| 甘秀敏,赵燕,马烨,吴亚松,赵德才.脱落率加权调整在医学重复测量资料敏感性分析中的应用及其SAS程序实现[J].Chinese journal of Epidemiology,2021,42(6):1118-1123 |
| 脱落率加权调整在医学重复测量资料敏感性分析中的应用及其SAS程序实现 |
| Application of weighted adjustments of dropout rates in sensitivity analysis of medical repeated measurements data and implementation with SAS |
| Received:December 17, 2020 |
| DOI：10.3760/cma.j.cn112338-20201217-01414 |
| KeyWord: 脱落 重复测量资料 模式混合模型 调整 敏感性分析 |
| English Key Word: Dropout Repeated measurements data Pattern-mixture models Adjustments Sensitivity analysis |
| FundProject:国家科技重大专项（2018ZX10302-102-003）；中国疾病预防控制中心性病艾滋病预防控制中心青年科研基金（2018AFQN006） |
| Author Name | Affiliation | E-mail | | Gan Xiumin | National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China | | | Zhao Yan | National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China | | | Ma Ye | National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China | | | Wu Yasong | National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China | | | Zhao Decai | National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China | zdcdc@chinaaids.cn |
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| Abstract: |
| 目的 探讨脱落率加权调整在医学重复测量资料敏感性分析中的应用和SAS实现过程。方法 运用SAS 9.4软件编写SAS程序，采用重复测量混合效应模型对多变量重复测量资料进行协方差分析；同时，分别引入试验总体脱落率和各组脱落率，构建基于脱落率加权调整的模式混合模型进行敏感性分析。结果 重复测量资料安慰剂组、低剂量组和高剂量组的脱落率分别为8.77%、11.79%和16.15%，各组脱落率之间的差异有统计学意义（P=0.025）；混合效应模型结果显示，试验高、低剂量组与安慰剂组疗效指标较基线改变量之间的差异均有统计学意义（P=0.008和P=0.002）；使用试验各组脱落率进行加权调整的模式混合模型敏感性分析结果与重复测量混合效应模型结果一致。结论 基于脱落率加权调整的模式混合模型可应用于医学重复测量资料敏感性分析中；SAS程序编写可为脱落率加权调整在医学重复测量资料敏感性分析中的推广应用提供实践依据。 |
| English Abstract: |
| Objective To explore the application of weighted adjustments of dropout rates in sensitivity analysis of medically repeated measurements data and the implementation with SAS 9.4 software. Methods By compiling SAS codes, mixed-effects models for repeated measures were used to conduct the covariance analysis of multivariable repeated measurements data. Meanwhile, the overall dropout rate and the dropout rates of each group were used to make weighted adjustments by applying pattern-mixture models, which was considered to be a sensitivity analysis to validate the stability of results. Results The dropout rates of placebo group, low-dose and high-dose groups were 8.77%, 11.79% and 16.15%, respectively, the differences were significant (P=0.025). The results of mixed-effects models for repeated measures showed the differences of curative effect indicators changes from baselines of between high-dose, low-dose groups and placebo group were significant (P=0.008 and P=0.002). The results of pattern-mixture models considering weighted adjustments of the respective groups' dropout rates were consistent with those of mixed-effects models for repeated measures. Conclusions The pattern-mixture models considering weighted adjustments of dropout rates can be used in the sensitivity analysis of repeated measurements data. The SAS codes can provide a practical basis for the popularization and application of weighted adjustments of dropout rates in the sensitivity analysis of repeated measurements data. |
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