Showing papers in "Statistics in Biopharmaceutical Research in 2010"
TL;DR: Results of a comprehensive simulation study are presented that compares and contrasts five new adaptive dose-ranging designs for a variety of different scenarios.
Abstract: The main goals in an adaptive dose-ranging study are to detect dose response, to determine if any doses(s) meets clinical relevance, to estimate the dose-response, and then to decide on the dose(s) (if any) to take into the confirmatory Phase III. Adaptive dose-ranging study designs may result in power gains to detect dose response and higher precision in estimating the target dose and the dose response curve. In this article, we complement the library of available methods with five new adaptive dose-ranging designs. Due to their inherent complexity, the operating characteristics can be assessed only through intensive simulations. We present here results of a comprehensive simulation study that compares and contrasts these designs for a variety of different scenarios.
68 citations
TL;DR: Adaptive dose allocation approaches (in particular, the Bayesian general adaptive dose allocation method) usually outperformed other fixed dose selection approaches with respect to both probability of success and dose selection.
Abstract: The purpose of this study was to assess the impact of phase II dose-selection strategies on the likelihood of success of phase III clinical programs, comparing both traditional and adaptive approaches.We evaluated the impact of the phase II approach to dose selection (including traditional, design-adaptive, and analysis-adaptive approaches), the sample size used in phase II, the number of doses studied in phase II, and the number of doses selected to advance into phase III on the probability of demonstrating efficacy, of demonstrating a lack of toxicity, of phase III trial success, and on the probability of overall success of the combined phase II/phase III programs. The expected net present value was used to quantify the financial implications of different strategies.We found that adaptive dose allocation approaches (in particular, the Bayesian general adaptive dose allocation method) usually outperformed other fixed dose allocation approaches with respect to both probability of success and dose selectio...
46 citations
TL;DR: In this article, the authors compare the OMR approach with standard response surface methodology software applications in an attempt to construct a design space, which may harbor operating conditions with a low probability of meeting process specifications.
Abstract: The ICH Q8 defines “design space” (DS) as “The multidimensional combination and interaction of input variables (e.g., material attributes) and process parameters that have been demonstrated to provide assurance of quality.” Unfortunately, some pharmaceutical scientists appear to misinterpret the definition of DS as a process monitoring strategy. A more subtle and possibly more misleading issue, however, is the application of standard response surface methodology software applications in an attempt to construct a DS. The methodology of “overlapping mean responses” (OMR), available in many point-and-click oriented statistical packages, provides a tempting opportunity to use this methodology to create a DS. Furthermore, a few recent (and two possibly very influential) papers have been published that appear to propose the use of OMR as a way to construct a DS. However, such a DS may harbor operating conditions with a low probability of meeting process specifications. In this article we compare the OMR approac...
44 citations
TL;DR: Results and conclusions from a new round of simulation-based evaluations conducted by the PhRMA Working Group on Adaptive Dose-Ranging Studies are presented, proposing a new set of recommendations to improve the accuracy and efficiency of dose-finding in clinical drug development.
Abstract: Poor dose-regimen selection remains a key cause of the high attrition rate of investigational drugs in confirmatory trials, being directly related to the escalating costs of drug development. This article is a follow-up to the first white paper put forward by the PhRMA Working Group (WG) on Adaptive Dose-Ranging Studies (Bornkamp et al. 2007). It presents results and conclusions from a new round of simulation-based evaluations conducted by the WG, proposing a new set of recommendations to improve the accuracy and efficiency of dose-finding in clinical drug development.
40 citations
TL;DR: A summary of key considerations in clinical trials with a sensitive subgroup, including multiplicity and enrichment adjustments as well as optimality considerations in the analysis strategy are provided.
Abstract: This article deals with clinical trials with a sensitive subpopulation of patients, that is, a subgroup that is more likely to benefit from the treatment than the overall population. Given a sensitive subgroup defined by a prespecified classifier, for example, a clinical marker or pharmacogenomic marker, the trial’s outcome is declared positive if the treatment effect is established in the overall population or in the subgroup. We provide a summary of key considerations in clinical trials with a sensitive subgroup, including multiplicity and enrichment adjustments as well as optimality considerations in the analysis strategy. The methodology proposed in this article is illustrated using a neuroscience clinical trial and its operating characteristics are assessed via a simulation study.
