Other subgrouping strategies were only used if the age category subgroup was unable to explain heterogeneity, then these further subgrouping strategies were applied in order of priority. Again, once a subgrouping strategy was found to explain heterogeneity from all derived subgroups, further subgrouping strategies were not used. If all predefined strategies of subgrouping were unable to explain statistical heterogeneity within each derived subgroup, then a random effects (DerSimonian and Laird) model was employed to the entire group of studies in the meta-analysis. A random-effects model assumes a distribution of populations, rather than a single population. This leads to a widening of the confidence interval around the overall estimate, thus providing a more realistic interpretation of the true distribution of effects across more than 1 population. If, however, the committee considered the heterogeneity was so large that meta-analysis was inappropriate, then the results were described narratively. 4.3.3.1.3 Complex analysis Where studies had used a crossover design, paired continuous data were extracted where possible, and forest plots were generated in RevMan5283 with the generic inverse variance function. When a crossover study had categorical data and the number of subjects with an event in both interventions Chronic Heart Failure Methods NICE 2018. All rights reserved. Subject to Notice of rights. ISBN: 978-1-4731-3093-7 was known, the standard error (of the log of the risk ratio) was calculated using the simplified Mantel-Haenszel method for paired outcomes. Forest plots were also generated in RevMan5283 with the generic inverse variance function. If paired continuous or categorical data were not available from the crossover studies, the separate group data were analysed in the same way as data from parallel groups, on the basis that this approach would overestimate the confidence intervals and thus artificially reduce study weighting resulting in a conservative effect. 