Statistical approaches to suicidal risk factor analysis.
Statistical approaches to suicidal risk factor analysis.
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DOI：
10.1111/j.1749-6632.1986.tb27883.x
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发表时间：
1986-01-01
影响因子：
5.2
通讯作者：
Cohen, J
中科院分区：
文献类型：
--
作者：
Cohen, J
自杀研究是一个特别困难的领域，主要是因为基本比率问题和病例查找不足。自杀研究中传统的项目分析和多元回归或判别函数数据分析方法在技术上受到了批评，包括随机大写、交叉验证失败以及相关程度与其统计意义的混淆。当研究数据库错误地反映了非常低的真实基准率时，这些错误就会进一步混淆。然而，项目分析以及传统和逐步多元回归方法最严重的缺陷是它们没有考虑到自杀危险因素的因果结构。逐步多元回归/相关分析为自杀研究提供了有效的工具。它利用了回归分析强大的一般数据分析功能，但以一种代表假定风险因素的因果结构的方式这样做。此外，还推荐了更为复杂的因果模型分析方法。然而，我不认为对自杀的理解的进步主要在于所采用的统计程序的改进。即使有了最优的程序，我们也不太可能利用心理社会风险因素来增加自杀的可预测性。最近关于自杀的生物化学的研究提供了一些希望。如果我们能在现有的社会心理因素的基础上，加入相关的生物因素及其与社会心理因素的相互作用，就有可能开发出理解、预测和预防自杀所必需的因果模型。
Suicide research is a particularly difficult area primarily because of the base rate problem and inadequate case finding. Traditional item-analytic and multiple regression or discriminant function data-analytic methods in suicide research are criticized on several technical grounds, including capitalization on chance, failure to cross-validate, and confusion of the degree of relationship with its statistical significance. These errors are further confounded when the research data base misrepresents the very low true base rate. However, the most serious defect in item-analytic and both conventional and stepwise multiple regression procedures is their failure to take into account the causal structure of suicide risk factors. Setwise hierarchical multiple regression/correlation analysis is offered as an effective tool for suicide research. It capitalizes on the powerful general data-analytic features of regression analysis, but does so in a way that represents the causal structure of the putative risk factors. The more complex methods of causal models analysis are also recommended. I do not believe, however, that progress in the understanding of suicide lies mainly in the improvement of the statistical procedures employed. Even with optimal procedures, the amount by which we can expect to increase the predictability of suicidality using psychosocial risk factors is not likely to be large. Recent research in the biochemistry of suicide offers some hope. If to the psychosocial factors now employed we can add relevant biological factors and their interactions with psychosocial factors, we may be able to develop the causal models necessary for the understanding, prediction, and prevention of suicide.