LDL-cholesterol differences predicted survival benefit in statin trials by the surrogate threshold effect (STE)

KR Johnson, Nick Freemantle, DM Anthony, MND Lassere

    Research output: Contribution to journalArticle

    10 Citations (Scopus)


    Objective: We describe anew statistical method called the surrogate threshold effect (STE) that estimates the threshold level of a surrogate needed in a clinical trial to predict a benefit in the target clinical outcome. In this article, we apply this method to the LDL-cholesterol biomarker surrogate and survival benefit-target outcome in statin trials. Study Design and Setting: We identified randomized trials comparing statin treatment to placebo treatment or no treatment and reporting all-cause and cardiovascular mortality. Trials with fewer than five all-cause deaths in at least one arm were excluded. Multiple regression modeled the reduction in all-cause and cardiovascular mortality as a function of LDL-cholesterol difference. The 95% confidence and 95% prediction bands were calculated and graphed to determine the minimum LDL-cholesterol difference (the surrogate threshold) below which there would be no predicted survival benefit. Results: In 16 qualifying trials, regression analysis yielded an all-cause mortality model whose prediction bands demonstrated no overall survival gain with LDL-cholesterol difference values below 1.5 mmol/L. The cardiovascular mortality model yielded prediction bands that demonstrated no cardiovascular survival benefit with LDL-cholesterol difference values below 1.4 mmol/L. Conclusions: In a multitrial setting, the STE approach is a promising yet straightforward statistical method for evaluating the surrogate validity of biomarkers. (C) 2008 Elsevier Inc. All fights reserved.
    Original languageEnglish
    Pages (from-to)328-336
    Number of pages9
    JournalJournal of Clinical Epidemiology
    Issue number3
    Publication statusPublished - 1 Mar 2009


    • Surrogate
    • Statins
    • LDL-cholesterol
    • Levels of evidence
    • Forecasting
    • Biomarker


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