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Hu C. Variability and uncertainty: interpretation and usage of pharmacometric simulations and intervals. J Pharmacokinet Pharmacodyn 2022; 49:487-491. [PMID: 35927373 DOI: 10.1007/s10928-022-09817-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2022] [Accepted: 06/27/2022] [Indexed: 10/16/2022]
Abstract
Variability and estimation uncertainty are important sources of variation in pharmacometric simulations. Different combinations of uncertainty and the variability components lead to a variety types of simulation intervals, and many realized and unrealized confusions exist among pharmacometricians on their interpretation and usage. This commentary aims to clarify some of the important underlying concepts and provide a convenient guideline on pharmacometric simulation conduct and interpretation.
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Affiliation(s)
- Chuanpu Hu
- Clinical Pharmacology and pharmacometrics, Janssen Research & Development, LLC, 1400 McKean Road, 19477, Spring House, PA, PO Box 776, USA.
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2
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Zhang T, Larson R, Dave K, Polson N, Zhang H. Developing patient-centric specifications for autologous chimeric antigen receptor T cell therapies. CURRENT OPINION IN BIOMEDICAL ENGINEERING 2021. [DOI: 10.1016/j.cobme.2021.100328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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3
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Guo B, Wang W, Wu Z, He X, Zhu Y. Exact equal-tailed β-expectation tolerance intervals for sample variances. COMMUN STAT-SIMUL C 2021. [DOI: 10.1080/03610918.2021.1986527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Baocai Guo
- School of Statistics, Zhejiang Gongshang University, Hangzhou, P.R. China
| | - Wei Wang
- School of Statistics, Zhejiang Gongshang University, Hangzhou, P.R. China
| | - Zhichao Wu
- School of Statistics, Zhejiang Gongshang University, Hangzhou, P.R. China
| | - Xixiang He
- School of Statistics, Zhejiang Gongshang University, Hangzhou, P.R. China
| | - Yumei Zhu
- School of Statistics, Zhejiang Gongshang University, Hangzhou, P.R. China
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4
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Chiang C, Hsiao CF. Tolerance interval testing for assessing accuracy and precision simultaneously. PLoS One 2021; 16:e0246642. [PMID: 33544743 PMCID: PMC7864420 DOI: 10.1371/journal.pone.0246642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 01/22/2021] [Indexed: 11/30/2022] Open
Abstract
Tolerance intervals have been recommended for simultaneously validating both the accuracy and precision of an analytical procedure. However, statistical inferences for the corresponding hypothesis testing are scarce. The aim of this study is to establish a whole statistical inference for tolerance interval testing, including sample size determination, power analysis, and calculation of p-value. More specifically, the proposed method considers the bounds of a tolerance interval as random variables so that a bivariate distribution can be derived. Simulations confirm the theoretical properties of the method. Furthermore, an example is used to illustrate the proposed method.
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Affiliation(s)
- Chieh Chiang
- Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan
| | - Chin-Fu Hsiao
- Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan
- * E-mail:
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5
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Chiang C, Chen CT, Hsiao CF. Use of a two-sided tolerance interval in the design and evaluation of biosimilarity in clinical studies. Pharm Stat 2020; 20:175-184. [PMID: 32869921 DOI: 10.1002/pst.2065] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2019] [Revised: 06/30/2020] [Accepted: 08/11/2020] [Indexed: 11/06/2022]
Abstract
In assessing biosimilarity between two products, the question to ask is always "How similar is similar?" Traditionally, the equivalence of the means between products is the primary consideration in a clinical trial. This study suggests an alternative assessment for testing a certain percentage of the population of differences lying within a prespecified interval. In doing so, the accuracy and precision are assessed simultaneously by judging whether a two-sided tolerance interval falls within a prespecified acceptance range. We further derive an asymptotic distribution of the tolerance limits to determine the sample size for achieving a targeted level of power. Our numerical study shows that the proposed two-sided tolerance interval test controls the type I error rate and provides sufficient power. A real example is presented to illustrate our proposed approach.
