Moore A R, Gavaghan David, Tramèr R M, Collins L S, McQuay J H
Pain Research, Nuffield Department of Anaesthetics, The Churchill, Oxford Radcliffe Hospital, Oxford OX3 7LJ, UK Computing Laboratory, Wolfson Building, Parks Rd, Oxford OX1 3QD, UK.
Pain. 1998 Dec;78(3):209-216. doi: 10.1016/S0304-3959(98)00140-7.
Variability in patients' response to interventions in pain and other clinical settings is large. Many explanations such as trial methods, environment or culture have been proposed, but this paper sets out to show that the main cause of the variability may be random chance, and that if trials are small their estimate of magnitude of effect may be incorrect, simply because of the random play of chance. This is highly relevant to the questions of 'How large do trials have to be for statistical accuracy?' and 'How large do trials have to be for their results to be clinically valid?' The true underlying control event rate (CER) and experimental event rate (EER) were determined from single-dose acute pain analgesic trials in over 5000 patients. Trial group size required to obtain statistically significant and clinically relevant (0.95 probability of number-needed-to-treat within -/+0.5 of its true value) results were computed using these values. Ten thousand trials using these CER and EER values were simulated using varying group sizes to investigate the variation due to random chance alone. Most common analgesics have EERs in the range 0.4-0.6 and CER of about 0.19. With such efficacy, to have a 90% chance of obtaining a statistically significant result in the correct direction requires group sizes in the range 30-60. For clinical relevance nearly 500 patients are required in each group. Only with an extremely effective drug (EER > 0.8) will we be reasonably sure of obtaining a clinically relevant NNT with commonly used group sizes of around 40 patients per treatment arm. The simulated trials showed substantial variation in CER and EER, with the probability of obtaining the correct values improving as group size increased. We contend that much of the variability in control and experimental event rates is due to random chance alone. Single small trials are unlikely to be correct. If we want to be sure of getting correct (clinically relevant) results in clinical trials we must study more patients. Credible estimates of clinical efficacy are only likely to come from large trials or from pooling multiple trials of conventional (small) size.
患者在疼痛及其他临床情境中对干预措施的反应存在很大差异。人们提出了许多解释，如试验方法、环境或文化等，但本文旨在表明，这种差异的主要原因可能是随机因素，而且如果试验规模较小，其对效应大小的估计可能是错误的，仅仅是因为随机因素的作用。这与“试验规模要多大才能保证统计准确性？”以及“试验规模要多大才能使其结果具有临床有效性？”这些问题高度相关。通过对5000多名患者进行的单剂量急性疼痛镇痛试验，确定了真实的潜在对照事件发生率（CER）和实验事件发生率（EER）。利用这些值计算出获得具有统计学显著性且具有临床相关性（治疗所需人数在其真实值的±0.5范围内的概率为0.95）结果所需的试验组规模。使用不同的组规模模拟了一万次使用这些CER和EER值的试验，以研究仅由随机因素导致的变异情况。大多数常见镇痛药的EER在0.4 - 0.6范围内，CER约为0.19。对于这样的疗效，要在正确方向上有90%的机会获得具有统计学显著性的结果，所需的组规模在30 - 60之间。对于临床相关性，每组需要近500名患者。只有使用极其有效的药物（EER > 0.8），我们才能合理地确定在每个治疗组常用的约40名患者的组规模下获得具有临床相关性的治疗所需人数。模拟试验显示CER和EER存在很大差异，随着组规模的增加，获得正确值的概率也会提高。我们认为，对照事件发生率和实验事件发生率的许多差异仅仅是由于随机因素。单个小规模试验不太可能是正确的。如果我们想在临床试验中确保获得正确（具有临床相关性）的结果，就必须研究更多的患者。临床疗效的可靠估计可能仅来自大规模试验或对多个常规（小规模）试验进行汇总。
