Identifying statistically significant combinatorial markers for survival analysis
Raissa Relator; Aika Terada; Jun Sese · 2018 · BMC Medical Genomics
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
BACKGROUND: Survival analysis methods have been widely applied in different areas of health and medicine, spanning over varying events of interest and target diseases. They can be utilized to provide relationships between the survival time of individuals and factors of interest, rendering them useful in searching for biomarkers in diseases such as cancer. However, some disease progression can be very unpredictable because the conventional approaches have failed to consider multiple-marker interactions. An exponential increase in the number of candidate markers requires large correction factor
Abstract by Raissa Relator; Aika Terada; Jun Sese, BMC Medical Genomics (2018) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1186/s12920-018-0346-x