Wald tests for variance-adjusted equivalence assessment with normal endpoints

Chen, Yue-Ming; Weng, Yu-Ting; Dong, Xiaoyu; Tsong, Yi

doi:10.1080/10543406.2016.1265542

Cited by 10 publications

(8 citation statements)

References 6 publications

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“…In this section, we describe the Howe and GCI methods for constructing confidence intervals on θ 1 and θ 2 . Chen et al and Dong et al recently developed Wald‐type and exact‐based confidence intervals, respectively, which are shown in the Appendix. Some of the theory used for developing these methods are briefly outlined for completeness.…”

Section: Notation and Statistical Testsmentioning

confidence: 99%

“…A detailed discussion on a statistical procedure for testing the effect size formulaton is in Burdick et al Another reformulation consists in rewriting the set of hypotheses in Equation as a linear combination of the parameters of interests. () These two reformulations will be denoted in this paper as effect size and linear formulations, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…The linear formulation was recently considered by Chen et al and Dong et al These authors focused primarily in testing the linear formulation based on test statistics. Alternatively, confidence intervals, derived from these test statistics, can be used for testing the linear formulation.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we investigate the performance of methods for constructing confidence intervals for the parameters of interest derived from the linear formulations using IUT procedures. One advantage of using confidence intervals is that it allows a direct comparison of the approaches proposed by Chen et al, Dong et al, and other previously developed methods for constructing bounds with well‐known statistical properties. Two such methods for constructing bounds for testing the linear formulation were previously developed by Howe and Weerahandi .…”

Section: Introductionmentioning

confidence: 99%

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A comparative study of confidence intervals to assess biosimilarity from analytical data

Quiróz

Montes

Shi

et al. 2019

Pharmaceutical Statistics

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Assessment of analytical similarity of tier 1 quality attributes is based on a set of hypotheses that tests the mean difference of reference and test products against a margin adjusted for standard deviation of the reference product. Thus, proper assessment of the biosimilarity hypothesis requires statistical tests that account for the uncertainty associated with the estimations of the mean differences and the standard deviation of the reference product. Recently, a linear reformulation of the biosimilarity hypothesis has been proposed, which facilitates development and implementation of statistical tests. These statistical tests account for the uncertainty in the estimation process of all the unknown parameters. In this paper, we survey methods for constructing confidence intervals for testing the linearized reformulation of the biosimilarity hypothesis and also compare the performance of the methods. We discuss test procedures using confidence intervals to make possible comparison among recently developed methods as well as other previously developed methods that have not been applied for demonstrating analytical similarity. A computer simulation study was conducted to compare the performance of the methods based on the ability to maintain the test size and power, as well as computational complexity. We demonstrate the methods using two example applications. At the end, we make recommendations concerning the use of the methods. KEYWORDSbiosimilarity, confidence interval, exact-based intervals, generalized confidence intervals, Howe's method, Wald-type confidence intervals 316 317 have no clinical impact. If this information is unavailable, the Food and Drug Administration (FDA) recommends using = f × R , where the constant f is a fixed multiplier, and R is the variability of RP. The value of f = 1.5 is justified using power-based calculations as rationalized in Tsong et al 1 and Chow. 2 In this case, the set of hypotheses in Equation 1 becomes:( 2) Currently, analytical similarity is demonstrated using a two one-sided tests (TOST) of the hypotheses in Equation 2. The TOST is conducted by computing a 100(1 − 2 )% confidence interval on the difference T − R , and equivalence is demonstrated if the entire confidence interval falls within the range from −1.5 R to 1.5 R . In this setting, is the test size. However, in practice, R is unknown and is replaced in the EAC with a sample estimate. This approach does not account for the uncertainty in the estimation of R and is not recommended because it inflates the type I error and reduces the powers of the TOST. 3 Alternative approaches have been explored to address this issue.Two reformulations of the set of hypotheses in Equation 2 that facilitate development and implementation of statistical procedures to account for the uncertainty in the estimations of all the parameters, and in particular R , have been recently proposed in the literature. Burdick et al 4 reformulated the set of hypotheses in Equation 2 as effect size by dividing both sides of the inequalities by R...

