LONG-TERM METABOLIC AND HORMONAL EFFECTS OF EXENATIDE ON ISLET TRANSPLANT RECIPIENTS WITH ALLOGRAFT DYSFUNCTION: 1318

The initial success of islet transplantation (ITx) is followed by graft dysfunction (GDF) and insulin reintroduction. Exenatide, a GLP-1 agonist, increases insulin and decreases glucagon secretion and has potential for β-cell regeneration. To improve functional islet mass, exenatide treatment was given to ITx recipients with GDF. The objective of this study was to assess metabolic and hormonal effects of exenatide in GDF. In this prospective, single-arm, nonrandomized study, 11 type 1 diabetes recipients of ITx with GDF had HbA1c, weight, insulin requirements, and 5-h mixed meal tolerance test (MMTT; with/without exenatide given before test) at baseline, 3, 6, and 12 months after initiating exenatide treatment. Baseline MMTT showed postprandial hyperglycemia and hyperglucagonemia. Daily exenatide treatment resulted in improved glucose, increased amylin/insulin ratio, and decreased proinsulin/insulin ratio as assessed by MMTT. Glucagon responses remained unchanged. Exenatide administration 1 h before MMTT showed decreased glucagon and glucose at 0 min and attenuation in their postprandial rise. Time-to-peak glucose was delayed, followed by insulin, proinsulin, amylin, and C-peptide, indicating glucose-driven insulin secretion. Five subjects completed 12-month follow-up. Glucose and glucagon suppression responses after MMTT with exenatide were no longer observed. Retrospective 3-month analysis of these subjects revealed higher and sustained glucagon levels that did not suppress as profoundly with exenatide administration, associated with higher glucose levels and increased C-peptide responses. In conclusion, Exenatide suppresses the abnormal postprandial hyperglucagonemia and hyperglycemia observed in GDF. Changes in amylin and proinsulin secretion may reflect more efficient insulin processing. Different degrees of responsiveness to exenatide were identified. These may help guide the clinical management of ITx recipients

Umbilical cord mesenchymal stem cells for COVID-19 acute respiratory distress syndrome: A double-blind, phase 1/2a, randomized controlled trial

Acute respiratory distress syndrome (ARDS) in COVID-19 is associated with high mortality. Mesenchymal stem cells are known to exert immunomodulatory and anti-inflammatory effects and could yield beneficial effects in COVID-19 ARDS. The objective of this study was to determine safety and explore efficacy of umbilical cord mesenchymal stem cell (UC-MSC) infusions in subjects with COVID-19 ARDS. A double-blind, phase 1/2a, randomized, controlled trial was performed. Randomization and stratification by ARDS severity was used to foster balance among groups. All subjects were analyzed under intention to treat design. Twenty-four subjects were randomized 1:1 to either UC-MSC treatment (n = 12) or the control group (n = 12). Subjects in the UC-MSC treatment group received two intravenous infusions (at day 0 and 3) of 100 ± 20 × 106 UC-MSCs; controls received two infusions of vehicle solution. Both groups received best standard of care. Primary endpoint was safety (adverse events [AEs]) within 6 hours; cardiac arrest or death within 24 hours postinfusion). Secondary endpoints included patient survival at 31 days after the first infusion and time to recovery. No difference was observed between groups in infusion-associated AEs. No serious adverse events (SAEs) were observed related to UC-MSC infusions. UC-MSC infusions in COVID-19 ARDS were found to be safe. Inflammatory cytokines were significantly decreased in UC-MSC-treated subjects at day 6. Treatment was associated with significantly improved patient survival (91% vs 42%, P =.015), SAE-free survival (P =.008), and time to recovery (P =.03). UC-MSC infusions are safe and could be beneficial in treating subjects with COVID-19 ARDS

Nonparametric paired tests for censored survival data incorporating prognostic covariate information.

