Corwin Zigler

Developmental validation of a point-of-care, salivary α-amylase biosensor.

The translation of salivary alpha-amylase (sAA) to the ambulatory assessment of stress hinges on the development of technologies capable of speedy and accurate reporting of sAA levels. Here, we describe the developmental validation and usability testing of a point-of-care, colorimetric, sAA biosensor. A disposable test strip allows for streamlined sample collection and a corresponding hand-held reader with integrated analytic capabilities permits rapid analysis and reporting of sAA levels. Bioanalytical validation utilizing saliva samples from 20 normal subjects indicates that, within the biosensors linear range (10-230 U/ml), its accuracy (R(2)=0.989), precision (CV<9%), and measurement repeatability (range -3.1% to +3.1%) approach more elaborate laboratory-based, clinical analyzers. The truncated sampling-reporting cycle (<1 min) and the excellent performance characteristics of the biosensor has the potential to take sAA analysis out of the realm of dedicated, centralized laboratories and facilitate future sAA biomarker qualification studies.

Area variations in health: A spatial multilevel modeling approach

Both space and membership in geographically-embedded administrative units can produce variations in health, resulting in geographic clusters of good and poor health. Despite important differences between these two types of dependence, one is easily mistaken for the other, and the possibility that both are at work is commonly ignored. We fit a series of hierarchical and spatially-explicit multilevel models to a U.S. county-level life dataset of life expectancy in 1999 to demonstrate approaches for data analysis and interpretation when multiple sources of area-clustering are present. We demonstrate the methods to detect, interpret, and differentiate evidence of spatial and geographic membership effects and discuss key considerations for analyzing data with spatial or/and membership dimensions. We find evidence that life expectancy is driven by both within-state geographic process, and by spatial processes. We argue that considering spatial and membership processes simultaneously yields valuable insights into the patterning of area variations in health.

The feasibility of ambulatory biosensor measurement of salivary alpha amylase: Relationships with self-reported and naturalistic psychological stress.

Recent developments in biosensor technology allow point-of-use reporting of salivary alpha amylase (sAA) levels while approaching the precision and accuracy of conventional laboratory-based testing. We deployed a portable prototype sAA biosensor in 54 healthy, male dental students during a low stress baseline and during final exams. At baseline, participants completed the Brief Symptom Inventory (BSI). At baseline and the exam week, participants provided saliva samples at 10 AM, 1 PM, and 5 PM, and rated concurrent subjective distress. Although subjective distress was higher during exams compared to baseline, sAA levels did not differ between baseline and exams. Higher sAA levels were related to higher concurrent subjective distress, and higher depressive and social isolation symptoms on the BSI were related to lower sAA during exams. Results from this study, in combination with previous validation data, suggest that the sAA biosensor is a promising tool for point-of-use measures of exposure to stress.

Uncertainty in propensity score estimation: : bayesian methods for variable selection and model-averaged causal effects

Causal inference with observational data frequently relies on the notion of the propensity score (PS) to adjust treatment comparisons for observed confounding factors. As decisions in the era of “big data” are increasingly reliant on large and complex collections of digital data, researchers are frequently confronted with decisions regarding which of a high-dimensional covariate set to include in the PS model to satisfy the assumptions necessary for estimating average causal effects. Typically, simple or ad hoc methods are employed to arrive at a single PS model, without acknowledging the uncertainty associated with the model selection. We propose three Bayesian methods for PS variable selection and model averaging that (a) select relevant variables from a set of candidate variables to include in the PS model and (b) estimate causal treatment effects as weighted averages of estimates under different PS models. The associated weight for each PS model reflects the data-driven support for that model’s ability to adjust for the necessary variables. We illustrate features of our proposed approaches with a simulation study, and ultimately use our methods to compare the effectiveness of surgical versus nonsurgical treatment for brain tumors among 2606 Medicare beneficiaries. Supplementary materials for this article are available online.

A Bayesian approach to improved estimation of causal effect predictiveness for a principal surrogate endpoint.

The literature on potential outcomes has shown that traditional methods for characterizing surrogate endpoints in clinical trials based only on observed quantities can fail to capture causal relationships between treatments, surrogates, and outcomes. Building on the potential-outcomes formulation of a principal surrogate, we introduce a Bayesian method to estimate the causal effect predictiveness (CEP) surface and quantify a candidate surrogates utility for reliably predicting clinical outcomes. In considering the full joint distribution of all potentially observable quantities, our Bayesian approach has the following features. First, our approach illuminates implicit assumptions embedded in previously-used estimation strategies that have been shown to result in poor performance. Second, our approach provides tools for making explicit and scientifically-interpretable assumptions regarding associations about which observed data are not informative. Through simulations based on an HIV vaccine trial, we found that the Bayesian approach can produce estimates of the CEP surface with improved performance compared to previous methods. Third, our approach can extend principal-surrogate estimation beyond the previously considered setting of a vaccine trial where the candidate surrogate is constant in one arm of the study. We illustrate this extension through an application to an AIDS therapy trial where the candidate surrogate varies in both treatment arms.

