Abstract
Observational studies in kidney transplantation often face confounding bias due to the absence of randomization, which can compromise validity and limit generalizability. Propensity score matching (PSM) helps mitigate this bias by mimicking random assignment. This guide outlines the implementation of PSM in kidney transplant research, focusing on methodology, practical considerations, common pitfalls, and reporting standards. We summarize key steps, including selecting covariates related to both treatment and the probability of receiving a transplant, estimating propensity scores, applying appropriate matching techniques, assessing balance, and conducting sensitivity analyses to test robustness. Practical considerations include ensuring sufficient overlap in propensity scores and balancing sample size with matching quality. Common challenges involve omitting relevant covariates, inadequate overlap, suboptimal matching, and loss of statistical power due to reduced sample size. By adhering to rigorous methodological practices and transparent reporting, researchers can improve the credibility and impact of their findings. When carefully implemented, PSM can substantially reduce confounding bias, enhance causal inference, and ultimately support better decision-making in kidney transplantation research. The example provided is illustrative only and does not replace a formal time-to-event analysis accounting for competing risks.
Keywords
Confounding factors; Kidney transplantation; Observational studies; Propensity score matching; Statistical analysis; Kidney Transplantation; Propensity Score; Humans; Observational Studies as Topic; Bias; Research Design