38 citations
TL;DR: The sequential parallel design has been proposed to improve the efficiency of clinical trials in psychiatry as mentioned in this paper, where patients are randomized into three treatment groups, each group has two phases, and the analysis of data in the second phase is restricted to placebo patients who failed to respond in the first phase of the trial.
Abstract: The sequential parallel design has been proposed to improve the efficiency of clinical trials in psychiatry. The design randomizes patients into three treatment groups. Each group has two phases. The three groups are (1) placebo in the first phase followed by placebo in the second phase, (2) placebo in the first phase followed by drug in the second phase, and (3) drug in the first phase followed by drug in the second phase. We consider the case of binary response data. The analysis of data in the second phase of the trial is restricted to placebo patients who failed to respond in the first phase of the trial. A crucial element in the choice of test statistics is the underlying assumptions of treatment effect in the two phases of the trial. We develop various likelihood-based statistics when the treatment effect is different in the two phases and for the special case in which the treatment effect is equal in the two phases. These statistics are compared in simulation studies to determine their underlying n...
36 citations
TL;DR: In this paper, the authors proposed the stratified Wilson confidence interval for multiple binomial proportions and stratified Newcombe confidence intervals for multinomial proportion differences for a vaccine trial to compute the sero-conversion rate and rate difference over multiple clinical centers.
Abstract: This article proposes the stratified Wilson confidence interval for multiple binomial proportions and the stratified Newcombe confidence intervals for multiple binomial proportion differences. Both confidence intervals are presented in closed forms to facilitate easy calculations. The confidence levels of the proposed intervals are theoretically justified and demonstrated through extensive simulations. The coverage rates are found to be rather satisfactory. When the Wilson and Newcombe methods are used in unstratified analysis, the proposed methods may serve as the counterparts for stratified analysis. The proposed methods are applied to a vaccine trial to compute the stratified sero-conversion rate and rate difference over multiple clinical centers.
36 citations
TL;DR: In this paper, the relationship between strictly standardized mean difference (SSMD) and the p-value of classical t-test for comparing two groups is explored from a theoretical basis.
Abstract: Statistical significance or p-value of t-test for testing mean difference has been widely used for the comparison of two groups. However, because of many issues that the statistical significance has, it has been intensively criticized in medical and social sciences. Consequently, effect sizes such as Cohen’s d have been proposed as an alternative to statistical significance. Recently, strictly standardized mean difference (SSMD) has been proposed for the comparison of two groups with applications in data analysis in high-throughput screening experiments. In this article, from a theoretical basis, I explore the relationships between SSMD, standardized mean difference, and p-value of classical t-test for comparing two groups. The relationships among these measures hint that SSMD may serve as an alternative to statistical significance in medical sciences and as an alternative to traditional effect sizes in social sciences.
26 citations
TL;DR: In this article, the authors proposed an optimal allocation for three treatments by giving an analytical solution for the optimization problem, which achieves a required power of a multivariate test of homogeneity in binary response experiments while minimizing expected treatment failures at the same time.
Abstract: A fundamental question in response–adaptive randomization is: What allocation proportion should we target to achieve required power while resulting in fewer treatment failures? For comparing two treatments, such optimal allocations are well studied in the literature. However, few authors address the question for multiple treatments and the generalization of optimal allocations is necessary in practice. We are interested in finding the optimal allocation proportion, which achieves a required power of a multivariate test of homogeneity in binary response experiments while minimizing expected treatment failures at the same time. We propose such an optimal allocation for three treatments by giving an analytical solution for the optimization problem. Numerical studies show that a response–adaptive randomization procedure that targets proposed optimal allocation is superior to complete randomization. We also discuss some future research topics and additional issues on optimal adaptive designs.
