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干擾機制對資料科學帶來的挑戰與契機:以抽煙及肺癌存活的因果關係為例

演講摘要

Complexity of the data may provide challenges for making valid causal inference about a scientific hypothesis (smoking vs. lung cancer mortality), particularly in the presence of unknown confounding (underlying lung function). Mendelian randomization (MR) addresses the issue of unknown confounding by using genetic information as an instrumental variable (IV) to estimate the causal effect of an exposure of interest on an outcome. Despite the popularity of IV analyses in fully observable outcomes, methodology is limited for time-to-event survival outcomes with censoring, a common data structure in biomedical sciences. We propose an IV analysis method in the survival context, estimating causal effects on a transformed survival time and the survival probabilities using semiparametric transformation models. We construct unbiased estimating equations to circumvent the difficulty in deriving joint likelihood of the exposure and the outcome, due to the unknown covariance by confounding. Asymptotic properties of the proposed estimators are established. We apply our methods to conduct an MR study for lung cancer survival, which suggests a harmful prognostic effect by smoking intensity (p=0.0067) that would have been missed by the conventional methods.

講者簡介

黃彥棕
  • 黃彥棕 個人網站
  • 中研院統計所 / 副研究員
  • 黃博士於 2003 自台大醫學系畢業,退伍後前往美國哈佛大學先後取得公共衛生碩士(2006, 主修計量方法)、生物統計碩士(2009)、流行病學及生物統計理學博士雙學位(2012)。研究所畢業後於美國布朗大學流行病學系及生物統計系擔任助理教授(2012-2016),於 2016 年 9 月返台於中研院統計所任職。黃博士之研究曾獲得國際生統學會東北美地區 Research Advisory Board Poster Award(2012)、美國統計學會 David P Byar Young Investigator Travel Award(2012)、布朗大學 Richard Salomon Faculty Research Award(2013)及布朗大學 Junior Research Award in Genetic Studies(2015)。黃博士研究領域包括:生物統計、因果推論、存活分析、流行病學及基因體學。

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