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Affiliation(s)
- Chieh Chiang
- Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan
| | | | - Chin-Fu Hsiao
- Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan
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6
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Zou Y, Young DS. Improving coverage probabilities for parametric tolerance intervals via bootstrap calibration. Stat Med 2020; 39:2152-2166. [PMID: 32249974 DOI: 10.1002/sim.8537] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Revised: 02/27/2020] [Accepted: 03/09/2020] [Indexed: 11/10/2022]
Abstract
Statistical tolerance intervals are commonly employed in biomedical and pharmaceutical research, such as in lifetime analysis, the assessment of biosimilarity of branded and generic versions of biopharmaceutical drugs, and in quality control of drug products to ensure that a specified proportion of the products are covered within established acceptance limits. Exact two-sided parametric tolerance intervals are only available for the normal distribution, while exact one-sided parametric tolerance limits are available for a limited number of distributions. Approximations to two-sided parametric tolerance intervals often use the Bonferroni correction on the one-sided tolerance interval calculation; however, this often incurs a higher coverage probability than the nominal level. Recently, the usage of a bootstrap calibration has been demonstrated as a way to improve coverage probabilities of tolerance intervals for very specific and complex distributional settings. We present a focused treatment on using a single-layer bootstrap calibration to improve the coverage probabilities of two-sided parametric tolerance intervals. Simulation results clearly demonstrate the improved coverage probabilities towards the nominal level over the uncalibrated setting. Applications to medical data for various parametric distributions also highlight the utility of constructing these calibrated tolerance intervals.
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Affiliation(s)
- Yixuan Zou
- Department of Statistics, University of Kentucky, Lexington, Kentucky, USA
| | - Derek S Young
- Department of Statistics, University of Kentucky, Lexington, Kentucky, USA
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Francq BG, Lin D, Hoyer W. Confidence and Prediction in Linear Mixed Models: Do Not Concatenate the Random Effects. Application in an Assay Qualification Study. Stat Biopharm Res 2020. [DOI: 10.1080/19466315.2020.1776762] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Affiliation(s)
| | - Dan Lin
- Pre-Clinical & Research – Biostatistics and Statistical Programming, GSK, Rixensart, Belgium
| | - Walter Hoyer
- TRD – CMC Statistical Sciences, GSK, Marburg, Germany
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Francq BG, Lin D, Hoyer W. Confidence, prediction, and tolerance in linear mixed models. Stat Med 2019; 38:5603-5622. [PMID: 31659784 PMCID: PMC6916346 DOI: 10.1002/sim.8386] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Revised: 08/05/2019] [Accepted: 09/13/2019] [Indexed: 11/15/2022]
Abstract
The literature about Prediction Interval (PI) and Tolerance Interval (TI) in linear mixed models is usually developed for specific designs, which is a main limitation to their use. This paper proposes to reformulate the two‐sided PI to be generalizable under a wide variety of designs (one random factor, nested and crossed designs for multiple random factors, and balanced or unbalanced designs). This new methodology is based on the Hessian matrix, namely, the inverse of (observed) Fisher Information matrix, and is built with a cell mean model. The degrees of freedom for the total variance are calculated with the generalized Satterthwaite method and compared to the Kenward‐Roger's degrees of freedom for fixed effects. Construction of two‐sided TIs are also detailed with one random factor, and two nested and two crossed random variables. An extensive simulation study is carried out to compare the widths and coverage probabilities of Confidence Intervals (CI), PIs, and TIs to their nominal levels. It shows excellent coverage whatever the design and the sample size are. Finally, these CIs, PIs, and TIs are applied to two real data sets: one from orthopedic surgery study (intralesional resection risk) and the other from assay validation study during vaccine development.
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Affiliation(s)
| | - Dan Lin
- Pre-Clinical & Research - Biostatistics and Statistical Programming, GSK, Rixensart, Belgium
| | - Walter Hoyer
- TRD - CMC Statistical Sciences, GSK, Marburg, Germany
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Mockus L, Reklaitis G, Morris K, LeBlond D. Risk-Based Approach to Lot Release. J Pharm Sci 2019; 109:1035-1042. [PMID: 31610180 DOI: 10.1016/j.xphs.2019.10.011] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2018] [Revised: 09/28/2019] [Accepted: 10/04/2019] [Indexed: 11/20/2022]
Abstract
In this work, a novel risk-based methodology for lot release is proposed. Its objective is to assess the risk that a lot declared to have passed truly meets product specifications. The methodology consists of 3 parts: adaptive sample size determination, estimation of the probability that the product was within specifications, and the lot-release decision. The methodology provides a probabilistic statement about the true quality of the batch. Having a probability estimate is the essential condition of risk-based decision-making. We demonstrate the proposed methodology on experimental data generated from 17 immediate-release solid oral drug products from a number of different manufacturers with 5 to 10 lots per manufacturer.