25 citations
TL;DR: An extensive simulation is done to compare the performance of conventional imputation-based analysis methods for such data with an existing nonparametric interval-censored data analysis method by Finkelstein (1986) in a typical setting for confirmatory clinical trials, and results clearly favor the non Parametric method.
Abstract: Interval-censored time-to-event data occurs frequently in randomized clinical trials. Many new methods for analysis of interval-censored time-to-event data have been proposed in the last two decades. However, most of these methods either rely on assumptions that are hard to verify in practice or are computationally challenging. As a result, none of them has been accepted by the pharmaceutical industry as a standard method. Taking advantage of modern computational power, we did an extensive simulation to compare the performance of conventional imputation-based analysis methods for such data with an existing nonparametric interval-censored data analysis method by Finkelstein (1986) in a typical setting for confirmatory clinical trials. The simulation results clearly favor the nonparametric method, which provides credible inference even under extreme conditions. This helps ease a key concern of the regulatory agency and paves the way for wide acceptance of interval-censored clinical endpoint as a primary end...
22 citations
TL;DR: In this paper, the authors describe several similar methods for a general case of hypotheses without logical restrictions, including the fallback procedure and the fixed sequence procedure, and procedures with a data-driven ordering of hypotheses, which order testing according to the observed data.
Abstract: In a clinical trial with multiple statistical hypotheses of interest, control of the Type I error rate must be considered There are many ways to address control of the Type I error rate and often the various options differ only by subtle nuances In this article we describe several similar methods for a general case of hypotheses without logical restrictions, including the fallback procedure and the fixed sequence procedure, which order testing a priori, and procedures with a data-driven ordering of hypotheses, which order testing according to the observed data Comparisons on power, including the probability of rejecting at least one or of rejecting all hypotheses, are provided In general, if the treatment effects are known or accurately estimated, an optimal testing procedure can be chosen However, if the treatment effects are unknown and especially if there is doubt about whether one or more null hypotheses are true, an optimal testing procedure may not be available and compromise procedures which r
TL;DR: This work considers the penalized D-optimal design that achieves an appropriate balance between the efficient treatment of patients in the trial and the precise estimation of the model parameters to be used in the identification of the target dose.
Abstract: When a new drug is under development, a conventional dose-finding study involves learning about the dose–response curve in order to bring forward right doses of the drug to late-stage development. We propose an adaptive procedure for dose-finding in clinical trials in the presence of both efficacy and toxicity endpoints. We use the principles of optimal experimental designs for bivariate continuous endpoints.However, instead of using the traditional D-optimal design, which favors collective ethics but neglects the individual ethics, we consider the penalized D-optimal design that achieves an appropriate balance between the efficient treatment of patients in the trial (by penalizing allocation of patients to ineffective or toxic doses) and the precise estimation of the model parameters to be used in the identification of the target dose. This is compared with the traditional fixed allocation design in terms of allocation of subjects and precision of the identified dose–response curve and selection of the t...
TL;DR: In this paper, the authors developed frequentist and Bayesian methodologies for evaluating the agreement among multiple methods of clinical measurement, and two approaches for ranking method pairs were described for comparing method pairs based on simultaneous confidence bounds and simultaneous credible bounds.
Abstract: Evaluation of agreement among multiple methods of clinical measurement is a topic of considerable interest in health sciences. As in an analysis of variance comparing more than two treatment means, when more than two measurement methods are compared, performing multiple comparisons and ranking pairs of methods on the basis of their extent of agreement are of primary concern. This article develops frequentist and Bayesian methodologies for this purpose. In particular, simultaneous confidence bounds and simultaneous credible bounds are developed for multiple comparisons. Moreover, two approaches are described for ranking method pairs—one based on simultaneous bounds and the other based on posterior probabilities of possible orderings. The proposed methodologies can be used with any scalar measure of agreement. Their small-sample performance is evaluated using simulation. Extension of the basic methodologies to incorporate covariates is illustrated using a blood pressure dataset.
TL;DR: It is argued that standards based on margins and/or percent preservation are inherently arbitrary and lacking in objective clinical or scientific justification and should not require an arbitrarily higher standard of evidence than placebo-controlled trials.
