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Cross-sectional design with a short-term follow-up for prognostic imaging biomarkers

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  • Won, Joong-Ho
  • Wu, Xiao
  • Lee, Sang Han
  • Lu, Ying

Abstract

Medical imaging techniques are being rapidly developed and used for diagnosis and prognostic predictions. To validate a prognostic predictive utility of a new imaging marker, a temporal association needs to be established to show an association between its baseline value with a subsequent chance of having the relevant clinical outcome. Validation of such techniques has several difficulties. First, different from techniques based on blood or tissue specimen, imaging techniques often have no historical samples to study and require new studies to collect data. For rare events, it can be costly. Second, the rapid technology evolution requires such validation studies to be short in order to keep the evaluation relevant. A new statistical design is proposed that extends traditional prospective cohort study by adding cases with known time of events and including a short-term follow-up to estimate the prospective odds ratio for the clinical endpoint of interest within a reasonably short duration of time. Under a Markov model, this new design can deliver a consistent estimate of the odds ratio and a formula for asymptotic variance. Numerical studies suggest that the new design induces a smaller variance than the corresponding prospective cohort study within three follow-ups. An application to Alzheimer’s disease data demonstrates that the proposed design has a potential to be useful to rapidly establish a prognostic validity of a new imaging marker within a reasonable time, with a small sample size.

Suggested Citation

  • Won, Joong-Ho & Wu, Xiao & Lee, Sang Han & Lu, Ying, 2017. "Cross-sectional design with a short-term follow-up for prognostic imaging biomarkers," Computational Statistics & Data Analysis, Elsevier, vol. 113(C), pages 154-176.
    • Handle: RePEc:eee:csdana:v:113:y:2017:i:c:p:154-176
    DOI: 10.1016/j.csda.2016.12.017
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    References listed on IDEAS

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    1. Mullen, Katharine M., 2014. "Continuous Global Optimization in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 60(i06).
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