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1.
J Biopharm Stat ; : 1-13, 2019 Nov 10.
Artigo em Inglês | MEDLINE | ID: mdl-31707908

RESUMO

In medical product development, there has been an increased interest in utilizing real-world data which have become abundant with recent advances in biomedical science, information technology, and engineering. High-quality real-world data may be analyzed to generate real-world evidence that can be utilized in the regulatory and healthcare decision-making. In this paper, we consider the case in which a single-arm clinical study, viewed as the primary data source, is supplemented with patients from a real-world data source containing both clinical outcome and covariate data at the patient-level. Propensity score methodology is used to identify real-world data patients that are similar to those in the single-arm study in terms of the baseline characteristics, and to stratify these patients into strata based on the proximity of the propensity scores. In each stratum, a composite likelihood function of a parameter of interest is constructed by down-weighting the information from the real-world data source, and an estimate of the stratum-specific parameter is obtained by maximizing the composite likelihood function. These stratum-specific estimates are then combined to obtain an overall population-level estimate of the parameter of interest. The performance of the proposed approach is evaluated via a simulation study. A hypothetical example based on our experience is provided to illustrate the implementation of the proposed approach.

2.
J Biopharm Stat ; 29(5): 749-759, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31590626

RESUMO

A question that routinely arises in medical device clinical studies is the homogeneity across demographic subgroups, geographical regions, or investigational sites of the enrolled patients in terms of treatment effects or outcome variables. The main objective of this paper is to discuss statistical concepts and methods for the assessment of such homogeneity and to provide the practitioner a statistical framework and points to consider in conducting homogeneity assessment. Demographic subgroups, geographical regions, and investigational sites are discussed separately as each has its unique issues. Specific considerations are also given to randomized controlled trials, non-randomized comparative studies, and single-arm studies. We point out that judicious use of statistical methods, in conjunction with sound clinical judgment, is essential in handling the issue of homogeneity of treatment effect in medical device clinical studies.

3.
J Biopharm Stat ; 29(5): 731-748, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31530111

RESUMO

We are now at an amazing time for medical product development in drugs, biological products and medical devices. As a result of dramatic recent advances in biomedical science, information technology and engineering, ``big data'' from health care in the real-world have become available. Although big data may not necessarily be attuned to provide the preponderance of evidence to a clinical study, high-quality real-world data can be transformed into scientific evidence for regulatory and healthcare decision-making using proven analytical methods and techniques, such as propensity score methodology and Bayesian inference. In this paper, we extend the Bayesian power prior approach for a single-arm study (the current study) to leverage external real-world data. We use propensity score methodology to pre-select a subset of real-world data containing patients that are similar to those in the current study in terms of covariates, and to stratify the selected patients together with those in the current study into more homogeneous strata. The power prior approach is then applied in each stratum to obtain stratum-specific posterior distributions, which are combined to complete the Bayesian inference for the parameters of interest. We evaluate the performance of the proposed method as compared to that of the ordinary power prior approach by simulation and illustrate its implementation using a hypothetical example, based on our regulatory review experience.

4.
J Biopharm Stat ; 29(4): 580-591, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31257999

RESUMO

Observational (non-randomized) comparative studies have been adopted in the pre-market safety/effectiveness evaluation of medical devices. There has been an increased interest in utilizing this design with the growing available real-world data. However, in such studies, biases that are introduced in every stage and aspect of study need to be addressed. Otherwise, the objectivity of study design and validity of study results will be compromised. In this paper, challenges and opportunities are discussed from the regulatory perspective. Considerations and good statistical practice to mitigate the potential bias are presented.

