Mean corrected generalized estimating equations for longitudinal binary outcomes with report bias

Cocaine addiction is an important public health problem worldwide. Cognitive-behavioral therapy is a counseling intervention for supporting cocaine-dependent individuals through recovery and relapse prevention. It may reduce patients’ cocaine uses by improving their motivations and enabling them to recognize risky situations. To study the effect of cognitive behavioral therapy on cocaine dependence, the self-reported cocaine use with urine test data were collected at the Primary Care Center of Yale-New Haven Hospital. Its outcomes are binary, including both the daily self-reported drug uses and weekly urine test results. To date, the generalized estimating equations are widely used to analyze binary data with repeated measures. However, due to the existence of significant self-report bias in the self-reported cocaine use with urine test data, a direct application of the generalized estimating equations approach may not be valid. In this paper, we proposed a novel mean corrected generalized estimating equations approach for analyzing longitudinal binary outcomes subject to reporting bias. The mean corrected generalized estimating equations can provide consistently and asymptotically normally distributed estimators under true contamination probabilities. In the self-reported cocaine use with urine test study, accurate weekly urine test results are used to detect contamination. The superior performances of the proposed method are illustrated by both simulation studies and real data analysis.

Concordance correlation coefficients estimated by modified variance components and generalized estimating equations for longitudinal overdispersed Poisson data

For longitudinal overdispersed Poisson data sets, estimators of the intra-, inter-, and total concordance correlation coefficient through variance components have been proposed. However, biased estimators of quadratic forms are used in concordance correlation coefficient estimation. In addition, the generalized estimating equations approach has been used in estimating agreement for longitudinal normal data and not for longitudinal overdispersed Poisson data. Therefore, this paper proposes a modified variance component approach to develop the unbiased estimators of the concordance correlation coefficient for longitudinal overdispersed Poisson data. Further, the indices of intra-, inter-, and total agreement through generalized estimating equations are also developed considering the correlation structure of longitudinal count repeated measurements. Simulation studies are conducted to compare the performance of the modified variance component and generalized estimating equation approaches for longitudinal Poisson and overdispersed Poisson data sets. An application of corticospinal diffusion tensor tractography study is used for illustration. In conclusion, the modified variance component approach performs outstandingly well with small mean square errors and nominal 95% coverage rates. The generalized estimating equation approach provides in model assumption flexibility of correlation structures for repeated measurements to produce satisfactory concordance correlation coefficient estimation results.

Penalized generalized estimating equations for relative risk regression with applications to brain lesion data

Motivated by a brain lesion application, we introduce penalized generalized estimating equations for relative risk regression for modelling correlated binary data. Brain lesions can have varying incidence across the brain and result in both rare and high incidence outcomes. As a result, odds ratios estimated from generalized estimating equations with logistic regression structures are not necessarily directly interpretable as relative risks. On the other hand, use of log-link regression structures with the binomial variance function may lead to estimation instabilities when event probabilities are close to 1. To circumvent such issues, we use generalized estimating equations with log-link regression structures with identity variance function and unknown dispersion parameter. Even in this setting, parameter estimates can be infinite, which we address by penalizing the generalized estimating functions with the gradient of the Jeffreys prior. Our findings from extensive simulation studies show significant improvement over the standard log-link generalized estimating equations by providing finite estimates and achieving convergence when boundary estimates occur. The real data application on UK Biobank brain lesion maps further reveals the instabilities of the standard log-link generalized estimating equations for a large-scale data set and demonstrates the clear interpretation of relative risk in clinical applications.

1330. Clinical Associations and Trajectory of “Long COVID”

Background Persistent symptoms after acute COVID-19 are being increasingly reported. To date, little is known about the cause, clinical associations, and trajectory of “Long COVID”. Methods Participants of an outpatient clinical trial of Peginterferon-Lambda as treatment for uncomplicated SARS-CoV-2 infection were invited to long term follow-up visits 4, 7, and 10 months after initial COVID-19 diagnosis. Ongoing symptoms and functional impairment measures (work productivity and activity index (WPAI), NIH toolbox smell test, 6-minute walk test) were assessed and blood samples obtained. “Long COVID” was defined as presence of 2 or more typical symptoms (fatigue, hyposmia/hypogeusia, dyspnea, cough, palpitations, memory problems, joint pain) at follow up. Associations between baseline characteristics, initial COVID-19 clinical course, and presence of “Long COVID” during follow-up were assessed using generalized estimating equations accounting for repeated measurements within individuals. Results Eighty-seven participants returned for at least one follow-up visit. At four months, 29 (34.1%) had “Long COVID”; 19 (24.7%) met criteria at 7 months and 18 (23.4%) at 10 months (Figure 1). Presence of “Long COVID” symptoms did not correlate significantly with functional impairment measures. Female gender (OR 3.01, 95% CI 1.37-6.61) and having gastrointestinal symptoms during acute COVID-19 illness (OR 5.37, 95% CI 1.02-28.18) were associated with “Long COVID” during follow-up (Figure 2). No significant associations with baseline immunologic signatures were observed. Figure 1. Alluvial plot of long term follow-up participants showing outcomes of symptoms at each visit. Figure 2. Generalized Estimating Equations Model showing associations with “Long COVID” (presence of 2+ symptoms) at month 4, 7, and 10 following acute infection using unstructured correlation matrix. Conclusion “Long COVID” was prevalent in this outpatient trial cohort and had low rates of resolution over 10 months of follow up. Female sex and gastrointestinal symptoms during acute illness were associated with “Long COVID”. Identifying modifiable risk factors associated with the development of persistent symptoms following SARS-CoV-2 infection remains a critical need. Disclosures All Authors: No reported disclosures