Semiparametric time varying coefficient model for matched case-crossover studies.

In matched case-crossover studies, it is generally accepted that the covariates on which a case and associated controls are matched cannot exert a confounding effect on independent predictors included in the conditional logistic regression model. This is because any stratum effect is removed by the conditioning on the fixed number of sets of the case and controls in the stratum. Hence, the conditional logistic regression model is not able to detect any effects associated with the matching covariates by stratum. However, some matching covariates such as time often play an important role as an effect modification leading to incorrect statistical estimation and prediction. Therefore, we propose three approaches to evaluate effect modification by time. The first is a parametric approach, the second is a semiparametric penalized approach, and the third is a semiparametric Bayesian approach. Our parametric approach is a two-stage method, which uses conditional logistic regression in the first stage and then estimates polynomial regression in the second stage. Our semiparametric penalized and Bayesian approaches are one-stage approaches developed by using regression splines. Our semiparametric one stage approach allows us to not only detect the parametric relationship between the predictor and binary outcomes, but also evaluate nonparametric relationships between the predictor and time. We demonstrate the advantage of our semiparametric one-stage approaches using both a simulation study and an epidemiological example of a 1-4 bi-directional case-crossover study of childhood aseptic meningitis with drinking water turbidity. We also provide statistical inference for the semiparametric Bayesian approach using Bayes Factors. Copyright © 2016 John Wiley & Sons, Ltd.

Impact of extramural DAIT/NIAID pharmacy programs, research pharmacist scientist oversight on study performance, and lessons learned.

PURPOSE: Over the past two decades, the involvement of a Pharmacist Scientist in clinical settings has improved patient safety, decreased medication errors, and enabled successful conduct of clinical trials and faster product development [1-5]. The impact of an oversight by a Pharmacist Scientist on clinical trial performance and execution in terms of Pharmacy and Investigational Product (IP)-related deviations has not been evaluated by a sponsor. METHODS: This was a retrospective observational study conducted by the Division of Allergy, Immunology and Transplantation (DAIT), National Institute of Allergy and Infectious Diseases (NIAID). We assessed the association of the number of Pharmacy and Investigational Product (IP)-related deviations with Pharmacist oversight and use of DAIT Pharmacy/ Pharmaceutical services in two groups: Intervention Group (IG) and the Control Group (CG). RESULTS: We evaluated monitoring data from 116 studies conducted between 2006 through 2020. Forty-one eligible clinical trials were included and analyzed: 13 trials were in the IG with Pharmacist oversight and use of Pharmacy Services; 28 trials were in the CG with no Pharmacist oversight and zero to partial use DAIT Pharmacy/ Pharmaceutical Services. The evaluation revealed the expected risk of having a pharmacy and IP-related deviations were 2.94 times higher (95% CI 1.28, 6.67, = 0.01) in trials not having Pharmacist oversight and zero to partial use of Pharmaceutical/ Pharmacy Program services. This significant finding was associated with having Pharmacist oversight when adjusting for study size (# of sites and patients needed), anticipated study duration, design complexity, and whether recruitment was completed or not. CONCLUSION: We found a statistically significant association between Pharmacist Scientist involvement and oversight from protocol development to study execution and a reduction in Pharmacy and IP-related deviations.

Characteristics and outcome of facial nerve palsy from Lyme neuroborreliosis in the United States.

OBJECTIVES: Facial palsy is the most common manifestation of Lyme neuroborreliosis (LNB) in the United States. This study aimed to describe features of patients with early LNB presenting with facial palsy and to determine if corticosteroids in addition to antibiotic therapy was associated with unfavorable outcome. METHODS: Retrospective analysis of participants enrolled in clinical studies investigating Lyme disease (N = 486) identified 44 patients who had facial palsy from LNB. The House-Brackmann scale was used to quantify the facial nerve dysfunction. RESULTS: Most patients presented in the summer months. Erythema migrans, frequently associated with systemic symptoms, occurred in 29 patients. Thirteen patients presented with bilateral facial palsy, usually with sequential involvement. Fourteen patients had painful radiculopathy. Of the 38 patients treated with antibiotics before the resolution of the palsy who had complete follow-up, 24 received both antibiotics and corticosteroids. Of these 38 patients, 34 recovered completely, 3 had nearly complete recovery, and 1 had moderate dysfunction. There were no differences between the treatment groups in achieving complete resolution of the palsy at 12 months or in time to complete recovery. INTERPRETATION: A history of rash compatible with erythema migrans or febrile illness in the weeks preceding the palsy are helpful clues pointing toward LNB and should be actively sought when evaluating patients with acute-onset peripheral facial palsy, particularly bilateral facial palsy. Treatment with antibiotic therapy is highly effective and most patients will fully recover facial nerve function. Adjunctive corticosteroid therapy appears to not affect the speed of recovery or overall outcome in this retrospective observational study.

