Score tests for scale effects, with application to genomic analysis

Tests for variance or scale effects due to covariates are used in many areas and recently, in genomic and genetic association studies. We study score tests based on location-scale models with arbitrary error distributions that allow incorporation of additional adjustment covariates. Tests based on Gaussian and Laplacian double generalized linear models are examined in some detail. Numerical properties of the tests under Gaussian and other error distributions are examined. Our results show that the use of model-based asymptotic distributions with score tests for scale effects does not control type 1 error well in many settings of practical relevance. We consider simple statistics based on permutation distribution approximations, which correspond to well-known statistics derived by another approach. They are shown to give good type 1 error control under different error distributions and under covariate distribution imbalance. The methods are illustrated through a differential gene expression analysis involving breast cancer tumor samples.

Adaptive Seamless Design for Establishing Pharmacokinetic and Efficacy Equivalence in Developing Biosimilars

BACKGROUND

Recently, numerous pharmaceutical sponsors have expressed a great deal of interest in the development of biosimilars, which requires clinical trials to demonstrate that the pharmacokinetic (PK) and clinical efficacy are equivalent. Pharmacodynamics (PD) may be used in evaluating efficacy if there are relevant PD markers available. However, in their absence, it is necessary to design the associated clinical trials to include efficacy measures as the primary endpoint.

METHODS

In this study, we propose a novel adaptive seamless PK and efficacy design with an efficient framework to remedy the risk of misspecification of efficacy parameters and to discontinue the trial evaluating the efficacy for futility based on the PK evaluation. Here, we consider the clinical development of biosimilars including their evaluation in patients rather than healthy volunteers under a situation where both PK and efficacy parameters are required to demonstrate equivalence. The original idea of the proposed method was to organize a clinical trial that includes the statistical analysis of PK as an interim analysis, with sample size recalculation of the efficacy data.

RESULTS

Our simulation study indicated that the proposed design would allow trials to be more efficient than with the classical design.

CONCLUSIONS

This proposal provides appealing advantages, such as a shorter time period, additional cost savings, and a smaller number of patients required.

A Calibrated Power Prior Approach to Borrow Information from Historical Data with Application to Biosimilar Clinical Trials

A biosimilar refers to a follow-on biologic intended to be approved for marketing based on biosimilarity to an existing patented biological product (i.e., the reference product). To develop a biosimilar product, it is essential to demonstrate biosimilarity between the follow-on biologic and the reference product, typically through two-arm randomization trials. We propose a Bayesian adaptive design for trials to evaluate biosimilar products. To take advantage of the abundant historical data on the efficacy of the reference product that is typically available at the time a biosimilar product is developed, we propose the calibrated power prior, which allows our design to adaptively borrow information from the historical data according to the congruence between the historical data and the new data collected from the current trial. We propose a new measure, the Bayesian biosimilarity index, to measure the similarity between the biosimilar and the reference product. During the trial, we evaluate the Bayesian biosimilarity index in a group sequential fashion based on the accumulating interim data, and stop the trial early once there is enough information to conclude or reject the similarity. Extensive simulation studies show that the proposed design has higher power than traditional designs. We applied the proposed design to a biosimilar trial for treating rheumatoid arthritis.

Biosimilarity Assessments of Model IgG1-Fc Glycoforms Using a Machine Learning Approach

Biosimilarity assessments are performed to decide whether 2 preparations of complex biomolecules can be considered "highly similar." In this work, a machine learning approach is demonstrated as a mathematical tool for such assessments using a variety of analytical data sets. As proof-of-principle, physical stability data sets from 8 samples, 4 well-defined immunoglobulin G1-Fragment crystallizable glycoforms in 2 different formulations, were examined (see More et al., companion article in this issue). The data sets included triplicate measurements from 3 analytical methods across different pH and temperature conditions (2066 data features). Established machine learning techniques were used to determine whether the data sets contain sufficient discriminative power in this application. The support vector machine classifier identified the 8 distinct samples with high accuracy. For these data sets, there exists a minimum threshold in terms of information quality and volume to grant enough discriminative power. Generally, data from multiple analytical techniques, multiple pH conditions, and at least 200 representative features were required to achieve the highest discriminative accuracy. In addition to classification accuracy tests, various methods such as sample space visualization, similarity analysis based on Euclidean distance, and feature ranking by mutual information scores are demonstrated to display their effectiveness as modeling tools for biosimilarity assessments.