Information for authors

The below text is part of The CHBG Module, referenced in every issue of The Cochrane Library. The text is usually updated after discussions with CHBG editors and other people involved in the review preparation, or whenever the Cochrane Editorial and Methods Department updates methodological requirements.

Depending on the type of review, review authors are working on, the Cochrane Handbooks, i.e. The Cochrane Handbook for Systematic Reviews of Interventions (Version 6) (available as PDF chapters to registered Cochrane contributors (Archie login required)), or The Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy must be  followed. The Methodological Expectations of Cochrane Intervention Reviews (MECIR) Standards present a guide to the conduct and reporting of Cochrane Intervention Reviews. Each set of Standards includes links to Cochrane Training resources, the Cochrane Handbook for Systematic Reviews of Interventions (the Handbook) and other available resources.

Authors shall prepare and submit their protocols and reviews only after ensuring that all co-authors have approved of it and that the style of the respective protocol or review is consistent with the Cochrane Style Manual.  Often, review authors forget to use active voice where sensible. In a sentence written in the active voice, the subject of the sentence performs the action [We will include only randomised clinical trials.]. In a sentence written in the passive voice, the subject receives the action [Randomised clinical trials were included.].

The following text may help review authors to write the Methods section of an intervention review protocol.

Outcomes
The CHBG works on standardisation of hepato-biliary outcomes in CHBG review protocols, based on the disease condition reviewed. We do already have a standardised set of outcomes for hepatitis B and C. Suggestions for standardised outcomes in other diseases are most welcome.

In general, selection of outcomes in review protocols and their listing shall follow the Guidelines of The Cochrane Handbook for Systematic Reviews of Interventions. See Chapter 18: Patient-reported outcomes

Study selection
The CHBG recommends the inclusion of randomised clinical trials for assessment of benefits and harms of interventions. As adverse events may not be caught in small or even large randomised clinical trials, The CHBG encourages authors to look at quasi-randomised studies, cohort studies, and case-control studies, retrieved with the searches for randomised clinical trials, for reports of harmful effects of interventions. Evidence on harm from non-randomised studies shall not be combined with evidence on harms from randomised trials in meta-analyses. The gathered information on harm from non-randomised studies can be presented in a narrative way. The CHBG does not recommend extensive searches for non-randomised studies because our knowledge on how to do this best is limited. 

Authors must follow the guidelines in Chapter 19 of the The Cochrane Handbook for Systematic Reviews of Interventions, Version 6 about adverse events. Two authors should generally perform the selection of studies and data extraction independently. Therefore, the Editorial Team encourages at least two authors to work on a systematic review.

Authors of reviews should follow The Cochrane Handbook for Systematic Reviews of InterventionsVersion 6. All authors are able to access up-to-date material with their Archie account (cochrane.org - My account). All authors are advised to regularly check the Cochrane training webiste (log in required). 

Bias risk assessment
Authors of new reviews should use RoB2 (See  Chapter 8: Assessing risk of bias in a randomized trial  and Chapter 23: Including variants on randomized trials). Please listen to recorded webinars

Data collection
Generally, two or more authors should extract data independently regarding inclusion criteria (design, participants, interventions, and outcomes), criteria for risk of bias, and results. When data are missing in a published report, authors should contact the corresponding author of the trial report. Collection of data from unpublished studies must be performed by writing to authors of previously published studies as well as the industry or manufacturers of the intervention. Any substantial piece of information regarding unpublished data should be entered as a reference. For the correct type of reference, please see The Cochrane Style Manual.

Data analysis
We urge authors of systematic reviewers to follow the instructions in The Cochrane Handbook for Systematic Reviews of Interventions regarding statistical analyses. 

The following instructions may assist review authors in writing some parts of the statistical methods in an intervention review.