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Affiliation(s)
- Linas Mockus
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana 47907-2100.
| | - Gintaras Reklaitis
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana 47907-2100
| | - Kenneth Morris
- The Arnold and Marie Schwartz College of Pharmacy, Long Island University, Brooklyn, New York 11201-8423
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Pawar P, Talwar S, Reddy D, Bandi CK, Wu H, Sowrirajan K, Friedman R, Drazer G, Drennen JK, Muzzio FJ. A "Large-N" Content Uniformity Process Analytical Technology (PAT) Method for Phenytoin Sodium Tablets. J Pharm Sci 2018; 108:494-505. [PMID: 30009795 DOI: 10.1016/j.xphs.2018.06.031] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2017] [Revised: 06/14/2018] [Accepted: 06/20/2018] [Indexed: 10/28/2022]
Abstract
Accurate assessment of tablet content uniformity is critical for narrow therapeutic index drugs such as phenytoin sodium. This work presents a near-infrared (NIR)-based analytical method for rapid prediction of content uniformity based on a large number of phenytoin sodium formulation tablets. Calibration tablets were generated through an integrated experimental design by varying formulation and process parameters, and scale of manufacturing. A partial least squares model for individual tablet content was developed based on tablet NIR spectra. The tablet content was obtained from a modified United States Pharmacopeia phenytoin sodium high-performance liquid chromatography assay method. The partial least squares model with 4 latent variables explained 92% of the composition variability and yielded a root mean square error of prediction of 0.48% w/w. The resultant NIR model successfully assayed the composition of tablets manufactured at the pilot scale. For one such batch, bootstrapping was applied to calculate the confidence intervals on the mean, acceptance value, and relative SD for different sample sizes, n = 10, 30, and 100. As the bootstrap sample size increased, the confidence interval on the mean, acceptance value, and relative SD became narrower and symmetric. Such a 'large N' NIR-based process analytical technology method can increase reliability of quality assessments in solid dosage manufacturing.
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Affiliation(s)
- Pallavi Pawar
- Department of Chemical and Biochemical Engineering, Rutgers University, Piscataway, New Jersey 08854
| | - Sameer Talwar
- Graduate School of Pharmaceutical Sciences, Duquesne University, Pittsburgh, Pennsylvania 15282
| | - Dheerja Reddy
- Department of Chemical and Biochemical Engineering, Rutgers University, Piscataway, New Jersey 08854
| | - Chandra Kanth Bandi
- Department of Chemical and Biochemical Engineering, Rutgers University, Piscataway, New Jersey 08854
| | - Huiquan Wu
- Division of Product Quality Research, Office of Testing and Research, Office of Pharmaceutical Quality, CDER, FDA, Silver Spring, Maryland 20993.
| | - Koushik Sowrirajan
- Division of Product Quality Research, Office of Testing and Research, Office of Pharmaceutical Quality, CDER, FDA, Silver Spring, Maryland 20993
| | - Rick Friedman
- Office of Manufacturing Quality, Office of Compliance, CDER, FDA, Silver Spring, Maryland 20993
| | - German Drazer
- Mechanical and Aerospace Engineering, Rutgers University, Piscataway, New Jersey 08854
| | - James K Drennen
- Graduate School of Pharmaceutical Sciences, Duquesne University, Pittsburgh, Pennsylvania 15282
| | - Fernando J Muzzio
- Department of Chemical and Biochemical Engineering, Rutgers University, Piscataway, New Jersey 08854.
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11
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Novick S, Hudson-Curtis B. Content uniformity testing for stratified samples via parametric tolerance interval testing. J Biopharm Stat 2017; 28:463-474. [PMID: 28422566 DOI: 10.1080/10543406.2017.1321005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Historically in the biopharmaceutical setting, USP<905> has been used to establish that a batch of drug product has acceptable content uniformity. More recently, alternative approaches such as the two one-sided parametric tolerance interval test (PTI-TOST) have been proposed to establish content uniformity. Traditionally, the PTI-TOST is implemented as a sequential, two-tiered test, under the generally accepted assumption that the data are independently and identically distributed. Since the material is sequenced through the manufacturing process over a period of time, there are conceptually arguable locations within each batch, for instance: beginning, middle, and end. In such a situation, a practitioner may wish to evaluate potential effects of these batch locations, for example, during process validation. If location (stratified) differences exist within the batch and if multiple samples are taken from each location, significant within-location correlations may be induced in the data. In such a case, the traditional PTI-TOST underestimates the total variability, thereby improperly boosting the power of the test method. When there is reason to believe that location variances exist, the batch may be evaluated using stratified sampling, and the location effect may be modeled. In this paper, a two-tiered PTI-TOST that accounts for both between-location and within-location variance components is introduced. Operating characteristic curves and practical advice are given to aid the practitioner's uptake of the proposed method.
