BACKGROUND/AIMS Quantitative imaging biomarkers have the potential to detect change in disease early and noninvasively, providing information about the diagnosis and prognosis of a patient, aiding in monitoring disease, and informing when therapy is effective. In clinical trials testing new therapies, there has been a tendency to ignore the variability and bias in quantitative imaging biomarker measurements. Unfortunately, this can lead to underpowered studies and incorrect estimates of the treatment effect. We illustrate the problem when non-constant measurement bias is ignored and show how treatment effect estimates can be corrected. METHODS Monte Carlo simulation was used to assess the coverage of 95% confidence intervals for the treatment effect when non-constant bias is ignored versus when the bias is corrected for. Three examples are presented to illustrate the methods: doubling times of lung nodules, rates of change in brain atrophy in progressive multiple sclerosis clinical trials, and changes in proton-density fat fraction in trials for patients with nonalcoholic fatty liver disease. RESULTS Incorrectly assuming that the measurement bias is constant leads to 95% confidence intervals for the treatment effect with reduced coverage (<95%); the coverage is especially reduced when the quantitative imaging biomarker measurements have good precision and/or there is a large treatment effect. Estimates of the measurement bias from technical performance validation studies can be used to correct the confidence intervals for the treatment effect. CONCLUSION Technical performance validation studies of quantitative imaging biomarkers are needed to supplement clinical trial data to provide unbiased estimates of the treatment effect.

中文翻译：

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在估计治疗效果时纳入定量成像生物标志物技术性能特征的重要性

背景/目的 定量成像生物标志物具有早期和无创检测疾病变化的潜力，提供有关患者诊断和预后的信息，帮助监测疾病，并告知治疗何时有效。在测试新疗法的临床试验中，倾向于忽略定量成像生物标志物测量的可变性和偏差。不幸的是，这可能导致研究动力不足和对治疗效果的错误估计。我们说明了忽略非常量测量偏差时的问题，并展示了如何校正治疗效果估计。方法 使用蒙特卡罗模拟来评估忽略非常量偏差时与校正偏差时治疗效果的 95% 置信区间的覆盖率。提供了三个例子来说明这些方法：肺结节的倍增时间，进行性多发性硬化症临床试验中脑萎缩的变化率，以及非酒精性脂肪肝患者试验中质子密度脂肪分数的变化。结果 错误地假设测量偏差是恒定的，导致治疗效果的置信区间为 95%，覆盖率降低 (<95%)；当定量成像生物标志物测量具有良好的精确度和/或存在较大的治疗效果时，覆盖率尤其降低。技术性能验证研究的测量偏差估计可用于校正治疗效果的置信区间。