Types of studies

We will include RCTs comparing simvastatin with placebo. Randomization ensures an equal likelihood of receiving either allocated intervention, and often results in a more balanced distribution of confounding characteristics. Cluster‐randomized studies and quasi randomized trials are not eligible. For a cross‐over RCT, we will include only data from the period of the trial prior to the first cross‐over. Due to possible carry‐over effects, we will exclude the second arm of cross‐over trials. We will include studies reported as full‐text and unpublished data.

Types of participants

Participants may be of any age, with and without evidence of cardiovascular disease. They can have normal lipid parameters or any type of hyperlipidemia or dyslipidemia. We will accept participants with various comorbid conditions, including type 2 diabetes mellitus, hypertension, metabolic syndrome, chronic renal failure or cardiovascular disease. Studies with subsets of eligible participants will be included only if data for that subset are provided separately. If not, we will contact authors and request the data.

Types of interventions

Simvastatin and placebo must be administered at a constant daily dose of 5 mg/day, 10 mg/day, 20 mg/day, 40 mg/day and 80 mg/day or any other dose for a period of three to 12 weeks. We have chosen this administration time window to allow at least three weeks for a steady‐state effect of simvastatin to occur and to keep it short enough to minimize participants dropping out. We will include studies where simvastatin is administered in the morning or evening or where it is not specified. Trials require a washout baseline dietary stabilization period of at least three weeks, where all previous lipid‐altering medication is withdrawn. This baseline phase ensures participants follow a standard lipid‐regulating diet and helps to stabilize baseline lipid values prior to treatment. In trials where participants were not receiving lipid‐altering medications or dietary supplements before receiving the test drug, we will not require a washout baseline dietary stabilization period. We will not include studies where participants receive co‐interventions that affect blood lipids. We will list all treatment arms of each study, even if they are not used in the review, in the 'Characteristics of included studies' table.

Types of outcome measures

The outcomes will not determine the eligibility of inclusion into the review. We will assess the following lipid parameters with data reported at 3, 4, 5, 6, 7, 8, 9, 10, 11 and 12 weeks.

Electronic searches

The Cochrane Hypertension Information Specialist (CIS) will search the following databases without language, publication year or publication status restrictions:

1. Cochrane Central Register of Controlled Trials (CENTRAL) via the Cochrane Register of Studies;

2. Ovid MEDLINE;

3. Ovid Embase;

4. US National Institutes of Health Ongoing Trials Register ClinicalTrials.gov (www.clinicaltrials.gov);

5. World Health Organization International Clinical Trials Registry Platform (trialsearch.who.int).

The information specialist will model the subject strategies for databases on the search strategy designed for MEDLINE. Where appropriate, the CIS will combine the subject strategy adaptations with the sensitivity‐ and precision‐maximizing version of the highly sensitive search strategy designed by Cochrane for identifying RCTs as described in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2021). We present the MEDLINE search strategy in Appendix 1.

Searching other resources

1. We will check the bibliographies of included studies and any relevant systematic reviews identified for further references to relevant trials.

2. We will check the included studies for retractions and errata via PubMed (pubmed.ncbi.nlm.nih.gov) and the Retraction Watch Database (retractiondatabase.org), and report the search dates in the review.

3. We will search Epistemonikos (www.epistemonikos.org) for related systematic reviews.

4. Where necessary, we will contact experts/organizations in the field to obtain additional information on relevant trials.

5. We may contact original authors of included studies for clarification and further data if trial reports are unclear.

6. We will include gray literature by searching the following additional resources:

   1. US Food and Drug Administration (www.fda.gov);

   2. European Patent Office (worldwide.espacenet.com).

Selection of studies

Two review authors (SA and NA) will independently screen titles and abstracts and remove reports that are obviously irrelevant. We will obtain the full text of the remaining reports. We will remove duplicates when importing reports into Covidence. Two review authors (SA and NA) will independently screen full‐text articles to decide on the trials to be included using Covidence software. We will resolve disagreements by recourse to the third review author (JMW). We will list studies/reports excluded at this stage in the 'Characteristics of excluded studies' table. The unit of interest for the review is the study, and we will group papers related to a single study under a single reference ID. We will record the selection process in sufficient detail to complete a PRISMA flow diagram (Liberati 2009).

