■在整群随机试验中,内相关系数是确定样本量的关键输入参数。样本量对团内相关系数的微小差异非常敏感,因此,有一个稳健的场内相关系数估计是至关重要的。这通常是有问题的,因为相关的簇内相关系数估计不可用,或者可用的估计由于基于具有低数量的簇的小规模研究而不精确。错误的规范可能会导致动力不足或效率低下的大型审判,并可能导致不道德的审判。
■我们应用贝叶斯方法来产生脑内相关系数估计,并因此提出了一项计划中的集群随机试验的样本量,该试验是针对中风后尿失禁的系统排尿计划的有效性。贝叶斯分层模型用于结合来自其他相关试验的内相关系数估计,并利用已发表研究中可用的大量内相关系数信息。我们采用知识启发过程来评估每个内部相关系数估计与计划的试验设置的相关性。专家评审团队为每项研究分配了相关性权重,研究中的每个结果,因此告知贝叶斯建模的参数。为了衡量专家的表现,采用了协议和可靠性方法。
■从16项先前发表的试验中提取的34种内相关系数估计在贝叶斯分层模型中使用专家得出的汇总相关性权重进行组合。可从外部来源获得的簇内相关系数用于构建目标簇内相关系数的后验分布,该后验分布被总结为后验中位数,具有95%的可信间隔,可告知研究人员合理的样本量值范围。估计的聚集内相关系数确定了450(25个簇)和480(20个簇)之间的样本量,与经典方法的500-600相比。分位数的使用,和其他参数,从估计的后验分布进行了说明,并描述了对样本量的影响。
■考虑未知的内部相关系数的不确定性,试验可以用更强大的样本量设计。所提出的方法提供了从不同的集群随机试验设置中纳入内部相关系数的可能性,这些系数可能与计划的研究不同。在建模中考虑到了差异。通过使用专家知识来引出相关性权重,并综合外部可用的集中相关系数估计,信息的使用比经典方法更有效,其中,池内相关系数估计往往不太稳健,过于保守。与选择保守的聚集内相关系数估计的常规策略相比,构建的聚集内相关系数估计平均可能产生较小的样本量。因此,这可以导致大量的时间和资源节省。
The intracluster correlation coefficient is a key input parameter for sample size determination in cluster-randomised trials. Sample size is very sensitive to small differences in the intracluster correlation coefficient, so it is vital to have a robust intracluster correlation coefficient estimate. This is often problematic because either a relevant intracluster correlation coefficient estimate is not available or the available estimate is imprecise due to being based on small-scale studies with low numbers of clusters. Misspecification may lead to an underpowered or inefficiently large and potentially unethical trial.
We apply a Bayesian approach to produce an intracluster correlation coefficient estimate and hence propose sample size for a planned cluster-randomised trial of the effectiveness of a systematic voiding programme for post-stroke incontinence. A Bayesian hierarchical model is used to combine intracluster correlation coefficient estimates from other relevant trials making use of the wealth of intracluster correlation coefficient information available in published research. We employ knowledge elicitation process to assess the relevance of each intracluster correlation coefficient estimate to the planned trial setting. The team of expert reviewers assigned relevance weights to each study, and each outcome within the study, hence informing parameters of Bayesian modelling. To measure the performance of experts, agreement and reliability methods were applied.
The 34 intracluster correlation coefficient estimates extracted from 16 previously published trials were combined in the Bayesian hierarchical model using aggregated relevance weights elicited from the experts. The intracluster correlation coefficients available from external sources were used to construct a posterior distribution of the targeted intracluster correlation coefficient which was summarised as a posterior median with a 95% credible interval informing researchers about the range of plausible sample size values. The estimated intracluster correlation coefficient determined a sample size of between 450 (25 clusters) and 480 (20 clusters), compared to 500-600 from a classical approach. The use of quantiles, and other parameters, from the estimated posterior distribution is illustrated and the impact on sample size described.
Accounting for uncertainty in an unknown intracluster correlation coefficient, trials can be designed with a more robust sample size. The approach presented provides the possibility of incorporating intracluster correlation coefficients from various cluster-randomised trial settings which can differ from the planned study, with the difference being accounted for in the modelling. By using expert knowledge to elicit relevance weights and synthesising the externally available intracluster correlation coefficient estimates, information is used more efficiently than in a classical approach, where the intracluster correlation coefficient estimates tend to be less robust and overly conservative. The intracluster correlation coefficient estimate constructed is likely to produce a smaller sample size on average than the conventional strategy of choosing a conservative intracluster correlation coefficient estimate. This may therefore result in substantial time and resources savings.