6.
J Biopharm Stat ; 26(6): 1136-1145, 2016.
Artigo em Inglês | MEDLINE | ID: mdl-27540636

RESUMO

Regulatory decisions are made based on the assessment of risk and benefit of medical devices at the time of pre-market approval and subsequently, when post-market risk-benefit balance needs reevaluation. Such assessments depend on scientific evidence obtained from pre-market studies, post-approval studies, post-market surveillance studies, patient perspective information, as well as other real world data such as national and international registries. Such registries provide real world evidence and are playing a more and more important role in enhancing the safety and effectiveness evaluation of medical devices. While these registries provide large quantities of data reflecting real world practice and can potentially reduce the cost of clinical trials, challenges arise concerning (1) data quality adequate for regulatory decision-making, (2) bias introduced at every stage and aspect of study, (3) scientific validity of study designs, and (4) reliability and interpretability of study results. This article will discuss related statistical and regulatory challenges and opportunities with examples encountered in medical device regulatory reviews.


Assuntos
Aprovação de Equipamentos , Regulamentação Governamental , Sistema de Registros , Viés , Confiabilidade dos Dados , Tomada de Decisões , Humanos , Reprodutibilidade dos Testes , Projetos de Pesquisa , Medição de Risco
7.
J Biopharm Stat ; 26(6): 1067-1077, 2016.
Artigo em Inglês | MEDLINE | ID: mdl-27541859

RESUMO

Due to rapid technological development, innovations in diagnostic devices are proceeding at an extremely fast pace. Accordingly, the needs for adopting innovative statistical methods have emerged in the evaluation of diagnostic devices. Statisticians in the Center for Devices and Radiological Health at the Food and Drug Administration have provided leadership in implementing statistical innovations. The innovations discussed in this article include: the adoption of bootstrap and Jackknife methods, the implementation of appropriate multiple reader multiple case study design, the application of robustness analyses for missing data, and the development of study designs and data analyses for companion diagnostics.


Assuntos
Técnicas e Procedimentos Diagnósticos/instrumentação , Equipamentos e Provisões , Humanos , Projetos de Pesquisa , Estatística como Assunto , Estados Unidos , United States Food and Drug Administration
9.
J Biopharm Stat ; 26(1): 3-16, 2016.
Artigo em Inglês | MEDLINE | ID: mdl-26372890

RESUMO

The world of medical devices while highly diverse is extremely innovative, and this facilitates the adoption of innovative statistical techniques. Statisticians in the Center for Devices and Radiological Health (CDRH) at the Food and Drug Administration (FDA) have provided leadership in implementing statistical innovations. The innovations discussed include: the incorporation of Bayesian methods in clinical trials, adaptive designs, the use and development of propensity score methodology in the design and analysis of non-randomized observational studies, the use of tipping-point analysis for missing data, techniques for diagnostic test evaluation, bridging studies for companion diagnostic tests, quantitative benefit-risk decisions, and patient preference studies.


Assuntos
Interpretação Estatística de Dados , Equipamentos e Provisões/estatística & dados numéricos , Teorema de Bayes , Ensaios Clínicos como Assunto/estatística & dados numéricos , Aprovação de Equipamentos , Aprovação de Teste para Diagnóstico , Humanos , Estudos Observacionais como Assunto/estatística & dados numéricos , Pontuação de Propensão , Projetos de Pesquisa , Medição de Risco , Estados Unidos , United States Food and Drug Administration
10.
J Biopharm Stat ; 24(5): 994-1010, 2014.
Artigo em Inglês | MEDLINE | ID: mdl-25013971

RESUMO

Due to the special nature of medical device clinical studies, observational (nonrandomized) comparative studies play important roles in the premarket safety/effectiveness evaluation of medical devices. While historical data collected in earlier investigational device exemption studies of a previously approved medical device have been used to form control groups in comparative studies, high-quality registry data are emerging to provide opportunities for the premarket evaluation of new devices. However, in such studies, various biases could be introduced in every stage and aspect of study and may compromise the objectivity of study design and validity of study results. In this article, challenges and opportunities in the design of such studies using propensity score methodology are discussed from regulatory perspectives.


