Bayesian analysis of compositional data

Compositional data are constrained vectors of multivariate observations whose elements are referred to as components. Such vectors often result when raw data are normalized or when data is obtained as proportions of a certain heterogeneous quantity. These conditions are fairly common in geology, economics and biology. Compositional data are subject to two restrictions; non-negativity and unit sum constraint on its components. The sample space is therefore a simplex subset of real space, whose dimension is a function of number of components.^ Usual multivariate procedures are seldom adequate and appropriate modeling techniques were slow to emerge because these data do not entertain known concepts of independence and the simplex also lacks a rich class of parametric distributions. In the past, Dirichlet distributions were involved in parametric modeling of compositional data although Dirichlet class is inherently unsuitable for describing such data. More recently Aitchison (1982) proposed a statistically feasible methodology in frequentist paradigm using logistic normal distributions.^ Aitchison\u27s idea primarily relies on the fact that an additive logratio transformation produces data that can be modeled under assumption of normality (equivalently, compositions are logistic normal). Clearly this may not always be valid. Further, a caveat in connection with Aitchison\u27s approach is that there is no satisfactory technique to verify logistic normality. As possible answers, marginal tests have been adopted but these tests may uncover the partial truth only.^ In an attempt to provide a general methodology that is more tolerant of data behavior, Box-Cox transformations are studied here in a Bayesian paradigm. Rayens and Srinivasan (1991) have examined this in a classical setup with certain strong assumptions on the transformed data to enable theory. Here, a general Bayesian methodology to this problem is presented and simulation based methods are adopted to weed out most appropriate choice of parameters. Dynamic modeling for correlated compositional data is also investigated under Box-Cox transformation and compared to regression models with vector autoregressive moving average errors. Finally, semiparametric Bayesian modeling under generalized Liouville distribution is presented as a viable alternative to model compositional data within the simplex. To summarize, existing methods for analysis of compositional data have been extended significantly.

Bayesian Analysis of Compositional Data

Compositional data often result when raw data are normalized or when data is obtained as proportions of a certain heterogeneous quantity. These conditions are fairly common in Geology, Economics and Biology. The result is therefore, a vector of such observations per specimen. The usual multivariate procedures are seldom adequate for the analysis of compositional data and there is a relative dearth of alternative techniques suitable for the same. The presence of covariates further adds to complexity of the situation. In this paper, a complete Bayesian methodology to model such data is developed and is illustrated on a real data set comprising Sand, Silt and Clay compositions taken at various water depths in an Arctic lake. Alternative methods such as maximum likelihood estimates are compared with the proposed Bayesian estimates. Simulation based approach is adopted to ascertain adequacy of the fit. Robustness issues are probed through exponential power family of distributions. Several m..

Paroxetine in the treatment of generalized anxiety disorder: results of a placebo-controlled, flexible-dosage trial.

Objective: This study assessed the efficacy of two fixed doses of paroxetine in the treatment of generalized anxiety disorder. Method: Outpatients (N=566) with generalized anxiety disorder and no other axis I disorder were eligible if they scored ≥20 on the Hamilton Rating Scale for Anxiety (with a score of 2 or higher on the anxious mood and tension items). Following a 1-week placebo run-in phase, patients were randomly assigned to 8 weeks of treatment with paroxetine, 20 or 40 mg/day, or placebo. The primary outcome measure was the change from baseline in total score on the Hamilton anxiety scale. Response was defined as a rating of "very much improved" or "much improved" on the Clinical Global Impression global improvement measure; remission was defined as a Hamilton anxiety scale score ≤7. Change in functional impairment was measured with the Sheehan Disability Scale. Results: At 8 weeks, reductions in total score on the Hamilton anxiety scale were significantly greater for both paroxetine groups. Response was achieved by 62% and 68% of the patients receiving 20 and 40 mg of paroxetine, respectively, compared with a 46% response rate in the placebo group. Remission was achieved by 30% and 36% of patients in the 20-and 40-mg paroxetine groups, respectively, compared with 20% given placebo. For all three domains of the Sheehan Disability Scale, significantly greater improvement was seen with paroxetine than placebo. Both doses of paroxetine were well tolerated. Conclusions: This study demonstrates that paroxetine is an efficacious and welltolerated treatment for generalized anxiety disorder

A phase I/IIa, open label, clinical trial evaluating the safety and efficacy of autologous T cells expressing enhanced TCRs specific for NY-ESO-1 in patients with recurrent or treatment refractory ovarian cancer (NCT01567891)

