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Cancer Stem Cells: Some statistical issues. What you would like to do: Identify ways to design studies with increased statistical “power” in clinical trials of targeted therapies Develop statistically meaningful biologic response criteria First things first:
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Cancer Stem Cells:Some statistical issues • What you would like to do: • Identify ways to design studies with increased statistical “power” in clinical trials of targeted therapies • Develop statistically meaningful biologic response criteria • First things first: • Current in vivo assays/measures have limitations • How well is the biology understood?
Measuring Response • Relapse-free survival, Overall survival • Pros: these are the “gold-standards” • Problems: takes too long, too costly • Biomarkers (“correlative” outcomes) • Pros: feasible in the short-term • Cons: • can be costly • might have many to measure • might not know all the relevant markers • might not know how they all “fit together” • If Biomarkers are used as “surrogates” for response, then they need to be TRUE surrogates. • “Correlative” outcome is not good enough
“True” Surrogate Marker • Defining Characteristic: • a marker must predict clinical outcome, in addition to predicting the effect of treatment on clinical outcome • Operational Definition • establish an association between marker & clinical outcome • establish an association between marker, treatment & clinical outcome, in which marker mediates relationship between clinical outcome and treatment
Surrogate Markers 1) establish an association between marker & clinical outcome. marker Clinical outcome 2) establish an association between marker, treatment & clinical outcome, in which marker completely mediates relationship between clinical outcome and treatment. marker treatment Clinical outcome
NOT Surrogate Markers treatment Clinical outcome marker Clinical outcome marker treatment
Alternative Approach:Bayesian Networks • Bayesian networks are complex diagrams that organize data • They map out cause-and-effect relationships among key variables • They encode them with numbers that represent the extent to which one variable is likely to affect another. • Use “network inference algorithms” to predict causal models of molecular networks from correlational data. • These systems can automatically generate optimal predictions or decisions even when key pieces of information are missing. • How to do this? • HYPOTHESIZE BIOLOGICAL MODEL • Collect data on hypothesized markers in the pathway/biologic model. • Collect data serially, over a time course that fits with biologic model.
Example of Bayesian Network Yu, J., Smith, V., Wang, P., Hartemink, A., & Jarvis, E. (2002) “Using Bayesian Network Inference Algorithms to Recover Molecular Genetic Regulatory Networks.” International Conference on Systems Biology 2002 (ICSB02), December 2002.
Ongoing Optimization of Assays • Ideally, assays are “perfect” before clinical trial opens • In reality, many of our assays are still pretty rough • Can incorporate assay “sub-studies” within clinical trial • RELIABILITY • How reproducible are the results? • Two samples taken from the same patient on the same day • One sample analyzed twice using the same method? • Subjectivity? Inter-rater and Intra-rater agreement • In what ways can ‘error’ come into the procedure? • Provides understanding of measurement error in practice • Benefit: Quantification of the ‘believability’ of the results • Drawback: what will reviewers think?
