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\begin{document}

\title{BJOG-21-1256 Systematic reviews and meta-analyses: bigger is not
necessarily better}



\author[1]{Laurine van der Slink}%
\affil[1]{Amsterdam UMC Locatie Meibergdreef}%


\vspace{-1em}



  
  \date{June 14, 2024}


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\textbf{BJOG-21-1256 Systematic reviews and meta-analyses: bigger is not
necessarily better}

Systematic reviews and often concomitant meta-analyses are designed to
summarize the existing and sometimes overwhelming amount of published
literature on a specific topic. Ideally, a systematic review and
meta-analysis is reproducible, rigorous and strengthens the evidence on
the specific topic, but there are potential pitfalls in designing such
studies.

O'Byrne et al. (\emph{BJOG} 2021;) performed a systematic review and
meta-analysis regarding the pregnancy outcomes of pregnant women with
chronic inflammatory disease exposed to biologics. In the past years
some other summarized data have been published on this relevant topic.
The authors of the current study tried to distinguish their study from
these previous systematic reviews and meta-analyses by using broad
inclusion criteria and by expanding the control group population. For
reference, a study from Tsao et al.
(\emph{Rheumatology}2020;59:1808--1817) included 24 individual studies
and approximately 5600 pregnancies in women with inflammatory systemic
diseases exposed to biologics. They compared the outcomes with a
disease-matched control group. Komaki et al. (\emph{Journal of
Autoimmunity} 2017;76:38-52) studied 5600 pregnancies of women with
immune mediated diseases and the use of anti-tumor necrosis factor-\selectlanguage{greek}α
\selectlanguage{english}agents. They included 13 studies and both a disease-matched control
group and a general population control group. O'Byrne et al. in
contrast, also included case series and case reports in addition,
resulting in a study population of more than 11000 exposed cases.

Then, the methods of data analysis were fitted to the wide design of the
study. The most applicable method of measuring an effect of an exposure
on maternal and fetal outcomes is with relative effect measures, also
known as odds ratios. In the current study the authors used proportions
in their meta-analyses. Proportions represent the absolute risk of an
outcome in the exposed and non-exposed groups and are not perfectly
suited to draw conclusions about the association of an exposure and an
outcome. But the wide study design, in particular the inclusion of case
series and case reports, left the authors with no choice but to use
proportions.

Further, this broad inclusion has two more major drawbacks. The
meta-analysis showed in most analyses a high heterogeneity. This can be
attributed to the inclusion of different study designs and comparisons
of different diseases and medications, as the authors mentioned. In
addition, there is a serious risk of bias, firstly due to the high
selectivity applied when choosing the population of case series and case
reports, and secondly due to the use of proportions, making it
impossible to adjust for confounders.

Well-designed systematic reviews and meta-analyses can generate a clear
overview and provide valuable information for clinicians and patients
alike. However, although including a large number of cases is
recommended to strengthen the evidence of a systematic
review/meta-analysis, this should not be done at any cost - it can
sometimes weaken the quality of the study. The most appropriate method
should be applied and individualized to the specific study.

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