Assuntos
Aprovação de Equipamentos , Equipamentos e Provisões , Estudos Observacionais como Assunto/métodos , Projetos de Pesquisa , Grupos Controle , Aprovação de Equipamentos/normas , Humanos , Estudos Observacionais como Assunto/estatística & dados numéricos , Pontuação de Propensão , Tamanho da Amostra , Amostragem , Resultado do Tratamento
12.
J Biopharm Stat ; 23(1): 110-21, 2013.
Artigo em Inglês | MEDLINE | ID: mdl-23331225

RESUMO

While randomized, well-controlled, clinical trials have been viewed as the gold standard in the evaluation of medical products, including drugs, biological products, and medical devices, it is not uncommon for safety assessment to be performed using observational studies, for ethical or practical reasons. In observational studies, various biases could be introduced in every stage and aspect of study, and consequently the resulting statistical inference may carry a lower level of scientific assurance, compared to randomized trials. To ensure the objectivity of study design and interpretability of the results, it is critical to address the challenges of such studies. In this paper, we share regulatory considerations on the prospective design of observational studies to address safety issues using propensity score methodology.


Assuntos
Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos , Segurança de Equipamentos , Pontuação de Propensão , Aprovação de Equipamentos/legislação & jurisprudência , Aprovação de Equipamentos/normas , Segurança de Equipamentos/normas , Humanos , Ensaios Clínicos Controlados Aleatórios como Assunto/legislação & jurisprudência , Ensaios Clínicos Controlados Aleatórios como Assunto/normas , Projetos de Pesquisa/legislação & jurisprudência , Projetos de Pesquisa/normas
13.
J Biopharm Stat ; 22(6): 1272-9, 2012.
Artigo em Inglês | MEDLINE | ID: mdl-23075022

RESUMO

In the evaluation of medical products, including drugs, biological products, and medical devices, comparative observational studies could play an important role when properly conducted randomized, well-controlled clinical trials are infeasible due to ethical or practical reasons. However, various biases could be introduced at every stage and into every aspect of the observational study, and consequently the interpretation of the resulting statistical inference would be of concern. While there do exist statistical techniques for addressing some of the challenging issues, often based on propensity score methodology, these statistical tools probably have not been as widely employed in prospectively designing observational studies as they should be. There are also times when they are implemented in an unscientific manner, such as performing propensity score model selection for a dataset involving outcome data in the same dataset, so that the integrity of observational study design and the interpretability of outcome analysis results could be compromised. In this paper, regulatory considerations on prospective study design using propensity scores are shared and illustrated with hypothetical examples.


Assuntos
Viés , Pesquisa Comparativa da Efetividade/estatística & dados numéricos , Modelos Estatísticos , Pontuação de Propensão , Projetos de Pesquisa , Pesquisa Comparativa da Efetividade/métodos , Pesquisa Comparativa da Efetividade/normas , Interpretação Estatística de Dados , Aprovação de Equipamentos/normas , Regulamentação Governamental , Humanos , Projetos de Pesquisa/legislação & jurisprudência , Projetos de Pesquisa/estatística & dados numéricos , Resultado do Tratamento , Estados Unidos , United States Food and Drug Administration
14.
Endocrinology ; 152(2): 414-23, 2011 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-21209021