Abstract: The double-blind placebo-controlled trial is the established standard for determining the efficacy of an experimental treatment. However, there are circumstances where the use of a placebo is unethical or impractical, and active-controlled trials are a common alternative. In an active-controlled trial, the objective is typically to show that the effect of the experimental treatment is within some prespecified margin of the control effect. The margin is often chosen specifically to guarantee 50% or 75% preservation of the control effect over placebo. An implicit assumption is that a higher standard of efficacy is required when a new treatment is evaluated in an active-controlled trial. In this article, we argue that standards based on margins and/or percent preservation are inherently arbitrary and lacking in objective clinical or scientific justification. The use of these ad hoc standards introduces logical inconsistencies for regulatory evaluation such that safe and effective treatments may be denied reg...
TL;DR: This article argues that it will be better to base the sample size decision for a confirmatory trial on the probability that the trial will produce a positive outcome than the traditional statistical power, and shows that sample sizes computed under the traditional method are often too small to yield a desirable success probability.
Abstract: When designing a phase III superiority trial, we often compute the sample size to detect a treatment effect observed in a previous trial. In this article, we propose to take into account uncertainties around the estimated treatment effect and the estimated variability in patients’ responses jointly. We argue that it will be better to base the sample size decision for a confirmatory trial on the probability that the trial will produce a positive outcome than the traditional statistical power. We show that sample sizes computed under the traditional method are often too small to yield a desirable success probability. We extend this argument to the “conditional probability” concept that is often the basis for futility decision and sample size reestimation in an adaptive design. We argue that in the latter case, the concept of predictive power is likely to provide a more realistic measure on the prospect of the trial. In our opinion, a trialist should be concerned when the required sample size for a confirmat...
TL;DR: In this paper, the authors proposed a two-stage design for a Phase II oncology trial with a long-term endpoint that does not suspend accrual when the interim analysis is conducted.
Abstract: Phase II trials are often designed with an interim analysis so they can be stopped early if a drug is ineffective. One problem with interim analyses is incomplete follow-up for some patients. Standard designs such as Simon (1989) require suspension of accrual until patient follow-up is completed. Case and Morgan (2003) proposed a two-stage design for a Phase II oncology trial with a long-term endpoint that does not suspend accrual when the interim analysis is conducted. We review the Case and Morgan design and propose modifications to ensure protection of Type I error rate and improved robustness to nonoptimal conditions likely to be encountered in practice. Software (an R package) is described to create the optimal designs and easily simulate their properties to check asymptotic approximations and the robustness of the performance of the designs in operational conditions.
TL;DR: This article proposes several new indices that measure the heterogeneity for individual studies in a meta-analysis that can be regarded as a generalization of the collective index of heterogeneity in meta-analyses proposed by various authors.
Abstract: This article proposes several new indices that measure the heterogeneity for individual studies in a meta-analysis. These indices directly assess how inconsistent an individual study is compared to the rest of studies used in the meta-analysis, that is, how much impact the specific study has on the scientific conclusion of the meta-analysis and further on the generalization of the conclusion. The proposed indices can be intuitively interpreted as the proportion of total variance from all studies in a meta-analysis that can be accounted for by the heterogeneity from specific studies. Further, each proposed index over all the studies sums to the collective measure of heterogeneity for the meta-analysis. Therefore our proposed study-specific indices of heterogeneity can be regarded as a generalization of the collective index of heterogeneity in meta-analyses proposed by various authors. We examine the difference among the proposed study-specific measures of heterogeneity and assess the variation associated w...
TL;DR: A web site is designed for generating and comparing different types of tailor-made optimal designs and user-supplied designs for the Michaelis-Menten and related models and evaluates robustness properties of the optimal designs under a variation in optimality criteria.
