Lees Jennifer S, Hanlon Peter, Butterly Elaine W, Wild Sarah H, Mair Frances S, Taylor Rod S, Guthrie Bruce, Gillies Katie, Dias Sofia, Welton Nicky J, McAllister David A
University of Glasgow, Glasgow, UK.
Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK.
BMJ Med. 2022 Sep 1;1(1):e000217. doi: 10.1136/bmjmed-2022-000217. eCollection 2022.
To estimate the association between individual participant characteristics and attrition from randomised controlled trials.
Meta-analysis of individual participant level data (IPD).
Clinical trial repositories (Clinical Study Data Request and Yale University Open Data Access).
Eligible phase 3 or 4 trials identified according to prespecified criteria (PROSPERO CRD42018048202).
Association between comorbidity count (identified using medical history or concomitant drug treatment data) and trial attrition (failure for any reason to complete the final trial visit), estimated in logistic regression models and adjusted for age and sex. Estimates were meta-analysed in bayesian linear models, with partial pooling across index conditions and drug classes.
In 92 trials across 20 index conditions and 17 drug classes, the mean comorbidity count ranged from 0.3 to 2.7. Neither age nor sex was clearly associated with attrition (odds ratio 1.04, 95% credible interval 0.98 to 1.11; and 0.99, 0.93 to 1.05, respectively). However, comorbidity count was associated with trial attrition (odds ratio per additional comorbidity 1.11, 95% credible interval 1.07 to 1.14). No evidence of non-linearity (assessed via a second order polynomial) was seen in the association between comorbidity count and trial attrition, with minimal variation across drug classes and index conditions. At a trial level, an increase in participant comorbidity count has a minor impact on attrition: for a notional trial with high level of attrition in individuals without comorbidity, doubling the mean comorbidity count from 1 to 2 translates to an increase in trial attrition from 29% to 31%.
Increased comorbidity count, irrespective of age and sex, is associated with a modest increased odds of participant attrition. The benefit of increased generalisability of including participants with multimorbidity seems likely to outweigh the disadvantages of increased attrition.
评估个体参与者特征与随机对照试验中失访之间的关联。
个体参与者水平数据（IPD）的荟萃分析。
临床试验资料库（临床研究数据请求和耶鲁大学开放数据访问）。
根据预先设定的标准（PROSPERO CRD42018048202）确定的符合条件的3期或4期试验。
在逻辑回归模型中估计并根据年龄和性别进行调整的合并症计数（使用病史或伴随药物治疗数据确定）与试验失访（因任何原因未能完成最终试验访视）之间的关联。在贝叶斯线性模型中对估计值进行荟萃分析，在指标疾病和药物类别之间进行部分合并。
在涉及20种指标疾病和17类药物的92项试验中，合并症计数的平均值范围为0.3至2.7。年龄和性别均与失访无明显关联（优势比分别为1.04，95%可信区间为0.98至1.11；以及0.99，0.93至1.05）。然而，合并症计数与试验失访有关（每增加一种合并症的优势比为1.11，95%可信区间为1.07至1.14）。在合并症计数与试验失访之间的关联中未发现非线性证据（通过二阶多项式评估），在药物类别和指标疾病之间的差异最小。在试验层面，参与者合并症计数的增加对失访影响较小：对于一个在无合并症个体中失访率较高的虚拟试验，将平均合并症计数从1翻倍至2会导致试验失访率从29%增加到31%。
无论年龄和性别如何，合并症计数增加与参与者失访几率适度增加相关。纳入患有多种疾病的参与者所带来的更高普遍性的益处似乎可能超过失访增加的弊端。
