Immortal time bias exaggerates the effect of metformin on the risk of gastric cancer: A meta-analysis

Abstract

High heterogeneity has been reported among epidemiological studies exploring the relationship between metformin and the risk of gastric cancer. Immortal time bias might be one of the vital factors causing heterogeneity because of its widespread existence in pharmacological observational studies and it could severely exaggerate the drug’s effectiveness. Immortal time bias could occur in an observational study if exposure status is determined based on a measurement or event that occurs after baseline. In this study, we aimed to assess whether immortal time bias is responsible for the false assumption that metformin reduces the risk of gastric cancer. We searched PubMed, Embase, Web of Science and Cochrane Library databases for relevant studies from the inception to August 9, 2020. The strength of the relationship was assessed using pooled relative risks (RRs) with corresponding 95% confidence intervals (95% CIs). Statistical analyses were carried out using a random-effects model. Pooled RR from 6 cohort studies with immortal time bias found a clear 33% reduced risk associated with metformin use (RR = 0.67, 95% CI = 0.59, 0.77; P < 0.001; I2 = 48.5%). However, pooled RR from 8 cohort studies without immortal time bias indicated no association between the use of metformin and gastric cancer risk (RR = 0.95, 95% CI = 0.85, 1.05; P = 0.317; I2 = 64.5%). From a univariate meta-regression model, the presence of immortal time bias was associated with a significant reduction of 29% in the effect estimate of metformin on gastric cancer risk (ratio of RR = 0.71, 95% CI = 0.58, 0.86; P = 0.002). This meta-analysis indicates that metformin use has no protective effect on gastric cancer risk. The relationship between metformin use and gastric cancer risk has been exaggerated as a result of the presence of immortal time bias. Further studies are required to confirm the results by controlling for immortal time bias based on appropriate study designs and statistical methods.