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基于假设的证据权重:一种评估因果关系的方法及其在监管毒理学中的应用。

Hypothesis-Based Weight of Evidence: An Approach to Assessing Causation and its Application to Regulatory Toxicology.

作者信息

Rhomberg Lorenz

机构信息

Gradient, 20 University Road, Cambridge, MA, USA.

出版信息

Risk Anal. 2015 Jun;35(6):1114-24. doi: 10.1111/risa.12206. Epub 2014 Apr 11.

Abstract

Other papers in this symposium focus on combining direct observations or measurements of a phenomenon of interest. Here, I consider the distinct problem of integrating diverse kinds of data to address the scientific case for toxicological causation in view of information that usually contains gaps and outright contradictions. Existing weight-of-evidence approaches have been criticized as either too formulaic or too vague, simply calling for professional judgment that is hard to trace to its scientific basis. I discuss an approach-hypothesis-based weight of evidence-that emphasizes articulation of the hypothesized generalizations, their basis, and span of applicability. Hypothesized common processes should be expected to act elsewhere as well-in different species or different tissues-and so outcomes that ought to be affected become part of the evidence evaluation. A compelling hypothesis is one that provides a common unified explanation for observed results. Any apparent exceptions and failures to account for some data must be plausibly explained. Ad hoc additions to the explanations introduced to "save" hypotheses from apparent contradiction weaken the degree to which available data test causal propositions. In the end, we need an "account" of all the results at hand, specifying what is ascribed to hypothesized common causal processes and what to special exceptions, chance, or other factors. Evidence is weighed by considering comparative plausibility of an account including the proposed causal effect versus an alternative that explains all of the results at hand otherwise.

摘要

本次研讨会的其他论文聚焦于对感兴趣的现象进行直接观察或测量的结合。在此,鉴于通常包含空白和明显矛盾的信息,我考虑整合各类数据以解决毒理学因果关系科学案例这一独特问题。现有的证据权重方法受到批评,要么过于公式化,要么过于模糊,只是要求难以追溯其科学依据的专业判断。我讨论一种基于假设的证据权重方法,该方法强调对假设的概括、其依据及适用范围的阐述。假设的共同过程应该也会在其他地方起作用——在不同物种或不同组织中——因此应该受到影响的结果成为证据评估的一部分。一个有说服力的假设是能为观察到的结果提供共同统一解释的假设。任何明显的例外情况以及未能解释某些数据的情况都必须得到合理说明。为使假设免遭明显矛盾而对解释进行的临时补充会削弱现有数据对因果命题的检验程度。最后,我们需要对所有现有结果作出“说明”,明确哪些归因于假设的共同因果过程,哪些归因于特殊例外、偶然性或其他因素。通过考虑一种解释(包括所提出的因果效应)与另一种以其他方式解释所有现有结果的解释的相对合理性来权衡证据。

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