Abstract: The Michaelis-Menten model has and continues to be one of the most widely used models in many diverse fields. In the biomedical sciences, the model continues to be ubiquitous in biochemistry, enzyme kinetics studies, nutrition science, and in the pharmaceutical sciences. Despite its wide-ranging applications across disciplines, design issues for this model are given short shrift. This article focuses on design issues and provides a variety of optimal designs of this model. In addition, we evaluate robustness properties of the optimal designs under a variation in optimality criteria. To facilitate use of optimal design ideas in practice, we design a web site for generating and comparing different types of tailor-made optimal designs and user-supplied designs for the Michaelis-Menten and related models.
TL;DR: Alternative measures for evaluating the proportion of treatment effect explained by a surrogate in logistic or probit regression models are provided which are less biased, less variable, and with narrower confidence intervals.
Abstract: Using surrogate endpoints in clinical trials is desirable for drug development because the trials can be shortened and therefore more cost-effective. Validating a surrogate for the clinical endpoint is critical in this context. One of the key steps in statistical validation of a surrogate for a single trial is to estimate the proportion of treatment effect explained (PTE or PE) by a surrogate. Often the measure for PTE is estimated from the difference in coefficients of treatment from two models with or without adjusting for the surrogate for clinical endpoint. Inherent problems with the method are: the two models may not be valid simultaneously; and the estimate can often lie outside the interval [0, 1]. In this article, we provide alternative measures for evaluating the proportion of treatment effect explained by a surrogate in logistic or probit regression models. Our measures can be estimated easily with any statistical programs capable of binary linear regression modeling, and the interpretation of the measures can be illustrated using Ordinal Dominance (OD) curves. The concept can be visually understood by any practical user. Simulation shows our alternative measures yield more accurate estimates which are less biased, less variable, and with narrower confidence intervals. A clinical trial example is provided.
TL;DR: The changing landscape of the 21st century is discussed and what statisticians should do to rise to the challenges and take advantage of the opportunities in an environment where “openness” is a major consideration that will govern many public and private decisions is discussed.
Abstract: Pharmaceutical statisticians have come a long way during the past 50 years in supporting product discovery, development, manufacturing, and commercialization. Statisticians are either equal partners or are consultants in all areas of pharmaceutical research and development. While statistics is enjoying a greater role in the current environment, our industry is facing unprecedented challenges. The number of approved new molecular entities has decreased in recent years. The public is expecting medicines that are more effective and have fewer side effects. Many of our customers are demanding greater data disclosure, greater transparency, and higher standards of evidence. At the same time, public trust in our industry and regulators is at its lowest point since the 1970s. To face this unfavorable external environment and to take advantage of the rapidly emerging information to drive innovations, many companies have joined forces to form collaborative partnerships. The collaborations cover not only scientific ...
TL;DR: What processes for interim monitoring, analysis, decision making and implementation, might be advantageous and appropriate for adaptive dose-ranging studies are discussed, and how processes were implemented and carried out in a particular case study are illustrated.
Abstract: In clinical studies that address dose-finding objectives, the use of trial designs that allow adaptations based upon accruing unblinded data have much potential to introduce added efficiency and to improve the decision making process, compared to traditional designs. In this paper we consider the processes by which such adaptations may be made. Conventions for interim data monitoring and decision making, particularly with regard to restricting access to data, have largely evolved based upon the needs in Phase III nonadaptive studies; as such, they may not be an ideal fit for adaptive dose-ranging studies, because of the different natures of the trials, their placement in the drug development process, and the objectives of the monitoring. In this paper, we specifically discuss what processes for interim monitoring, analysis, decision making and implementation, might be advantageous and appropriate for adaptive dose-ranging studies, and then illustrate how processes were implemented and carried out in a par...
TL;DR: This work proposes a joint likelihood method which addresses censoring and dropouts in a mixed effects model simultaneously, and a simulation is conducted to evaluate the proposed method.
Abstract: HIV viral dynamic models have received much interest in the literature in recent years. These models are useful for modeling the viral load trajectories during an anti-HIV treatment and for evaluating the efficacy of the treatment. In AIDS studies, patients may drop out of the study early due possibly to drug side-effects, and viral load measurements often have a lower limit of detection. Statistical analyses are therefore complicated by the censoring and dropouts in the data. We propose a joint likelihood method which addresses censoring and dropouts in a mixed effects model simultaneously. A real AIDS dataset is analyzed, and a simulation is conducted to evaluate the proposed method.
