Estimating key parameters in FOBT screening for colorectal cancer

Abstract

Objectives-The association between screening sensitivity, transition probability, and individual's age in FOBT for colorectal cancer are explored, for both males and females.Methods-We apply the statistical method developed by Wu et al.[1] using the Minnesota colorectal cancer study group data, to make Bayesian inference for the age-dependent screening test sensitivity, the age-dependent transition probability from disease-free to preclinical state, and the sojourn time distribution, for both male and female participants in a periodic screening program. This gives us more information on the effectiveness of the fecal occult blood test in colorectal cancer detection.Results-The sensitivity appears to increase with age for both genders. However, the posterior mean sensitivity is not monotonic with age for males; it has a peak around age 74. The standard errors of the sensitivity are not monotone either; there is a minimum at age 69 for males and at age 78 for females. The age-dependent transition probability is not a monotone function of age; it has a single maximum at age 72 for males and a single maximum at age 75 for females. The age dependency seems more dramatic for females than for males. The posterior mean sojourn time is 4.08 years for males and 2.41 years for females, with a posterior median of 1.66 years for males and 1.88 years for females. The 95% highest posterior density (HPD) interval is (0.97, 20.28) for males and (1.15, 5.96) for females, which are very large ranges, especially for males. The reason might be that there were fewer men than women in the annual screening program.Conclusion-Reliable estimates of age-dependent sensitivity and transition probability are of great value to policy-makers regarding the initial age for colorectal cancer screening exams. We found that the mean sojourn time for males is much longer than that for females, which may imply that FOBT screening for colorectal cancer may be more effective for males than for females.

Modelling and Inference in Screening: Exemplification with the Faecal Occult Blood Test

“…Where m is the average age-at-entry in the study and Wu et al 2005Wu et al , 2009b shows the detailed justifications on how these age effect functions were chosen. Models in equation 1-5 were estimated using programs C/C++ (Silicon Graphics, I, 2003, Stroustrup, B, 2011) and we applied the likelihood separately for men and women in the MCCCS.…”

“…Models in equation 1-5 were estimated using programs C/C++ (Silicon Graphics, I, 2003, Stroustrup, B, 2011) and we applied the likelihood separately for men and women in the MCCCS. Markov Chain Monte Carlo (MCMC) was used to generate random samples from the joint posterior distribution of the parameters in the likelihood for Bayesian inference (Wu et al, 2005(Wu et al, , 2009b. The posterior distribution within the MCMC was partitioned into four sub-chains, e.g.…”

“…Sensitivity is the probability that the screening exam is positive given that the individual is in the preclinical state p S . The sensitivity cannot be easily estimated from data collected during screening, but can be estimated using probability modelling (Wu et al, 2005(Wu et al, , 2009b. We exemplify the rationale for this issue using Table 1.…”

“…These three parameters are the key parameters for the estimation of other important indicators in cancer screening, and they cannot be easily estimated from data. We will briefly review the age-dependent likelihood method that we used in estimating these three parameters, and provide the key result using the MCCCS data (Wu et al, 2005(Wu et al, , 2009b.…”

“…Prevention efforts in the population requires reliable estimates of the sensitivity of the test, the sojourn time of the disease, the transition probabilities from the disease-free state to the preclinical state, the lead time of the disease and the indirect effects in the screening per se in the estimates of rates of the disease. The aim of this chapter is to introduce the concept of probability modelling in colorectal cancer screening, and the statistical methods developed by the authors in this area (Wu et al, 2005(Wu et al, , 2007(Wu et al, , 2009a(Wu et al, , 2009b. We will estimate these essential components from a population based perspective.…”