Substance use in vulnerable patients with orofacial injury: prevalence, correlates, and unmet service needs

BACKGROUND A large portion of the injuries treated at urban trauma centers are preventable with alcohol and substance use presenting as common antecedent risk factors. METHODS Alcohol and drug use characteristics of vulnerable adults treated for intentional orofacial injury at a regional trauma center were investigated. Patients (N = 154) presenting with intentional facial injury were recruited. Patients were considered eligible for recruitment if they were adults, recently used alcohol or drugs, and had a fracture within the 30 days preceding recruitment that involved the jaw, orbit, nose, or cheekbone as determined by clinical history, examination, and radiographic findings and that injury was due to interpersonal violence. RESULTS This patient cohort evidenced significant levels of alcohol use, with 58% of our patient cohort meeting the criteria for problem drinking. Although lower than alcohol use rates, the reported use of illicit drugs was substantial. Almost half of the sample reported other substance use in the previous month, with 24% meeting the criteria for problem drug use. CONCLUSIONS Despite the very high percentage of individuals needing alcohol or drug treatment, only a small proportion of the patient sample reported having seen a professional for alcohol or drug treatment. Integrating substance use services into trauma care is discussed.

Estimating causal effects of air quality regulations using principal stratification for spatially correlated multivariate intermediate outcomes

Methods for causal inference regarding health effects of air quality regulations are met with unique challenges because (1) changes in air quality are intermediates on the causal pathway between regulation and health, (2) regulations typically affect multiple pollutants on the causal pathway towards health, and (3) regulating a given location can affect pollution at other locations, that is, there is interference between observations. We propose a principal stratification method designed to examine causal effects of a regulation on health that are and are not associated with causal effects of the regulation on air quality. A novel feature of our approach is the accommodation of a continuously scaled multivariate intermediate response vector representing multiple pollutants. Furthermore, we use a spatial hierarchical model for potential pollution concentrations and ultimately use estimates from this model to assess validity of assumptions regarding interference. We apply our method to estimate causal effects of the 1990 Clean Air Act Amendments among approximately 7 million Medicare enrollees living within 6 miles of a pollution monitor.

Accounting for uncertainty in confounder and effect modifier selection when estimating average causal effects in generalized linear models

Confounder selection and adjustment are essential elements of assessing the causal effect of an exposure or treatment in observational studies. Building upon work by Wang et al. (2012, Biometrics 68, 661-671) and Lefebvre et al. (2014, Statistics in Medicine 33, 2797-2813), we propose and evaluate a Bayesian method to estimate average causal effects in studies with a large number of potential confounders, relatively few observations, likely interactions between confounders and the exposure of interest, and uncertainty on which confounders and interaction terms should be included. Our method is applicable across all exposures and outcomes that can be handled through generalized linear models. In this general setting, estimation of the average causal effect is different from estimation of the exposure coefficient in the outcome model due to noncollapsibility. We implement a Bayesian bootstrap procedure to integrate over the distribution of potential confounders and to estimate the causal effect. Our method permits estimation of both the overall population causal effect and effects in specified subpopulations, providing clear characterization of heterogeneous exposure effects that may vary considerably across different covariate profiles. Simulation studies demonstrate that the proposed method performs well in small sample size situations with 100-150 observations and 50 covariates. The method is applied to data on 15,060 US Medicare beneficiaries diagnosed with a malignant brain tumor between 2000 and 2009 to evaluate whether surgery reduces hospital readmissions within 30 days of diagnosis.

Willingness of Facial Injury Patients to Change Causal Substance Using Behaviors

Many injuries due to interpersonal violence among patients presenting to urban trauma centers for treatment are preventable, with alcohol and illicit drug use presenting as common antecedent risk factors. However, many patients with such problems do not seek treatment. Substance use patients were surveyed to determine how many recognized they had a problem and whether they had previously received treatment for substance use problems. Almost 60% of the patients treated for a facial injury screened for problem alcohol use, and slightly more than 25% screened for problem drug use. Only approximately one third of patients indicated any movement towards dealing with these problems and of these, only 20% had actually sought treatment. Employment had an effect on treatment seeking, with fewer employed patients seeking help. Utilizing the critical window of opportunity for emergency department (ED) personnel to make referrals may have an impact on treatment seeking for problem level substance use.

Comparative effectiveness of radiotherapy with vs. without temozolomide in older patients with glioblastoma

It is unknown whether the addition of temozolomide (TMZ) to radiotherapy (RT) is associated with improved overall survival (OS) among older glioblastoma patients. We performed a retrospective cohort SEER-Medicare analysis of 1652 patients aged ≥65 years with glioblastoma who received ≥10 fractions of RT from 2005 to 2009, or from 1995 to 1999 before TMZ was available. Three cohorts were assembled based on diagnosis year and treatment initiated within 60 days of diagnosis: (1) 2005–2009 and TMZ/RT, (2) 2005–2009 and RT only, or (3) 1995–1999 and RT only. Associations with OS were estimated using Cox proportional hazards models and propensity score analyses; OS was calculated starting 60 days after diagnosis. Pre-specified sensitivity analyses were performed among patients who received long-course RT (≥27 fractions). Median survival estimates were 7.4 (IQR, 3.3–14.7) months for TMZ/RT, 5.9 (IQR, 2.6–12.1) months for RT alone in 2005–2009, and 5.6 (IQR, 2.7–9.6) months for RT alone in 1995–1999. OS at 2 years was 10.1 % for TMZ/RT, 7.1 % for RT in 2005–2009, and 4.7 % for RT in 1995–1999. Adjusted models suggested decreased mortality risk for TMZ/RT compared to RT in 2005–2009 (AHR, 0.86; 95 % CI, 0.76–0.98) and RT in 1995–1999 (AHR, 0.71; 95 % CI, 0.57–0.90). Among patients from 2005 to 2009 who received long-course RT, however, the addition of TMZ did not significantly improve survival (AHR, 0.91; 95 % CI, 0.80–1.04). In summary, among a large cohort of older glioblastoma patients treated in a real-world setting, the addition of TMZ to RT was associated with a small survival gain.