TABLE 1.

Challenges and possible solutions in real‐world evidence studies on renal endpoints

Challenge	Explanation	Possible solution(s)
Channelling bias (confounding by indication)	Patients assigned to different treatments under routine care have different characteristics	Use PSM to obtain similar cohorts at baseline Apply adjustment and weighting methods in sensitivity analyses
Time lag bias	Patients assigned to different treatments under routine care locate at different disease stages even if they look similar	Match for eGFR slope in the pre‐index date year(s)
Conditioning on the future	eGFR slope analysis require post‐index date data available	Do impose strict schedules of eGFR availability, leaving it free as in routine practice
Heterogeneity of the database	Pooling crude data from multiple databases from multiple countries or healthcare setting generates heterogeneity that can affect the pooled results	Limit to databases from the same or highly similar healthcare setting (eg, specialist care).
Nonlinearity of eGFR change	The change in eGFR may not be linear over time or during limited periods, such that slope modelling is biased	Use nonlinear models to analyse eGFR changes. Compute the chronic (not total) total eGFR slope
Short observation	eGFR slope better predicts ESKD when calculated over 3 years	Prolong duration of observation to ≥3 years (or ≥ 2 years after the acute effect)

Abbreviations: eGFR, estimated glomerular filtration rate; ESKD, end‐stage kidney disease; PSM, propensity score matching.