Two-stage Case-Cohort Sampling Design for Estimating Multistate Disease Natural History of Colorectal Cancer
Date Issued
2015
Date
2015
Author(s)
Hsu, Wen-Feng
Abstract
Background Elucidating multi-state disease natural history is of paramount importance for the identification of subjects at greater risk for disease progression, the determination of appropriate inter-screening intervals, and the evaluation of efficacy of interventions such as population-based screening program. While the application of stochastic models to estimate the force of multi-state disease progression using data on population-based screening program is well developed, the collection of such big data is quite costly. Moreover, it is often not feasible to accrue costly biomarkers such as genetic determinants based on the whole target population to quantify their roles played in the identification of subject at increased risk for disease progression. The application of case-cohort design is an alternative solution to address this issue with efficiency. Objectives The thesis aimed to develop a generalized non-linear regression model for fitting the data obtained from the three-stage design in comparison with the conventional multi-state stochastic model taking the measurement error such as sensitivity into account using data on the population-based fecal immunochemical test (FIT) for colorectal cancer (CRC) screening in Taiwan for the disease natural history for CRC and the sensitivity of FIT. Methods In the Taiwanese Nationwide Colorectal Cancer Screening Program, residents aged 50 to 69 years w consisting of 1160884 subjects with repeat screen rate of 28.3% were invited to receive a biennial FIT, between January 1, 2004 and December 31, 2009. A total of 2494 and 195 CRCs were detected during the prevalent screen and the subsequent screen, and 694 interval cancers were ascertained. A continuous-time, progressive 3-state Markov model was constructed for estimating the natural history of CRC. We developed a generalized non-linear regression model based on the three-state progression model for the derivation of the force driving the initiation and the progression of disease. A method incorporating both the nature of multistate disease progression and a sampling scheme based on a case-cohort design to utilize the data with efficiency was developed. The performance of the proposed method compared that of full data using a range of sampling proportions for the states of disease progression was then explored. Results Applying the generalized non-linear regression model to the full data, the estimated annual rate of CRC preclinical detectable phase (PCDP) incidence was 75 per 105 (95% CI: 58-92 per 10¬5) and that for PCDP progress was 0.31 (95% CI: 0.23-0.40) yielding a mean sojourn time (MST) for 3.23 years (95% CI: 2.5-4.35 years). The MST was around 3.2-4.3 years and the test sensitivity was 78-82% after fitting the data. The model considering measurement error and the effect covariates (sex and age) on PCDP incidence rate give the estimated hazard ratio for male and the elders (older than 60 years) of 1.65 (95% CI: 1.31-2.08) and 2.05 (95% CI: 1.61-2.64) and the sensitivity was estimated at 80% (95% CI: 74-84%). Applying the proposed algorithm for two-stage sampling scheme to the data derived by a series of sampling ratio using the covariate of sex and age demonstrated the influence of the reduction in sample size in terms of screen detected cancers and interval cancers. Conclusion A novel algorithm with a two-stage sampling design was developed to efficiently estimate the multistate outcome of the disease natural history and assess the effect of covariates on stage-specific transition rates. This new algorithm has been well demonstrated by using Taiwanese nationwide colorectal cancer screening program and can be easily extended to assess the causal effect of certain costly biomarkers on stage-specific transition on the basis of such a large population-based screened cohort.
Subjects
Colorectal cancer (CRC)
screening
fecal occult blood test (FOBT)
disease natural history
case-cohort design
SDGs
Type
thesis