Incorporating Baseline Outcome Data in Individual Participant Data Meta-Analysis of Non-randomized Studies.
Incorporating Baseline Outcome Data in Individual Participant Data Meta-Analysis of Non-randomized Studies.
复制标题
DOI：
10.3389/fpsyt.2022.774251
复制
发表时间：
2022
影响因子：
4.7
通讯作者：
Del Giovane C
中科院分区：
文献类型：
--
作者：
Syrogiannouli L;Wildisen L;Meuwese C;Bauer DC;Cappola AR;Gussekloo J;den Elzen WPJ;Trompet S;Westendorp RGJ;Jukema JW;Ferrucci L;Ceresini G;Åsvold BO;Chaker L;Peeters RP;Imaizumi M;Ohishi W;Vaes B;Völzke H;Sgarbi JA;Walsh JP;Dullaart RPF;Bakker SJL;Iacoviello M;Rodondi N;Del Giovane C
在非随机化研究(NRSS)中，在基线和随访时评估连续的结果变量(例如，抑郁症状)，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这一问题的统计方法不同而不同。个体参与者数据(IPD)的分析允许跨研究方法的标准化。我们的目的是确定NRSS已发表的IPD-MA中用于持续结果的方法，并使用来自甲状腺研究合作(TSC)的两个经验例子来比较不同方法来解释NRSS的IPD-MA中结果变量的基线值。对于第一个目标，我们系统地搜索了MEDLINE、EMBASE和Cochrane，从最初到2021年2月，以确定已发表的NRSS的IPD-MA，这些IPD-MA在连续结果分析中根据基线结果衡量标准进行了调整。对于第二个目标，我们应用了协方差分析(ANCOVA)、改变评分、倾向评分和来自NRSS的IPD-MA中的天真方法(忽略基线结果数据)来研究亚临床甲亢与抑郁症状和肾功能之间的关系。我们估计研究和荟萃分析的平均差(MD)和相对标准差(SE)。我们同时使用了固定效果和随机效果的MA。在纳入的18项研究中，10项(56%)使用了变化评分法，7项(39%)使用了ANCOVA，1项使用了倾向性评分(5%)。在基线在结果变量方面保持平衡的研究中，研究估计在所有方法中都是相似的，但在基线不平衡的研究中不同。在我们的经验例子中，ANCOVA和Change Score显示了相同方向的研究结果，而不是倾向分数。在我们的应用中，与其他方法相比，ANCOVA在研究和荟萃分析水平上都提供了更准确的估计。当使用改变分数作为结果时，异质性较高，对于ANCOVA为中等，而与倾向性分数为零。ANCOVA在研究和荟萃分析水平上都提供了最准确的估计，因此在非随机研究的IPD的荟萃分析中似乎更可取。对于组间平衡良好的研究，Change Score和ANCOVA的表现相似。
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). 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. 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. 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.
登录
查看更多内容
影响因子：
6.1
作者：
Meuwese, Christiaan L.;van Diepen, Merel;den Elzen, Wendy P. J.
通讯作者：
den Elzen, Wendy P. J.
影响因子：
37.8
作者：
Baumgartner C;da Costa BR;Collet TH;Feller M;Floriani C;Bauer DC;Cappola AR;Heckbert SR;Ceresini G;Gussekloo J;den Elzen WPJ;Peeters RP;Luben R;Völzke H;Dörr M;Walsh JP;Bremner A;Iacoviello M;Macfarlane P;Heeringa J;Stott DJ;Westendorp RGJ;Khaw KT;Magnani JW;Aujesky D;Rodondi N;Thyroid Studies Collaboration
通讯作者：
Thyroid Studies Collaboration
影响因子：
9.8
作者：
McKenzie, Joanne E.;Herbison, G. Peter;Deeks, Jonathan J.
通讯作者：
Deeks, Jonathan J.
影响因子：
5.8
作者：
Imaizumi, M;Akahoshi, M;Eguchi, K
通讯作者：
Eguchi, K
影响因子：
4.1
作者：
Blum, Manuel R.;Wijsman, Liselotte W.;Rodondi, Nicolas
通讯作者：
Rodondi, Nicolas