Types of studies

Randomized controlled clinical trials.

Types of participants

Adults (18 years or older) with hypercholesterolemia.

Types of interventions

Types of outcome measures

Electronic searches

We will use the most recent version of the following sources for the identification of trials.

• The Cochrane Library

• MEDLINE

• EMBASE

• China National Knowledge Infrastructure

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 section 'Outcomes' in the 'Characteristics of included studies' table.

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 the 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,systematic reviews, meta‐analyses, and health technology assessment reports found as a result of the searches.

Selection of studies

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

Figure 1

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Study flow diagram.

Data extraction and management

For studies that fulfil the inclusion criteria, two review authors (YQ, KN) will independently abstract relevant population and intervention characteristics using standard data extraction templates (for details see 'Characteristics of included studies'; Table 1; Appendix 2; Appendix 3; Appendix 4; Appendix 5) with any disagreements to be resolved by discussion, or if required by a third party. We will send an email request to the contact persons of published studies to enquire whether authors are willing to answer questions regarding their trials. We will publish the results of this survey in Appendix 6. 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 review authors (YQ, KN) will assess each trial independently. Possible disagreements will be resolved by consensus, or with consultation with a third party. In cases of disagreement, we will consult the rest of the group and a judgement will be made based on consensus.

We will assess risk of bias using The Cochrane Collaboration’s tool (Higgins 2011). We will use the following bias 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 and a 'Risk of bias' summary as figures.

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

Measures of treatment effect

We will express dichotomous data as odds ratio (OR) or risk ratio (RR) with 95% confidence intervals (CI). We will express continuous data as differences in means (MD) with 95% CI.

Unit of analysis issues

We will take into account the level at which randomization occurred, such as cross‐over trials, cluster‐randomized 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, randomized 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:

• Asian and non‐Asian populations (due to their different genotype, diet style, and equol metabolism phenotype etc);

• degree of hypercholesterolemia;

• previous cardiovascular events;

• other cardiovascular risk factors.

Assessment of reporting biases

We will use funnel plots in case we include more than 10 studies 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

We will primarily summarize low‐risk of bias data by means of a random‐effects model. We will perform statistical analyses according to the statistical guidelines referenced in the most recent 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:

• Asian and non‐Asian population;

• male and female;

• hypercholesterolemia with or without hypertriglyceridemia;

• equol and non‐equol producers.

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 (RR, OR etc.) and different statistical models (fixed‐effect model and random‐effects model).