Evaluation of the natural history of disease by combining incident and prevalent cohorts: application to the Nun Study
Tóm tắt
The Nun study is a well-known longitudinal epidemiology study of aging and dementia that recruited elderly nuns who were not yet diagnosed with dementia (i.e., incident cohort) and who had dementia prior to entry (i.e., prevalent cohort). In such a natural history of disease study, multistate modeling of the combined data from both incident and prevalent cohorts is desirable to improve the efficiency of inference. While important, the multistate modeling approaches for the combined data have been scarcely used in practice because prevalent samples do not provide the exact date of disease onset and do not represent the target population due to left-truncation. In this paper, we demonstrate how to adequately combine both incident and prevalent cohorts to examine risk factors for every possible transition in studying the natural history of dementia. We adapt a four-state nonhomogeneous Markov model to characterize all transitions between different clinical stages, including plausible reversible transitions. The estimating procedure using the combined data leads to efficiency gains for every transition compared to those from the incident cohort data only.
Tài liệu tham khảo
Contador I, Stern Y, Bermejo-Pareja F, Sanchez-Ferro A, Benito-Leon J (2017) Is educational attainment associated with increased risk of mortality in people with dementia? A population-based study. Curr Alzheimer Res 14(5):571–576
Cox D, Miller H (1965) The theory of stochastic processes. Chapman and Itall, New York
Gorfine M, Keret N, Ben Arie A, Zucker D, Hsu L (2021) Marginalized frailty-based illness-death model: application to the UK-biobank survival data. J Am Stat Assoc 116(535):1155–1167
Hubbard R, Inoue L, Fann J (2008) Modeling nonhomogeneous Markov processes via time transformation. Biometrics 64(3):843–850. https://doi.org/10.1111/j.1541-0420.2007.00932.x
Kalbfleisch J, Lawless J (1985) The analysis of panel data under a Markov assumption. J Am Stat Assoc 80(392):863–871. https://doi.org/10.1080/01621459.1985.10478195
Kulathinal S, Säävälä M, Auranen K, Saarela O (2020) Estimation of marriage incidence rates by combining two cross-sectional retrospective designs: Event history analysis of two dependent processes. arXiv preprint arXiv:2009.01897
Lee C, Ning J, Kryscio R, Shen Y (2019) Analysis of combined incident and prevalent cohort data under a proportional mean residual life model. Stat Med 38(12):2103–2114. https://doi.org/10.1002/sim.8098
McVittie J, Wolfson D, Stephens D, Addona V, Buckeridge D (2020) Parametric models for combined failure time data from an incident cohort study and a prevalent cohort study with follow-up. Int J Biostatist. https://doi.org/10.1515/ijb-2020-0042
Omar R, Stallard N, Whitehead J (1995) A parametric multistate model for the analysis of carcinogenicity experiments. Lifetime Data Anal 1(4):327–346. https://doi.org/10.1007/BF00985448
Pak D, Li C, Todem D, Sohn W (2017) A multistate model for correlated interval-censored life history data in caries research. J R Stat Soc Ser C 66(2):413–423. https://doi.org/10.1111/rssc.12186
Pak D, Li C, Todem D (2019) Semiparametric analysis of correlated and interval-censored event-history data. Stat Methods Med Res 28(9):2754–2767. https://doi.org/10.1177/0962280218788383
Pérez-Ocón R, Ruiz-Castro J, Gámiz-Pérez M (2001) Non-homogeneous Markov models in the analysis of survival after breast cancer. J R Stat Soc Ser C 50(1):111–124. https://doi.org/10.1111/1467-9876.00223
Saarela O, Kulathinal S, Karvanen J (2009) Joint analysis of prevalence and incidence data using conditional likelihood. Biostatistics 10(3):575–587. https://doi.org/10.1093/biostatistics/kxp013
Shen Y, Ning J, Qin J (2009) Analyzing length-biased data with semiparametric transformation and accelerated failure time models. J Am Stat Assoc 104(487):1192–1202. https://doi.org/10.1198/jasa.2009.tm08614
Stern Y, Tang M, Denaro J, Mayeux R (1995) Increased risk of mortality in Alzheimer’s disease patients with more advanced educational and occupational attainment. Ann Neurol Official J Am Neurol Assoc Child Neurol Soc 37(5):590–595. https://doi.org/10.1002/ana.410370508
Titman A (2011) Flexible nonhomogeneous Markov models for panel observed data. Biometrics 67(3):780–787. https://doi.org/10.1111/j.1541-0420.2010.01550.x
Tyas S, Salazar J, Snowdon D, Desrosiers M, Riley K, Mendiondo M, Kryscio R (2007) Transitions to mild cognitive impairments, dementia, and death: findings from the nun study. Am J Epidemiol 165(11):1231–1238. https://doi.org/10.1093/aje/kwm085
Wei S, Xu L, Kryscio R (2014) Markov transition model to dementia with death as a competing event. Comput Stat Data Anal 80:78–88. https://doi.org/10.1016/j.csda.2014.06.014
Wolfson D, Best A, Addona V, Wolfson J, Gadalla S (2019) Benefits of combining prevalent and incident cohorts: an application to myotonic dystrophy. Stat Methods Med Res 28(10–11):3333–3345. https://doi.org/10.1177/0962280218804275