Before you start writing the 'statistical methods' section in a protocol for a Cochrane review, you need to consider thoroughly which methods would be most appropriate with regard to your specific question. You should consult Part 2 of The Cochrane Handbook for Systematic Reviews of Interventions where you will find a thorough presentation of statistical methods used in meta-analyses. Overall, the writing of 'statistical methods' in a review is not fixed and should be changed according to the need and characteristics of every unique systematic review.

We recommend that you always seek to perform intention-to-treat analysis.

Example of "Dealing with missing data" text
"We will perform an intention-to-treat analysis whenever possible. Otherwise, we will use the data that are available to us (e.g. a trial may have reported only per-protocol analysis results). As 'per-protocol' analyses may be biased, we plan to conduct best-worst case scenario analyses (good outcome in the intervention group and bad outcome in control group) and worst-best case scenario analyses (bad outcome in the intervention group and good outcome in control group) as sensitivity analyses whenever possible.

For continuous outcomes, we plan to impute the standard deviation from P values according to the guidance given in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2019). If the data are likely to be normally distributed, we plan to use the median for meta-analysis when the mean is not available. If it is not possible to calculate the standard deviation from the P-value or the Confidence Intervals, we plan to impute the standard deviation using the largest standard deviation in other trials for that outcome. This form of imputation may decrease the weight of the study for calculation of mean differences and may bias the effect estimate to no effect for calculation of standardised mean differences (Higgins 2019)."

Review authors can include missing data by considering them as treatment failures, or treatment successes. Furthermore, you could do extreme case analyses where you consider the drop-outs as failures or successes in the experimental group and as successes or failures in the control group. You need to consider what would be the most appropriate assumption for your specific review.

An example of the wording of each of the situations mentioned above is:
"Intention-to-treat analyses
Regarding the primary outcomes, we will include patients with incomplete or missing data in sensitivity analyses by imputing them according to the following scenarios.

- Poor outcome analysis: assuming that drop-outs/participants lost from both the experimental and the control arms experienced the outcome, including all randomised participants in the denominator.
- Good outcome analysis: assuming that none of the drop-outs/participants lost from the experimental and the control arms experienced the outcome, including all randomised participants in the denominator.
- Extreme case analysis that favours the experimental intervention ('best-worse' case scenario: none of the drop-outs/participants lost from the experimental arm, but all of the drop-outs/participants lost from the control arm experienced the outcome, including all randomised participants in the denominator.
- Extreme case analysis that favours the control ('worst-best' case scenario): all drop-outs/participants lost from the experimental arm, but none from the control arm experienced the outcome, including all randomised participants in the denominator."

Per protocol analyses 
Interpretation of per-protocol analyses should be cautious as they may be biased.

Cross-over trials
We recommend to those who want to include cross-over trials in their systematic reviews to consider using the analytical methods described by Elbourne 2002 as well as The Cochrane Handbook for Systematic Reviews of Interventions.

Risks of random errors and Trial Sequential Analysis
When few and small trials are combined in a meta-analysis, the risk of introducing random errors increases due to sparse data and due to multiplicity when conducting cumulative meta-analyses with repeating analyses of the same data. 

At a CHBG Editorial Team Group meeting in Copenhagen in April 2009, CHBG editors decided that CHBG review authors should be encouraged to use trial sequential analyses of their important meta-analyses. In the recent years, the central Cochrane editorial team suggested the use of Trial Sequential Analysis only as a sensitivity analysis of imprecision and compare this assessment with the assessment of imprecision with GRADE.

The Trial Sequential Analysis (TSA) software is free to download and use. The Trial Sequential Analysis Manual can be downloaded from the TSA website. Review authors should follow the insructions at www.ctu.dk/tools-and-links/tools-and-links.aspx when creating images for publication in their reviews with RevMan web.  

