A Bayesian model with application for adaptive platform trials having temporal changes.

Temporal changes exist in clinical trials. Over time, shifts in patients' characteristics, trial conduct, and other features of a clinical trial may occur. In typical randomized clinical trials, temporal effects, that is, the impact of temporal changes on clinical outcomes and study analysis, are largely mitigated by randomization and usually need not be explicitly addressed. However, temporal effects can be a serious obstacle for conducting clinical trials with complex designs, including the adaptive platform trials that are gaining popularity in recent medical product development. In this paper, we introduce a Bayesian robust prior for mitigating temporal effects based on a hidden Markov model, and propose a particle filtering algorithm for computation. We conduct simulation studies to evaluate the performance of the proposed method and provide illustration examples based on trials of Ebola virus disease therapeutics and hemostat in vascular surgery.

Methods for efficacy evaluations of antibacterial treatments in multiple body-site infection trials.

Unmet medical need exists for serious bacterial diseases caused by multidrug-resistant infections, necessitating an urgent need for newer therapies with greater treatment benefits to patients. For meeting this need, the usual approach has been to conduct separate clinical trials, each trial targeting infection at a single body-site, e.g., for respiratory tract, intra-abdominal site, urinary tract, or blood. However, for the unmet medical need situations, this approach seems inefficient for developing antibacterial drugs with activity against single species or against multiple species of bacteria for a broader indication. Instead, a streamlined clinical development program for such situations can benefit by considering multiple body-site infection trials. Such trials would enroll patients with infections at different body-sites, but with similar severity and comorbidity for avoiding potential treatment effect heterogeneity. Such trials, when properly designed and conducted, can be informative and can save time and resources in drug development. Goals for such trials would be to first demonstrate that there is evidence of an overall treatment effect, and then to show that the treatment effects at individual body-sites reveal consistency in contributing to the overall treatment effect, or to identify a subset of body-sites for which greater treatment effect can be supported by a specified statistical decision criterion. For this, we propose here an information-based procedure for the demonstration of treatment effect overall across all body-sites, or for a subset of body-sites, on considering two types of error rates of falsely concluding treatment effect.

Age Differences by Sex in Antiretroviral-Naïve Participants: Pooled Analysis from Randomized Clinical Trials.

Age and sex effects on antiretroviral therapy (ART) response are not well elucidated. Our pooled analysis of 40 randomized clinical trials measured the association of age and sex on CD4+ T cell count changes and virologic suppression using multivariable regression modeling. The average increase in CD4+ T cell count from baseline to week 48 was 17.3 cells/mm3 lower and clinically insignificant (95% confidence interval -30.8 to -3.8) among women ages ≥ 50 years (n = 573), compared to women ≤ 35 years (n = 3,939). Results were similar for men. Virologic suppression odds were 60% and 21% times greater among participants ≥50 years compared to ≤35 years, in women and men, respectively. In both sexes, larger increases in CD4+ T cell count changes were observed in younger, compared to older, participants; however, virologic suppression was higher in older, compared to younger, participants suggesting a non-sex-specific age effect response to ART.

Treatment evaluation for a data-driven subgroup in adaptive enrichment designs of clinical trials.

Adaptive enrichment designs (AEDs) of clinical trials allow investigators to restrict enrollment to a promising subgroup based on an interim analysis. Most of the existing AEDs deal with a small number of predefined subgroups, which are often unknown at the design stage. The newly developed Simon design offers a great deal of flexibility in subgroup selection (without requiring pre-defined subgroups) but does not provide a procedure for estimating and testing treatment efficacy for the selected subgroup. This article proposes a 2-stage AED which does not require predefined subgroups but requires a prespecified algorithm for choosing a subgroup on the basis of baseline covariate information. Having a prespecified algorithm for subgroup selection makes it possible to use cross-validation and bootstrap methods to correct for the resubstitution bias in estimating treatment efficacy for the selected subgroup. The methods are evaluated and compared in a simulation study mimicking actual clinical trials of human immunodeficiency virus infection.

Representation of Women and Minorities in Clinical Trials for New Molecular Entities and Original Therapeutic Biologics Approved by FDA CDER from 2013 to 2015.

