Calibrating Longitudinal Models to Cross-Sectional Data: The Effect of Temporal Changes in Health Practices

Abstract Objectives To assess the impact of simulating temporal changes in health-care practice patterns when calibrating longitudinal models to cross-sectional data. Methods A Markov model of cervical cancer was calibrated to recent age-specific US data on the prevalence of cervical abnormalities,...

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Bibliographic Details
Published in:Value in health 2011-07, Vol.14 (5), p.700-704
Main Authors: Taylor, Douglas C.A., MBA, Pawar, Vivek, PhD, Kruzikas, Denise, PhD, Gilmore, Kristen E., BA, Pandya, Ankur, MPH, Iskandar, Rowan, MA, Weinstein, Milton C., PhD
Format: Article
Language:English
Subjects:
Adolescent
Adult
Age Distribution
Aged
Algorithms
Calibration
cervical cancer
Computer Simulation
cross-sectional data
Cross-Sectional Studies
Delivery of Health Care - standards
Delivery of Health Care - trends
Female
Health Services Research
Humans
Incidence
Internal Medicine
Longitudinal Studies
Markov Chains
Mass Screening - standards
Mass Screening - trends
Middle Aged
model calibration
Models, Statistical
Practice Patterns, Physicians' - standards
Practice Patterns, Physicians' - trends
temporal changes
Time Factors
United States - epidemiology
Uterine Cervical Neoplasms - diagnosis
Uterine Cervical Neoplasms - epidemiology
Uterine Cervical Neoplasms - mortality
Young Adult
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Summary:Abstract Objectives To assess the impact of simulating temporal changes in health-care practice patterns when calibrating longitudinal models to cross-sectional data. Methods A Markov model of cervical cancer was calibrated to recent age-specific US data on the prevalence of cervical abnormalities, cervical cancer incidence, and related mortality. The impact of failing to account for temporal changes in screening practices was assessed by comparing results from 1) a conventional calibration that incorrectly assumed that all women had been exposed to current screening practices in the past and 2) an historically accurate calibration that reflected the fact that US women 65 years of age and older had not received currently available screening practices at younger ages. Results The parameter set derived from conventional calibration produced a cervical cancer incidence rate of 13.4 per 100,000 among women aged 65 years and older, which is equal to the target end point. However, when this parameter set was used in the model to simulate the effects of historically correct screening, cervical incidence and related mortality in the 65 years and older age group were overestimated by 18% and 47%, respectively. Finally, when the parameter set was correctly calibrated by assuming historical changes in screening in the calibration process, excellent calibration to both incidence and mortality was obtained. Conclusions Calibrating longitudinal models to cross-sectional data without accounting for temporal changes in clinical practice may result in a parameter set that is not as optimized as it appears and may lead to bias in evaluating the effectiveness of interventions.
ISSN:1098-3015
1524-4733
DOI:10.1016/j.jval.2011.01.002