The following is an exaple of Trial Sequential Analysis text: 
"Sensitivity analysis

In addition to the sensitivity analysis described in the 'Dealing with missing data' section, we will perform sensitivity analysis on trials at low risk of bias (or both). We also plan to assess imprecision with Trial Sequential Analysis (see below), using also the eight-step procedure for validation of meta-analytic results in systematic reviews as suggested by Jakobsen and colleagues (Jakobsen 2014).

Trial Sequential Analysis
Cumulative meta-analyses are at risk of producing random errors due to sparse data and multiple testing of accumulating data (Brok 2008; Wetterslev 2008; Brok 2009; Thorlund 2009; Wetterslev 2009; Thorlund 2010; Wetterslev 2017); therefore, Trial Sequential Analysis (TSA 2011) can be applied as a secondary analysis to control this risk (Thorlund 2011; Thomas 2019). We will use Trial Sequential Analysis as a sensitivity analysis to our GRADE assessments of imprecision. The former is taking meta-analytic model and diversity into consideration whereas the latter is based on a fixed-effect model and ignores diversity. The required information size (i.e. the number of participants needed in a meta-analysis to detect or reject a certain intervention effect) can be calculated in order to control random errors (Wetterslev 2008; Wetterslev 2009; Wetterslev 2017). The required information size takes into account the event proportion in the control group, the assumption of a plausible relative risk reduction, and the heterogeneity of the meta-analysis (Wetterslev 2008; Wetterslev 2009; Turner 2013; Wetterslev 2017). Trial Sequential Analysis enables testing for significance to be conducted each time a new trial is included in the meta-analysis. On the basis of the required information size, trial sequential monitoring boundaries can be constructed. This enables one to determine the statistical inference concerning cumulative meta-analysis that has not yet reached the required information size (Wetterslev 2008).

If the trial sequential monitoring boundary is crossed by the cumulative Z-curve before reaching the required information size, we may conclude that sufficient evidence is collected to validly assess benefit or harm, and that inclusion of additional trial data may be redundant. In contrast, if the boundaries for benefit or harm are not crossed, we may conclude that further trials are necessary before a certain intervention effect can be evaluated. Trial Sequential Analysis also allows for assessment of the sufficiency of evidence for a postulated intervention effect. A lack of effect is evident if the cumulative Z-score crosses the trial sequential monitoring boundaries for futility.

We will make relatively conservative estimations of the anticipated intervention effect to control the risks of random error (Jakobsen 2014). Large anticipated intervention effects lead to small required information sizes, and the thresholds for significance will be less strict after the information size has been reached (Jakobsen 2014).

We will analyse all primary and secondary outcomes using Trial Sequential Analysis. These analyses will allow us to calculate the Trial Sequential Analysis-adjusted CIs based on the following assumptions.

Primary outcomes
We will estimate the diversity-adjusted required information size (Wetterslev 2009), based on the proportion of participants with an outcome in the control group. We will use an alpha of 0.025 because of our three primary outcomes, a beta of 10%, and the diversity suggested by the trials in the meta-analysis (Jakobsen 2014; Castellini 2017).

As anticipated intervention effects for the primary outcomes in the Trial Sequential Analysis, we will use the following.

All-cause mortality: a relative risk reduction of 10% and the observed proportion of mortality in the control group.
Serious adverse events: a relative risk reduction of 20% and the observed proportion of serious adverse events in the control group.
Health-related quality of life: minimal relevant difference observed SD divided by two.

Secondary outcomes
We will estimate the diversity-adjusted required information size (Wetterslev 2009), based on the proportion of participants with an outcome in the control group when analysing dichotomous outcomes, and we will use the observed SD when analysing continuous outcomes. We will use an alpha of 0.033 because of the two secondary outcomes, a beta of 10%, and the diversity suggested by the trials in the meta-analysis (Jakobsen 2014; Castellini 2017).

As anticipated intervention effects for the secondary outcomes in the Trial Sequential Analysis, we will use the following relative risk reductions or increases.

Sepsis: a relative risk reduction of 20% and the observed incidence of failure treatment in the control group.
Non-serious adverse events: a relative risk reduction of 20%.