BACKGROUND: The U.S. Food and Drug Administration (FDA) has made efforts to encourage adequate assessment of women, racial/ethnic minorities, and geriatric participants in clinical trials through regulations and guidance documents. This study surveyed the demographics of clinical trial participants and the presence of efficacy and safety analyses by sex for new drugs approved between 2013 and 2015 by the FDA Center for Drug Evaluation and Research. METHODS: New drug marketing applications submitted to FDA were surveyed for demographic data (sex, race, ethnicity, and age) and the presence of sex-based analyses for efficacy and safety. The Ratio of the Proportion of women in clinical trials for the indicated disease population relative to the estimated Proportion of women in the disease population (PPR) was calculated for new drug indications. RESULTS: Of the 102 new drugs in this cohort (defined as new molecular entity drugs and original therapeutic biologics), sex was reported for >99.9% of trial participants, and women accounted for 40.4% of these participants. An estimated 77.2% of participants were White, 6.4% were Black/African American, and 29.1% were aged ≥65 years. Sex-based analyses for both efficacy and safety were conducted for 93.1% of applications. PPR was calculated for 82 new drugs for a total of 60 indications, of which 50 indications (83.3%) had a PPR ≥0.80. CONCLUSIONS: Sex data are now collected for almost all study participants, and this study shows appropriate sex participation for most new drugs when estimated disease prevalence by sex (PPR) is considered. Therapeutic area and disease indication are important considerations when assessing the sex of participants because variation occurs depending on the disease under study. Some racial minorities, especially Blacks/African Americans, are still not well represented in most drug development programs and remain an area where improvement is needed.

A Quantitative Concordance Measure for Comparing and Combining Treatment Selection Markers.

Motivated by an HIV example, we consider how to compare and combine treatment selection markers, which are essential to the notion of precision medicine. The current literature on precision medicine is focused on evaluating and optimizing treatment regimes, which can be obtained by dichotomizing treatment selection markers. In practice, treatment decisions are based not only on efficacy but also on safety, cost and individual preference, making it difficult to choose a single cutoff value for all patients in all settings. It is therefore desirable to have a statistical framework for comparing and combining treatment selection markers without dichotomization. We provide such a framework based on a quantitative concordance measure, which quantifies the extent to which higher marker values are predictive of larger treatment effects. For a given marker, the proposed concordance measure can be estimated from clinical trial data using a U-statistic, which can incorporate auxiliary covariate information through an augmentation term. For combining multiple markers, we propose to maximize the estimated concordance measure among a specified family of combination markers. A cross-validation procedure can be used to remove any re-substitution bias in assessing the quality of an optimized combination marker. The proposed methodology is applied to the HIV example and evaluated in simulation studies.

New methods for treatment effect calibration, with applications to non-inferiority trials.

In comparative effectiveness research, it is often of interest to calibrate treatment effect estimates from a clinical trial to a target population that differs from the study population. One important application is an indirect comparison of a new treatment with a placebo control on the basis of two separate randomized clinical trials: a non-inferiority trial comparing the new treatment with an active control and a historical trial comparing the active control with placebo. The available methods for treatment effect calibration include an outcome regression (OR) method based on a regression model for the outcome and a weighting method based on a propensity score (PS) model. This article proposes new methods for treatment effect calibration: one based on a conditional effect (CE) model and two doubly robust (DR) methods. The first DR method involves a PS model and an OR model, is asymptotically valid if either model is correct, and attains the semiparametric information bound if both models are correct. The second DR method involves a PS model, a CE model, and possibly an OR model, is asymptotically valid under the union of the PS and CE models, and attains the semiparametric information bound if all three models are correct. The various methods are compared in a simulation study and applied to recent clinical trials for treating human immunodeficiency virus infection.

Use of auxiliary covariates in estimating a biomarker-adjusted treatment effect model with clinical trial data.

