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Adaptive Modeling of Monthly Depression Levels in Terms of Daily Assessments of opioid medications Taken and Pain Levels for Cancer Patients

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Open Journal of Statistics 2024年第5期14卷 492-517页

作者： George J. Knafl Ryan Quinn Andrew Robinson Salimah H. Meghani School of Nursing University of North Carolina at Chapel Hill Chapel Hill NC USA Department of Biobehavioral Health Sciences New Courtland Center for Transitions and Health School of Nursing University of Pennsylvania Philadelphia PA USA Leonard Davis Institute for Health Economics University of Pennsylvania Philadelphia PA USA

A research study collected intensive longitudinal data from cancer patients on a daily basis as well as non-intensive longitudinal survey data on a monthly basis. Although the daily data need separate analysis, those data can also be utilized to generate predictors of monthly outcomes. Alternatives for generating daily data predictors of monthly outcomes are addressed in this work. Analyses are reported of depression measured by the Patient Health Questionnaire 8 as the monthly survey outcome. Daily measures include numbers of opioid medications taken, numbers of pain flares, least pain levels, and worst pain levels. Predictors are averages of recent non-missing values for each daily measure recorded on or prior to survey dates for depression values. Weights for recent non-missing values are based on days between measurement of a recent value and a survey date. Five alternative averages are considered: averages with unit weights, averages with reciprocal weights, weighted averages with reciprocal weights, averages with exponential weights, and weighted averages with exponential weights. Adaptive regression methods based on likelihood cross-validation (LCV) scores are used to generate fractional polynomial models for possible nonlinear dependence of depression on each average. For all four daily measures, the best LCV score over averages of all types is generated using the average of recent non-missing values with reciprocal weights. Generated models are nonlinear and monotonic. Results indicate that an appropriate choice would be to assume three recent non-missing values and use the average with reciprocal weights of the first three recent non-missing values.

关键词： Adaptive Regression Cancer Depression Intensive Longitudinal Data Factional Polynomials opioid medications Pain Levels

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