| 1. |
|
|
| 2. |
- Grosse Ruse, Mareile, et al.
(författare)
-
Simultaneous inference of a binary composite endpoint and its components
- 2017
-
Ingår i: Journal of Biopharmaceutical Statistics. - : Informa UK Limited. - 1520-5711 .- 1054-3406. ; 27:1, s. 56-69
-
Tidskriftsartikel (refereegranskat)abstract
- Binary composite endpoints offer some advantages as a way to succinctly combine evidence from a number of related binary endpoints recorded in the same clinical trial into a single outcome. However, as some concerns about the clinical relevance as well as the interpretation of such composite endpoints have been raised, it is recommended to evaluate the composite endpoint jointly with the involved components. We propose an approach for carrying out simultaneous inference based on separate model fits for each endpoint, yet controlling the family-wise type I error rate asymptotically. The key idea is to stack parameter estimates from the different fits and derive their joint asymptotic distribution. Simulations show that the proposed approach comes closer to nominal levels and has comparable or higher power as compared to existing approaches, even for moderate sample sizes (around 100-200 observations). The method is compared to the gatekeeping approach and results are provided in the Supplementary Material. In two data examples we show how the procedure may be adapted to handle local significance levels specified through a priori given weights.
|
|
| 3. |
- Jonsson, Fredrik, et al.
(författare)
-
The application of a Bayesian approach to the analysis of a complex, mechanistically based model
- 2007
-
Ingår i: Journal of Biopharmaceutical Statistics. - : Informa UK Limited. - 1054-3406 .- 1520-5711. ; 17:1, s. 65-92
-
Tidskriftsartikel (refereegranskat)abstract
- The Bayesian approach has been suggested as a suitable method in the context of mechanistic pharmacokinetic-pharmacodynamic (PK-PD) modeling, as it allows for efficient use of both data and prior knowledge regarding the drug or disease state. However, to this day, published examples of its application to real PK-PD problems have been scarce. We present an example of a fully Bayesian re-analysis of a previously published mechanistic model describing the time course of circulating neutrophils in stroke patients and healthy individuals. While priors could be established for all population parameters in the model, not all variability terms were known with any degree of precision. A sensitivity analysis around the assigned priors used was performed by testing three different sets of prior values for the population variance terms for which no data were available in the literature: “informative”, “semi-informative”, and “noninformative”, respectively. For all variability terms, inverse gamma distributions were used. It was possible to fit the model to the data using the “informative” priors. However, when the “semi-informative” and “noninformative” priors were used, it was impossible to accomplish convergence due to severe correlations between parameters. In addition, due to the complexity of the model, the process of defining priors and running the Markov chains was very time-consuming. We conclude that the present analysis represents a first example of the fully transparent application of Bayesian methods to a complex, mechanistic PK-PD problem with real data. The approach is time-consuming, but enables us to make use of all available information from data and scientific evidence. Thereby, it shows potential both for detection of data gaps and for more reliable predictions of various outcomes and “what if” scenarios.
|
|
| 4. |
|
|
| 5. |
- Miller, Frank, et al.
(författare)
-
A decision theoretical modeling for Phase III investments and drug licensing
- 2018
-
Ingår i: Journal of Biopharmaceutical Statistics. - : Informa UK Limited. - 1054-3406 .- 1520-5711. ; 28:4, s. 698-721
-
Tidskriftsartikel (refereegranskat)abstract
- For a new candidate drug to become an approved medicine, several decision points have to be passed. In this article, we focus on two of them: First, based on Phase II data, the commercial sponsor decides to invest (or not) in Phase III. Second, based on the outcome of Phase III, the regulator determines whether the drug should be granted market access. Assuming a population of candidate drugs with a distribution of true efficacy, we optimize the two stakeholders' decisions and study the interdependence between them. The regulator is assumed to seek to optimize the total public health benefit resulting from the efficacy of the drug and a safety penalty. In optimizing the regulatory rules, in terms of minimal required sample size and the Type I error in Phase III, we have to consider how these rules will modify the commercial optimization made by the sponsor. The results indicate that different Type I errors should be used depending on the rarity of the disease.
|
|
| 6. |
- Ondra, Thomas, et al.
