%A Nimmo,Ailish %A Latimer,Nicholas %A Oniscu,Gabriel C. %A Ravanan,Rommel %A Taylor,Dominic M. %A Fotheringham,James %D 2022 %J Transplant International %C %F %G English %K Observational studies,causal inference,confounding,propensity score,instrumental variable %Q %R 10.3389/ti.2022.10105 %W %L %M %P %7 %8 2022-June-27 %9 Statistical Insights %# %! Causal inference techniques in transplantation %* %< %T Propensity Score and Instrumental Variable Techniques in Observational Transplantation Studies: An Overview and Worked Example Relating to Pre-Transplant Cardiac Screening %U https://www.frontierspartnerships.org/articles/10.3389/ti.2022.10105 %V 35 %0 JOURNAL ARTICLE %@ 1432-2277 %X Inferring causality from observational studies is difficult due to inherent differences in patient characteristics between treated and untreated groups. The randomised controlled trial is the gold standard study design as the random allocation of individuals to treatment and control arms should result in an equal distribution of known and unknown prognostic factors at baseline. However, it is not always ethically or practically possible to perform such a study in the field of transplantation. Propensity score and instrumental variable techniques have theoretical advantages over conventional multivariable regression methods and are increasingly being used within observational studies to reduce the risk of confounding bias. An understanding of these techniques is required to critically appraise the literature. We provide an overview of propensity score and instrumental variable techniques for transplant clinicians, describing their principles, assumptions, strengths, and weaknesses. We discuss the different patient populations included in analyses and how to interpret results. We illustrate these points using data from the Access to Transplant and Transplant Outcome Measures study examining the association between pre-transplant cardiac screening in kidney transplant recipients and post-transplant cardiac events.