TPS3094 Background: Epithelial ovarian cancer comprises the majority of malignant ovarian neoplasms (~80%) and is the leading cause of death from gynecologic cancer in the US. Due to lack of effective screening strategies, the majority (63%) of patients are diagnosed with ovarian cancer at advanced stages. New therapies are needed to address the unmet medical need of patients with ovarian cancer. 11-40% of ovarian cancers express NY-ESO-1 cancer testis/antigen. This study is evaluating affinity enhanced autologous NY-ESO-1c259T cells recognizing an NY-ESO-1 derived peptide complexed with HLA-A*02 in ovarian cancer. Methods: This single arm, open label clinical trial is evaluating safety and tolerability, antitumor activity (response rate by RECIST v1.1, progression free survival, overall survival, duration of response), and translational research endpoints. The study evaluates two lymphodepleting regimens: cyclophosphamide (enrolment completed; n = 7) and cyclophosphamide plus fludarabine (at least 10 subjects to be enrolled). Subjects must be ≥ 18 years old; HLA-A*02:01, *02:05, or *02:06 positive; have recurrent epithelial ovarian, primary peritoneal or fallopian tube carcinoma with refractory or platinum-resistant disease expressing NY-ESO-1 by IHC; have measurable disease; have ECOG status 0 or 1; and have adequate organ function. Following apheresis, the T cells are isolated and expanded with CD3/CD28 beads, transduced with a lentiviral vector containing the NY-ESO-1c259 TCR, and 1 – 6 × 109 transduced T cells are infused intravenously on Day 0 after lymphodepletion with fludarabine 30 mg/m2/day and cyclophosphamide 600 mg/m2/day on days -7 to -5. Response is assessed at weeks 4, 8, 12 and 24, and then every 3 months until confirmation of disease progression. Clinical trial information: NCT01567891

A semiparametric model for compositional data analysis in presence of covariates on the simplex

Compositional data, Markov chain Monte Carlo methods, posterior predictive distribution, semiparametric density estimation, 62A15, 62F15,

Systemic and local immunity following adoptive transfer of NY-ESO-1 SPEAR T cells in synovial sarcoma.

BackgroundGene-modified autologous T cells expressing NY-ESO-1c259, an affinity-enhanced T-cell receptor (TCR) reactive against the NY-ESO-1-specific HLA-A*02-restricted peptide SLLMWITQC (NY-ESO-1 SPEAR T-cells; GSK 794), have demonstrated clinical activity in patients with advanced synovial sarcoma (SS). The factors contributing to gene-modified T-cell expansion and the changes within the tumor microenvironment (TME) following T-cell infusion remain unclear. These studies address the immunological mechanisms of response and resistance in patients with SS treated with NY-ESO-1 SPEAR T-cells.MethodsFour cohorts were included to evaluate antigen expression and preconditioning on efficacy. Clinical responses were assessed by RECIST v1.1. Engineered T-cell persistence was determined by qPCR. Serum cytokines were evaluated by immunoassay. Transcriptomic analyses and immunohistochemistry were performed on tumor biopsies from patients before and after T-cell infusion. Gene-modified T-cells were detected within the TME via an RNAish assay.ResultsResponses across cohorts were affected by preconditioning and intra-tumoral NY-ESO-1 expression. Of the 42 patients reported (data cut-off 4June2018), 1 patient had a complete response, 14 patients had partial responses, 24 patients had stable disease, and 3 patients had progressive disease. The magnitude of gene-modified T-cell expansion shortly after infusion was associated with response in patients with high intra-tumoral NY-ESO-1 expression. Patients receiving a fludarabine-containing conditioning regimen experienced increases in serum IL-7 and IL-15. Prior to infusion, the TME exhibited minimal leukocyte infiltration; CD163+ tumor-associated macrophages (TAMs) were the dominant population. Modest increases in intra-tumoral leukocytes (≤5%) were observed in a subset of subjects at approximately 8 weeks. Beyond 8 weeks post infusion, the TME was minimally infiltrated with a TAM-dominant leukocyte infiltrate. Tumor-associated antigens and antigen presentation did not significantly change within the tumor post-T-cell infusion. Finally, NY-ESO-1 SPEAR T cells trafficked to the TME and maintained cytotoxicity in a subset of patients.ConclusionsOur studies elucidate some factors that underpin response and resistance to NY-ESO-1 SPEAR T-cell therapy. From these data, we conclude that a lymphodepletion regimen containing high doses of fludarabine and cyclophosphamide is necessary for SPEAR T-cell persistence and efficacy. Furthermore, these data demonstrate that non-T-cell inflamed tumors, which are resistant to PD-1/PD-L1 inhibitors, can be treated with adoptive T-cell based immunotherapy.Trial registrationClinicalTrials.gov, NCT01343043 , Registered 27 April 2011