(författare)
-
Methods for identification and confirmation of targeted subgroups in clinical trials : A systematic review
- 2016
-
Ingår i: Journal of Biopharmaceutical Statistics. - : Informa UK Limited. - 1054-3406 .- 1520-5711. ; 26:1, s. 99-119
-
Tidskriftsartikel (refereegranskat)abstract
- Important objectives in the development of stratified medicines include the identification and confirmation of subgroups of patients with a beneficial treatment effect and a positive benefit-risk balance. We report the results of a literature review on methodological approaches to the design and analysis of clinical trials investigating a potential heterogeneity of treatment effects across subgroups. The identified approaches are classified based on certain characteristics of the proposed trial designs and analysis methods. We distinguish between exploratory and confirmatory subgroup analysis, frequentist, Bayesian and decision-theoretic approaches and, last, fixed-sample, group-sequential, and adaptive designs and illustrate the available trial designs and analysis strategies with published case studies.
|
|
| 7. |
- Sverdlov, Oleksandr, et al.
(författare)
-
Efficient and ethical response-adaptive randomization designs for multi-arm clinical trials with Weibull time-to-event outcomes.
- 2014
-
Ingår i: Journal of Biopharmaceutical Statistics. - : Informa UK Limited. - 1054-3406 .- 1520-5711. ; 24:4, s. 732-54
-
Tidskriftsartikel (refereegranskat)abstract
- We consider a design problem for a clinical trial with multiple treatment arms and time-to-event primary outcomes that are modeled using the Weibull family of distributions. The D-optimal design for the most precise estimation of model parameters is derived, along with compound optimal allocation designs that provide targeted efficiencies for various estimation problems and ethical considerations. The proposed optimal allocation designs are studied theoretically and are implemented using response-adaptive randomization for a clinical trial with censored Weibull outcomes. We compare the merits of our multiple-objective response-adaptive designs with traditional randomization designs and show that our designs are more flexible, realistic, generally more ethical, and frequently provide higher efficiencies for estimating different sets of parameters.
|
|
| 8. |
- Lee, MLT, et al.
(författare)
-
Nonparametric methods for microarray data based on exchangeability and borrowed power
- 2005
-
Ingår i: Journal of Biopharmaceutical Statistics. - 1520-5711. ; 15:5, s. 783-797
-
Tidskriftsartikel (refereegranskat)abstract
- This article proposes nonparametric inference procedures for analyzing microarray gene expression data that are reliable, robust, and simple to implement. They are conceptually transparent and require no special-purpose software. The analysis begins by normalizing gene expression data in a unique way. The resulting adjusted observations consist of gene-treatment interaction terms ( representing differential expression) and error terms. The error terms are considered to be exchangeable, which is the only substantial assumption. Thus, under a family null hypothesis of no differential expression, the adjusted observations are exchangeable and all permutations of the observations are equally probable. The investigator may use the adjusted observations directly in a distribution-free test method or use their ranks in a rank-based method, where the ranking is taken over the whole data set. For the latter, the essential steps are as follows: 1. Calculate a Wilcoxon rank-sum difference or a corresponding Kruskal-Wallis rank statistic for each gene. 2. Randomly permute the observations and repeat the previous step. 3. Independently repeat the random permutation a suitable number of times. Under the exchangeability assumption, the permutation statistics are independent random draws from a null cumulative distribution function (c.d.f.) approximated by the empirical c.d.f. Reference to the empirical c.d.f. tells if the test statistic for a gene is outlying and, hence, shows differential expression. This feature is judged by using an appropriate rejection region or computing a p-value for each test statistic, taking into account multiple testing. The distribution-free analog of the rank-based approach is also available and has parallel steps which are described in the article. The proposed nonparametric analysis tends to give good results with no additional refinement, although a few refinements are presented that may interest some investigators. The implementation is illustrated with a case application involving differential gene expression in wild-type and knockout mice of an E. coli lipopolysaccharide (LPS) endotoxin treatment, relative to a baseline untreated condition.
|
|
