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首页> 外文期刊>Quality of life research: An international journal of quality of life aspects of treatment, care and rehabilitation >Measuring fatigue in persons with multiple sclerosis: Creating a crosswalk between the modified fatigue impact scale and the PROMIS fatigue short form
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Measuring fatigue in persons with multiple sclerosis: Creating a crosswalk between the modified fatigue impact scale and the PROMIS fatigue short form

机译:测量多发性硬化症患者的疲劳:在修改后的疲劳影响量表和PROMIS疲劳简写之间创建人行横道

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Purpose To create cross-walk tables to associate scores for the Modified Fatigue Impact Scale (MFIS) with scores for the Patient Reported Outcome Measurement Information System (PROMIS) Fatigue Short Form (SF) in persons with Multiple Sclerosis (MS). Methods Cross-walk tables were created using equipercentile linking and based on data collected at one time point in a longitudinal study of persons with MS (N = 458). Validation of the tables was conducted using data collected at a subsequent time point (N = 444). Deviations between estimates and actual scores were compared across levels of fatigue. The impact of sample size on the precision of sample mean estimates was evaluated using bootstrapping. Results Correlations between deviations and fatigue level for the PROMIS Fatigue SF and MFIS were (-0.31) and (-0.30), respectively, indicating moderately greater deviations with lower fatigue scores. Estimated sample means were impacted by sample size. Conclusions Cross-walk tables allow data from studies using different measures of fatigue to be combined to achieve larger sample sizes and to compare results. These tables are valid for group-level analyses with sample sizes of 150 or greater.
机译:目的创建交叉表,以将修正的疲劳影响量表(MFIS)的得分与多发性硬化症(MS)患者的患者报告结果测量信息系统(PROMIS)疲劳简短形式(SF)的得分相关联。方法采用等百分位数链接并根据一项针对MS患者的纵向研究(N = 458)在某个时间点收集的数据创建横走表。使用在随后的时间点(N = 444)收集的数据对表进行验证。在疲劳水平上比较了估计值和实际分数之间的差异。使用自举法评估样本量对样本均值估计精度的影响。结果PROMIS Fatigue SF和MFIS的偏差与疲劳水平之间的相关性分别为(-0.31)和(-0.30),表明偏差适度较大,且疲劳评分较低。估计的样本均值受到样本大小的影响。结论交叉行走表可以将使用不同疲劳测量方法的研究数据组合起来,以获取更大的样本量并比较结果。这些表对于样本大小为150或更大的组级别分析有效。

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