Assessment of imprecision
In order to have a better judgement of imprecision in the included trials, we will compare GRADE and Trial Sequential Analysis results regarding our Primary outcomes and Secondary outcomes (Castellini 2018; Gartlehner 2018; Thomas 2019).

Assessment of significance
We will assess the intervention effects using the random-effects model meta-analysis (DerSimonian 1986). For analysis of the three primary outcomes, we will consider significant a P value less than 0.025 (Jakobsen 2014), as this will secure a family-wise error rate (FWER) below 0.05. We will apply an eight-step procedure to assess if the results from the meta-analyses have passed the thresholds for significance (Jakobsen 2014).

We may perform further sensitivity analysis if deemed necessary (Higgins 2019)."

The following is an example of a Summary of Findings’ table text
"We will create a 'Summary of Findings' table including the following outcomes: [up to seven outcomes  are permitted and should be listed ]. We will report the longest follow-up with a range of follow-up for each outcome. We will use the GRADE approach and software to assess the quality of a body of evidence (GRADEpro GDT www.gradepro.org). GRADE considers the following criteria: study risk of bias (methodological quality); inconsistency of results (unexplained heterogeneity); indirectness of evidence (population, intervention, comparator, or outcome); imprecision of results (wide CIs); and publication bias.
We will define levels of certainty as 'high', 'moderate', 'low', or 'very low' as follows.
  • High certainty: we are very confident that the true effect lies close to that of the estimate of the effect.
  • Moderate certainty: we are moderately confident in the effect estimate: the true effect is likely to be close to the estimate of the effect, but there is a possibility that it is substantially different.
  • Low certainty: our confidence in the effect estimate is limited: the true effect may be substantially different from the estimate of the effect.
  • Very low certainty: we have very little confidence in the effect estimate: the true effect is likely to be substantially different from the estimate of effect."

Reporting of reviews
For policies on the reporting of reviews (for example on the discussion of results, the use of tables and figures, and the naming of studies), authors must follow the recommendations of the Cochrane Handbook for Systematic Reviews of Interventions.

The Cochrane Collaboration's online learning page for authors is a good source of information and developing skills.