TL;DR: Some strategies for the search of optimal two-stage designs are presented, which minimizes the expected total sample size under null hypothesis and has its Type I and II errors bounded above by prespecified values.
Abstract: In phase II cancer clinical trials of newly developed cytostatic or molecularly targeted agents, it is proposed to use both response and early progression rate as co-primary endpoints of the trials. An agent is considered promising if either its response rate is high or its early progression rate is low. In this article, we present some strategies for the search of optimal two-stage designs, which minimizes the expected total sample size under null hypothesis and has its Type I and II errors bounded above by prespecified values. Examples are given for several scenarios appearing in clinical practice.
TL;DR: A unified derivation of both the Shapiro-Wilk test and Vasicek’s test based on estimated entropy divergence is presented and some existing confusion is clarified.
Abstract: The normal distribution is among the most useful distributions in statistical applications. Accordingly, testing for normality is of fundamental importance in many fields including biopharmaceutical research. A generally powerful test for normality is the Shapiro-Wilk test, which can be derived based on estimated entropy divergence. Another well-known test for normality based on entropy divergence was proposed by Vasicek (1976) which has inspired the development of many goodness-of-fit tests for other important distributions. Despite extensive research on the subject, there still exists considerable confusion concerning the fundamental characteristics of Vasicek’s test. This article presents a unified derivation of both the Shapiro-Wilk test and Vasicek’s test based on estimated entropy divergence and clarifies some existing confusion. A comparative study of power performance for these two well-known tests for normality is presented with respect to a wide range of alternatives.
TL;DR: In this article, a general multistage gatekeeping procedure (GMGP) is proposed for testing multiple hypotheses grouped into ordered families under the assumption of isotonic gains, where a family consists of all hypotheses corresponding to noncomparable combinations.
Abstract: Recently, Dmitrienko, Tamhane, and Wiens (2008) proposed a general multistage gatekeeping procedure (GMGP) as a novel procedure for testing multiple hypotheses grouped into ordered families. This article discusses an application of the GMGP to combination drug efficacy trial with the goal of identifying all effective, superior or simultaneously effective and superior combinations. A general framework to formulate and solve these problems is introduced. Under the assumption of isotonic gains the hypotheses are grouped into ordered families, where a family consists of all hypotheses corresponding to noncomparable combinations. The GMGP with the truncated Holm component is then used to test the hypotheses in a stepwise manner. The main advantage of this approach is the strong control of the overall error rate. Moreover, the GMGP can be applied to designs of relatively large dimensions. In general, the performance of the procedure depends not only on parameters but also on a prespecified value (values) of a t...
TL;DR: This work addresses the problem of establishing two-sided equivalence using paired-sample analysis of two treatments or two laboratory tests with a binary endpoint through real data examples and Monte Carlo simulations and provides suggestions on the choice of these three testing parameters.
Abstract: We address the problem of establishing two-sided equivalence using paired-sample analysis of two treatments or two laboratory tests with a binary endpoint. Through real data examples and monte carlo simulations, we compare three commonly used testing parameters, namely, the difference of response probabilities, the ratio of response probabilities, and the ratio of discordant probabilities based on score test statistics for constructing equivalence hypothesis tests of paired binary data. We provide suggestions on the choice of these three testing parameters and proper equivalence margins in hypothesis formulation of equivalence testing. In addition, we describe the implementation of a group sequential design in the context of equivalence testing with early stopping to reject, as well as to declare equivalence.
TL;DR: This paper is primarily written from a U.S. point of view, but the discussion is from a European standpoint, so it is possible that some of the differences in perspective may result from differences in EU and US.
