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它究竟何时真正开始或停止:删失观测值对持续时间分析的影响。

When did it really start or stop: the impact of censored observations on the analysis of duration.

作者信息

Bressers M, Meelis E, Haccou P, Kruk M

机构信息

Institute of Theoretical Biology, Leiden University, PO Box 9516, 2300 RA Leiden, Netherlands.

Ethofarmacology Group, Medical Faculty, Leiden University, PO Box 9503, 2300 RA Leiden, Netherlands.

出版信息

Behav Processes. 1991 Feb;23(1):1-20. doi: 10.1016/0376-6357(91)90102-6.

Abstract

Behaviour is often described in terms of bout lengths. Because of censoring, some of these bout lengths may only be observed partially. For instance, when observation is finished after a fixed period the end moment of the last bout remains unknown. The only available information on such a bout length is that it exceeds a certain value. This value is the censored observed bout length. Censored data are quite common in ethology, but the problem is often not recognized. Therefore, the well established statistical methods that account for censoring are rarely used in ethology. We report on the consequences of using standard methods instead of methods adjusted to account for censoring. We demonstrate that the usual methods of dealing with censored observations, such as treating them as uncensored observations or omitting them altogether, leads more often to erroneous conclusions. When an unadjusted test is used for testing the equality of two censored samples of bout lengths, the probability of rejecting the null hypothesis when the samples are different is much lower than when an adjusted test is used. Moreover, especially when censoring patterns differ between samples, the probability of wrongly rejecting the null hypothesis may be increased.

摘要

行为通常根据发作时长来描述。由于删失,其中一些发作时长可能只能部分被观察到。例如,当在固定时间段后观察结束时,最后一次发作的结束时刻仍然未知。关于这样一个发作时长的唯一可用信息是它超过了某个值。这个值就是删失观测到的发作时长。删失数据在动物行为学中很常见,但这个问题往往未被认识到。因此,考虑到删失的成熟统计方法在动物行为学中很少被使用。我们报告了使用标准方法而非针对删失进行调整的方法的后果。我们证明,处理删失观测值的常用方法,比如将它们当作未删失观测值处理或完全忽略它们,更常导致错误的结论。当使用未经调整的检验来检验两个删失的发作时长样本是否相等时,样本不同时拒绝原假设的概率比使用经调整的检验时要低得多。此外,特别是当样本之间的删失模式不同时,错误地拒绝原假设的概率可能会增加。

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