Used and other used in systematic reviews references

Higgins JPT, Green S (editors). Cochrane Handbook for Systematic Reviews of Interventions Version 5.1.0 [updated March 2011]. The Cochrane Collaboration, 2009. Available from www.cochrane-handbook.org.
Schulz KF, Chalmers I, Hayes RJ, Altman DG. Empirical evidence of bias. Dimensions of methodological quality associated with estimates of treatment effects in controlled trials. JAMA 1995;273:408-12.
Moher D, Pham B, Jones A, Cook DJ, Jadad AR, Moher M, et al. Does quality of reports of randomised trials affect estimates of intervention efficacy reported in meta-analyses? Lancet 1998;352:609-13.
Kjaergard LL, Villumsen J, Gluud C. Reported methodologic quality and discrepancies between large and small randomized trials in meta-analyses. Annals of Internal Medicine 2001;135:982-9.
Jüni P, Altman D, Egger M. Assessing the quality of controlled clinical trials. BMJ (Clinical Research Ed.) 2001;323:42-6.
Egger M, Jüni P, Bartlett C, Holenstein F, Sterne J. How important are comprehensive literature searches and the assessment of trial quality in systematic reviews? Empirical study. Health Technology Assessment 2003;7:1-76.
Als-Nielsen B, Chen W, Gluud C, Kjaergard LL. Association of funding and conclusions in randomized drug trials: a reflection of treatment effect or adverse events. JAMA 2003;290:921-8.
 Kunz R, Vist G, Oxman AD. Randomisation to protect against selection bias in healthcare trials. The Cochrane Database of Methodology Reviews 2002, Issue 4. Art. No.: MR000012. DOI: 10.1002/14651858.MR000012.
Kjaergard LL, Liu JP, Als-Nielsen B, Gluud C. Artificial and bioartificial support systems for acute and acute-on-chronic liver failure: a systematic review. JAMA 2003;289:217-22.
Wood L, Egger M, Gluud LL, Schulz KF, Jüni P, Altman DG, Gluud C, et al. Empirical evidence of bias in treatment effect estimates in controlled trials with different interventions and outcomes: meta-epidemiological study. BMJ (Clinical Research Ed.) 2008;336(7644):601-5.
Gluud LL, Thorlund K, Gluud C, Woods L, Harris R, Sterne JA. Correction: reported methodologic quality and discrepancies between large and small randomized trials in meta-analyses. Annals of Internal Medicine 2008;149(3):219.
Savović J, Jones HE, Altman DG, Harris RJ, Jüni P, Pildal J et.al. Influence of reported study design characteristics on intervention effect estimates from randomized, controlled trials. Annals of Internal Medicine 2012;157:429-38.
Savović J, Jones HE, Altman DG, Harris RJ, Jüni P, Pildal J, Als-Nielsen B, Balk EM, Gluud C, Gluud LL, A Ioannidis JP, Schulz KF, Beynon R, Welton NJ, Wood L, Moher D, Deeks JJ, Sterne JA. Influence of reported study design characteristics on intervention effect estimates from randomized, controlled trials. Health Technology Assessment 2012;16(35):1-82.
Lundh A, Sismondo S, Lexchin J, Busuioc OA, Bero L. Industry sponsorship and research outcome. Cochrane Database of Systematic Reviews 2012, Issue 12. Art. No.: MR000033. DOI: 10.1002/14651858.MR000033.pub2.
Lundh A, Sismondo S, Lexchin J, Busuioc OA, Bero L. Industry sponsorship and research outcome. Cochrane Database of Systematic Reviews 2017, Issue 2. Art. No.: MR000033 DOI: 10.1002/14651858.MR000033.pub3.
DerSimonian R, Laird N. Meta-analysis in clinical trials. Controlled Clinical Trials 1986;7(3):177-88.
DeMets DL. Methods of combining randomized clinical trials: strengths and limitations. Statistics in Medicine 1987;6(3):341-50.
Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ (Clinical Research Ed.) 2003;327:557-60.
Hollis S, Campbell F. What is meant by intention to treat analysis? Survey of published randomised controlled trials. BMJ (Clinical Research Ed.) 1999;319:670-4.
Elbourne DR, Altman DG, Higgins JP, Curtin F, Worthington HV, Vail A. Meta-analyses involving cross-over trials: methodological issues. International Journal of Epidemiology 2002;31:140-9.