Data extraction and management

Two review authors (SA and NA) will independently extract the appropriate data from each included trials. If there is disagreement over a value, we will reach consensus by data recalculation to determine the correct value. We will use a piloted data extraction form. We will directly extract the mean percentage change from the data. We will collect endpoint data if provided instead of change data. To extract numeric data from graphs we will use a WebPlotDigitizer, which calculates the appropriate data. We will add the calculated data to the 'Data and analyses' section of the review. We will extract standard deviations (SDs) and standard errors (SEs) from the report or will calculate them when possible. We will contact experts/organizations and original authors for clarification and further data if trial reports are unclear.

In Covidence, we will extract the following data for each study.

1. Identification: sponsorship source, country and setting of where the trial was done

2. Author's contact details

3. Methods: trial design, group, additional methods data

4. Population: inclusion criteria, exclusion criteria, group differences, baseline characteristics

5. Interventions and comparisons: doses of simvastatin and placebo

6. Outcomes: percentage change from baseline in total cholesterol, LDL‐C, HDL‐C, triglycerides for treatment period of 3 to 12 weeks and WDAEs with no time restriction.

We will enter data from Covidence into Review Manager Web as continuous data (RevMan Web 2022).

Assessment of risk of bias in included studies

Two review authors (SPA and NA) will assess all trials using the Cochrane RoB 1 tool under the domains as follows:

1. Random sequence generation

2. Allocation concealment

3. Blinding of participants and personnel (performance bias)

4. Blinding of outcome assessment (detection bias): all lipids

5. Blinding of outcome assessment (detection bias): WDAEs

6. Selective reporting (reporting bias): total cholesterol

7. Selective reporting (reporting bias): LDL‐C

8. Selective reporting (reporting bias): HDL‐C

9. Selective reporting (reporting bias): triglycerides

10. Selective reporting (reporting bias): WDAEs

11. Attrition bias: total cholesterol

12. Attrition bias: LDL‐C

13. Attrition bias: HDL‐C

14. Attrition bias: triglycerides

15. Attrition bias: WDAEs

16. Other bias

If there are disagreements, we will reach consensus by discussion with a third review author (JMW).

We will produce risk of bias tables as outlined in the Cochrane Handbook for Systematic Reviews of Interventions, Chapter 8 (Higgins 2011).

Measures of treatment effect

We will analyze the treatment effects for continuous data as percent reduction from baseline, as mean difference (MD) with 95% confidence intervals (CIs) for each simvastatin dose versus placebo for blood total cholesterol, LDL‐C, HDL‐C and triglycerides. If the study does not report data in a format that can be entered directly into a meta‐analysis, we will convert them to the required format using the information in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2021). We will analyze the dichotomous data, WDAE, as risk ratio (RR) with its 95% CI.

Unit of analysis issues

The unit of analysis will be the mean values for the people completing each trial. In the case of trials with multiple treatment arms with different doses of simvastatin, we will correct the number of participants of the placebo group by dividing it by the number of comparisons. For a cross‐over RCT, we will only analyze the data before the cross‐over point. When there are data at different times, we will calculate a single weighted mean from all the time points as, in a previous review, we established that the lipid‐lowering effect of atorvastatin was stable over time (Adams 2015).

Dealing with missing data

We expect follow‐up to be reasonably high for these short‐term trials. However, the data will represent treatment efficacy and not real‐world effectiveness of simvastatin on these lipid parameters. When data are missing, we will request them from the study authors. The most common type of value that is not reported is the SD of the change.

In the case of a missing SD for the change in lipid parameters, we will impute the SD using the following hierarchy (listed from high to low preference).

1. SD calculated either from the t statistics corresponding to the exact P value reported or from the 95% CI of the MD between treatment groups.

