A More Powerful Test For Three-Arm Non-inferiority Via Risk Difference: Frequentist and Bayesian approaches

Necessity for finding improved intervention in many legacy therapeutic areas are of high priority. This has the potential to decrease the expense of medical care and poor outcomes for many patients. Typically, clinical efficacy is the primary evaluating criteria to measure any beneficial effect of a treatment. Albeit, there could be situations when several other factors (e.g. side-effects, cost-burden, less debilitating, less intensive, etc.) which can permit some slightly less efficacious treatment options favorable to a subgroup of patients. This often leads to non-inferiority (NI) testing. NI trials may or may not include a placebo arm due to ethical reasons. However, when included, the resulting three-arm trial is more prudent since it requires less stringent assumptions compared to a two-arm placebo-free trial. In this article, we consider both Frequentist and Bayesian procedures for testing NI in the three-arm trial with binary outcomes when the functional of interest is risk difference. An improved Frequentist approach is proposed first, which is then followed by a Bayesian counterpart. Bayesian methods have a natural advantage in many active-control trials, including NI trial, as it can seamlessly integrate substantial prior information. In addition, we discuss sample size calculation and draw an interesting connection between the two paradigms

Proteogenomic analysis of carboplatin response in ovarian cancer cell lines and PDX models.

Non UBCUnreviewedAuthor affiliation: Icahn School of Medicine at Mount SinaiPostdoctora

Common Variance Fractional Factorial Designs for Model Comparisons

In designing a fractional factorial experiment, a class of models with some common parameters is considered for describing the data to be obtained from the experiment. The uncommon parameters of these models are to be estimated with the same variance as best as possible. Fractional factorial designs are obtained with the various variance structures in terms of their equalities. A special variance structure having the equal variances of the estimators of all uncommon parameters is the main theme of this thesis. In particular the 2-factor interaction effect is considered as the uncommon parameter in each model. Such plans with the ability of estimating the uncommon parameter with equal precision are called Common Variance (CV) designs. From the class of all CV designs for particular values of the number of factors m and the number of runs n designs giving smallest value of CV are obtained. Such designs are called Optimum CV designs. Both symmetric and asymmetric factorial experiments are considered with factors at two and three levels. Two series of CV designs are obtained for general 3^m factorial experiment with different number of runs. The common variance property is characterized for general fractional factorial designs. Several sufficient conditions are obtained using projection matrix and runs of the designs. The projection matrices of the series of CV designs for general m are investigated and a special structure of the projection matrix is presented for the CV designs including the optimum CV designs. Optimum CV designs are also presented for these two series for different m. CV designs are obtained with replicated runs. It is shown that a 3^2 CV design which is optimum in the class of all CV designs for n=6 remains CV after replicating any of its six runs any number of times. Several other 3^2 CV designs for n=6 are presented which satisfy this general replication property. Condition is derived for obtaining hierarchical CV designs for a general fractional factorial experiment. The determination of CV designs was also extended to a mixed level factorial experiment with factors at two and three levels. For a 2x3 factorial experiment CV designs exist only under a constraint of replications, for 2^mx3 and 2^mx3^3 factorial experiments designs are presented which give common variance within groups of similar structured interactions

Differential expression of single-cell RNA-seq data using Tweedie models

The performance of computational methods and software to identify differentially expressed features in single-cell RNA-sequencing (scRNA-seq) has been shown to be influenced by several factors, including the choice of the normalization method used and the choice of the experimental platform (or library preparation protocol) to profile gene expression in individual cells. Currently, it is up to the practitioner to choose the most appropriate differential expression (DE) method out of over 100 DE tools available to date, each relying on their own assumptions to model scRNA-seq expression features. To model the technological variability in cross-platform scRNA-seq data, here we propose to use Tweedie generalized linear models that can flexibly capture a large dynamic range of observed scRNA-seq expression profiles across experimental platforms induced by platform- and gene-specific statistical properties such as heavy tails, sparsity, and gene expression distributions. We also propose a zero-inflated Tweedie model that allows zero probability mass to exceed a traditional Tweedie distribution to model zero-inflated scRNA-seq data with excessive zero counts. Using both synthetic and published plate- and droplet-based scRNA-seq datasets, we perform a systematic benchmark evaluation of more than 10 representative DE methods and demonstrate that our method (Tweedieverse) outperforms the state-of-the-art DE approaches across experimental platforms in terms of statistical power and false discovery rate control. Our open-source software (R/Bioconductor package) is available at https://github.com/himelmallick/Tweedieverse

Controlling Charge Carrier Dynamics in Porphyrin Nanorings by Optically Active Templates

Understanding the dynamics of photogenerated charge carriers is essential for enhancing the performance of solar and optoelectronic devices. Using atomistic quantum dynamics simulations, we demonstrate that a short π-conjugated optically active template can be used to control hot carrier relaxation, charge carrier separation, and carrier recombination in light-harvesting porphyrin nanorings. Relaxation of hot holes is slowed by 60% with an optically active template compared to that with an analogous optically inactive template. Both systems exhibit subpicosecond electron transfer from the photoactive core to the templates. Notably, charge recombination is suppressed 6-fold by the optically active template. The atomistic time-domain simulations rationalize these effects by the extent of electron and hole localization, modification of the density of states, participation of distinct vibrational motions, and changes in quantum coherence. Extension of the hot carrier lifetime and reduction of charge carrier recombination, without hampering charge separation, demonstrate a strategy for enhancing efficiencies of energy materials with optically active templates

DAGBagM: learning directed acyclic graphs of mixed variables with an application to identify protein biomarkers for treatment response in ovarian cancer.

BackgroundApplying directed acyclic graph (DAG) models to proteogenomic data has been shown effective for detecting causal biomarkers of complex diseases. However, there remain unsolved challenges in DAG learning to jointly model binary clinical outcome variables and continuous biomarker measurements.ResultsIn this paper, we propose a new tool, DAGBagM, to learn DAGs with both continuous and binary nodes. By using appropriate models, DAGBagM allows for either continuous or binary nodes to be parent or child nodes. It employs a bootstrap aggregating strategy to reduce false positives in edge inference. At the same time, the aggregation procedure provides a flexible framework to robustly incorporate prior information on edges.ConclusionsThrough extensive simulation experiments, we demonstrate that DAGBagM has superior performance compared to alternative strategies for modeling mixed types of nodes. In addition, DAGBagM is computationally more efficient than two competing methods. When applying DAGBagM to proteogenomic datasets from ovarian cancer studies, we identify potential protein biomarkers for platinum refractory/resistant response in ovarian cancer. DAGBagM is made available as a github repository at https://github.com/jie108/dagbagM