A biomarker-adjusted treatment effect (BATE) model describes the effect of one treatment versus another on a subpopulation of patients defined by a biomarker. Such a model can be estimated from clinical trial data without relying on additional modeling assumptions, and the estimator can be made more efficient by incorporating information on the main effect of the biomarker on the outcome of interest. Motivated by an HIV trial known as THRIVE, we consider the use of auxiliary covariates, which are usually available in clinical trials and have been used in overall treatment comparisons, in estimating a BATE model. Such covariates can be incorporated using an existing augmentation technique. For a specific type of estimating functions for difference-based BATE models, the optimal augmentation depends only on the joint main effects of marker and covariates. For a ratio-based BATE model, this result holds in special cases but not in general; however, simulation results suggest that the augmentation based on the joint main effects of marker and covariates is virtually equivalent to the theoretically optimal augmentation, especially when the augmentation terms are estimated from data. Application of these methods and results to the THRIVE data yields new insights on the utility of baseline CD4 cell count and viral load as predictive or treatment selection markers.

Hierarchical nested trial design (HNTD) for demonstrating treatment efficacy of new antibacterial drugs in patient populations with emerging bacterial resistance.

In the last decade or so, pharmaceutical drug development activities in the area of new antibacterial drugs for treating serious bacterial diseases have declined, and at the same time, there are worries that the increased prevalence of antibiotic-resistant bacterial infections, especially the increase in drug-resistant Gram-negative infections, limits available treatment options . A recent CDC report, 'Antibiotic Resistance Threats in the United States', indicates that antimicrobial resistance is one of our most serious health threats. However, recently, new ideas have been proposed to change this situation. An idea proposed in this regard is to conduct randomized clinical trials in which some patients, on the basis of a diagnostic test, may show presence of bacterial pathogens that are resistant to the control treatment, whereas remaining patients would show pathogens that are susceptible to the control. The control treatment in such trials can be the standard of care or the best available therapy approved for the disease. Patients in the control arm with resistant pathogens can have the option for rescue therapies if their clinical signs and symptoms worsen. A statistical proposal for such patient populations is to use a hierarchical noninferiority-superiority nested trial design that is informative and allows for treatment-to-control comparisons for the two subpopulations without any statistical penalty. This design can achieve in the same trial dual objectives: (i) to show that the new drug is effective for patients with susceptible pathogens on the basis of a noninferiority test and (ii) to show that it is superior to the control in patients with resistant pathogens. This paper addresses statistical considerations and methods for achieving these two objectives for this design. Published 2014. This article is a U.S. Government work and is in the public domain in the USA.

Network meta-analysis of randomized clinical trials: reporting the proper summaries.

BACKGROUND: In the absence of sufficient data directly comparing multiple treatments, indirect comparisons using network meta-analyses (NMAs) can provide useful information. Under current contrast-based (CB) methods for binary outcomes, the patient-centered measures including the treatment-specific event rates and risk differences (RDs) are not provided, which may create some unnecessary obstacles for patients to comprehensively trade-off efficacy and safety measures. PURPOSE: We aim to develop NMA to accurately estimate the treatment-specific event rates. METHODS: A Bayesian hierarchical model is developed to illustrate how treatment-specific event rates, RDs, and risk ratios (RRs) can be estimated. We first compare our approach to alternative methods using two hypothetical NMAs assuming a fixed RR or RD, and then use two published NMAs to illustrate the improved reporting. RESULTS: In the hypothetical NMAs, our approach outperforms current CB NMA methods in terms of bias. In the two published NMAs, noticeable differences are observed in the magnitude of relative treatment effects and several pairwise statistical significance tests from previous report. LIMITATIONS: First, to facilitate the estimation, each study is assumed to hypothetically compare all treatments, with unstudied arms being missing at random. It is plausible that investigators may have selected treatment arms on purpose based on the results of previous trials, which may lead to 'nonignorable missingness' and potentially bias our estimates. Second, we have not considered methods to identify and account for potential inconsistency between direct and indirect comparisons. CONCLUSIONS: The proposed NMA method can accurately estimate treatment-specific event rates, RDs, and RRs and is recommended.

The Use of Covariates and Random Effects in Evaluating Predictive Biomarkers Under a Potential Outcome Framework.

