Pre-statistical harmonization of behavrioal instruments across eight surveys and trials.
Pre-statistical harmonization of behavrioal instruments across eight surveys and trials.
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DOI：
10.1186/s12874-021-01431-6
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发表时间：
2021-10-25
影响因子：
4
通讯作者：
Gross AL
中科院分区：
文献类型：
--
作者：
Chen D;Jutkowitz E;Iosepovici SL;Lin JC;Gross AL
数据协调是平衡评估相同基础构造的度量中的项目的强大方法。有多种措施可以评估痴呆症相关的行为症状。痴呆症研究中行为工具的统计前协调是在测量之间建立统计交叉的第一步。对行为工具进行统计前协调的研究很少以结构化、可重复的方式记录其方法。这是至关重要的一步，需要对源数据进行仔细审查、记录和审查，以确保数据池之前项目之间具有足够的可比性。在这里，我们记录了测量痴呆症患者行为和心理症状的项目的统计前协调。我们为未来的研究提供了一盒推荐的程序。我们确定了用于临床实践、全国调查和痴呆症护理干预随机试验的行为工具。我们严格审查了问题内容和评分程序，以便在数据池之前建立项目之间以及项目质量的足够可比性。此外，我们将编码标准化为 Stata 可读格式，这使我们能够自动识别项目和低质量项目中潜在的交叉研究差异。为了确保模型合理地适合统计协同校准，我们在八项研究的每一项中估计了双参数逻辑项目响应理论模型。我们从 8 个数据集中的 11 个行为工具中识别出 59 个项目。我们发现测量相同属性的项目的管理和编码程序存在相当大的交叉研究异质性。即使看似相似的项目，在行为症状的方向性和量化方面也存在差异。在估计用于统计协同校准的项目响应理论模型之前，我们解决了项目响应异质性、缺失和偏度、条件依赖性。我们使用了几种严格的数据转换程序来解决这些问题，包括重新编码和截断。这项研究强调了行为工具统计前协调过程中涉及的各个方面的重要性。我们为未来的研究如何检测和解释汇集行为和相关工具中的类似问题提供指南和建议。在线版本包含可在 10.1186/s12874-021-01431-6 获取的补充材料。
Data harmonization is a powerful method to equilibrate items in measures that evaluate the same underlying construct. There are multiple measures to evaluate dementia related behavioral symptoms. Pre-statistical harmonization of behavioral instruments in dementia research is the first step to develop a statistical crosswalk between measures. Studies that conduct pre-statistical harmonization of behavioral instruments rarely document their methods in a structured, reproducible manner. This is a crucial step which entails careful review, documentation and scrutiny of source data to ensure sufficient comparability between items prior to data pooling. Here, we document the pre-statistical harmonization of items measuring behavioral and psychological symptoms among people with dementia. We provide a box of recommended procedure for future studies. We identified behavioral instruments that are used in clinical practice, a national survey, and randomized trials of dementia care interventions. We rigorously reviewed question content and scoring procedures to establish sufficient comparability across items as well as item quality prior to data pooling. Additionally, we standardized coding to Stata-readable format, which allowed us to automate approaches to identify potential cross-study differences in items and low-quality items. To ensure reasonable model fit for statistical co-calibration, we estimated two-parameter logistic Item Response Theory models within each of the eight studies. We identified 59 items from 11 behavioral instruments across the eight datasets. We found considerable cross-study heterogeneity in administration and coding procedures for items that measure the same attribute. Discrepancies existed in terms of directionality and quantification of behavioral symptoms for even seemingly comparable items. We resolved item response heterogeneity, missingness and skewness, conditional dependency prior to estimation of item response theory models for statistical co-calibration. We used several rigorous data transformation procedures to address these issues, including re-coding and truncation. This study highlights the importance of each aspect involved in the pre-statistical harmonization process of behavioral instruments. We provide guidelines and recommendations for how future research may detect and account for similar issues in pooling behavioral and related instruments. The online version contains supplementary material available at 10.1186/s12874-021-01431-6.
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DOI：
10.1080/10705519909540118
发表时间：
1999-01-01
影响因子：
6
作者：
Hu, Li-tze;Bentler, Peter M.
通讯作者：
Bentler, Peter M.
影响因子：
5.7
作者：
Gross AL;Sherva R;Mukherjee S;Newhouse S;Kauwe JS;Munsie LM;Waterston LB;Bennett DA;Jones RN;Green RC;Crane PK;Alzheimer's Disease Neuroimaging Initiative;GENAROAD Consortium;AD Genetics Consortium
通讯作者：
AD Genetics Consortium
DOI：
10.1016/j.jalz.2017.04.011
发表时间：
2017-12
期刊：
Alzheimer's & dementia : the journal of the Alzheimer's Association
影响因子：
--
作者：
Crane PK;Trittschuh E;Mukherjee S;Saykin AJ;Sanders RE;Larson EB;McCurry SM;McCormick W;Bowen JD;Grabowski T;Moore M;Bauman J;Gross AL;Keene CD;Bird TD;Gibbons LE;Mez J;Executive Prominent Alzheimer's Disease: Genetics and Risk Factors (EPAD:GRF) Investigators
通讯作者：
Executive Prominent Alzheimer's Disease: Genetics and Risk Factors (EPAD:GRF) Investigators
影响因子：
5.7
作者：
Langa, KM;Plassman, BL;Willis, RJ
通讯作者：
Willis, RJ
影响因子：
3.6
作者：
Bangdiwala, Shrikant I.;Bhargava, Alok;Pratt, Charlotte A.
通讯作者：
Pratt, Charlotte A.