RESUMO

The euglycemic glucose clamp is the reference method for assessing insulin sensitivity in humans and animals. However, clamps are ill-suited for large studies because of extensive requirements for cost, time, labor, and technical expertise. Simple surrogate indexes of insulin sensitivity/resistance including quantitative insulin-sensitivity check index (QUICKI) and homeostasis model assessment (HOMA) have been developed and validated in humans. However, validation studies of QUICKI and HOMA in both rats and mice suggest that differences in metabolic physiology between rodents and humans limit their value in rodents. Rhesus monkeys are a species more similar to humans than rodents. Therefore, in the present study, we evaluated data from 199 glucose clamp studies obtained from a large cohort of 86 monkeys with a broad range of insulin sensitivity. Data were used to evaluate simple surrogate indexes of insulin sensitivity/resistance (QUICKI, HOMA, Log HOMA, 1/HOMA, and 1/Fasting insulin) with respect to linear regression, predictive accuracy using a calibration model, and diagnostic performance using receiver operating characteristic. Most surrogates had modest linear correlations with SI(Clamp) (r ≈ 0.4-0.64) with comparable correlation coefficients. Predictive accuracy determined by calibration model analysis demonstrated better predictive accuracy of QUICKI than HOMA and Log HOMA. Receiver operating characteristic analysis showed equivalent sensitivity and specificity of most surrogate indexes to detect insulin resistance. Thus, unlike in rodents but similar to humans, surrogate indexes of insulin sensitivity/resistance including QUICKI and log HOMA may be reasonable to use in large studies of rhesus monkeys where it may be impractical to conduct glucose clamp studies.


Assuntos
Técnica Clamp de Glucose/métodos , Hiperinsulinismo/sangue , Resistência à Insulina/fisiologia , Animais , Modelos Lineares , Macaca mulatta , Ratos
15.
Am J Physiol Endocrinol Metab ; 297(5): E1023-9, 2009 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-19706785

RESUMO

Assessing insulin resistance in rodent models gives insight into mechanisms that cause type 2 diabetes and the metabolic syndrome. The hyperinsulinemic euglycemic glucose clamp, the reference standard for measuring insulin sensitivity in humans and animals, is labor intensive and technically demanding. A number of simple surrogate indexes of insulin sensitivity/resistance have been developed and validated primarily for use in large human studies. These same surrogates are also frequently used in rodent studies. However, in general, these indexes have not been rigorously evaluated in animals. In a recent validation study in mice, we demonstrated that surrogates have a weaker correlation with glucose clamp estimates of insulin sensitivity/resistance than in humans. This may be due to increased technical difficulties in mice and/or intrinsic differences between human and rodent physiology. To help distinguish among these possibilities, in the present study, using data from rats substantially larger than mice, we compared the clamp glucose infusion rate (GIR) with surrogate indexes, including QUICKI, HOMA, 1/HOMA, log (HOMA), and 1/fasting insulin. All surrogates were modestly correlated with GIR (r = 0.34-0.40). Calibration analyses of surrogates adjusted for body weight demonstrated similar predictive accuracy for GIR among all surrogates. We conclude that linear correlations of surrogate indexes with clamp estimates and predictive accuracy of surrogate indexes in rats are similar to those in mice (but not as substantial as in humans). This additional rat study (taken with the previous mouse study) suggests that application of surrogate insulin sensitivity indexes developed for humans may not be appropriate for determining primary outcomes in rodent studies due to intrinsic differences in metabolic physiology. However, use of surrogates may be appropriate in rodents, where feasibility of clamps is an obstacle and measurement of insulin sensitivity is a secondary outcome.


Assuntos
Técnica Clamp de Glucose , Hiperinsulinismo/metabolismo , Resistência à Insulina/fisiologia , Animais , Peso Corporal/fisiologia , Calibragem , Jejum/metabolismo , Feminino , Homeostase/fisiologia , Modelos Lineares , Valor Preditivo dos Testes , Ratos , Ratos Sprague-Dawley
16.
Am J Physiol Endocrinol Metab ; 294(2): E261-70, 2008 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-18003716

