Methodologic Issues in the Validation of Putative Biomarkers and Surrogate Endpoints in Treatment Evaluation for Systemic Lupus Erythematosus

Matthew   H.   Liang; Julia   F.   Simard; Karen      Costenbader; Benjamin   T.   Dore; Michael      Ward; Paul   R.   Fortin; Gabor   G.   Illei; Susan      Manzi; Barbara      Mittleman; Jill      Buyon; Samardeep      Gupta; Michal      Abrahamowicz

Abstract

No new drugs have been approved for the treatment of systemic lupus erythematosus (SLE) by the Food and Drug Administration for the last 30 years. One barrier has been the lack of validated biomarkers and surrogate endpoints. Validation of SLE biomarkers in the past have been methodologically flawed. We put forth a conceptual framework and five critical criterion for validating putative biomarkers and bio-surrogates in this heterogeneous multi-system disease with protean manifestations. Using the example of a putative biomarker for end-stage lupus nephritis, we performed computer simulations for planning a biomarker bio-repository to support the validation process. “Random time window” sampling where a biomarker is obtained in an interval randomly selected from the total follow-up time for that subject creates survival bias. This can be avoided by the “fixed calendar window” design, in which biomarkers are measured within the same, pre-specified period for all cohort members who remain at risk during that period. In lupus nephritis where the incidence rate of end-stage renal disease is relatively low, to accumulate 300 instances of end-stage renal disease, at risk patients would have to be followed for about 5,000 person-years, implying 500 subjects followed, on average, for about 10 years. Increasing the number of biomarker determinations per subject from one to five reduces the required number of subjects by 10-15%, while further increases in the number of observations per subject yielded much smaller gains. The large numbers of subjects required for a bio-repository, makes it essential to maximize the efficiency of study designs and analyses and provides the strongest rationale for collaboration and the use of standardized measures to ensure comparability.

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