The Use of Molecular and Imaging Biomarkers in Lung Cancer Risk Prediction

In hypothesis testing, p-value is routinely used as a measure of statistical evidence against the null hypothesis, where a smaller p-value indicates stronger evidence substantiating the alternative hypothesis. P-value is the probability of type-I error made in a hypothesis testing, namely, the chance that one falsely reject the null hypothesis when the null holds true. 

In a disease genome wide association study (GWAS), p-value potentially tells us how likely a putative disease associated variant is due to random chance. For a long time p-values have been taken seriously by the GWAS community as a safeguard against false positives. Every disease-associated mutation reported in a GWAS must reach a stringent p-value cutoff (e.g., 10-8) in order to survive the multiple testing corrections. This is reasonable because after testing millions of variants in the genome, some random variants ought to yield small p-values purely by chance.

Bayesian Estimation of the Three Key Parameters in CT for the National Lung Screening Trial Data

In this study cancer screening likelihood method was used to analyze the CT scan group in the National Lung Screening Trial (NLST) data. Three key parameters: screening sensitivity, transition probability densityfrom disease free to preclinical state, and sojourn time in the preclinical state, were estimated using Bayesian approach and Markov Chain Monte Carlo simulations. 

The sensitivity for lung cancer screening using CT scan is high; it does not depend on a patient’s age, and is slightly higher in females than in males. The transition probability from the disease-free to the preclinicalstate has a peak around age 70 for both genders, which agrees with the fact that the highest lung cancer incidence rate appears between age 65 and 74. The posterior mean sojourn time is around 1.5 years for all groups, and that explains why screening only have a short time interval to catch lung cancer. Accurate estimation of the three key parameters is critical for other estimations such as lead time and over-diagnosis, because these quantities are functions of the three key parameters.