RESUMO

Insulin resistance contributes to the pathophysiology of diabetes, obesity, and their cardiovascular complications. Mouse models of these human diseases are useful for gaining insight into pathophysiological mechanisms. The reference standard for measuring insulin sensitivity in both humans and animals is the euglycemic glucose clamp. Many studies have compared surrogate indexes of insulin sensitivity and resistance with glucose clamp estimates in humans. However, regulation of metabolic physiology in humans and rodents differs and comparisons between surrogate indexes and the glucose clamp have not been directly evaluated in rodents previously. Therefore, in the present study, we compared glucose clamp-derived measures of insulin sensitivity (GIR and SI(Clamp)) with surrogate indexes, including quantitative insulin-sensitivity check index (QUICKI), homeostasis model assessment (HOMA), 1/HOMA, log(HOMA), and 1/fasting insulin, using data from 87 mice with a wide range of insulin sensitivities. We evaluated simple linear correlations and performed calibration model analyses to evaluate the predictive accuracy of each surrogate. All surrogate indexes tested were modestly correlated with both GIR and SI(Clamp). However, a stronger correlation between body weight per se and both GIR and SI(Clamp) was noted. Calibration analyses of surrogate indexes adjusted for body weight demonstrated improved predictive accuracy for GIR [e.g., R = 0.68, for QUICKI and log(HOMA)]. We conclude that linear correlations of surrogate indexes with clamp data and predictive accuracy of surrogate indexes in mice are not as substantial as in humans. This may reflect intrinsic differences between human and rodent physiology as well as increased technical difficulties in performing glucose clamps in mice.


Assuntos
Hiperinsulinismo/sangue , Resistência à Insulina/fisiologia , Animais , Peso Corporal/fisiologia , Calibragem , Jejum/metabolismo , Técnica Clamp de Glucose , Homeostase/fisiologia , Insulina/sangue , Camundongos , Camundongos Endogâmicos C57BL , Camundongos Transgênicos , Modelos Estatísticos
17.
J Biopharm Stat ; 18(1): 20-30, 2008.
Artigo em Inglês | MEDLINE | ID: mdl-18161539

RESUMO

While randomized, well-controlled, clinical trials have been viewed as the gold standard in the evaluation of medical products, it is not uncommon for medical device clinical studies to depart from the paradigm of randomized trials, due to ethical or practical reasons. In nonrandomized studies, the advantages of well-designed and conducted randomized clinical trials are no longer available, and consequently the statistical inference obtained from such studies may carry a lower level of scientific assurance, compared to randomized trials. This paper provides a brief overview of nonrandomized medical device clinical studies in terms of design and statistical analysis as well as regulatory issues, including some challenges that frequently arise in those endeavors.


Assuntos
Ensaios Clínicos como Assunto/estatística & dados numéricos , Ensaios Clínicos como Assunto/normas , Equipamentos e Provisões/estatística & dados numéricos , Equipamentos e Provisões/normas , Aprovação de Equipamentos/legislação & jurisprudência , Aprovação de Equipamentos/normas , Humanos , Estados Unidos
18.
J Biopharm Stat ; 17(1): 1-13; discussion 15-7, 19-21, 23-7 passim, 2007.
Artigo em Inglês | MEDLINE | ID: mdl-17219753

RESUMO

Propensity score analysis is a versatile statistical method used mainly in observational studies for improving treatment comparison by adjusting for up to a relatively large number of potentially confounding covariates. Recently, there has been an increased interest in applying this method to nonrandomized medical device clinical studies. In the application of the methodology, some statistical and regulatory issues arise in both study design and analysis of study results, such as the need for pre-specifying clinically relevant covariates to be measured, appropriate patient populations, and the essential elements of statistical analysis, planning sample size in the context of propensity score methodology, handling missing covariates in generating propensity scores, and assessing the success of the propensity score method by evaluating treatment group overlap in terms of the distributions of propensity scores. In this paper, the advantages and limitations of this methodology will be revisited, and the above issues will be discussed and illustrated with examples from a regulatory perspective.


Assuntos
Ensaios Clínicos Controlados como Assunto/estatística & dados numéricos , Aprovação de Equipamentos/normas , Modelos Estatísticos , Algoritmos , Viés , Humanos , Projetos de Pesquisa , Tamanho da Amostra , Reino Unido , Estados Unidos , United States Food and Drug Administration
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