On the efficiency of targeted clinical trials

Two-stage stratified designs with survival outcomes and adjustment for misclassification in predictive biomarkers

Biomarker stratified clinical trial designs are versatile tools to assess biomarker clinical utility and address its relationship with clinical endpoints. Due to imperfect assays and/or classification rules, biomarker status is prone to errors. To account for biomarker misclassification, we consider a two-stage stratified design for survival outcomes with an adjustment for misclassification in predictive biomarkers. Compared to continuous and/or binary outcomes, the test statistics for survival outcomes with an adjustment for biomarker misclassification is much more complicated and needs to take special care. We propose to use the information from the observed biomarker status strata to construct adjusted log-rank statistics for true biomarker status strata. These adjusted log-rank statistics are then used to develop sequential tests for the global (composite) hypothesis and component-wise hypothesis. We discuss the power analysis with the control of the type-I error rate by using the correlations between the adjusted log-rank statistics within and between the design stages. Our method is illustrated with examples of the recent successful development of immunotherapy in nonsmall-cell lung cancer.

New insights into adaptive enrichment designs

The transition towards personalized medicine is happening and the new experimental framework is raising several challenges, from a clinical, ethical, logistical, regulatory, and statistical perspective. To face these challenges, innovative study designs with increasing complexity have been proposed. In particular, adaptive enrichment designs are becoming more attractive for their flexibility. However, these procedures rely on an increasing number of parameters that are unknown at the planning stage of the clinical trial, so the study design requires particular care. This review is dedicated to adaptive enrichment studies with a focus on design aspects. While many papers deal with methods for the analysis, the sample size determination and the optimal allocation problem have been overlooked. We discuss the multiple aspects involved in adaptive enrichment designs that contribute to their advantages and disadvantages. The decision-making process of whether or not it is worth enriching should be driven by clinical and ethical considerations as well as scientific and statistical concerns.

A systematic literature review for the prediction of anticancer drug response using various machine-learning and deep-learning techniques

Computational methods have gained prominence in healthcare research. The accessibility of healthcare data has greatly incited academicians and researchers to develop executions that help in prognosis of cancer drug response. Among various computational methods, machine-learning (ML) and deep-learning (DL) methods provide the most consistent and effectual approaches to handle the serious aftermaths of the deadly disease and drug administered to the patients. Hence, this systematic literature review has reviewed researches that have investigated drug discovery and prognosis of anticancer drug response using ML and DL algorithms. Fot this purpose, PRISMA guidelines have been followed to choose research papers from Google Scholar, PubMed, and Sciencedirect websites. A total count of 105 papers that align with the context of this review were chosen. Further, the review also presents accuracy of the existing ML and DL methods in the prediction of anticancer drug response. It has been found from the review that, amidst the availability of various studies, there are certain challenges associated with each method. Thus, future researchers can consider these limitations and challenges to develop a prominent anticancer drug response prediction method, and it would be greatly beneficial to the medical professionals in administering non-invasive treatment to the patients.

The Current Landscape of Prevention Trials in Dementia

As the prevalence of dementia and Alzheimer's disease (AD) increases worldwide, it is imperative to reflect on the major clinical trials in the prevention of dementia and the challenges that surround them. The pharmaceutical industry has focused on developing drugs that primarily affect the Aβ cascade and tau proteinopathy, while academics have focused on repurposed therapeutics and multi-domain interventions for prevention studies. This paper highlights significant primary, secondary, and tertiary prevention trials for dementia and AD, overall design, methods, and systematic issues to better understand the current landscape of prevention trials. We included 32 pharmacologic intervention trials and 9 multi-domain trials. Fourteen could be considered primary prevention, and 18 secondary or tertiary prevention trials. Major categories were Aβ vaccines, Aβ antibodies, tau antibodies, anti-inflammatories, sex hormones, and Ginkgo biloba extract. The 9 multi-domain studies mainly focused on lifestyle modifications such as blood pressure management, socialization, and physical activity. The lack of validated drug targets, and the complexity of the diagnostic frameworks, eligibility criteria, and outcome measurements for trials, make it difficult to show efficacy for both pharmacological and multi-domain interventions. We hope that this summative analysis of trials will stimulate discussion for scientists and clinicians interested in reviewing and developing preventative interventions for AD.