2. Mean weighted SD of the change from other trials in the review (Furukawa 2006 [Furukawa 2006]).

It is common for the SD to be miscalculated. Therefore, in order to avoid overestimating results from trials where it is inaccurately calculated and much lower than expected, we will use the imputed value by the method of Furukawa 2006 when SD values are less than 40% of the mean weighted SDs.

Assessment of heterogeneity

The Chi² test to identify heterogeneity is not appropriate because it has low power when there are few studies but has excessive power to detect clinically unimportant heterogeneity when there are many studies; therefore, the I² statistic is more preferable. The I² statistic calculates between‐study variance/(between‐study variance + within‐study variance). This measures the proportion of total variation in the estimate of the treatment effect that is due to heterogeneity between studies. This statistic is also independent of the number of studies in the analysis (Higgins 2002). We will explore the cause of heterogeneity when an I² statistic is more than 50% using subgroup and sensitivity analyses.

Assessment of reporting biases

When there are more than 10 trials for an individual outcome, we will assess publication bias using funnel plots, as outlined in Chapter 13 of the Cochrane Handbook for Systematic Reviews of Interventions (Page 2021).

Data synthesis

We will undertake meta‐analysis using standard Cochrane methodology (Deek 2020). We will enter all study data for meta‐analysis into Review Manager Web using an MD fixed‐effect model to determine the weighted treatment effect and 95% CIs for blood total cholesterol, LDL‐C, HDL‐C and triglycerides (RevMan Web 2022). We will record trial data of each study and dose in GraphPad Prism 4, to yield a weighted least squares analysis based on the inverse of the square of the SE for each lipid parameter, to generate weighted log dose–response curves (Tallarida 1987). We will enter the number of participants in who prematurely withdrew due to at least one adverse effect as dichotomous data for all combined doses of simvastatin and report it as RRs (RevMan Web 2022).

Subgroup analysis and investigation of heterogeneity

We will assess heterogeneity using the I² statistic (Higgins 2002). If the I² statistic is 50% or greater, we will attempt to identify possible causes for this by conducting planned subgroup analyses, provided there are sufficient numbers of trials (see below). The outcome will be LDL‐C.

If we find at least 10 trials for each subgroup, we will analyze subgroups based on the following factors.

1. Men versus women (effect may be greater in women because of their smaller size on average).

2. Morning administration versus evening administration (effect may be greater when administered in the evening).

3. Twice daily versus single dose (effect may be greater with twice daily dosing).

4. Participants with cardiovascular disease versus those without cardiovascular disease (effect may be greater in people with cardiovascular disease due to better adherence).

5. Participants with chronic renal failure versus those without chronic renal failure (effect may be diminished in people with chronic renal failure).

6. Familial hypercholesterolemia versus non‐familial hypercholesterolemia (effect appeared to be less in familial hypercholesterolemia in previous reviews) (Adams 2015; Adams 2018).

7. Asian people versus non‐Asian people (Asian people have a higher incidence of poor metabolizers).

Subgroup comparisons will be made in the same analysis (forest plot) and all the subgroup comparisons will be added to the data and analysis section. We plan to use the tests for interaction to test for differences between the subgroups.

Sensitivity analysis

We will assess the influence of high/unclear risk of bias studies by deselecting them to determine if only low risk of bias studies retain the direction and strength of effects observed for each outcome. We will assess the influence of low adherence and the effect of our approach of combining results at different times and the influence of industry funded trials similarly.

Summary of findings and assessment of the certainty of the evidence

Two review authors (SA and JMW) will independently apply the GRADE approach to assess the certainty of the evidence behind each estimate of treatment effect using the five GRADE domains (risk of bias, inconsistency, indirectness, imprecision and publication bias) (Schünemann 2021a; Schünemann 2021b). We will justify all decisions to downgrade the certainty of the evidence using footnotes and make comments to aid reader's understanding of the review where necessary. We will resolve disagreements by discussion or with a third review author (NA). We will present key findings of the review in summary of findings tables for each outcome separately: LDL‐C, total cholesterol and triglycerides as this will provide the dose–response relationship for the drug. We will also present a separate summary of findings table for WDAEs for all doses as the best measure of harm.