New insights into modeling exposure measurements below the limit of detection.

In environmental epidemiology, it is of interest to assess the health effects of environmental exposures. Some exposure analytes present values that are below the laboratory limit of detection (LOD). There have been many methods proposed for handling this issue to incorporate exposures subject to LOD in risk modeling using logistic regression. We present a fresh look at proposed methods to handle exposure analytes that present values that are below the LOD. METHODS: We performed comparisons through an extensive simulation study and a cancer epidemiology example. The methods we considered were a maximum-likelihood approach, multiple imputation, Cox regression, complete case analysis, filling in values below the LOD with , and a missing indicator method. RESULTS: We found that the logistic regression coefficient associated with the exposure (subject to LOD) can be severely biased when underlying assumptions are not met, even with a relatively small proportion (under 20%) of measurements below the LOD. CONCLUSIONS: We propose the use of a simple method where the relationship between the analyte and disease risk is modeled only above the detection limit with an intercept term at the LOD and a slope parameter, which makes no assumptions about what happens below the LOD. In most practical situations, our results suggest that this simple method may be the best choice for analyzing analytes with detection limits.

Risk Prediction of Death in Inpatient Adults With COVID-19 from Mexico.

Background: There is substantial variation in COVID-19 lethality across countries. In addition, in countries with populations with extreme economic inequalities, such as Mexico, there are regional and local differences in risk factors for COVID-19 death. The goal of this study was to test the hypothesis that the risk of death in Mexican COVID-19 patients was associated with the time between symptom onset and hospitalization and/or with the healthcare site. Also, death prognostic models were developed. Methods: The study included two COVID-19 inpatient cohorts, one prospective and one retrospective from Chiapas, Mexico. Demographic, clinical and laboratory variables were collected, and the diagnosis of SARS-CoV-2 infection was performed using RT-qPCR in samples collected seven days since symptom onset. The 30-day mortality, since symptom onset, was the outcome, and clinical variables at the first 48 hours of hospitalization were independent factors. Multivariate logistic regression analyses were conducted. Results: Of the 392 patients included, 233 died (59.4%). The time between symptom onset and hospitalization, the healthcare site and sex were not related to the 30-day mortality. Three death prognostic models were developed (AUC between 0.726 and 0.807). Age, LDH, AST, and lymphocyte count were included in all models, OSI-WHO Classification (Non-invasive ventilation or high-flow oxygen, and mechanical ventilation with or without organ support/ECMO) and leukocyte count in two models, and diabetes and diarrhea in one model. Conclusion: The population evaluated had underlying deteriorated health before COVID-19 compared with regional and country population. The factors that determine the COVID-19 mortality risk in a relatively healthy population are sex, age and comorbidities. However, as this study shows, when populations have underlying poor health, some of these factors lose their associations with mortality risk, and others become more important.

Estimating onset time from longitudinal and cross-sectional data with an application to estimating gestational age from longitudinal maternal anthropometry during pregnancy and neonatal anthropometry at birth.

Determining the date of conception is important for estimating gestational age and monitoring whether the fetus and mother are on track in their development and pregnancy. Various methods based on ultrasound have been proposed for dating a pregnancy in high resource countries. However, such techniques may not be available in under-resourced countries. We develop a shared random parameter model for estimating the date of conception using longitudinal assessment of multiple maternal anthropometry and cross-sectional neonatal anthropometry. The methodology is evaluated with a training-test set paradigm as well as with simulations to examine the robustness of the method to model misspecification. We illustrate this new methodology with data from the NICHD Fetal Growth Studies.