Jason J. Z. Liao

Sample size calculation for an agreement study

It is often necessary to compare two measurement methods in medicine and other experimental sciences. This problem covers a broad range of data. Many authors have explored ways of assessing the agreement of two sets of measurements. However, there has been relatively little attention to the problem of determining sample size for designing an agreement study. In this paper, a method using the interval approach for concordance is proposed to calculate sample size in conducting an agreement study. The philosophy behind this is that the concordance is satisfied when no more than the pre-specified k discordances are found for a reasonable large sample size n since it is much easier to define a discordance pair. The goal here is to find such a reasonable large sample size n. The sample size calculation is based on two rates: the discordance rate and tolerance probability, which in turn can be used to quantify an agreement study. The proposed approach is demonstrated through a real data set.

Comparability of critical quality attributes for establishing biosimilarity

To develop a biosimilar product, it is essential to demonstrate the biosimilarity between the proposed biosimilar product and the reference product first in terms of quality in a stepwise approach that can then help inform the extent of safety and efficacy data that will be required to establish biosimilarity. These comparability studies should have direct side-by-side comparisons of the test and the reference products. In this paper, we develop a statistical method for unpaired head-to-head quality attribute comparisons. The method uses a plausibility interval derived from comparing the reference against the reference itself as the goalpost for claiming comparability. The idea behind this is that any observed difference between the reference and the reference itself should be considered as the random noise and as a part of the variability. We illustrate the performance of the proposed method by using simulation and real data sets.

A new approach for outliers in a bioavailability/bioequivalence study.

It is well known that outliers can have a significant effect on the conclusion of a bioavailability/bioequivalence study. Existing approaches for outlier detection are ANOVA type based on the assumptions on log-AUC, and they are disconnected from the pharmacokinetics (PK) literature. However, the observations from a bioavailability/bioequivalence study are the correlated concentrations, not the AUCs. Thus, the estimate of AUC and the related variance estimate may not be accurate because of the exclusion of the correlation nature. In this paper, based on the predicted concentrations from a functional linear model which takes into consideration of the correlation structure of concentrations, a residual analysis is proposed to detect the outliers. With this approach, the distributional assumption is on the observed raw concentration instead of the summarized parameter AUC, and this approach takes the repeated measurements nature of the concentration curve into consideration, which is in line with population PK concept and could result in a more accurate variance estimate. A real data set is used to demonstrate the proposed approach.

An Improved Bland-Altman Method for Concordance Assessment

It is often necessary to compare two measurement methods in medicine and other experimental sciences. This problem covers a broad range of data with applications arising from many different fields. The Bland-Altman method has been a favorite method for concordance assessment. However, the Bland-Altman approach creates a problem of interpretation for many applications when a mixture of fixed bias, proportional bias and/or proportional error occurs. In this paper, an improved Bland-Altman method is proposed to handle more complicated scenarios in practice. This new approach includes Bland-Altmans approach as its special case. We evaluate concordance by defining an agreement interval for each individual paired observation and assessing the overall concordance. The proposed interval approach is very informative and offers many advantages over existing approaches. Data sets are used to demonstrate the advantages of the new method.

An insight into linear calibration: univariate case

In the linear controlled calibration literature, the classical least-squares estimator and the inverse estimator are the two main estimators. These two have different advantages and disadvantages. Investigation of these differences leads us to propose a class of weighted least-squares estimators that includes the classical, the inverse, and the orthogonal-regression approaches as special cases. Instead of pre-choosing the weight, a method is proposed to choose the optimal weight. An example is used to demonstrate the advantages of the new approach.

QUALIFYING ELISA DATA: COMBINING INFORMATION

With immunoassay or bioassay data, the assay standards often exhibit considerable inter-assay variability. However, the assay controls, which are used to monitor the assay performance and set acceptance criteria, should have no or less interassay variability. In this paper, we develop a mixed-effect calibration model for the assay controls to set new acceptance criteria and qualify the enzyme-linked immunosorbent assay (ELISA) data, which incorporates the interassay variation of assay standards and the nature of the assay controls, and overcomes the problems caused by traditional fixed-effect calibration model.

Biosimilarity for Follow-on Biologics

With dramatic increased spending on biologics and approaching patent expirations for existing biological products, there is a need to consolidate thinking on the regulatory approval pathway of biosimilars. However, biologics have much greater complexity by nature. The traditional paradigm currently used for generic chemical drugs, where bioequivalence is the focus, cannot be extrapolated to biologics. In the biosimilars scenario, the comparability of pharmacokinetic and pharmacodynamic parameters, and the comparability of efficacy and safety from clinical trials are the keys for the success of follow-on biologics. Developing sensitive bioanalytical methods to detect small, meaningful differences is critical. This article proposes a novel reference-scaled method to evaluate the comparability of pharmacokinetics parameters, and illustrates the method using a study comparing a test drug to a reference drug in a cancer study.

Transfer of Methods Supporting Biologics and Vaccines

The transfer of analytical methods supporting biologics and vaccines is complicated by the complexity and variability of biological systems. Many of these assays may be linked to clinical performance, and thus subject to specifications established from materials that were tested in the development laboratory. Thus, transfer must account for the risk that the method characteristics have changed, and may generate results for commercial lots that either earmarks a satisfactory lot as failing, or an unsatisfactory lot as passing specification. Transfer study strategies have been proposed based on method parameters or on tolerance intervals. This article describes a framework for establishing the equivalence between two laboratories with emphasis on the associated risks, and compares and contrasts the parametric and tolerance approaches.

Quantifying an Agreement Study

Abstract In medical and other related sciences, clinical or experimental measurements usually serve as a basis for diagnostic, prognostic, therapeutic, and performance evaluations. Examples can be assessing the reliability of multiple raters (or measurement methods), assessing the suitability for tumor evaluation of using a local laboratory or a central laboratory in a randomized clinical trial (RCT), validating surrogate endpoints in a study, determining that the important outcome measurements are interchangeable among the evaluators in an RCT. Any elegant study design cannot overcome the damage by unreliable measurement. Many methods have been developed to assess the agreement of two measurement methods. However, there is little attention to quantify how good the agreement of two measurement methods is. In this paper, similar to the type I error and the power in describing a hypothesis testing, we propose quantifying an agreement assessment using two rates: the discordance rate and the tolerance probability. This approach is demonstrated through examples.

Analyzing highly variable potency data using a linear mixed-effects measurement error model

A house standard lot is tested along with experimental samples in a variable TCID50 assay in order to monitor and control assay performance. Instead of being simply a control, it is proposed to use this lot as a calibration standard to reduce the systematic variability in the assay caused by acknowledged sources of variability such as the age of the cells used in the assay and interlaboratory differences. Because of this new proposal, the consistency of the relationship between the test sample and the house standard is assessed within the acceptance range of the house standard. A linear mixed-effects measurement error model is proposed for the data. The slope curve is then used to assess the dynamic relationship between the sample and the house standard within the house standard range. It is shown with these analyses that the sample and the house standard have uniformly good agreement within the house standard range.