Empirical versus theoretical power and type I error (false-positive) rates estimated from real murine aging research data.
Empirical versus theoretical power and type I error (false-positive) rates estimated from real murine aging research data.
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DOI：
10.1016/j.celrep.2021.109560
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发表时间：
2021-08-17
期刊：
影响因子：
8.8
通讯作者：
de Cabo R
中科院分区：
文献类型：
--
作者：
Alfaras I;Ejima K;Vieira Ligo Teixeira C;Di Germanio C;Mitchell SJ;Hamilton S;Ferrucci L;Price NL;Allison DB;Bernier M;de Cabo R
我们评估了一组24个月大的雄性C57 BL/6小鼠的表型变异程度。由于鼠类研究通常使用小样本量，如果不满足通常依赖的残差正态分布假设，则可能会增加I类错误率。在这项研究中，从376只小鼠的经验分布中重新采样3-20只小鼠以创建等离子体模式，这是一种用于计算I型错误率和常用统计检验功效的方法，而无需假设残差的正态分布。虽然研究的所有表型和代谢变量均显示出相当大的变异性，但根据所进行的统计检验，达到足够把握度所需的动物数量明显不同。总的来说，这项工作提供了一个分析，研究人员可以作出明智的决定所需的样本量，以实现统计功率从特定的测量，而无需先验假设的理论分布。Alfaras等人报告说，等离子体模式方法揭示了不同测量的性状具有不同影响力的分布，并且性状类型影响所需的最小样本量。他们的发现扩展了衰老研究的统计和推理工具箱。
We assess the degree of phenotypic variation in a cohort of 24-month-old male C57BL/6 mice. Because murine studies often use small sample sizes, if the commonly relied upon assumption of a normal distribution of residuals is not met, it may inflate type I error rates. In this study, 3–20 mice are resampled from the empirical distributions of 376 mice to create plasmodes, an approach for computing type I error rates and power for commonly used statistical tests without assuming a normal distribution of residuals. While all of the phenotypic and metabolic variables studied show considerable variability, the number of animals required to achieve adequate power is markedly different depending on the statistical test being performed. Overall, this work provides an analysis with which researchers can make informed decisions about the sample size required to achieve statistical power from specific measurements without a priori assumptions of a theoretical distribution. Alfaras et al. report that the plasmode approach reveals that differently measured traits have distributions that affect power differently, and that trait type affects the minimal required sample size. Their findings expand the statistical and inferential toolbox of aging research.
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影响因子：
7.7
作者：
Petr MA;Alfaras I;Krawcyzk M;Bair WN;Mitchell SJ;Morrell CH;Studenski SA;Price NL;Fishbein KW;Spencer RG;Scheibye-Knudsen M;Lakatta EG;Ferrucci L;Aon MA;Bernier M;de Cabo R
通讯作者：
de Cabo R
影响因子：
1.8
作者：
Kaiser, Kathryn A.;Gadbury, Gary L.
通讯作者：
Gadbury, Gary L.
影响因子：
29
作者：
Mitchell SJ;Madrigal-Matute J;Scheibye-Knudsen M;Fang E;Aon M;González-Reyes JA;Cortassa S;Kaushik S;Gonzalez-Freire M;Patel B;Wahl D;Ali A;Calvo-Rubio M;Burón MI;Guiterrez V;Ward TM;Palacios HH;Cai H;Frederick DW;Hine C;Broeskamp F;Habering L;Dawson J;Beasley TM;Wan J;Ikeno Y;Hubbard G;Becker KG;Zhang Y;Bohr VA;Longo DL;Navas P;Ferrucci L;Sinclair DA;Cohen P;Egan JM;Mitchell JR;Baur JA;Allison DB;Anson RM;Villalba JM;Madeo F;Cuervo AM;Pearson KJ;Ingram DK;Bernier M;de Cabo R
通讯作者：
de Cabo R
影响因子：
29
作者：
Mitchell SJ;Bernier M;Mattison JA;Aon MA;Kaiser TA;Anson RM;Ikeno Y;Anderson RM;Ingram DK;de Cabo R
通讯作者：
de Cabo R
影响因子：
16.6
作者：
Martin-Montalvo, Alejandro;Mercken, Evi M.;Mitchell, Sarah J.;Palacios, Hector H.;Mote, Patricia L.;Scheibye-Knudsen, Morten;Gomes, Ana P.;Ward, Theresa M.;Minor, Robin K.;Blouin, Marie-Jose;Schwab, Matthias;Pollak, Michael;Zhang, Yongqing;Yu, Yinbing;Becker, Kevin G.;Bohr, Vilhelm A.;Ingram, Donald K.;Sinclair, David A.;Wolf, Norman S.;Spindler, Stephen R.;Bernier, Michel;de Cabo, Rafael
通讯作者：
de Cabo, Rafael