Fitting a progressive 3-state colorectal cancer model to interval-censored surveillance data under outcome-dependent sampling using a weighted likelihood approach

To optimize colorectal cancer (CRC) surveillance, accurate information on the risk of developing CRC from premalignant lesions is essential. However, directly observing this risk is challenging since precursor lesions, that is, advanced adenomas (AAs), are removed upon detection. Statistical methods for multistate models can estimate risks, but estimation is challenging due to low CRC incidence. We propose an outcome-dependent sampling (ODS) design for this problem in which we oversample CRCs. More specifically, we propose a 3-state model for jointly estimating the time distributions from baseline colonoscopy to AA and from AA onset to CRC accounting for the ODS design using a weighted likelihood approach. We applied the methodology to a sample from a Norwegian adenoma cohort (1993-2007), comprising 1495 individuals (median follow-up $6.8$ years; IQR, 1.1-12.8) of whom 648 did and 847 did not develop CRC. We observed a 5-year AA risk of 13% and 34% for individuals having nonadvanced adenoma (NAA) and AA removed at baseline colonoscopy, respectively. Upon AA development, the subsequent risk to develop CRC in 5 years was 17% and age-dependent. These estimates provide a basis for optimizing surveillance intensity and determining the optimal trade-off between CRC prevention, costs, and use of colonoscopy resources.

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Publication Details

Journal
American Journal of Epidemiology
Published
2024-08-30
DOI
https://doi.org/10.1093/aje/kwae307
Citations
1
Primary Topic
Statistical Methods and Bayesian Inference
Type
article
Field-Weighted Citation Impact
0.71

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article

Fitting a progressive 3-state colorectal cancer model to interval-censored surveillance data under outcome-dependent sampling using a weighted likelihood approach

Johannes Berkhof, Thomas Klausch, Magnus Løberg, Eddymurphy U. Akwiwu et al.
1 citations
American Journal of Epidemiology
Statistical Methods and Bayesian Inference
0.71
article

Fitting a progressive 3-state colorectal cancer model to interval-censored surveillance data under outcome-dependent sampling using a weighted likelihood approach

Johannes Berkhof, Thomas Klausch, Magnus Løberg, Eddymurphy U. Akwiwu, Veerle M.H. Coupé, Henriette C. Jodal, Mette Kalager, Beatriz G. Carvalho
article en
1 citations

Abstract

To optimize colorectal cancer (CRC) surveillance, accurate information on the risk of developing CRC from premalignant lesions is essential. However, directly observing this risk is challenging since precursor lesions, that is, advanced adenomas (AAs), are removed upon detection. Statistical methods for multistate models can estimate risks, but estimation is challenging due to low CRC incidence. We propose an outcome-dependent sampling (ODS) design for this problem in which we oversample CRCs. More specifically, we propose a 3-state model for jointly estimating the time distributions from baseline colonoscopy to AA and from AA onset to CRC accounting for the ODS design using a weighted likelihood approach. We applied the methodology to a sample from a Norwegian adenoma cohort (1993-2007), comprising 1495 individuals (median follow-up $6.8$ years; IQR, 1.1-12.8) of whom 648 did and 847 did not develop CRC. We observed a 5-year AA risk of 13% and 34% for individuals having nonadvanced adenoma (NAA) and AA removed at baseline colonoscopy, respectively. Upon AA development, the subsequent risk to develop CRC in 5 years was 17% and age-dependent. These estimates provide a basis for optimizing surveillance intensity and determining the optimal trade-off between CRC prevention, costs, and use of colonoscopy resources.

American Journal of EpidemiologyVol. 194(6)
Oslo University Hospital (NO), University of Oslo (NO), Drammen Hospital (NO), Vestre Viken Hospital Trust (NO), The Netherlands Cancer Institute (NL), Amsterdam Neuroscience (NL), Amsterdam University Medical Centers (NL), Vrije Universiteit Amsterdam (NL)
ZonMw, Norges Forskningsråd
Partnerships for the goals
Openalex Percentile: Top 27%
Statistical Methods and Bayesian Inference
0.71
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