The Fitting Optimization Path Analysis on Scale Missing Data: Based on the 507 Patients of Poststroke Depression Measured by SDS

Xiaoying Lv; Ruonan Zhao; Tongsheng Su; Liyun He; Rui Song; Qizhen Wang; Xueyun Yu; Yanbo Zhu

doi:10.1155/2022/5630748

The Fitting Optimization Path Analysis on Scale Missing Data: Based on the 507 Patients of Poststroke Depression Measured by SDS

Evid Based Complement Alternat Med. 2022 Jan 13:2022:5630748. doi: 10.1155/2022/5630748. eCollection 2022.

Authors

Xiaoying Lv^{1

2}, Ruonan Zhao³, Tongsheng Su⁴, Liyun He², Rui Song⁴, Qizhen Wang², Xueyun Yu², Yanbo Zhu⁵

Affiliations

¹ School of Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China.
² Institute of Basic Research In Clinical Medicine, China Academy Of Chinese Medical Sciences, Beijing, China.
³ Xi'an Encephalopathy Hospital of Traditional Chinese Medicine, Shanxi University of Chinese Medicine, Xi'an, China.
⁴ Shanxi Provincial Hospital of Chinese Medicine, Xi'an, Shanxi, China.
⁵ School of Management, Beijing University of Chinese Medicine, Beijing, China.

Abstract

Objective: To explore the optimal fitting path of missing data of the Scale to make the fitting data close to the real situation of patients' data.

Methods: Based on the complete data set of the SDS of 507 patients with stroke, the data simulation sets of Missing Completely at Random (MCAR), Missing at Random (MAR), and Missing Not at Random (MNAR) were constructed by R software, respectively, with missing rates of 5%, 10%, 15%, 20%, 25%, 30%, 35%, and 40% under three missing mechanisms. Mean substitution (MS), random forest regression (RFR), and predictive mean matching (PMM) were used to fit the data. Root mean square error (RMSE), the width of 95% confidence intervals (95% CI), and Spearman correlation coefficient (SCC) were used to evaluate the fitting effect and determine the optimal fitting path.

Results: when dealing with the problem of missing data in scales, the optimal fitting path is ① under the MCAR deletion mechanism, when the deletion proportion is less than 20%, the MS method is the most convenient; when the missing ratio is greater than 20%, RFR algorithm is the best fitting method. ② Under the Mar mechanism, when the deletion ratio is less than 35%, the MS method is the most convenient. When the deletion ratio is greater than 35%, RFR has a better correlation. ③ Under the mechanism of MNAR, RFR is the best data fitting method, especially when the missing proportion is greater than 30%. In reality, when the deletion ratio is small, the complete case deletion method is the most commonly used, but the RFR algorithm can greatly expand the application scope of samples and save the cost of clinical research when the deletion ratio is less than 30%. The best way to deal with data missing should be based on the missing mechanism and proportion of actual data, and choose the best method between the statistical analysis ability of the research team, the effectiveness of the method, and the understanding of readers.