权所有