| Autor: |
Teresa Pérez, Núria Malats, Dipender Gill, Raquel Benítez, Claudia Coscia, Stephen Burgess |
| Rok vydání: |
2021 |
| Předmět: |
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| Popis: |
BackgroundMendelian randomization (MR) uses genetic variants as instrumental variables to investigate the causal effect of a risk factor on an outcome. A collider is a variable influenced by two or more other variables. Naive calculation of MR estimates in strata of the population defined by a variable affected by the risk factor can result in collider bias.MethodsWe propose an approach that allows MR estimation in strata of the population while avoiding collider bias. This approach constructs a new variable, the residual collider, as the residual from regression of the collider on the genetic instrument, and then calculates causal estimates in strata defined by quantiles of the residual collider. Estimates stratified on the residual collider will typically have an equivalent interpretation to estimates stratified on the collider, but they are not subject to collider bias. We apply the approach in several simulation scenarios considering different characteristics of the collider variable and strengths of the instrument. We then apply the proposed approach to investigate the causal effect of smoking on bladder cancer in strata of the population defined by bodyweight.ResultsThe new approach generated unbiased estimates in all the simulation settings. In the applied example, we observed a trend in the stratum-specific MR estimates at different bodyweight levels that suggested stronger effects of smoking on bladder cancer among individuals with lower bodyweight.ConclusionsThe proposed approach can be used to perform MR studying heterogeneity among subgroups of the population while avoiding collider bias. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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