@article{5f13e22a857a4197b86c4ffbde5e920c,
title = "Estimating and comparing cancer progression risks under varying surveillance protocols",
abstract = "Outcomes after cancer diagnosis and treatment are often observed at discrete times via doctor-patient encounters or specialized diagnostic examinations. Despite their ubiquity as endpoints in cancer studies, such outcomes pose challenges for analysis. In particular, comparisons between studies or patient populations with different surveillance schema may be confounded by differences in visit frequencies. We present a statistical framework based on multistate and hidden Markov models that represents events on a continuous time scale given data with discrete observation times. To demonstrate this framework, we consider the problem of comparing risks of prostate cancer progression across multiple active surveillance cohorts with different surveillance frequencies. We show that the different surveillance schedules partially explain observed differences in the progression risks between cohorts. Our application permits the conclusion that differences in underlying cancer progression risks across cohorts persist after accounting for different surveillance frequencies.",
keywords = "Active surveillance, Hidden Markov model, Multistate model, Panel data, Prostate cancer",
author = "Lange, {Jane M.} and Roman Gulati and Leonardson, {Amy S.} and Lin, {Daniel W.} and Newcomb, {Lisa F.} and Trock, {Bruce J.} and Carter, {H. Ballentine} and Carroll, {Peter R.} and Cooperberg, {Matthew R.} and Cowan, {Janet E.} and Klotz, {Lawrence H.} and Ruth Etzioni",
note = "Funding Information: Supported by the National Cancer Institute Award Number R01 CA183570 for the Prostate Modeling to Identify Surveillance Strategies (PROMISS) consortium. Supported by the National Cancer Institute Award Number U01 CA199338 as part of the Cancer Intervention and Surveillance Modeling Network (CISNET). Supported by the National Cancer Institute Award Number P50 CA097186 as part of the Pacific Northwest Prostate Cancer Specialized Program in Research Excellence (SPORE).4 Supported by the Canary Foundation. Supported by Genomic Health Inc. and the US Department of Defense Translational Impact Award for Prostate Cancer Award Number W81XWH-13-2-0074.6 Supported by Prostate Cancer Canada. Funding Information: Received May 2017; revised September 2017. 1Supported by the National Cancer Institute Award Number R01 CA183570 for the Prostate Modeling to Identify Surveillance Strategies (PROMISS) consortium. 2Supported by the National Cancer Institute Award Number U01 CA199338 as part of the Cancer Intervention and Surveillance Modeling Network (CISNET). 3Supported by the National Cancer Institute Award Number P50 CA097186 as part of the Pacific Northwest Prostate Cancer Specialized Program in Research Excellence (SPORE). 4Supported by the Canary Foundation. 5Supported by Genomic Health Inc. and the US Department of Defense Translational Impact Award for Prostate Cancer Award Number W81XWH-13-2-0074. 6Supported by Prostate Cancer Canada. Key words and phrases. Hidden Markov model, multistate model, panel data, prostate cancer, active surveillance. Publisher Copyright: {\textcopyright} Institute of Mathematical Statistics, 2018.",
year = "2018",
month = sep,
doi = "10.1214/17-AOAS1130",
language = "English (US)",
volume = "12",
pages = "1773--1795",
journal = "Annals of Applied Statistics",
issn = "1932-6157",
publisher = "Institute of Mathematical Statistics",
number = "3",
}