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Comparison of Clinical Outcomes and Adverse Events Associated with Glucose-Lowering Drugs in Patients with Type 2 Diabetes: a Meta-Analysis

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Comparison of Clinical Outcomes and Adverse Events Associated with Glucose-Lowering Drugs in Patients with Type 2 Diabetes: a Meta-Analysis Online Supplementary Content Palmer SC, Mavridis D, Nicolucci A, et al. Comparison of clinical outcomes and adverse events associated with glucose-lowering drugs in patients with type 2 diabetes: a meta-analysis. JAMA. doi:10.1001/jama.2016.9400. eMethods. Summary of Statistical Analysis eTable 1. Search Strategies eTable 2. Description of Included Clinical Trials Evaluating Drug Classes Given as Monotherapy eTable 3. Description of Included Clinical Trials Evaluating Drug Classes Given as Dual Therapy Added to Metformin eTable 4. Description of Included Clinical Trials Evaluating Drug Classes Given as Triple Therapy When Added to Metformin Plus Sulfonylurea eTable 5. Risks of Bias in Clinical Trials Evaluating Drug Classes Given as Monotherapy eTable 6. Risks of Bias in Clinical Trials Evaluating Drug Classes Given as Dual Therapy Added to Metformin eTable 7. Risks of Bias in Clinical Trials Evaluating Drug Classes Given as Triple Therapy When Added to Metformin plus Sulfonylurea eTable 8. Estimated Global Inconsistency in Networks of Outcomes eTable 9. Estimated Heterogeneity in Networks eTable 10. Definitions of Treatment Failure Outcome eTable 11. Contributions of Direct Evidence to the Networks of Treatments eTable 12. Network Meta-analysis Estimates of Comparative Treatment Associations for Drug Classes Given as Monotherapy eTable 13. Network Meta-analysis Estimates of Comparative Treatment Associations for Drug Classes When Used in Dual Therapy (in Addition to Metformin) eTable 14. Network Meta-analysis Estimates of Comparative Treatment Effects for Drug Classes Given as Triple Therapy eTable 15. Meta-regression Analyses for Drug Classes Given as Monotherapy (Compared With Metformin) eTable 16. Subgroup Analyses of Individual Sulfonylurea Drugs (as Monotherapy) on Hypoglycemia eTable 17. Sensitivity Analysis—Summary Treatment Estimates of Glucose-Lowering Interventions Restricted to Clinical Trials at Low Risk of Bias From Allocation Concealment Methods eFigure 1. Summary Study-Level Characteristics According to Drug Class eFigure 2. Networks of Secondary Outcomes eFigure 3. Evaluation of Loop Specific Consistency in Effect Estimates in Triangular and Quadratic Treatment Loops Within Each Network for Drug Classes Given as Monotherapy eFigure 4. Evaluation of Loop Specific Consistency in Effect Estimates in Triangular and Quadratic Treatment Loops Within Each Network for Drug Classes Given as Dual Therapy in Addition to Metformin eFigure 5. Evaluation of Loop Specific Consistency in Effect Estimates in Triangular and Quadratic Treatment Loops Within Each Network of Drug Classes Given as Triple Therapy in Addition to Metformin and Sulfonylurea eFigure 6. Direct (Pairwise) and Network Estimates of Treatment Effects for Drug Classes Given as Monotherapy © 2016 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ on 10/02/2021 eFigure 7. Direct (Pairwise) and Network Estimates of Treatment Effects for Drug Classes Given as Dual Therapy in Addition to Metformin eFigure 8. Direct (Pairwise) and Network Estimates of Treatment Effects for Drug Classes Given as Triple Therapy in Addition to Metformin and Sulfonylurea eFigure 9. Rankograms for Odds of Hypoglycemia Associated With Individual Sulfonylurea Drugs Given as Monotherapy eReferences This supplementary material has been provided by the authors to give readers additional information about their work. © 2016 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ on 10/02/2021 eMethods. Summary of Statistical Analysis The statistical analyses are presented in detail in the pre-specified protocol which is available at http://tinyurl.com/onjketx. First, pairwise random-effects meta-analysis was conducted by synthesizing data from trials comparing two different drug classes. Using a random-effects model, different studies were assumed to assess different yet related treatment effects. The relative treatment effects of the competing interventions were estimated using standardized mean differences (SMD) for continuous outcomes (glycated hemoglobin (HbA1C) and body weight) and odds ratios for the dichotomous outcomes (cardiovascular mortality, all-cause mortality, treatment failure, hypoglycemia, and serious adverse events). An SMD equal to zero meant that the compared treatment strategies had equivalent effects. For both HbA1C and body weight, an improvement was assumed to be associated with lower values on the continuous outcomes of interest. Therefore, an SMD below zero indicated the degree to which the intervention was associated with lower