Journal of general internal medicine
-
Meta Analysis
The Longer-Term Benefits and Harms of Glucagon-Like Peptide-1 Receptor Agonists: a Systematic Review and Meta-Analysis.
Previous meta-analyses of the benefits and harms of glucagon-like peptide-1 receptor agonists (GLP1RAs) have been limited to specific outcomes and comparisons and often included short-term results. We aimed to estimate the longer-term effects of GLP1RAs on cardiovascular risk factors, microvascular and macrovascular complications, mortality, and adverse events in patients with type 2 diabetes, compared to placebo and other anti-hyperglycemic medications. ⋯ GLP1RAs reduced cardiovascular risk factors and increased gastrointestinal events compared to placebo and other anti-hyperglycemic medications. GLP1RAs also reduced MACE, stroke, renal events, and mortality in comparisons with placebo; however, analyses were inconclusive for comparisons with other anti-hyperglycemic medications. Given the high costs of GLP1RAs, the lack of long-term evidence comparing GLP1RAs to other anti-hyperglycemic medications has significant policy and clinical practice implications.
-
Sodium-glucose cotransporter-2 inhibitors (SGLT2Is) are a recent class of medication approved for the treatment of type 2 diabetes (T2D). Previous meta-analyses have quantified the benefits and harms of SGLT2Is; however, these analyses have been limited to specific outcomes and comparisons and included trials of short duration. We comprehensively reviewed the longer-term benefits and harms of SGLT2Is compared to placebo or other anti-hyperglycemic medications. ⋯ We found that SGLT2Is led to durable reductions in cardiovascular risk factors compared to both placebo and other anti-hyperglycemic medications. Reductions in macrovascular complications and mortality were only observed in comparisons with placebo, although trials comparing SGLT2Is vs. other anti-hyperglycemic medications were not designed to assess longer-term outcomes.
-
Meta Analysis
Empirical Comparisons of 12 Meta-analysis Methods for Synthesizing Proportions of Binary Outcomes.
Meta-analysis is increasingly used to synthesize proportions (e.g., disease prevalence). It can be implemented with widely used two-step methods or one-step methods, such as generalized linear mixed models (GLMMs). Existing simulation studies have shown that GLMMs outperform the two-step methods in some settings. It is, however, unclear whether these simulation settings are common in the real world. We aim to compare the real-world performance of various meta-analysis methods for synthesizing proportions. ⋯ Although different methods produced similar overall proportion estimates in most datasets, one-step methods should be considered in the presence of small total event counts or sample sizes and very low or high event rates.