In clinical trials, complications in the data structure can arise by design, as when treatment groups are dependent, or by happenstance, as when selection bias is present. In this dissertation, we develop nonparametric paired tests based on weighted integrated survival that incorporate prognostic covariate information, and adjust for selection bias as well as informative censoring. In the absence of these sources of bias, these new methods can improve power over current nonparametric tests in two ways. First, by extending existing methods that take advantage of covariate information to the paired setting, we gain power over methods that either ignore the covariates or the dependence between the treatment groups. Second, the methods we use to adjust for selection bias can produce additional efficiency gains when treatment groups are comparable with respect to baseline covariates. When information collected post-baseline is incorporated, additional efficiency gains are made as long as censoring is uninformative. Additionally, we develop paired stratified tests using both weighted rank-based and weighted integrated survival based methods. In addition to adjusting for potential bias from baseline covariate imbalances and weakening assumptions concerning uninformative censoring, stratification may improve power when treatment alternatives are more clearly detectable within each covariate stratum. We investigate the strengths and limitations of the different methods presented using a number of simulation scenarios. Data from the Early Treatment Diabetic Retinopathy Study are used throughout to illustrate methodology in comparing time to severe vision loss between treatment groups receiving either early or deferred photocoagulation therapy.Ph.D.Biological SciencesBiostatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/131477/2/3057914.pd

Conditional uncertainty: Misinterpretations of “significant” p values

Misapplication of p values can lead to very misleading conclusions. The frequent insignificance of a “significant” p value 1provides examples illustrating situations where p values are misused in interpretation and describes alternatives to p values to consider. The concern about p values, and their potential shortcomings and misinterpretations, has been widely discussed and has received a lot of debate from the statistical community. While we agree with much of the content presented in the article, the issue fundamentally represents a misinterpretation of conditional probability, misapplication of measures regarding diagnostic accuracy, and consequently violations of assumptions inherent in experimental design and the validity of many approaches to statistical analysis and inference. One way this can be remedied is through team science, and collaboration between clinical and biostatistical scientists in research

A conditional approach for the receiver operating characteristic curve construction to evaluate diagnostic test performance in a family-matched case–control design

Receiver operating characteristic curves are widely used in medical research to illustrate biomarker performance in binary classification, particularly with respect to disease or health status. Study designs that include related subjects, such as siblings, usually have common environmental or genetic factors giving rise to correlated biomarker data. The design could be used to improve detection of biomarkers informative of increased risk, allowing initiation of treatment to stop or slow disease progression. Available methods for receiver operating characteristic construction do not take advantage of correlation inherent in this design to improve biomarker performance. This paper will briefly review some developed methods for receiver operating characteristic curve estimation in settings with correlated data from case–control designs and will discuss the limitations of current methods for analyzing correlated familial paired data. An alternative approach using conditional receiver operating characteristic curves will be demonstrated. The proposed approach will use information about correlation among biomarker values, producing conditional receiver operating characteristic curves that evaluate the ability of a biomarker to discriminate between affected and unaffected subjects in a familial paired design

Use of an indirect sampling method to produce reference intervals for hematologic and biochemical analyses in psittaciform species

As with other animal species, comprehensive reference intervals (RI) for psittaciform species are rare and plagued by common issues, including sparse information regarding methods used to analyze specimens, low sample sizes, and improper statistical analyses. The purpose of this study was to examine the use of an indirect sampling method of RI generation from several years of data collected from specimens of multiple psittaciform species submitted to a veterinary diagnostic laboratory. These data were unselected for health status. A previously published method for indirect RI generation was applied to data collected for routine hematologic and biochemical analyses. Seven species groups were examined, and sample size ranged from 346 to 2358. Results showed that RI varied by species and appeared to represent a broader range than expected compared with other RI and traditional clinical expectations for core health assessments, such as total white blood cell count and white blood cell differential results. Some biochemical results reflected more narrow ranges, and a few were consistent with other published ranges. The intervals were likely influenced by changes related to stress and underlying disease. The results of the current study reflect the imprecision of this method related to data obtained from the population served by this laboratory. Overall, this method is not suitable for the production of comprehensive RI, although it may provide rough estimates for some limited analyses until traditional RI can be generated

Abstract A01: Prism regression: A new statistical tool for understanding determinants of cancer health disparities