Stedman MR, Curtin F, Elbourne DR, Kesselheim AS, Brookhart MA. Meta-analyses involving cross-over trials: methodological issues. International Journal of Epidemiology. 2011; 40(6): 1732-4.
Macaskill P, Walter SD, Irwig L. A comparison of methods to detect publication bias in meta-analysis. Statistics in Medicine 2001:20:641-54.
Begg CB, Mazumdar M. Operating characteristics of a rank correlation test for publication bias. Biometrics 1994;50:(4):1088-101.
Egger M, Smith GD, Schneider M, Minder C. Bias in meta-analysis detected by a simple graphical test. BMJ (Clinical Research Ed.) 1997;315(7109):629-34.
Egger M, Jüni P, Bartlett C, Holenstein F, Sterne J. How important are comprehensive literature searches and the assessment of trial quality in systematic reviews? Empirical study. Health Technology Assessment 2003;7:1-76.
Kjaergard LL, Liu JP, Als-Nielsen B, Gluud C. Artificial and bioartificial support systems for acute and acute-on-chronic liver failure: a systematic review. JAMA 2003;289:217-22.
Gluud LL, Thorlund K, Gluud C, Woods L, Harris R, Sterne JA. Correction: reported methodologic quality and discrepancies between large and small randomized trials in meta-analyses. Annals of Internal Medicine 2008;149(3):219.
Wetterslev J, Thorlund K, Brok J, Gluud C. Trial sequential analysis may establish when firm evidence is reached in cumulative meta-analysis. Journal of Clinical Epidemiology 2008;61:64-75.
Brok J, Thorlund K, Gluud C, Wetterslev J. Trial sequential analysis reveals insufficient information size and potentially false positive results in many meta-analyses. Journal of Clinical Epidemiology. 2008. DOI:10.1016/j.jclinepi.2007.10.007.
Brok J, Thorlund K, Wetterslev J, Gluud C. Apparently conclusive meta-analyses may be inconclusive - Trial sequential analysis adjustment of random error risk due to repetitive testing of accumulating data in apparently conclusive neonatal meta-analyses. International Journal of Epidemiology. 2009;38(1):287-98.
Thorlund K, Devereaux PJ, Wetterslev J, Guyatt G, Ioannidis JP, Thabane L, Gluud LL, Als-Nielsen B, Gluud C. Can trial sequential monitoring boundaries reduce spurious inferences from meta-analyses? International Journal of Epidemiology. 2009;38(1):276-86.
Wetterslev J, Thorlund K, Brok J, Gluud C. Estimating required information size by quantifying diversity in random-effects model meta-analyses. BMC Medical Research Methodology 2009;9:86.
Thorlund K, Engstrøm J, Wetterslev J, Brok J, Imberger G, Gluud C. User manual for trial sequential analysis (TSA). Copenhagen Trial Unit, Centre for Clinical Intervention Research, Copenhagen, Denmark. 2011;1-115. Available from www.ctu.dk/tsa.
Jakobsen J, Wetterslev J, Winkel P, Lange T, Gluud C. Thresholds for statistical and clinical significance in systematic reviews with meta-analytic methods. BMC Medical Research Methodology 2014;14:120.
Balshem H, Helfand M, Schunemann HJ, Oxman AD, Kunz R, Brozek J, et al. GRADE guidelines: 3. Rating the quality of evidence. Journal of Clinical Epidemiology 2011;64(4):401-6. [ PubMed: 21208779]
Guyatt G, Oxman AD, Akl EA, Kunz R, Vist G, Brozek J, et al. GRADE guidelines: 1. Introduction-GRADE evidence profiles and summary of findings tables. Journal of Clinical Epidemiology 2011;64(4):383-94. [ PubMed: 21195583]
Guyatt GH, Oxman AD, Kunz R, Atkins D, Brozek J, Vist G, et al. GRADE guidelines: 2. Framing the question and deciding on important outcomes. Journal of Clinical Epidemiology 2011;64(4):395-400. [ PubMed: 21194891]
Guyatt GH, Oxman AD, Vist G, Kunz R, Brozek J, Alonso-Coello P, et al. GRADE guidelines: 4. Rating the quality of evidence--study limitations (risk of bias). Journal of Clinical Epidemiology 2011;64(4):407-15. [ PubMed: 21247734]