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Affiliation(s)
- Steven Novick
- a Director, Statistical Sciences , MedImmune, Gaithersburg , MD , USA
| | - Buffy Hudson-Curtis
- b Manager of Statistics, GMS Technical, GlaxoSmithKline , Zebulon , NC , USA
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Chen YM, Weng YT, Dong X, Tsong Y. Wald tests for variance-adjusted equivalence assessment with normal endpoints. J Biopharm Stat 2016; 27:308-316. [PMID: 27906607 DOI: 10.1080/10543406.2016.1265542] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
Equivalence tests may be tested with mean difference against a margin adjusted for variance. The justification of using variance adjusted non-inferiority or equivalence margin is for the consideration that a larger margin should be used with large measurement variability. However, under the null hypothesis, the test statistic does not follow a t-distribution or any well-known distribution even when the measurement is normally distributed. In this study, we investigate asymptotic tests for testing the equivalence hypothesis. We apply the Wald test statistic and construct three Wald tests that differ in their estimates of variances. These estimates of variances include the maximum likelihood estimate (MLE), the uniformly minimum variance unbiased estimate (UMVUE), and the constrained maximum likelihood estimate (CMLE). We evaluate the performance of these three tests in terms of type I error rate control and power using simulations under a variety of settings. Our empirical results show that the asymptotic normalized tests are conservative in most settings, while the Wald tests based on ML- and UMVU-method could produce inflated significance levels when group sizes are unequal. However, the Wald test based on CML-method provides an improvement in power over the other two Wald tests for medium and small sample size studies.
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Affiliation(s)
- Yue-Ming Chen
- a Department of Biostatistics , The University of Texas School of Public Health , Houston , Texas , USA
| | - Yu-Ting Weng
- b Division of Biometrics VI, Office of Biostatistics, CDER, FDA , Silver Spring , Maryland , USA
| | - Xiaoyu Dong
- b Division of Biometrics VI, Office of Biostatistics, CDER, FDA , Silver Spring , Maryland , USA
| | - Yi Tsong
- b Division of Biometrics VI, Office of Biostatistics, CDER, FDA , Silver Spring , Maryland , USA
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Tsong Y, Dong X, Shen M, Lostritto RT. Quality assurance test of delivered dose uniformity of multiple-dose inhaler and dry powder inhaler drug products. J Biopharm Stat 2015; 25:328-38. [PMID: 25357132 DOI: 10.1080/10543406.2014.972510] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
The delivered dose uniformity is one of the most critical requirements for dry powder inhaler (DPI) and metered dose inhaler products. In 1999, the Food and Drug Administration (FDA) issued a Draft Guidance entitled Nasal Spray and Inhalation Solution, Suspension, and Spray Drug Products-Chemistry, Manufacturing and Controls Documentation and recommended a two-tier acceptance sampling plan that is a modification of the United States Pharmacopeia (USP) sampling plan of dose content uniformity (USP34<601>). This sampling acceptance plan is also applied to metered dose inhaler (MDI) and DPI drug products in general. The FDA Draft Guidance method is shown to have a near-zero probability of acceptance at the second tier. In 2000, under the request of The International Pharmaceutical Aerosol Consortium, the FDA developed a two-tier sampling acceptance plan based on two one-sided tolerance intervals (TOSTIs) for a small sample. The procedure was presented in the 2005 Advisory Committee Meeting of Pharmaceutical Science and later published in the Journal of Biopharmaceutical Statistics (Tsong et al., 2008). This proposed procedure controls the probability of the product delivering below a pre-specified effective dose and the probability of the product delivering over a pre-specified safety dose. In this article, we further propose an extension of the TOSTI procedure to single-tier procedure with any number of canisters.
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Affiliation(s)
- Yi Tsong
- a Office of Biostatistics/Office of Translational Sciences , Center for Drug Evaluation and Research, U.S. Food and Drug Administration , Silver Spring , Maryland , USA
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