未经评估:在非随机研究(NRS)中,连续结果变量(例如,抑郁症状)在基线和随访时进行评估,通常观察到治疗/暴露组和对照组之间基线值的不平衡.这可能会使研究产生偏差,从而导致荟萃分析(MA)估计。这些估计可能因用于处理此问题的统计方法而异。个体参与者数据(IPD)的分析允许跨研究的方法标准化。我们的目标是确定已发表的NRSIPD-MA中用于连续结果的方法,并使用来自甲状腺研究协作(TSC)的两个经验示例比较不同的方法来解释NRS的IPD-MA中结果变量的基线值。
未经评估:对于我们在MEDLINE中系统地搜索的第一个目标,EMBASE,和Cochrane从成立到2021年2月,以确定已发布的NRS的IPD-MA,这些IPD-MA在连续结果分析中针对基线结果指标进行了调整。对于第二个目标,我们应用协方差分析(ANCOVA),更改分数,IPD-MA中的倾向评分和幼稚方法(忽略基线结果数据)来自NRS的亚临床甲状腺功能亢进与抑郁症状和肾功能之间的关联.我们估计了研究和荟萃分析平均差异(MD)和相对标准误差(SE)。我们使用了固定和随机效果MA。
未经评估:纳入的18项研究中有10项(56%)使用了变化评分法,7项(39%)研究使用ANCOVA,1项倾向评分(5%).在基线对结果变量进行平衡的研究中,研究估计与方法相似,但在基线失衡的研究中有所不同。在我们的经验例子中,ANCOVA和变化评分显示了相同方向的研究结果,不是倾向得分。在我们的应用中,ANCOVA提供了更精确的估计,在研究和荟萃分析层面,与其他方法相比。当使用变化评分作为结果时,异质性更高,ANCOVA为中度,倾向评分为零。
UNASSIGNED:ANCOVA在研究和荟萃分析水平上提供了最精确的估计,因此在非随机研究的IPD荟萃分析中似乎更可取。对于组间平衡良好的研究,更改分数,ANCOVA的表现类似。
UNASSIGNED: In non-randomized studies (NRSs) where a continuous outcome variable (e.g., depressive symptoms) is assessed at baseline and follow-up, it is common to observe imbalance of the baseline values between the treatment/exposure group and control group. This may bias the study and consequently a meta-analysis (MA) estimate. These estimates may differ across statistical methods used to deal with this issue. Analysis of individual participant data (IPD) allows standardization of methods across studies. We aimed to identify methods used in published IPD-MAs of NRSs for continuous outcomes, and to compare different methods to account for baseline values of outcome variables in IPD-MA of NRSs using two empirical examples from the Thyroid Studies Collaboration (TSC).
UNASSIGNED: For the first aim we systematically searched in MEDLINE, EMBASE, and Cochrane from inception to February 2021 to identify published IPD-MAs of NRSs that adjusted for baseline outcome measures in the analysis of continuous outcomes. For the second aim, we applied analysis of covariance (ANCOVA), change score, propensity score and the naïve approach (ignores the baseline outcome data) in IPD-MA from NRSs on the association between subclinical hyperthyroidism and depressive symptoms and renal function. We estimated the study and meta-analytic mean difference (MD) and relative standard error (SE). We used both fixed- and random-effects MA.
UNASSIGNED: Ten of 18 (56%) of the included studies used the change score method, seven (39%) studies used ANCOVA and one the propensity score (5%). The study estimates were similar across the methods in studies in which groups were balanced at baseline with regard to outcome variables but differed in studies with baseline imbalance. In our empirical examples, ANCOVA and change score showed study results on the same direction, not the propensity score. In our applications, ANCOVA provided more precise estimates, both at study and meta-analytical level, in comparison to other methods. Heterogeneity was higher when change score was used as outcome, moderate for ANCOVA and null with the propensity score.
UNASSIGNED: ANCOVA provided the most precise estimates at both study and meta-analytic level and thus seems preferable in the meta-analysis of IPD from non-randomized studies. For the studies that were well-balanced between groups, change score, and ANCOVA performed similarly.