Triangulation is not a separate methodological approach, but rather a research paradigm aiming to enhance the confidence in inferred causal relationships. Triangulation in aetiological epidemiology (Int J Epidemiol. 2016;45(6):1866-86) defines triangulation as “the practice of obtaining more reliable answers to research questions through integrating results from several different approaches, where each approach has different key sources of potential bias that are unrelated to each other.” Triangulation differs from replication by explicitly choosing data sources/data collection approaches, study designs and/or analytical approaches with different bias structures.
In Triangulation of pharmacoepidemiology and laboratory science to tackle otic quinolone safety (Basic Clin Pharmacol Toxicol. 2022;Suppl 1:75-80), laboratory studies using cell culture and rodent models were complemented with real-world data from pharmacoepidemiological studies to translate mechanistic findings and corroborate real-world evidence. In Identifying Antidepressants Less Likely to Cause Hyponatremia: Triangulation of Retrospective Cohort, Disproportionality, and Pharmacodynamic Studies (Clin Pharmacol Ther. 2022; 111(6):1258-67), analyses of three different types of data with their respective analyses are presented: a retrospective cohort study, a disproportionality analysis of patients in the Japanese Adverse Drug Event Report database, and a pharmacodynamic study examining the binding affinity for serotonin transporter.
Triangulation does not require the use of different data sources and can readily be employed in studies using electronic healthcare data, which allow investigators to use a multitude of study designs and analytical approaches. For example, in Prenatal Antidepressant Exposure and the Risk of Attention-deficit/Hyperactivity Disorder in Childhood: A Cohort Study With Triangulation (Epidemiology. 2022;33(4):581-592), a negative control analysis, a sibling analysis, and a former-user analysis were used to triangulate results.
In recent years, the use of genetic tools has become popular for the investigation of drug effects. The complementary application of drug target mendelian randomisation and colocalisation analyses can provide another layer of genetic evidence for causality, as demonstrated by Genetically proxied therapeutic inhibition of antihypertensive drug targets and risk of common cancers: A mendelian randomization analysis (PLoS Med. 2022 Feb 3;19(2):e1003897). It is recommended to use triangulation methods and formalise sensitivity analyses using a priori specification of potential biases and their (assumed) directions in the main analysis and by performing sensitivity/triangulation analyses explicitly addressing these biases.