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Section: Notation and Statistical Testsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A comparative study of confidence intervals to assess biosimilarity from analytical data

Quiróz

Montes

Shi

et al. 2019

Pharmaceutical Statistics

View full text Add to dashboard Cite

show abstract

“…Since this analysis inflates both the probability of rejecting H 0 under H a (Type I error rate) and the probability of failing to reject H 0 under H a (type II error rate), we recommend that the margin be estimated with the data and Wald method [18] be used for hypothesis test in …”

Section: Equivalence Test For Comparing Means Obtained In Two Laboratmentioning

confidence: 99%

Design and Statistical Analysis of Method Transfer Studies for Biotechnology Products

Shen

2017

Bioanalysis

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During biotechnology product development, the analytical instrumentation and methodology are often carefully selected based on the intended purpose and the scope of the analytical method. Specifically, parameters such as specificity, linearity, limit of detection (LOD) and limit of qualification (LOQ), range, accuracy and precision are evaluated [1]. After an analytical method is successfully validated and implemented, the updates of the method with the standard operating procedure will be conducted during the life-cycle of the product. The life-cycle management of analytical methods includes, but is not limited to, the following:• trend analyses of the method performance at regular intervals;• evaluation of the need to optimize the analytical method by requalification or even revalidation of all or a part of the analytical procedure due to any proposed changes (e.g., critical raw material supplier change);• development and validation of a new or an alternative analytical method for a new impurity;• transferring a validated analytical method from a sending laboratory to a testing laboratory.A method transfer is common practice during the life-cycle management of pharmaceutical products. Since the analytical method to be transferred has been already thoroughly evaluated and fully validated for its intended purpose at the sending laboratory, the main purpose of method transfer studies is usually the qualification to evaluate if the two laboratories generate comparable results across the parameter ranges of interest, and to assure that the method after the transfer is still suitable for its intended use.While the US FDA guidance to industry [1,2] and further publications [3][4][5][6][7][8][9][10][11][12][13][14][15] discuss the general principles for the design, analysis and evaluation of method transfer studies, they do not explicitly specify the acceptance criteria by which assay transfers are considered acceptable. Although these general principles apply to the majority of assay transfer studies, depending on the type and the intended purpose of the assays, different statistical analysis methods have been proposed to suit specific needs. Below, we briefly summarize published proposals with regard to testing materials, study [6,7]. Wieling [6] proposed to split the sample into two subsets. Lin et al. [7] recommended that bioanalytical methods use the incurred samples or a set of QC samples prepared to cover the low, medium and high concentrations in both laboratories. Samples of commercial production batches are recommended to be used [5,[8][9][10][11][12][13] as testing materials. Samples from at least one batch are recommended to be tested [4,[9][10][11][12].With regard to the study designs, method transfer studies should evaluate the impact of factors including analysts, days, number of runs, equipment (brand and/or model), environment and reagent suppliers on the results obtained in the sending and receiving laboratories, using the same testing materials. Wieling proposed to keep the methods, chemicals, rea...

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Sample Size Consideration for Equivalent Test of Tier-1 Quality Attributes for Analytical Biosimilarity Assessment

Wang

Tsong

Shen

2019

Springer Proceedings in Mathematics &Amp; Statistics

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Wald tests for variance-adjusted equivalence assessment with normal endpoints

Cited by 10 publications

References 6 publications

A comparative study of confidence intervals to assess biosimilarity from analytical data

A comparative study of confidence intervals to assess biosimilarity from analytical data

Design and Statistical Analysis of Method Transfer Studies for Biotechnology Products

Sample Size Consideration for Equivalent Test of Tier-1 Quality Attributes for Analytical Biosimilarity Assessment

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