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Information measures and design issues in the study of mortality deceleration: findings for the gamma-Gompertz model
Lifetime Data Analysis ( IF 1.2 ) Pub Date : 2021-02-25 , DOI: 10.1007/s10985-021-09518-4
Marie Böhnstedt 1, 2 , Jutta Gampe 1 , Hein Putter 2
Affiliation  

Mortality deceleration, or the slowing down of death rates at old ages, has been repeatedly investigated, but empirical studies of this phenomenon have produced mixed results. The scarcity of observations at the oldest ages complicates the statistical assessment of mortality deceleration, even in the parsimonious parametric framework of the gamma-Gompertz model considered here. The need for thorough verification of the ages at death can further limit the available data. As logistical constraints may only allow to validate survivors beyond a certain (high) age, samples may be restricted to a certain age range. If we can quantify the effects of the sample size and the age range on the assessment of mortality deceleration, we can make recommendations for study design. For that purpose, we propose applying the concept of the Fisher information and ideas from the theory of optimal design. We compute the Fisher information matrix in the gamma-Gompertz model, and derive information measures for comparing the performance of different study designs. We then discuss interpretations of these measures. The special case in which the frailty variance takes the value of zero and lies on the boundary of the parameter space is given particular attention. The changes in information related to varying sample sizes or age ranges are investigated for specific scenarios. The Fisher information also allows us to study the power of a likelihood ratio test to detect mortality deceleration depending on the study design. We illustrate these methods with a study of mortality among late nineteenth-century French-Canadian birth cohorts.



中文翻译:

死亡率减速研究中的信息测量和设计问题:伽马-Gompertz 模型的发现

死亡率减速,或老年死亡率的减缓,已经被反复研究,但对这种现象的实证研究产生了不同的结果。即使在此处考虑的 gamma-Gompertz 模型的简约参数框架中,对最古老年龄的观察的稀缺也使死亡率减速的统计评估变得复杂。需要彻底核实死亡年龄可能会进一步限制可用数据。由于后勤限制可能只允许验证超过某个(高)年龄的幸存者,因此样本可能仅限于某个年龄范围。如果我们可以量化样本量和年龄范围对死亡率减速评估的影响,我们就可以为研究设计提出建议。为了这个目的,我们建议应用Fisher信息的概念和优化设计理论的思想。我们计算 gamma-Gompertz 模型中的 Fisher 信息矩阵,并推导出用于比较不同研究设计性能的信息度量。然后我们讨论对这些措施的解释。脆弱方差取零值且位于参数空间边界上的特殊情况受到特别关注。针对特定场景调查了与不同样本量或年龄范围相关的信息变化。Fisher 信息还允许我们研究似然比检验的功效,以根据研究设计检测死亡率减速。我们通过对 19 世纪晚期法裔加拿大出生队列死亡率的研究来说明这些方法。

更新日期:2021-02-25
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