Antiepileptic drug monotherapy for epilepsy: an overview of systematic reviews andnetwork meta-analysis

All time-to-event outcomes in this overview and network meta-analysis will be summarised using the hazard ratio and 95% Confidence Interval as the measure of treatment effect. Outcomes will calculated from individual participant data (IPD) provided where possible or extracted from published studies.

We will not attempt to analyse or synthesise adverse event data; we will report adverse event data from IPD or extracted from published studies narratively.

We will take an intention-to-treat approach to analysis; in other words, participants will be analysed in the group to which they were randomised in an individual trial, irrespective of which treatment they actually received. Therefore for time-to-event
outcomes 'Time to 6 month remission', 'Time to 12 month remission' and 'Time to first seizure post randomisation' participants will not be censored if treatment was withdrawn. For the primary outcome, time to withdrawal of allocated treatment, we will consider withdrawals due to lack of efficacy (i.e recurrent seizures), poor tolerability (i.e. adverse events) or a combination of both poor efficacy and tolerability. Other withdrawals such as losses to follow up, non-treatment related deaths, administrative trial reasons etc. will be censored at the time of withdrawal.

For all time-to-event outcomes, the relationship between the time-to-event and treatment effect of the anti-epileptic drugs will be investigated. Cox proportional hazards regression models will be used to obtain study-specific estimates of log(hazard
ratio) or treatment effect and associated standard errors. The model assumes that ratio of hazards (risks) between the two treatment groups is constant over time (i.e. hazards are proportional). This proportional hazards assumption of the Cox regression model will be tested for each outcome of each study by testing the statistical significance of a time-varying covariate in the model for each study. To preserve the within-trial randomisation, Cox regression models will be stratified by trial

A key assumption made in network meta-analysis is that treatment effect is exchangeable" across all included trials; in other words, the indirect comparison made between two treatments is a feasible comparison to make (known as the transitivity assumption) and that the indirect evidence is consistent with the direct evidence where a comparison exists (known as the consistency assumption). Transitivity requires that all treatments are "jointly randomisable," in other words all ten AEDs could feasibly be randomised in the same trial and those which are not treatment arms in any given trial are "missing at random". This assumption can not be formally tested statistically; transitivity must be judged by careful consideration of trial settings and characteristics, treatment mechanisms and participant demographics to investigate if any differences would be expected to modify relative treatment effects. The consistency assumption can be evaluated statistically via the Bucher Method which applies a z-test to the difference between the direct treatment effect estimate and the indirect estimate. If inconsistency is found to be present, it can be adjusted for in network meta-analysis via inconsistency factors.
As in pairwise meta-analysis, we will assess the presence of heterogeneity statistically using the Q test (P value < 0.10) for significance and the I² statistic with the following interpretation;

0% to 40% might not be important
30% to 60% may represent moderate heterogeneity
50-90% may represent substantial heterogeneity
75%-100%: considerable heterogeneity.

We will also assess the presence of heterogeneity by visually inspecting forest plots, particularly in terms of the magnitude and direction of effects. If considerable heterogeneity is found to be present, we will perform network meta-analysis with
random effects and inspect trial and participant characteristics for sources of heterogeneity

There are strong clinical beliefs that certain AEDs are more effective in certain seizure types than others, for example CBZ is more effective in partial onset seizures and VPA is more effective in generalised onset seizures, suggesting that there is a treatment by seizure type (partial or generalised) interaction; if such an interaction exists then the key assumption of an exchangable treatment effect across all included trials would be violated. To account for this, all analyses will be conducted separately by epilepsy type (partial onset or generalised onset) according to the classification of main seizure type at baseline. Partial seizures (simple or complex) and partial secondarily generalised seizures were classified as partial epilepsy. Primarily generalised seizures were classified as generalised epilepsy. Exchangability of treatment effect will then be judged separately by analyses of seizure type

For the ten AEDs included in the treatment network, 45 possible pairwise comparisons can be made for both partial and generalised onset seizures. For clinical interest and relevance, we will present hazard ratio estimates for each AED in the network compared to the current recommended first line treatments (CBZ or LTG for partial onset seizures and VPA for generalised onset seizures) in the main results of this overview and results for all 45 pairwise comparisions by seizure type will be presented as an additional table for each outcome.