IntroductionDetermining whether associations are causal is central to much addiction research but is challenging, with many observational associations unlikely to reflect causal relationships . Randomized controlled trials (RCTs), which support stronger causal inference, are not suited to all research questions—particularly as their external validity may be limited . Randomizing long‐term behaviours or environmental exposures in humans is unethical and impractical. Many causal questions, such as the long‐term consequences of consuming potentially harmful, addictive substances, cannot be answered with RCTs.Mendelian randomization (MR) provides a tool for assessing the causal effects of behaviours on outcomes, although only when genetic variants associated with behaviours are known . While previous reviews of MR exist , here we provide an up‐to‐date general introduction targeted specifically at addiction researchers. We note that other approaches to causal inference using observational data exist (including natural experiment approaches and statistical techniques such as propensity score‐matching, time–series analysis and structural equation modelling) . We start by revisiting challenges to causal inference in traditional observational studies, explain how MR studies potentially overcome them and outline challenges and possible solutions when applying MR. Throughout, we illustrate MR's principles with two case studies: tobacco smoking as a possible cause of mental health
Addiction – Wiley
Published: Jan 1, 2018
Keywords: ; ; ; ; ;
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
All the latest content is available, no embargo periods.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud