Translated by WANG Weiwei 1 , ZHANG Ge 2 , ZHOU Qingxin 3 , LV Xiaozhen 4 , MENG Ruogu 5 , YANG Zhirong 6 , SUN Feng 3
  • 1. The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, P.R.China;
  • 2. Zhangfan Information Technology (Shanghai) Co., Ltd, Shanghai, 200090, P.R.China;
  • 3. Department of Epidemiology and Biostatistics, School of Public Health, Center of Evidence-based Medicine and Clinical Research, Peking University, Beijing, 100191, P.R.China;
  • 4. Peking University Sixth Hospital, National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing Municipal Key Laboratory for Translational Research on Diagnosis and Treatment of Dementia, Beijing, 100191, P.R.China;
  • 5. National Institute of Health Data Science, Center for Data Science in Health and Medicine, Peking University, Beijing, 100191, P.R.China;
  • 6. Primary Care Unit, School of Clinical Medicine, University of Cambridge, Cambridge, CB1 8RN, UK;
SUN Feng, Email: sunfeng@bjmu.edu.cn
潛類別; LCGA; LGMM; 混合模型; 結構方程模型
10.7507/1672-2531.201910053
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混合模型框架下的模型，如潛變量增長混合模型（latent growth mixture modeling，LGMM）或潛類別增長分析（latent class growth analysis，LCGA），因估算過程中涉及多個決策過程，導致潛變量軌跡分析結果的報告呈現多樣性。為解決這一問題，指南制訂小組按照系統化的制訂流程，通過 4 輪德爾菲法調查，遵循專家小組意見，提出了各領域報告潛變量軌跡分析結果時需采用統一的標準，最終確定了報告軌跡研究結果必要的關鍵條目，發布了潛變量軌跡研究報告規范（guidelines for reporting on latent trajectory studies，GRoLTS），并利用 GRoLTS 評價了 38 篇使用 LGMM 或 LCGA 研究創傷后應激軌跡的論文的報告情況。

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