Abstract As part of our ongoing collaboration with community stakeholders throughout the Miami metropolitan area, we have become increasingly aware of the limitations imposed by current measurements of race. Our stakeholders uphold that the categorical variable (black vs. white) most commonly used to approximate this social construct lacks necessary dimension for understanding its significance and contribution to persistent health disparity. In response to this input, we introduce a class of generalized regression models for cancer outcomes, which allow the coefficient of race to vary as a function of other variables. These other variables involve factors that typically interact with the race focus variable. Existing varying coefficient models (VCMs) have been limited in a number of ways: they do not easily extend to allow multiple effect modifiers at once, and they do not allow interactions between the effect modifier variables. We developed “prism regression” to overcome these limitations in a data-adaptive, non-parametric manner. Joint modeling of the effect modifiers can be viewed as a prism through which the overall race coefficient is “refracted” in different directions (the variables defining the prism are what we term prism variables). This allows one to delineate patterns of racial differences in cancer outcomes that were previously hidden or masked. Our particular implementation of prism regression uses a rule-based decision tree structure, which is extremely intuitive to understand. Importantly, these tree-like structures can be extended in elegant ways to allow for much more complex effect modification. One such example, which we term hierarchical prism regression, allows the effect of race to vary as a function of layered, directional effect modifiers (i.e. prism variables and hierarchical variables together). This cannot be accomplished by existing methods such as multiway interactions, mediation or moderation. We detail the prism regression methodology and demonstrate some optimality properties based on a new constrained parameter estimation algorithm called “prism fusion” that provides not only more accurate predictions due to shrinkage, but also evidence for or against the presence of effect modification. We compare prism regression to existing methods using a series of simulation studies and show significant gains in predictive accuracy. In addition, we analyzed data from the cancer registry of Florida to search for complex effect modification of racial disparity. The Florida Cancer Data System is Florida's legislatively mandated, population-based, statewide cancer registry. It is the second largest population based, cancer incidence registry in the nation processing over 180,000 new cases are from patient medical records annually, corresponding to 1155,000 newly diagnosed tumors since 1981. Cancer cases are submitted by hospitals, freestanding ambulatory surgical facilities, radiation therapy facilities, private physicians and death certificates. Information includes routine personal and demographic data, includes diagnosis, stage of disease, tissue pathology, and first course of medical treatment as well as passive death certificate linkage information. We illustrate the usefulness of prism regression focusing on breast cancer patients from the FCDS registry to investigate the individual and contextual level factors that lead to differential effects of race in relation to stage of disease at diagnosis. Individual level variables obtained from the FCDS registry are linked by geocode to obtain contextual variables from census data. We consider the individual level information as prism variables, and the geocode linked contextual level variables as hierarchical variables. Citation Format: Shari Messinger, Erin Kobetz, J Sunil Rao. Prism regression: A new statistical tool for understanding determinants of cancer health disparities. [abstract]. In: Proceedings of the Seventh AACR Conference on The Science of Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; Nov 9-12, 2014; San Antonio, TX. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2015;24(10 Suppl):Abstract nr A01.</jats:p

Alternative parameterization approaches for modelling risk assessment in the presence of imbalance: An example using parotid malignancy

In collaborative statistics, logistic regression models are commonly used with binary outcomes and reference cell coding for categorical predictors. However, despite the usefulness of reference cell coding schemes under many investigative objectives, it is not always appropriate to address research questions of interest. Investigators often consider modifying research questions to align with inference possible using reference cell coding. Alternative coding schemes can offer a more appropriate approach for the investigation. We explored application of deviation from means coding in determining how results from a diagnostic tool provide additional information on a patient's risk of disease with respect to the overall (naive) risk at clinical presentation. We compared model parameterizations between using reference cell coding and deviation from means coding, by both unweighted and weighted approaches, for assessing risk of parotid malignancy, comparing patients with indeterminate FNAB results with the general (naive) risk among presenting patients. Unweighted deviation from means coding estimates a 1.2-fold increase in the odds of malignancy with an indeterminate FNAB result compared to the naive odds of malignancy at clinical presentation (OR: 1.21 [95% CI: 1.63-2.32], p = 0.5699). The weighted approach takes into account the imbalance in the presenting population and estimates an increased risk (OR: 2.54 [95% CI: 1.52-4.26], p = 0.0004), which is more accurately representing the naive risk at presentation and to answer the research question of interest. Using standard reference cell coding, an indeterminate result is associated with significantly higher odds than that of a negative result (OR: 5.20 [95% CI: 2.22-12.20], p = 0.0001), but this does not inform us as to the risk with respect to that inherent at clinical presentation and thus may not be useful for clinical decision making