慢性病 3/4 期行业资助试验的参与者特征和排除情况:对参与者个人水平数据的荟萃分析。

BMJ medicine Pub Date : 2024-05-03 eCollection Date: 2024-01-01 DOI:10.1136/bmjmed-2023-000732
Jennifer Lees, Jamie Crowther, Peter Hanlon, Elaine W Butterly, Sarah H Wild, Frances Mair, Bruce Guthrie, Katie Gillies, Sofia Dias, Nicky J Welton, Srinivasa Vittal Katikireddi, David A McAllister
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引用次数: 0

摘要

目的: 评估年龄、性别、合并症数量、种族和民族是否与试验参与者因任何原因未被纳入试验(即筛查失败)的可能性相关:评估年龄、性别、合并症数量、种族和民族是否与试验参与者因任何原因(即筛选失败)而未被纳入试验的可能性相关:设计:对参与者个人数据进行贝叶斯荟萃分析:环境:行业资助的慢性病 3/4 期试验:利用参与者个人数据确定参与者属于入组组还是筛选失败组。52项试验涉及72178名筛查对象,其中24733人(34%)在筛查阶段被排除在试验之外:对每项试验建立逻辑回归模型,以评估受邀参加筛查者筛查失败的可能性,并根据年龄(每 10 年递增一次)、性别(男性与女性)、合并症数量(每增加一种合并症)以及种族或民族进行回归。在贝叶斯分层模型中结合试验水平分析,并对不同条件进行汇总:在所有试验的年龄和性别调整模型中,年龄和性别均与筛查失败几率的增加无关,但在对合并症进行额外调整后,发现两者之间存在微弱的关联(年龄每增加 10 岁的几率比为 1.02(95% 可信区间为 1.01 至 1.04),男性性别的几率比为 0.95(0.91 至 1.00))。合并症计数与筛查失败有微弱的相关性,但方向出乎意料(经年龄和性别调整后,每增加一个合并症,相关性为 0.97(0.94 至 1.00))。自称为黑人的人筛查失败的可能性似乎略高于自称为白人的人(1.04 (0.99 to 1.09));在对年龄、性别和合并症数量(1.05 (0.98 to 1.12))进行调整后,这种微弱的影响似乎依然存在。试验间的异质性一般较低,但有证据表明在不同条件下存在性别异质性(几率比的对数变化为0.01-0.13):虽然结论受到了非随机参与者数据收集完整性或准确性不确定性的限制,但我们发现,在年龄、性别、合并症计数、黑人种族或民族群体方面,筛查失败的可能性增加大多与弱相关性有关。按比例增加对这些服务不足人群的筛查可能会提高试验的代表性:ProCORD42018048202.
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Participant characteristics and exclusion from phase 3/4 industry funded trials of chronic medical conditions: meta-analysis of individual participant level data.

Objectives: To assess whether age, sex, comorbidity count, and race and ethnic group are associated with the likelihood of trial participants not being enrolled in a trial for any reason (ie, screen failure).

Design: Bayesian meta-analysis of individual participant level data.

Setting: Industry funded phase 3/4 trials of chronic medical conditions.

Participants: Participants were identified using individual participant level data to be in either the enrolled group or screen failure group. Data were available for 52 trials involving 72 178 screened individuals of whom 24 733 (34%) were excluded from the trial at the screening stage.

Main outcome measures: For each trial, logistic regression models were constructed to assess likelihood of screen failure in people who had been invited to screening, and were regressed on age (per 10 year increment), sex (male v female), comorbidity count (per one additional comorbidity), and race or ethnic group. Trial level analyses were combined in Bayesian hierarchical models with pooling across condition.

Results: In age and sex adjusted models across all trials, neither age nor sex was associated with increased odds of screen failure, although weak associations were detected after additionally adjusting for comorbidity (odds ratio of age, per 10 year increment was 1.02 (95% credibility interval 1.01 to 1.04) and male sex (0.95 (0.91 to 1.00)). Comorbidity count was weakly associated with screen failure, but in an unexpected direction (0.97 per additional comorbidity (0.94 to 1.00), adjusted for age and sex). People who self-reported as black seemed to be slightly more likely to fail screening than people reporting as white (1.04 (0.99 to 1.09)); a weak effect that seemed to persist after adjustment for age, sex, and comorbidity count (1.05 (0.98 to 1.12)). The between-trial heterogeneity was generally low, evidence of heterogeneity by sex was noted across conditions (variation in odds ratios on log scale of 0.01-0.13).

Conclusions: Although the conclusions are limited by uncertainty about the completeness or accuracy of data collection among participants who were not randomised, we identified mostly weak associations with an increased likelihood of screen failure for age, sex, comorbidity count, and black race or ethnic group. Proportionate increases in screening these underserved populations may improve representation in trials.

Trial registration number: PROSPERO CRD42018048202.

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