65 We will evaluate sequence generation, allocation sequence conc

65 We will evaluate sequence generation, allocation sequence concealment; blinding of participants and study personnel; and incomplete outcome data.66

We will resolve any disagreements between reviewers by discussion. We will contact study authors if limitations in reporting lead to uncertainties in eligibility, risk of bias, or outcome. Direct comparisons meta-analyses In comparison to fixed Vandetanib mechanism of action effect models, random effect models are conservative in that they consider the within-study as well as the among-study variability. Recent methodological research has shown that while popular, the DerSimonian-Laird method67 can produce narrow CIs when the number of studies is small or when they are substantively heterogeneous.68 69 Therefore, to pool outcome data for trials that make direct comparisons between interventions and alternatives, we will use the likelihood profile approach.70 We will pool cross-over trials with parallel design RCTs using methods outlined in the Cochrane handbook to derive effect estimates.66

Specifically, we will perform a paired t test for each cross-over trial if any of the following are available: (1) the individual participant data; (2) the mean and SD or SE of the participant-specific differences, and between the intervention and control measurement; (3) the mean difference (MD) and one of the following: (a) a t-statistic from a paired t test; (b) a p value from a paired t-test; (c) a CI from a paired analysis; or (4)

a graph of measurements of the intervention arm and control arm from which we can extract individual data values, so long as the matched measurement for each individual can be identified.66 If these data are not available, we will approximate paired analyses by calculating the MDs and the corresponding SEs for the paired analyses.66 If the SE or SD of within-participant differences are not available, we will impute the SD using the methods outlined in the Cochrane Handbook.66 Ensuring interpretable results We will use a number of approaches to provide interpretable results from our meta-analyses. Batimastat For studies that provide binary outcome measures, we will calculate relative risks (RRs) to inform relative effectiveness. To generate measures of absolute effect (risk differences), we will use estimates of baseline risk from the control arm of eligible RCTs. When pooling across studies reporting continuous endpoints that use the same instrument, we will calculate the weighted mean difference (WMD), which maintains the original unit of measurement and represents the average difference between groups. Once the WMD has been calculated, we will contextualise this value by noting the corresponding MID—the smallest change in instrument score that patients perceive is important. We will prioritise use of anchor-based MIDs when they are available, and calculate distribution-based MIDs when they are not.

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