Department of Psychological Science, University of Bristol, 12a Priory Road, Bristol, BS8 1TU, UK.
Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
Trials. 2024 Aug 12;25(1):532. doi: 10.1186/s13063-024-08374-5.
To assess the cost-effectiveness of using cheaper-but-noisier outcome measures, such as a short questionnaire, for large simple clinical trials.
To detect associations reliably, trials must avoid bias and random error. To reduce random error, we can increase the size of the trial and increase the accuracy of the outcome measurement process. However, with fixed resources, there is a trade-off between the number of participants a trial can enrol and the amount of information that can be collected on each participant during data collection.
To consider the effect on measurement error of using outcome scales with varying numbers of categories, we define and calculate the variance from categorisation that would be expected from using a category midpoint; define the analytic conditions under which such a measure is cost-effective; use meta-regression to estimate the impact of participant burden, defined as questionnaire length, on response rates; and develop an interactive web-app to allow researchers to explore the cost-effectiveness of using such a measure under plausible assumptions.
An outcome scale with only a few categories greatly reduced the variance of non-measurement. For example, a scale with five categories reduced the variance of non-measurement by 96% for a uniform distribution. We show that a simple measure will be more cost-effective than a gold-standard measure if the relative increase in variance due to using it is less than the relative increase in cost from the gold standard, assuming it does not introduce bias in the measurement. We found an inverse power law relationship between participant burden and response rates such that a doubling the burden on participants reduces the response rate by around one third. Finally, we created an interactive web-app ( https://benjiwoolf.shinyapps.io/cheapbutnoisymeasures/ ) to allow exploration of when using a cheap-but-noisy measure will be more cost-effective using realistic parameters.
Cheaper-but-noisier questionnaires containing just a few questions can be a cost-effective way of maximising power. However, their use requires a judgement on the trade-off between the potential increase in risk of information bias and the reduction in the potential of selection bias due to the expected higher response rates.
评估使用更便宜但更嘈杂的结果测量方法（例如简短问卷）进行大型简单临床试验的成本效益。
为了可靠地检测关联，试验必须避免偏倚和随机误差。为了减少随机误差，我们可以增加试验的规模并提高结果测量过程的准确性。但是，在固定资源的情况下，试验可以招募的参与者数量和在数据收集过程中可以收集到的每个参与者的信息量之间存在权衡。
为了考虑使用具有不同分类数的结果量表对测量误差的影响，我们定义并计算了使用分类中点预期的分类方差；定义了在分析条件下，这种衡量标准具有成本效益；使用荟萃回归来估计参与者负担（定义为问卷长度）对反应率的影响；并开发了一个交互式网络应用程序，允许研究人员在合理的假设下探索使用此类测量的成本效益。
一个只有几个类别的结果量表大大降低了非测量的方差。例如，对于均匀分布，一个具有五个类别的量表将非测量的方差降低了 96%。我们表明，如果由于使用它而导致的方差增加相对小于黄金标准的成本增加，那么简单的测量将比黄金标准的测量更具成本效益，前提是它不会在测量中引入偏差。我们发现参与者负担和反应率之间存在逆幂律关系，即参与者负担增加一倍会使反应率降低约三分之一。最后，我们创建了一个交互式网络应用程序（https://benjiwoolf.shinyapps.io/cheapbutnoisymeasures/），以允许使用现实参数探索使用廉价但嘈杂的测量方法何时更具成本效益。
包含几个问题的更便宜但嘈杂的问卷可以是一种经济有效的方法，可以最大限度地提高功效。但是，它们的使用需要权衡潜在的信息偏倚风险增加与由于预期的更高反应率而导致的选择偏倚减少之间的权衡。
