Coffman Cynthia J, Edelman David, Woolson Robert F
Health Services Research, Durham Veterans Affairs Medical Center, Durham, North Carolina, USA.
Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, North Carolina, USA.
BMJ Open. 2016 Dec 30;6(12):e013096. doi: 10.1136/bmjopen-2016-013096.
The statistical analysis for a 2-arm randomised controlled trial (RCT) with a baseline outcome followed by a few assessments at fixed follow-up times typically invokes traditional analytic methods (eg, analysis of covariance (ANCOVA), longitudinal data analysis (LDA)). 'Constrained' longitudinal data analysis (cLDA) is a well-established unconditional technique that constrains means of baseline to be equal between arms. We use an analysis of fasting lipid profiles from the Group Medical Clinics (GMC) longitudinal RCT on patients with diabetes to illustrate applications of ANCOVA, LDA and cLDA to demonstrate theoretical concepts of these methods including the impact of missing data.
For the analysis of the illustrated example, all models were fit using linear mixed models to participants with only complete data and to participants using all available data.
With complete data (n=195), 95% CI coverage are equivalent for ANCOVA and cLDA with an estimated 11.2 mg/dL (95% CI -19.2 to -3.3; p=0.006) lower mean low-density lipoprotein (LDL) cholesterol in GMC compared with usual care. With all available data (n=233), applying the cLDA model yielded an LDL improvement of 8.9 mg/dL (95% CI -16.7 to -1.0; p=0.03) for GMC compared with usual care. The less efficient, LDA analysis yielded an LDL improvement of 7.2 mg/dL (95% CI -17.2 to 2.8; p=0.15) for GMC compared with usual care.
Under reasonable missing data assumptions, cLDA will yield efficient treatment effect estimates and robust inferential statistics. It may be regarded as the method of choice over ANCOVA and LDA.
对于双臂随机对照试验（RCT），若基线结果之后在固定随访时间进行多次评估，其统计分析通常采用传统分析方法（如协方差分析（ANCOVA）、纵向数据分析（LDA））。“约束”纵向数据分析（cLDA）是一种成熟的无条件技术，它约束双臂之间基线均值相等。我们通过对糖尿病患者进行的团体医疗诊所（GMC）纵向RCT的空腹血脂谱分析，来说明ANCOVA、LDA和cLDA的应用，以展示这些方法的理论概念，包括缺失数据的影响。
对于所举示例的分析，所有模型均使用线性混合模型对仅具有完整数据的参与者以及使用所有可用数据的参与者进行拟合。
在完整数据（n = 195）情况下，ANCOVA和cLDA的95%置信区间覆盖范围相当，与常规护理相比，GMC中估计的平均低密度脂蛋白（LDL）胆固醇水平低11.2mg/dL（95%置信区间 -19.2至 -3.3；p = 0.006）。在所有可用数据（n = 233）情况下，与常规护理相比，应用cLDA模型得出GMC的LDL改善为8.9mg/dL（95%置信区间 -16.7至 -1.0；p = 0.03）。效率较低的LDA分析得出，与常规护理相比，GMC的LDL改善为7.2mg/dL（95%置信区间 -17.2至2.8；p = 0.15）。
在合理的缺失数据假设下，cLDA将产生有效的治疗效果估计值和稳健的推断统计量。它可被视为优于ANCOVA和LDA的首选方法。
