Abstract：
BACKGROUND: In trials, subgroup analyses are used to examine whether treatment effects differ by important patient characteristics. However, which subgroups are most commonly reported has not been comprehensively described.
METHODS: Using a set of trials identified from the US clinical trials register (ClinicalTrials.gov), we describe every reported subgroup for a range of conditions and drug classes.
METHODS: We obtained trial characteristics from ClinicalTrials.gov via the Aggregate Analysis of ClinicalTrials.gov database. We subsequently obtained all corresponding PubMed-indexed papers and screened these for subgroup reporting. Tables and text for reported subgroups were extracted and standardised using Medical Subject Headings and WHO Anatomical Therapeutic Chemical codes. Via logistic and Poisson regression models we identified independent predictors of result reporting (any vs none) and subgroup reporting (any vs none and counts). We then summarised subgroup reporting by index condition and presented all subgroups for all trials via a web-based interactive heatmap (https://ihwph-hehta.shinyapps.io/subgroup_reporting_app/).
RESULTS: Among 2235 eligible trials, 23% (524 trials) reported subgroups. Follow-up time (OR, 95%CI: 1.13, 1.04-1.24), enrolment (per 10-fold increment, 3.48, 2.25-5.47), trial starting year (1.07, 1.03-1.11) and specific index conditions (eg, hypercholesterolaemia, hypertension, taking asthma as the reference, OR ranged from 0.15 to 10.44), predicted reporting, sponsoring source and number of arms did not. Results were similar on modelling any result reporting (except number of arms, 1.42, 1.15-1.74) and the total number of subgroups. Age (51%), gender (45%), racial group (28%) were the most frequently reported subgroups. Characteristics related to the index condition (severity/duration/types etc) were frequently reported (eg, 69% of myocardial infarction trials reported on its severity/duration/types). However, reporting on comorbidity/frailty (five trials) and mental health (four trials) was rare.
CONCLUSIONS: Other than age, sex, race ethnicity or geographic location and characteristics related to the index condition, information on variation in treatment effects is sparse.
UNASSIGNED: CRD42018048202.
METHODS: Using a set of trials identified from the US clinical trials register (ClinicalTrials.gov), we describe every reported subgroup for a range of conditions and drug classes.
METHODS: We obtained trial characteristics from ClinicalTrials.gov via the Aggregate Analysis of ClinicalTrials.gov database. We subsequently obtained all corresponding PubMed-indexed papers and screened these for subgroup reporting. Tables and text for reported subgroups were extracted and standardised using Medical Subject Headings and WHO Anatomical Therapeutic Chemical codes. Via logistic and Poisson regression models we identified independent predictors of result reporting (any vs none) and subgroup reporting (any vs none and counts). We then summarised subgroup reporting by index condition and presented all subgroups for all trials via a web-based interactive heatmap (https://ihwph-hehta.shinyapps.io/subgroup_reporting_app/).
RESULTS: Among 2235 eligible trials, 23% (524 trials) reported subgroups. Follow-up time (OR, 95%CI: 1.13, 1.04-1.24), enrolment (per 10-fold increment, 3.48, 2.25-5.47), trial starting year (1.07, 1.03-1.11) and specific index conditions (eg, hypercholesterolaemia, hypertension, taking asthma as the reference, OR ranged from 0.15 to 10.44), predicted reporting, sponsoring source and number of arms did not. Results were similar on modelling any result reporting (except number of arms, 1.42, 1.15-1.74) and the total number of subgroups. Age (51%), gender (45%), racial group (28%) were the most frequently reported subgroups. Characteristics related to the index condition (severity/duration/types etc) were frequently reported (eg, 69% of myocardial infarction trials reported on its severity/duration/types). However, reporting on comorbidity/frailty (five trials) and mental health (four trials) was rare.
CONCLUSIONS: Other than age, sex, race ethnicity or geographic location and characteristics related to the index condition, information on variation in treatment effects is sparse.
UNASSIGNED: CRD42018048202.
摘要：
背景：在试验中，亚组分析用于检查治疗效果是否因患者的重要特征而不同.然而,哪些亚组最常报告尚未得到全面描述。
方法：使用美国临床试验注册（ClinicalTrials.gov）中确定的一组试验，我们描述了一系列疾病和药物类别的每个报告亚组.
方法：我们通过ClinicalTrials.gov数据库的综合分析从ClinicalTrials.gov获得了试验特征。随后，我们获得了所有相应的PubMed索引论文，并筛选了这些论文的亚组报告。使用医学主题标题和WHO解剖学治疗化学代码提取并标准化报告的亚组的表格和文本。通过逻辑和泊松回归模型，我们确定了结果报告（任何vs无）和亚组报告（任何vs无和计数）的独立预测因子。然后，我们按索引条件总结了分组报告，并通过基于网络的交互式热图（https://ihwph-hehta）呈现了所有试验的所有分组。shinyapps.io/subgroup_reporting_app/)。
结果：在2235项符合条件的试验中，23%(524项试验)报告亚组。随访时间(OR，95CI：1.13，1.04-1.24），入学率(每10倍增加，3.48,2.25-5.47),试验开始年(1.07、1.03-1.11)和具体指标条件(例如，高胆固醇血症,高血压,以哮喘为参考，OR范围从0.15到10.44)，预测报告，赞助来源和武器数量没有。在对任何结果报告进行建模时，结果相似(除了武器数量，1.42、1.15-1.74)和子组总数。年龄（51%）性别（45%），种族组(28%)是最常见的亚组.经常报告与指标状况相关的特征（严重程度/持续时间/类型等）（例如，69%的心肌梗死试验报告了其严重程度/持续时间/类型)。然而,关于共病/虚弱(5项试验)和心理健康(4项试验)的报告很少.
结论：除了年龄，性别,种族、民族或地理位置以及与指标状况相关的特征，关于治疗效果变化的信息很少。
■CRD42018048202。
方法：使用美国临床试验注册（ClinicalTrials.gov）中确定的一组试验，我们描述了一系列疾病和药物类别的每个报告亚组.
方法：我们通过ClinicalTrials.gov数据库的综合分析从ClinicalTrials.gov获得了试验特征。随后，我们获得了所有相应的PubMed索引论文，并筛选了这些论文的亚组报告。使用医学主题标题和WHO解剖学治疗化学代码提取并标准化报告的亚组的表格和文本。通过逻辑和泊松回归模型，我们确定了结果报告（任何vs无）和亚组报告（任何vs无和计数）的独立预测因子。然后，我们按索引条件总结了分组报告，并通过基于网络的交互式热图（https://ihwph-hehta）呈现了所有试验的所有分组。shinyapps.io/subgroup_reporting_app/)。
结果：在2235项符合条件的试验中，23%(524项试验)报告亚组。随访时间(OR，95CI：1.13，1.04-1.24），入学率(每10倍增加，3.48,2.25-5.47),试验开始年(1.07、1.03-1.11)和具体指标条件(例如，高胆固醇血症,高血压,以哮喘为参考，OR范围从0.15到10.44)，预测报告，赞助来源和武器数量没有。在对任何结果报告进行建模时，结果相似(除了武器数量，1.42、1.15-1.74)和子组总数。年龄（51%）性别（45%），种族组(28%)是最常见的亚组.经常报告与指标状况相关的特征（严重程度/持续时间/类型等）（例如，69%的心肌梗死试验报告了其严重程度/持续时间/类型)。然而,关于共病/虚弱(5项试验)和心理健康(4项试验)的报告很少.
结论：除了年龄，性别,种族、民族或地理位置以及与指标状况相关的特征，关于治疗效果变化的信息很少。
■CRD42018048202。