Guyatt GH, Oxman AD, Montori V, Vist G, Kunz R, Brozek J, et al. GRADE guidelines: 5. Rating the quality of evidence--publication bias. Journal of Clinical Epidemiology 2011;64(12):1277-82. [ PubMed: 21802904]
Guyatt GH, Oxman AD, Kunz R, Brozek J, Alonso-Coello P, Rind D, et al. GRADE guidelines 6. Rating the quality of evidence--imprecision. Journal of Clinical Epidemiology 2011;64(12):1283-93. [ PubMed: 21839614]
Guyatt GH, Oxman AD, Kunz R, Woodcock J, Brozek J, Helfand M, et al. GRADE guidelines: 7. Rating the quality of evidence--inconsistency. Journal of Clinical Epidemiology 2011;64(12):1294-302. [ PubMed: 21803546]
Guyatt GH, Oxman AD, Kunz R, Woodcock J, Brozek J, Helfand M, et al. GRADE guidelines: 8. Rating the quality of evidence--indirectness. Journal of Clinical Epidemiology 2011;64(12):1303-10. [ PubMed: 21802903]
Guyatt GH, Oxman AD, Sultan S, Glasziou P, Akl EA, Alonso-Coello P, et al. GRADE guidelines: 9. Rating up the quality of evidence. Journal of Clinical Epidemiology 2011;64(12):1311-6. [ PubMed: 21802902]
Guyatt G, Oxman AD, Sultan S, Brozek J, Glasziou P, Alonso-Coello P, et al. GRADE guidelines: 11. Making an overall rating of confidence in effect estimates for a single outcome and for all outcomes. Journal of Clinical Epidemiology 2013;66(2):151-7. [ PubMed: 22542023]
Guyatt GH, Oxman AD, Santesso N, Helfand M, Vist G, Kunz R, et al. GRADE guidelines: 12. Preparing summary of findings tables-binary outcomes. Journal of Clinical Epidemiology 2013;66(2):158-72. [ PubMed: 22609141]
Guyatt GH, Thorlund K, Oxman AD, Walter SD, Patrick D, Furukawa TA, et al. GRADE guidelines: 13. Preparing summary of findings tables and evidence profiles-continuous outcomes. Journal of Clinical Epidemiology 2013;66(2):173-83. [ PubMed: 23116689]
Mustafa RA, Santesso N, Brozek J, Akl EA, Walter SD, Norman G, et al. The GRADE approach is reproducible in assessing the quality of evidence of quantitative evidence syntheses. Journal of Clinical Epidemiology 2013;66(7):736-42; quiz 742.e1-5. [ PubMed: 23623694]
Jakobsen JC, Wetterslev J, Winkel P, Lange T, Gluud C. Thresholds for statistical and clinical significance in systematic reviews with meta-analytic methods. BMC Medical Research Methodology 2014;14(1):120.
Hrobjartsson A, Thomsen AS, Emanuelsson F, Tendal B, Hilden J, Boutron I, et al. Observer bias in randomised clinical trials with binary outcomes: systematic review of trials with both blinded and non-blinded outcome assessors. BMJ (Clinical Research Ed.) 2012;344:e1119.
Hrobjartsson A, Thomsen ASS, Emanuelsson F, Tendal B, Hilden J, Boutron I, et al. Observer bias in randomized clinical trials with measurement scale outcomes: a systematic review of trials with both blinded and nonblinded assessors. CMAJ : Canadian Medical Association Journal 2013;185(4):E201-11.
(Hrobjartsson 2014a) Hrobjartsson A, Emanuelsson F, Skou Thomsen AS, Hilden J, Brorson S. Bias due to lack of patient blinding in clinical trials. A systematic review of trials randomizing patients to blind and nonblind sub-studies. International Journal of Epidemiology 2014;43(4):1272-83.
(Hrobjartsson 2014b) Hrobjartsson A, Thomsen AS, Emanuelsson F, Tendal B, Rasmussen JV, Hilden J, et al. Observer bias in randomized clinical trials with time-to-event outcomes: systematic review of trials with both blinded and non-blinded outcome assessors. International Journal of Epidemiology 2014;43(3):937-48.
Gluud C, Nikolova D, Klingenberg SL. Cochrane Hepato-Biliary Group. About Cochrane (Cochrane Review Groups (CRGs)) 2018, Issue (changes - see The CLib issue). Art. No.: LIVER.
Savović J, Turner RM, Mawdsley D, Jones HE, Beynon R, Higgins JPT, et al. Association between risk-of-bias assessments and results of randomized trials in cochrane reviews: The ROBES meta-epidemiologic study. Am J Epidemiol 2018;187(5):1113-22.