HbA1C than the comparator and an SMD above zero indicated the degree to which the intervention was associated with a higher HbA1C than the comparator. An odds ratio below 1 indicated that the treatment was associated with a lower odds of the outcome (all-cause mortality, cardiovascular mortality, hypoglycemia, serious adverse events) than the comparator while an odds ratio above 1 indicated that the treatment was associated with a greater odds of the outcome than the comparator. Risk differences were also estimated for all binary outcomes (all-cause mortality, cardiovascular mortality, serious adverse events, hypoglycemia) where they are reported in the text. For indirect and mixed treatment estimates, network meta-analysis was conducted to compare different glucose- lowering drug classes. Network meta-analysis synthesized both direct evidence (from head to head trial comparisons) and indirect evidence (estimating the relative effectiveness between pairs of interventions even if these have never been compared directly in trials).2-4 Network analysis was then used to rank interventions. A key assumption of network analysis was that of consistency; that one can learn about the relative effectiveness between the two interventions indirectly. Consistency implied that the distribution of effect modifiers was the same across treatment comparisons.5 For example, potential effect modifiers were similar across all included trials. The variables of age, baseline glycemic control, body weight, duration of diagnoses type 2 diabetes, and duration of treatment were all considered when assessing this assumption. Consistency implied that the studies comparing different drugs (for example metformin versus basal insulin or metformin versus placebo) were similar in terms of these patient and study characteristics. To maximize consistency, trials were stratified according to the intensity of drug treatment, assuming that people receiving three drugs for glucose control were likely to be different from those receiving only one- or two-drugs for glucose control. Networks of drug treatments were generated separately according to whether patients were receiving no, one (metformin), or two (metformin plus sulfonylurea) other drugs as glucose-lowering medication. Frequentist random-effects network meta-analysis was performed in Stata using the mvmeta and network commands and self-programmed Stata routines available at http://www.mtm.uoi.gr/index.php/stata-routines-for-network-meta- analysis.6,7 The ranking probabilities for all treatments being at each possible rank for each intervention were estimated.8 The treatment hierarchy of the competing interventions was obtained by using rankograms, the surface under the cumulative ranking curve (SUCRA), and mean ranks.6,9 SUCRA expressed as a percentage the efficacy or safety of each intervention relative to an imaginary intervention that was always the best without uncertainty. A SUCRA of 80% meant that the intervention of interest was associated with 80% of the effectiveness of this imaginary intervention. The larger the SUCRA value for an intervention, the larger the association with treatment effect the intervention had. In network meta-analysis, heterogeneity was assumed to be the same for all treatment comparisons within a single network. The restricted maximum likelihood (REML) method was used to estimate heterogeneity assuming a common estimate for the heterogeneity variance across the different drug classes. The assessment of heterogeneity © 2016 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ on 10/02/2021 in the entire network was based on the magnitude of the heterogeneity variance parameter estimated from the network meta-analysis models. The heterogeneity variance was compared with the empirical distribution derived by Spiegelhalter et al.10 Disagreement between direct and indirect evidence challenges the consistency assumption. To evaluate for the presence of inconsistency, the loop-specific approach was used to evaluate the difference between direct and indirect estimates for a specific comparison in the loop (consistency factor). A common heterogeneity estimate within each loop was assumed. The results of this approach were also presented graphically in a forest plot in which the ratio of odds ratios between the direct and indirect evidence was plotted. A ratio with a 95% confidence interval excluding 1 indicated evidence of statistical inconsistency between a direct treatment estimate and an indirect treatment estimate derived from treatments within the same triangular or quadratic loop of evidence.11,12 To check the assumption of consistency in the entire network, the ‘design-by-treatment’ model was used as has been described by Higgins and colleagues.13 This method accounted for different potential sources of inconsistency that can occur when studies with different designs
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