Types of studies

Randomised controlled clinical trials.

Types of participants

Women with a diagnosis of gestational diabetes mellitus (GDM) in the index pregnancy.

Types of interventions

Types of outcome measures

Electronic searches

We will use the following sources for the identification of trials:

• The Cochrane Library (last issue);

• MEDLINE (until recent);

• EMBASE (until recent).

We will also search databases of ongoing trials (http://www.controlled‐trials.com/ with links to several databases and https://www.clinicaltrialsregister.eu/). We will provide information including trial identifier about recognized studies in the table 'Characteristics of ongoing studies' and the appendix 'matrix of study endpoints'.

For detailed search strategies please see under Appendix 1 (searches will not be older than six months at the moment the final review draft is checked into the Cochrane Information and Management System for editorial approval).

If additional key words of relevance are detected during any of the electronic or other searches we will modify the electronic search strategies to incorporate these terms. We will include studies published in any language.

We will send results of electronic searches to the Editorial Base of the Cochrane Metabolic and Endocrine Disorders Group.

Searching other resources

We will try to identify additional studies by searching the reference lists of included trials and (systematic) reviews, meta‐analyses and health technology assessment reports noticed.

Selection of studies

To determine the studies to be assessed further, two authors (PM, CAC) will independently scan the abstract, title or both sections of every record retrieved. All potentially relevant articles will be investigated as full text. Where differences in opinion exist, they will be resolved by a third party. If resolving disagreement is not possible, the article will be added to those 'awaiting assessment' and we will contact authors for clarification. A PRISMA (preferred reporting items for systematic reviews and meta‐analyses) flow‐chart of study selection (Figure 1) will be attached (Liberati 2009).

Figure 1

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Study flow diagram.

Data extraction and management

For studies that fulfil inclusion criteria, two authors (PM, CAC) will independently abstract relevant population and intervention characteristics using standard data extraction templates (for details see Table 1, Appendix 2, Appendix 3, Appendix 4, Appendix 5, Appendix 6, Appendix 7) with any disagreements to be resolved by discussion, or if required by a third party. We will send an email request to contact persons of published studies to enquire whether authors are willing to answer questions regarding their trials. The results of this survey will be published in Appendix 8. Thereafter, we will seek relevant missing information on the trial from the original author(s) of the article, if required.

Assessment of risk of bias in included studies

Two authors (PM, CAC) will assess each trial independently. We will resolve possible disagreements by consensus, or in consultation with a third party.

We will assess risk of bias using the Cochrane Collaboration’s tool (Higgins 2011). We will use the following criteria:

• random sequence generation (selection bias);

• allocation concealment (selection bias);

• blinding (performance bias and detection bias), separated for blinding of participants and personnel and blinding of outcome assessment;

• incomplete outcome data (attrition bias);

• selective reporting (reporting bias);

• other bias.

We will judge risk of bias criteria as 'low risk', 'high risk' or 'unclear risk' and use individual bias items as described in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011). We will attach a 'risk of bias graph' figure and 'risk of bias summary' figure.

We will assess the impact of individual bias domains on study results at endpoint and study levels.

Measures of treatment effect

Dichotomous data will be expressed as risk ratio (RR) with 95% confidence intervals (CI). Continuous data will be expressed as differences in means (MD) with 95% CI.

Unit of analysis issues

We will take into account the level at which randomisation occurred, such as cross‐over trials, cluster‐randomised trials and multiple observations for the same outcome.

Dealing with missing data

We will obtain relevant missing data from authors, if feasible and carefully perform evaluation of important numerical data such as screened, randomised patients as well as intention‐to‐treat (ITT), as‐treated and per‐protocol (PP) populations. We will investigate attrition rates, for example drop‐outs, losses to follow‐up and withdrawals and critically appraise issues of missing data and imputation methods (for example last‐observation‐carried‐forward (LOCF)).

Assessment of heterogeneity

In the event of substantial clinical or methodological or statistical heterogeneity we will not report study results as meta‐analytically pooled effect estimates.

We will identify heterogeneity by visual inspection of the forest plots and by using a standard Chi² test with a significance level of α = 0.1, in view of the low power of this test. We specifically will examine heterogeneity employing the I² statistic which quantifies inconsistency across studies to assess the impact of heterogeneity on the meta‐analysis (Higgins 2002; Higgins 2003), where an I² statistic of 75% and more indicates a considerable level of inconsistency (Higgins 2011).

When heterogeneity is found, we will attempt to determine potential reasons for it by examining individual study and subgroup characteristics.

We expect the following characteristics to introduce clinical heterogeneity:

• severity of GDM (e.g. required insulin during index pregnancy);

• type of test ‐ OGTT or other glucose test;

• result of glucose test(s) ‐ normal/abnormal; high or low results if abnormal;

• thresholds used in glucose tests for defining normal and abnormal;

• parity;

• maternal age;

• maternal BMI;

• reason for not having a glucose test;

• modality of reminder;

• who was reminded (clinician or woman or both).

Assessment of reporting biases

We will use funnel plots in case we include 10 studies or more for a given outcome to assess small study bias. There are a number of explanations for the asymmetry of a funnel plot (Sterne 2001) and we will carefully interpret results (Lau 2006).

Data synthesis

Data will be summarised statistically if they are available, sufficiently similar and of sufficient quality. We will perform statistical analyses according to the statistical guidelines referenced in the newest version of the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011).

Subgroup analysis and investigation of heterogeneity

We will mainly carry out subgroup analyses of the primary outcome parameter(s) (see above) and investigate interaction.

The following subgroup analyses are planned:

• severity of GDM in index pregnancy (need for insulin or other diabetes medication);

• maternal age (> 35 years versus ≤ 35 years);

• maternal BMI (normal, overweight or obese) ‐ either pre‐pregnancy or after giving birth.

Sensitivity analysis

We will perform sensitivity analyses in order to explore the influence of the following factors on effect size:

• restricting the analysis to published studies;

• restricting the analysis taking account risk of bias, as specified above;

• restricting the analysis to very long or large studies to establish how much they dominate the results;

• restricting the analysis to studies using the following filters: diagnostic criteria, language of publication, source of funding (industry versus other), country.

We will also test the robustness of the results by repeating the analysis using different measures of effect size (relative risk, odds ratio etc.) and different statistical models (fixed‐effect and random‐effects model).