Core outcomes for chronic hepatitis B (CHB) virus infection

Primary outcomes

  • All-cause mortality or hepatitis B-related morbidity (number of participants who developed cirrhosis, ascites, variceal bleeding, hepato-renal syndrome, hepatocellular carcinoma, or hepatic encephalopathy and who have not died). These outcomes will be tested as a composite outcome as well as individually (mortality or morbidity). Such composite outcomes need to be interpreted with caution, especially if the components are influenced differently by the intervention.
  • Health-related quality of life (any valid assessment scale, filled out by the participant).
  • Serious adverse events, that is, any untoward medical occurrence that results in death, is life threatening, requires hospitalisation or prolongation of existing hospitalisation, results in persistent or significant disability or incapacity, or is a congenital anomaly or birth defect (The International Conference on Harmonization (ICH) Guidelines for Good Clinical Practice (ICH_GCP 1997)).

Secondary outcomes

  • Mortality due to hepatitis B-related liver disease.
  • Non-serious adverse events. Any untoward medical occurrence in a participant or clinical investigation participant that does not meet the above criteria for a serious adverse event is defined as a non-serious adverse event.
  • Number of participants without histological improvement.
  • Number of participants with detectable HBsAg in serum or plasma.
  • Number of participants with detectable HBV DNA in serum or plasma. 

Exploratory outcomes

  • Number of participants with detectable HBeAg in serum or plasma (this outcome is only relevant for HBeAg-positive participants). 
  • Number of participants without HBeAg seroconversion in serum or plasma (this outcome is only relevant for HBeAg-positive participants). 
  • Number of participants without normalisation of transaminases.

References:
International Conference on Harmonisation Expert Working Group. International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use. ICH Harmonised Tripartite Guideline. Guideline for Good Clinical Practice CFR & ICH Guidelines. Vol. 1. Philadelphia (PA): Barnett International/PAREXEL, 1997.

Core outcomes for chronic hepatitis C virus infection
Primary outcomes

  • All-cause mortality or hepatitis C-related morbidity (number of participants who developed cirrhosis, ascites, variceal bleeding, hepato-renal syndrome, hepatocellular carcinoma, or hepatic encephalopathy and who have not died). These outcomes will be tested as a composite outcome as well as individually (mortality or morbidity). Such composite outcomes need to be interpreted with caution, especially if the components are influenced differently by the intervention.
  • Health-related quality of life (any valid assessment scale, filled out by the participant).  
  • Serious adverse events, that is, any untoward medical occurrence that results in death, is life threatening, requires hospitalisation or prolongation of existing hospitalisation, results in persistent or significant disability or incapacity, or is a congenital anomaly or birth defect (The International Conference on Harmonization (ICH) Guidelines for Good Clinical Practice (ICH-GCP 1997)).

Secondary outcomes

  • Mortality due to hepatitis C-related liver disease.
  • Non-serious adverse events. Any untoward medical occurrence in a participant or clinical investigation participant that does not meet the above criteria for a serious adverse event is defined as a non-serious adverse events.
  • Number of participants without histological improvement.
  • Failure of virological response: number of participants without sustained virological response, i.e., number of participants with detectable hepatitis C virus RNA (i.e., above lower limit of detection) in the serum by a sensitive PCR-based essay or by a transcription-mediated amplification testing 12 and 24 weeks after end of treatment.

Exploratory outcomes

  • Number of participants without normalisation of transaminases.

References:
International Conference on Harmonisation Expert Working Group. International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use. ICH Harmonised Tripartite Guideline. Guideline for Good Clinical Practice CFR & ICH Guidelines. Vol. 1. Philadelphia (PA): Barnett International/PAREXEL, 1997.

Information in general on core outcome sets can be found on www.comet-initiative.org/ 
If review authors wish to see more standard guidance, please contact Dimitrinka Nikolova, the Managing Editor.