Multivariable risk prediction can greatly enhance the statistical power of clinical trial subgroup analysis.
Multivariable risk prediction can greatly enhance the statistical power of clinical trial subgroup analysis.
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DOI：
10.1186/1471-2288-6-18
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发表时间：
2006-04-13
影响因子：
4
通讯作者：
Hofer, Timothy P
中科院分区：
文献类型：
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作者：
Hayward, Rodney A;Kent, David M;Hofer, Timothy P
背景技术背景：当阳性临床试验的亚组分析无法揭示时，这些发现通常用于论证治疗的益处适用于整个研究人群;然而，这种分析通常受到统计能力差的限制。多变量风险分层分析被认为是研究治疗获益异质性的一个重要进展，但还没有人对影响这种方法与传统亚组分析的相对优点的情况进行系统的统计学检查。方法：使用模拟临床试验，其中个体患者结局的概率由风险因素的存在和以下因素的影响随机确定：在治疗中，我们在各种情况下检查了常规与“风险分层”亚组分析的相对优点，其中有少量均匀分布的治疗相关损害。计算风险分层和常规亚组分析检测治疗效应异质性的统计把握度，同时改变：1）在没有治疗的情况下，个体风险因素的数量、患病率和比值比，2）多变量风险模型的预测性（包括其权重的准确性），3）治疗相关损害的程度，和5）研究人群的平均未治疗风险。常规亚组分析（其中单个患者属性被评估为“一次一个”）最多具有中等统计功效（30%至45%），以检测治疗相关损害导致的治疗净相对风险降低的变化，即使在最佳情况下（该研究的总体统计功效良好，并且在主要风险因素[OR = 3]上评估了治疗效果的异质性）。在某些情况下，多变量风险分层方法也具有低至中等的统计功效（特别是当多变量风险预测工具具有低区分度时）。然而，多变量风险分层方法可以具有很好的统计功效来检测各种情况下净治疗获益的异质性，在这种情况下，传统的亚组分析具有很差的统计功效。这些结果表明，在许多可能的情况下，多变量风险-分层方法在检测治疗获益的异质性方面的统计功效显著高于常规亚组分析，与之前未识别的治疗相关损害相关的安全性。亚组分析必须始终有充分的理由，并谨慎解释，在某些情况下，传统的亚组分析可能是有用的;但是，当有足够的外部开发的风险预测工具时，临床试验报告应包括多变量风险分层分析。
BACKGROUND: When subgroup analyses of a positive clinical trial are unrevealing, such findings are commonly used to argue that the treatment's benefits apply to the entire study population; however, such analyses are often limited by poor statistical power. Multivariable risk-stratified analysis has been proposed as an important advance in investigating heterogeneity in treatment benefits, yet no one has conducted a systematic statistical examination of circumstances influencing the relative merits of this approach vs. conventional subgroup analysis.METHODS: Using simulated clinical trials in which the probability of outcomes in individual patients was stochastically determined by the presence of risk factors and the effects of treatment, we examined the relative merits of a conventional vs. a "risk-stratified" subgroup analysis under a variety of circumstances in which there is a small amount of uniformly distributed treatment-related harm. The statistical power to detect treatment-effect heterogeneity was calculated for risk-stratified and conventional subgroup analysis while varying: 1) the number, prevalence and odds ratios of individual risk factors for risk in the absence of treatment, 2) the predictiveness of the multivariable risk model (including the accuracy of its weights), 3) the degree of treatment-related harm, and 5) the average untreated risk of the study population.RESULTS: Conventional subgroup analysis (in which single patient attributes are evaluated "one-at-a-time") had at best moderate statistical power (30% to 45%) to detect variation in a treatment's net relative risk reduction resulting from treatment-related harm, even under optimal circumstances (overall statistical power of the study was good and treatment-effect heterogeneity was evaluated across a major risk factor [OR = 3]). In some instances a multi-variable risk-stratified approach also had low to moderate statistical power (especially when the multivariable risk prediction tool had low discrimination). However, a multivariable risk-stratified approach can have excellent statistical power to detect heterogeneity in net treatment benefit under a wide variety of circumstances, instances under which conventional subgroup analysis has poor statistical power.CONCLUSION: These results suggest that under many likely scenarios, a multivariable risk-stratified approach will have substantially greater statistical power than conventional subgroup analysis for detecting heterogeneity in treatment benefits and safety related to previously unidentified treatment-related harm. Subgroup analyses must always be well-justified and interpreted with care, and conventional subgroup analyses can be useful under some circumstances; however, clinical trial reporting should include a multivariable risk-stratified analysis when an adequate externally-developed risk prediction tool is available.