2DkcTXceO

338 Lessons in biostatisticsprognosis in Wilms tumor that resulted in greater appreciation for the adverseoutcomes associated with tumor invasion of regional lymph nodes and ultimatelyto changes in the staging system. Fascinated by how well Knudson’s(Knudson, Jr., 1971) 2-hit mutational model explained the genetic epidemiologyof retinoblastoma, another embryonal tumor of a paired organ (in thiscase the eye rather than the kidney), I conducted studies of the epidemiologyof Wilms tumor that provided strong evidence for genetic heterogeneity, anexplanation for its lower incidence and younger ages-at-onset in Asians and ahypothesis regarding which survivors were at especially high risk of end stagerenal disease in young adulthood (Breslow and Beckwith, 1982; Breslow et al.,2006; Lange et al., 2011). Since 1991, I have served as Principal Investigatoron the NIH grant that funds the continued follow-up of NWTS survivors for“late effects” associated with Wilms tumor and its treatment. This study hasoccupied an increasingly important place in my research repertoire.30.3 Immortal timeIn my opening lecture to a class designed primarily for second year doctoralstudents in epidemiology, I state the First Rule of survival analysis: Selectioninto the study cohort, or into subgroups to be compared in the analysis, mustnot depend on events that occur after the start of follow-up. While this pointmay be obvious to a statistician, certainly one trained to use martingale argumentsto justify inferences about how past history influences rates of futureevents, it was not obvious to many of the epidemiologists. The “immortaltime” bias that results from failure to follow the rule has resulted, and continuesregularly to result, in grossly fraudulent claims in papers published inthe most prestigious medical journals.My first exposure to the issue came soon after I started work with thechildren’s cancer group. The group chair was puzzled by a recently publishedarticle that called into question the standard criteria for evaluation of treatmentresponse in acute leukemia. These included the stipulation that patientswith a high bone marrow lymphocyte count (BMLC) be excluded from the excellentresponse category. Indeed, a high BMLC often presaged relapse, definedas 5% or higher blast cells in the marrow. The article in question, however,reported that patients whose lymphocytes remained below the threshold levelof 20% of marrow cells throughout the period of remission tended to haveshorter remissions than patients whose BMLC exceeded 20% on at least oneoccasion. Although I knew little about survival analysis, and had not yet articulatedthe First Rule, I was familiar with random variation and the tendencyof maxima to increase with the length of the series. Intuition suggested thatthe article’s conclusion, that there was “no justification for excluding a patient