On the Need for Landmark Analysis or Time-dependent Covariates

As statistical editors for The Journal of Urology we have identified one of the most common serious statistical errors in recent submissions is ignoring the time-dependency of covariates in survival analyses, despite being identified and described as guideline 4.15 1 in the shared statistical guidelines published across the four major urological journals (The Journal of Urology, European Urology, BJUI and Urology).In this paper we seek to highlight this issue, provide examples of scenarios involving time-dependent covariates, and introduce appropriate statistical methods for use in this setting.In many urological studies the primary endpoint is the time to an event of interest, such as death, recurrence, or progression.It is common that not all individuals in a study will have experienced the event of interest at the time of analysis, a phenomenon known as censoring.For time-to-event endpoints where some subjects are censored, specialized statistical methods known as survival analysis are used, in which the status and the time-toevent are analyzed simultaneously.The time to event is defined as the time from the start of follow-up (e.g.date of randomization, date of treatment initiation, date of diagnosis) to either the event of interest (among patients who experienced the event) or the last follow-up at which the subject was known to be event-free (among patients who did not yet experience the event) (for further description see guideline 4.13 1 ).Common survival analysis techniques, such as Cox regression or the Kaplan-Meier method, assume that all covariates are known at the start of follow-up.However, at times we may wish to analyze the association between a covariate that is not known at the start of follow-up and a time-to-event endpoint.In 1983 a seminal paper by Anderson et al. in the Journal of Clinical Oncology drew attention to the issue of post-baseline covariates in survival analysis. 6The authors demonstrate that comparing survival between responders and non-responders using traditional survival analysis methods is biased in favor of responders zabore2@ccf.org .