Predictive or treatment selection biomarkers are usually evaluated in a subgroup or regression analysis with focus on the treatment-by-marker interaction. Under a potential outcome framework (Huang, Gilbert and Janes [Biometrics68 (2012) 687-696]), a predictive biomarker is considered a predictor for a desirable treatment benefit (defined by comparing potential outcomes for different treatments) and evaluated using familiar concepts in prediction and classification. However, the desired treatment benefit is un-observable because each patient can receive only one treatment in a typical study. Huang et al. overcome this problem by assuming monotonicity of potential outcomes, with one treatment dominating the other in all patients. Motivated by an HIV example that appears to violate the monotonicity assumption, we propose a different approach based on covariates and random effects for evaluating predictive biomarkers under the potential outcome framework. Under the proposed approach, the parameters of interest can be identified by assuming conditional independence of potential outcomes given observed covariates, and a sensitivity analysis can be performed by incorporating an unobserved random effect that accounts for any residual dependence. Application of this approach to the motivating example shows that baseline viral load and CD4 cell count are both useful as predictive biomarkers for choosing antiretroviral drugs for treatment-naive patients.

A note on partial covariate-adjustment and design considerations in noninferiority trials when patient-level data are not available.

The traditional fixed margin approach to evaluating an experimental treatment through an active-controlled noninferiority trial is simple and straightforward. However, its utility relies heavily on the constancy assumption of the experimental data. The recently developed covariate-adjustment method permits more flexibility and improved discriminatory capacity compared to the fixed margin approach. However, one major limitation of this covariate-adjustment methodology is its adherence on the patient-level data, which may not be accessible to investigators in practice. In this article, under some assumptions, we examine the feasibility of a partial covariate-adjustment approach based on data typically available from journal publications or other public data when the patient-level data are unavailable. We illustrate the usefulness of this approach through two real examples. We also provide design considerations on the efficiency of the partial covariate-adjustment approach.

Assessing the heterogeneity of treatment effects via potential outcomes of individual patients.

There is growing interest in understanding the heterogeneity of treatment effects (HTE), which has important implications in treatment evaluation and selection. The standard approach to assessing HTE (i.e. subgroup analyses based on known effect modifiers) is informative about the heterogeneity between subpopulations but not within. It is arguably more informative to assess HTE in terms of individual treatment effects, which can be defined by using potential outcomes. However, estimation of HTE based on potential outcomes is challenged by the lack of complete identifiability. The paper proposes methods to deal with the identifiability problem by using relevant information in baseline covariates and repeated measurements. If a set of covariates is sufficient for explaining the dependence between potential outcomes, the joint distribution of potential outcomes and hence all measures of HTE will then be identified under a conditional independence assumption. Possible violations of this assumption can be addressed by including a random effect to account for residual dependence or by specifying the conditional dependence structure directly. The methods proposed are shown to reduce effectively the uncertainty about HTE in a trial of human immunodeficiency virus.

Assessing overall evidence from noninferiority trials with shared historical data.

For regulatory approval of a new drug, the United States Code of Federal Regulations (CFR) requires 'substantial evidence' from 'adequate and well-controlled investigations'. This requirement is interpreted in the Food and Drug Administration guidance as the need of 'at least two adequate and well-controlled studies, each convincing on its own to establish effectiveness'. The guidance also emphasizes the need of 'independent substantiation of experimental results from multiple studies'. However, several authors have noted the loss of independence between two noninferiority trials that use the same set of historical data to make inferences, raising questions about whether the CFR requirement is met in noninferiority trials through current practice. In this article, we first propose a statistical interpretation of the CFR requirement in terms of trial-level and overall type I error rates, which captures the essence of the requirement and can be operationalized for noninferiority trials. We next examine four typical regulatory settings in which the proposed requirement may or may not be fulfilled by existing methods of analysis (fixed margin and synthesis). In situations where the criteria are not met, we then propose adjustments to the existing methods. As illustrated with several examples, our results and findings can be helpful in designing and analyzing noninferiority trials in a way that is both compliant with the regulatory interpretation of the CFR requirement and reasonably powerful.

Meta-analysis of gender differences in efficacy outcomes for HIV-positive subjects in randomized controlled clinical trials of antiretroviral therapy (2000-2008).

