Table 2 Suggestions for a more valid assessment of intervention effects in systematic reviews

Step 1

Calculate and report the P-values and the 95% confidence intervals from all fixed-effect and random-effects meta-analyses. The most conservative result should be the main result.

Step 2

Explore the reasons behind substantial statistical heterogeneity by performing subgroup analyses and sensitivity analyses (see step 6).

Step 3

Adjust the thresholds for significance (P-values and the confidence intervals from the meta-analyses and the risks of type I error in the trial sequential analysis) according to the number of primary outcome comparisons.

Step 4

Calculate and report a realistic diversity-adjusted required information size and analyse all of the outcomes with trial sequential analysis. Report if the trial sequential monitoring boundaries for benefit, harm, or futility are crossed.

Step 5

Calculate and report Bayes factor for the primary outcome/s based on the anticipated intervention effect used to estimate the required information size (http://www.ctu.dk/tools-and-links/bayes-factor-calculation.aspx). A Bayes factor less than 0.1 (a ten-fold higher likelihood of compatibility with the alternative hypothesis than with the null hypothesis) may be chosen as threshold for significance.

Step 6

Use subgroup analysis and sensitivity analyses to assess the potential impact of systematic errors (‘bias’).

Step 7

Assess the risk of publication bias.

Step 8

Assess clinical significance of the review results if the prior seven steps have shown statistically significant results.