How much missing data is too much? Monte Carlo simulations to develop SISAQOL guidelines for missing data handling

Mazza Gina L.; Coens Corned; Pe Madeline; Dorme Lien; Oliver Kathy; Bottomley Andrew; Sloan Jeff A.; Dueck Amylou C.

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How much missing data is too much? Monte Carlo simulations to develop SISAQOL guidelines for missing data handling

机译：多少缺少数据太多了？ Monte Carlo模拟开发Sisaqol用于缺少数据处理的指南

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来源
《Quality of life research: An international journal of quality of life aspects of treatment, care and rehabilitation》 |2018年第1期|共1页
作者
Mazza Gina L.; Coens Corned; Pe Madeline; Dorme Lien; Oliver Kathy; Bottomley Andrew; Sloan Jeff A.; Dueck Amylou C.;
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Mayo Clin Scottsdale AZ USA;

European Org Res Treatment Canc Brussels Belgium;

European Org Res Treatment Canc Brussels Belgium;

European Org Res Treatment Canc Brussels Belgium;

Int Brain Tumour Alliance Tadworth England;

European Org Res Treatment Canc Brussels Belgium;

Mayo Clin Rochester MN USA;

Mayo Clin Scottsdale AZ USA;

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原文格式 PDF
正文语种 eng
中图分类医药、卫生;
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1. How much missing data is too much? Monte Carlo simulations to develop SISAQOL guidelines for missing data handling [J] . Mazza Gina L., Coens Corned, Pe Madeline, Quality of life research: An international journal of quality of life aspects of treatment, care and rehabilitation . 2018,第Suppla1期

机译：多少缺少数据太多了？ Monte Carlo模拟开发Sisaqol用于缺少数据处理的指南
2. Monte carlo simulations evaluating the impact of missing data on between-arm comparisons on summary measures in randomized trials [J] . Mazza Gina L., Kunze Katie L., DeWees Todd A., Quality of life research: An international journal of quality of life aspects of treatment, care and rehabilitation . 2019,第Suppla1期

机译：蒙特卡罗模拟评估缺失数据对随机试验中的总结措施之间逐步比较的影响
3. Planned missing-data designs in experience-sampling research: Monte Carlo simulations of efficient designs for assessing within-person constructs [J] . Paul J. Silvia, Thomas R. Kwapil, Molly A. Walsh, Behavior Research Methods . 2014,第1期

机译：经验采样研究中的计划缺失数据设计：蒙特卡罗评估人内部建筑的高效设计模拟
4. Design Guidelines for Mobile Group Recommender Systems to Handle Inaccurate or Missing Location Data [C] . Markus Tschersich Proceedings of the Fifth ACM conference on recommender systems. . 2011

机译：移动组推荐系统处理不正确或丢失位置数据的设计准则
5. Streamlining Missing Data Analysis by Aggregating Multiple Imputations at the Data Level: A Monte Carlo Simulation to Assess the Tenability of the SuperMatrix Approach [D] . Lang, Kyle M. 2013

机译：通过在数据级别聚合多个插补来简化丢失的数据分析：评估超矩阵方法的持久性的蒙特卡洛模拟
6. Planned Missing Data Designs in Experience Sampling Research: Monte Carlo Simulations of Efficient Designs for Assessing Within-Person Constructs [O] . Paul J. Silvia, Thomas R. Kwapil, Molly A. Walsh, -1

机译：经验抽样研究中的计划中的缺失数据设计：评估个人内部构造的高效设计的蒙特卡洛模拟
7. Missing data in multiple item scales: A Monte Carlo analysis of missing data techniques [O] . Philip L. Roth, Fred S. Switzer Iii, Deborah M. Switzer 1999

机译：多项目规模中缺少数据：对缺失数据技术的蒙特卡洛分析

How much missing data is too much? Monte Carlo simulations to develop SISAQOL guidelines for missing data handling

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