LEGEND-T2DM Evidence Explorer    

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Large-scale Evidence Generation and Evaluation across a Network of Database for Type 2 Diabetes Mellitus (LEGEND-T2DM)

PLEASE NOTE: All results are preliminary and subject to change

Terms of Use:

These results are being shared as part of OHDSI's open science community efforts to study the comparative cardiovascular effectiveness and safety of treatments for diabetes under the LEGEND-T2DM initiative, for the purpose of enabling collaborative research within the community. Synthesis of the results and interpretation of the findings is underway and manuscripts are being prepared. All manuscripts must be reviewed and approved by all co-authors and data partner contributors prior to submission. Until final publication, all results are to be considered preliminary and subject to change, and may only be used under the terms of use of the respective data partner contributors.

Objectives:

  1. To determine, through systematic evaluation, the comparative effectiveness of traditionally second-line T2DM agents, SGLT2 inhibitors and GLP1 receptor agonists, with each other and with DPP4 inhibitors and sulfonylureas, for cardiovascular outcomes.
  2. To determine, through systematic evaluation, the comparative safety of traditionally second-line T2DM agents among patients with T2DM.
  3. To assess heterogeneity in effectiveness and safety of traditionally second-line T2DM agents among key patient subgroups: Using stratified patient cohorts, we will quantify differential effectiveness and safety across subgroups of patients based on age, sex, race, renal impairment, and baseline cardiovascular risk.

Resources:

  • The study protocol is available here
  • All analytic code is available on GitHub

Cohort Diagnostics:

Table 3. Fitted propensity model, listing all coviates with non-zero coefficients. Positive coefficients indicate predictive of the target exposure.
Figure 2. Preference score distribution. The preference score is a transformation of the propensity score that adjusts for differences in the sizes of the two treatment groups. A higher overlap indicates subjects in the two groups were more similar in terms of their predicted probability of receiving one treatment over the other.
Figure 4. Systematic error. Effect size estimates for the negative controls (true hazard ratio = 1) and positive controls (true hazard ratio > 1), before and after calibration. Estimates below the diagonal dashed lines are statistically significant (alpha = 0.05) different from the true effect size. A well-calibrated estimator should have the true effect size within the 95 percent confidence interval 95 percent of times.
Figure 8. Fitted null distributions per data source.