Schiff Michael
Denver Arthritis Clinic, Denver, Colorado 80230, USA.
Clin Ther. 2003 Mar;25(3):993-1001. doi: 10.1016/s0149-2918(03)80119-4.
Rheumatoid arthritis (RA) is a chronic disease with diverse and fluctuating manifestations. Because no single variable fully captures disease activity or severity, clinical trials of antirheumatic drugs typically employ composite indices, such as the American College of Rheumatology (ACR) core criteria, to assess disease status. Drug effects (as demonstrated with these indices) are usually assessed at discrete time points, which may obscure information about the time of onset or duration of improvements. An alternative methodology is the use of summary measurements based on area under the curve (AUC) analyses of disease activity. For these analyses, response is plotted over time, and the area under the response curve is calculated. Because disease variables are quantified over time, AUC measures summarize the therapeutic effects during the entire course of the trial or treatment course. Trials of RA agents have used AUC-based calculations in data analyses.
We examined the use of summary AUC measurements for the assessment of the effects of antirheumatic therapies.
Results provided by summary measurements from studies identified by a MEDLINE search (years, 1990-2002; search terms, rheumatoid arthritis, clinical trial, American College of Rheumatology, disease activity, and radiographic progression) were evaluated to assess the relevance of AUC analyses in the determination of disease activity.
Results from these trials suggest that summary AUC measurements produce more precise treatment-effect estimates and are more sensitive than end-of-study data to differences between slower and more rapidly acting agents. Some research suggests that AUC analyses may be more stable and more sensitive to interpatient differences than other measures. AUC measures based on numeric ACR scores have been used successfully to determine treatment effects over time.
Summary measurements are more sensitive to treatment differences than single-time-point assessments and should be considered for use in future RA clinical trials.
类风湿关节炎(RA)是一种临床表现多样且波动的慢性疾病。由于没有单一变量能完全反映疾病活动度或严重程度,抗风湿药物的临床试验通常采用复合指标,如美国风湿病学会(ACR)核心标准,来评估疾病状态。药物疗效(通过这些指标体现)通常在离散时间点进行评估,这可能会掩盖改善的起始时间或持续时间的信息。另一种方法是使用基于疾病活动度曲线下面积(AUC)分析的汇总测量值。对于这些分析,将反应随时间作图,并计算反应曲线下的面积。由于疾病变量是随时间进行量化的,AUC测量值总结了整个试验疗程或治疗过程中的治疗效果。RA药物试验在数据分析中已采用基于AUC的计算方法。
我们研究了汇总AUC测量值在评估抗风湿治疗效果中的应用。
对通过MEDLINE检索(年份,1990 - 2002;检索词,类风湿关节炎、临床试验、美国风湿病学会、疾病活动度和影像学进展)确定的研究中的汇总测量结果进行评估,以评估AUC分析在确定疾病活动度方面的相关性。
这些试验的结果表明,汇总AUC测量值能产生更精确的治疗效果估计值,并且比研究结束时的数据对作用较慢和较快的药物之间的差异更敏感。一些研究表明,AUC分析可能比其他测量方法更稳定,对患者间差异更敏感。基于数字ACR评分的AUC测量值已成功用于确定随时间的治疗效果。
汇总测量值比单时间点评估对治疗差异更敏感,应考虑在未来的RA临床试验中使用。