Abstract: I’d like to thank the authors for a very interesting paper. The issue of how to interpret the results of noninferiority trials and how to select the noninferiority margin is complex and this paper makes a valuable contribution to the scientific discussion. It is agreed that the first and minimal objective of an active-control trial should be to demonstrate superiority (indirectly) to placebo. This is in full agreement with the CHMP guideline on the choice of noninferiority margin (CHMP 2006). Another of the main thrusts of the article, that percentage preservation methods (where a fixed margin is chosen as a percentage of the estimated effect of the control treatment over placebo) are not desirable, can also be wholeheartedly endorsed. In fact the CHMP Guideline explicitly states “It is not appropriate to define the noninferiority margin as a proportion of the difference between active comparator and placebo.” You can hardly get clearer than that! Rather than trying to set a higher standard for active-controlled trials, another interpretation of such approaches is that they are flawed attempts to demonstrate superiority to placebo, with the thinking being that “if we are worse by no more than this % of the reference difference from placebo, then we are sure to be better than placebo.” Of course this takes no account of the variability of the estimate of the reference product effect, and therefore does not demonstrate superiority to placebo, and approaches such as the synthesis method that do take variability into account are to be strongly preferred. Having stated broad agreement with the main arguments of the paper, I will now try to playfully challenge the logic of a few of the paper’s other assertions, hopefully to show that some of the scenarios described are not as ridiculous and illogical as they may seem at first reading. All quotations are drawn from Section 3, One Standard of Evidence. It should be noted at the outset that while the paper is primarily written from a U.S. point of view, this discussion is from a European standpoint, so it is possible that some of the differences in perspective may result from differences in EU and U.S. legislation. For example Recital 7 of Council Directive 2001/83/EC (European Parliament 2001) provides: “The concepts of harmfulness and therapeutic efficacy can only be examined in relation to each other and have only relative significance depending on the progress of scientific knowledge and the use for which the medicinal product is intended. The particulars and documents which must accompany an applica-
TL;DR: This study uses three modeling approaches commonly used in the surrogate marker validation theory to select and evaluate a set of genes and metabolites as possible biomarkers for depression, as measured by the HAMD score.
Abstract: Recently, preclinical microarray experiments have become increasingly common laboratory tools to investigate the activity of thousands of genes simultaneously and their response to a certain treatment (Amaratunga and Cabrera 2004). In some experiments, in addition to the gene expressions, other responses are also available. In such situations, the primary question of interest is to identify whether or not the gene expressions can serve as biomarkers for the responses. In addition to gene expressions, metabolites are potential biomarkers for some responses as well. In the present study, we focus on the identification of genomic biomarkers, based on gene and metabolite expression for depression. One measure of the level of depression is the Hamilton Depression Scale (HDS or HAMD) which is a test measuring the severity of depressive symptoms in individuals. The data for this study are a result of a clinical trial in which both HAMD and gene/metabolites expression were measured. We use three modeling approach...
TL;DR: This paper summarizes several current methods of adaptive dose-ranging (ADR) approaches and is a good reference for biostatisticians/investigators who design ADR trials and concludes with some recommendations.
Abstract: Dose-finding is one of the most important steps during the drug development process in all disease areas. Failure to determine the appropriate dose leads to lower success rates of clinical trials in the late phases of development. Use of an adaptive design for dose-finding would help sponsors investigate a broader range of doses efficiently, thereby increasing the success rate of clinical trials and facilitating the clinical drug development process. Although adaptive dose-ranging (ADR) approaches have been widely studied, a clear summarization and discussion of the methods is lacking. This paper summarizes several current methods and is a good reference for biostatisticians/investigators who design ADR trials. An increasing number of clinical trial consultation meetings are being conducted to discuss ADR trials within the Pharmaceuticals and Medical Devices Agency (PMDA). To date, these meetings primarily discuss the necessity and the advantages of such trials. However, the methodology of ADR will be discussed in detail in the near future, making this paper a useful resource. We begin with a brief mention of our experiences and perspectives in reviewing ADR trials based on the PMDA clinical trial consultation meetings (Section 2). In Section 3, there is a description of the simulation results and their findings with respect to the ADR approaches of the paper. In Section 4, we conclude our discussion with some recommendations.