Women are often underrepresented in randomized clinical trials (RCT) of HIV-1 drugs. As a result, determining whether women have different virologic outcomes compared to men is not always possible because the gender-related analyses usually lack statistical power. To address this important public health concern, the Food and Drug Administration's (FDA) Division of Antiviral Products (DAVP) created a database including 20,328 HIV-positive subjects from 40 RCTs in 18 New Drug Applications (NDAs) submitted to the FDA between 2000 and 2008. These RCTs were conducted for at least 48 weeks in duration and were used to support approval of new molecular entity, new formulation, or major label change. To delineate potential gender differences in antiretroviral treatment (ART), we evaluated the percentage of subjects with HIV RNA less than 50 copies per milliliter at 48 weeks. Analyses of the database represent the most systematic review of gender-related ART efficacy data to date. Overall, the meta-analyses did not demonstrate statistically or clinically significant gender differences in virologic outcome at week 48. However, the corresponding subgroup analyses appear to show several statistically significant gender differences favoring males.

Meeting the demand for more sophisticated study designs. A proposal for a new type of clinical trial: the hybrid design.

Background Treatment effect is traditionally assessed through either superiority or non-inferiority clinical trials. Investigators may find that because of safety concerns and/or wide variability across strata of the superiority margin of active controls over placebo, neither a superiority nor a non-inferiority trial design is ethical or practical in some disease populations. Prior knowledge may allow and drive study designers to consider more sophisticated designs for a clinical trial. Design In this paper, the authors propose hybrid designs which may combine a superiority design in one subgroup with a non-inferiority design in another subgroup or combine designs with different control regimens in different subgroups in one trial when a uniform design is unethical or impractical. The authors show how the hybrid design can be planned and how inferences can be made. Through two examples, the authors illustrate the scenarios where hybrid designs are useful while the conventional designs are not preferable. Conclusion The hybrid design is a useful alternative to current superiority and non-inferiority designs.

Antiviral Information Management System (AIMS): a prototype for operational innovation in drug development.

This article presents a prototype for an operational innovation in knowledge management (KM). These operational innovations are geared toward managing knowledge efficiently and accessing all available information by embracing advances in bioinformatics and allied fields. The specific components of the proposed KM system are (1) a database to archive hepatitis C virus (HCV) treatment data in a structured format and retrieve information in a query-capable manner and (2) an automated analysis tool to inform trial design elements for HCV drug development. The proposed framework is intended to benefit drug development by increasing efficiency of dose selection and improving the consistency of advice from US Food and Drug Administration (FDA). It is also hoped that the framework will encourage collaboration among FDA, industry, and academic scientists to guide the HCV drug development process using model-based quantitative analysis techniques.

A covariate-adjustment regression model approach to noninferiority margin definition.

To maintain the interpretability of the effect of experimental treatment (EXP) obtained from a noninferiority trial, current statistical approaches often require the constancy assumption. This assumption typically requires that the control treatment effect in the population of the active control trial is the same as its effect presented in the population of the historical trial. To prevent constancy assumption violation, clinical trial sponsors were recommended to make sure that the design of the active control trial is as close to the design of the historical trial as possible. However, these rigorous requirements are rarely fulfilled in practice. The inevitable discrepancies between the historical trial and the active control trial have led to debates on many controversial issues. Without support from a well-developed quantitative method to determine the impact of the discrepancies on the constancy assumption violation, a correct judgment seems difficult. In this paper, we present a covariate-adjustment generalized linear regression model approach to achieve two goals: (1) to quantify the impact of population difference between the historical trial and the active control trial on the degree of constancy assumption violation and (2) to redefine the active control treatment effect in the active control trial population if the quantification suggests an unacceptable violation. Through achieving goal (1), we examine whether or not a population difference leads to an unacceptable violation. Through achieving goal (2), we redefine the noninferiority margin if the violation is unacceptable. This approach allows us to correctly determine the effect of EXP in the noninferiority trial population when constancy assumption is violated due to the population difference. We illustrate the covariate-adjustment approach through a case study.