The Uprising of Mitochondrial DNA Biomarker in Cancer

Cancer is a heterogeneous group of diseases, the progression of which demands an accumulation of genetic mutations and epigenetic alterations of the human nuclear genome or possibly in the mitochondrial genome as well. Despite modern diagnostic and therapeutic approaches to battle cancer, there are still serious concerns about the increase in death from cancer globally. Recently, a growing number of researchers have extensively focused on the burgeoning area of biomarkers development research, especially in noninvasive early cancer detection. Intergenomic cross talk has triggered researchers to expand their studies from nuclear genome-based cancer researches, shifting into the mitochondria-mediated associations with carcinogenesis. Thus, it leads to the discoveries of established and potential mitochondrial biomarkers with high specificity and sensitivity. The research field of mitochondrial DNA (mtDNA) biomarkers has the great potential to confer vast benefits for cancer therapeutics and patients in the future. This review seeks to summarize the comprehensive insights of nuclear genome cancer biomarkers and their usage in clinical practices, the intergenomic cross talk researches that linked mitochondrial dysfunction to carcinogenesis, and the current progress of mitochondrial cancer biomarker studies and development.

Design and Analysis of Cancer Clinical Trials for Personalized Medicine

Biomarkers play a key role in the development of personalized medicine. Cancer clinical trials with biomarker should be appropriately designed and analyzed reflecting the various factors, such as the phase of trials, the type of biomarker, the study objectives, and whether the used biomarker is already validated or not. In this paper, we demonstrate design and analysis of two phase II cancer clinical trials, one with a predictive biomarker and the other with a prognostic biomarker. A statistical testing method and its sample size calculation method are presented for each of the trials. We assume that the primary endpoint of these trials is a time to event variable, but this concept can be used for any type of endpoint with associated testing methods. The test statistics and their sample size formulas are derived using the large sample approximation based on the martingale central limit theorem. Using simulations, we find that the test statistics control the type I error rate accurately and the sample sizes calculated using the formulas maintain the statistical power specified at the design stage.

A Bayesian Decision-Theoretic Design for Simultaneous Biomarker-Based Subgroup Selection and Efficacy Evaluation

The success of drug development of targeted therapy often hinges on an appropriate selection of the sensitive patient population, mostly based on patients' biomarker levels. At the planning stage of a phase II study, although a potential biomarker may have been identified, a threshold value for defining sensitive patient population is often unavailable for adopting many existing biomarker-guided designs. To address this issue, we propose a two-stage design that allows for simultaneous biomarker threshold selection and efficacy evaluation while accommodating situations where the drug is efficacious in the entire patient population. The design uses a Bayesian decision-theoretic approach and incorporates the benefit and cost considerations of the study into a utility function. The operating characteristics of the proposed design under different scenarios are investigated via simulations. We also provide a discussion on the choice of the benefit and cost parameters in practice.

Targeted design for adaptive clinical trials via semiparametric model

Precision medicine approach that assigns treatment according to an individual's personal (including molecular) profile is revolutionizing health care. Existing statistical methods for clinical trial design typically assume a known model to estimate characteristics of treatment outcomes, which may yield biased results if the true model deviates far from the assumed one. This article aims to achieve model robustness in a phase II multi-stage adaptive clinical trial design. We propose and study a semiparametric regression mixture model in which the mixing proportions are specified according to the subjects' profiles, and each sub-group distribution is only assumed to be unimodal for robustness. The regression parameters and the error density functions are estimated by semiparametric maximum likelihood and isotonic regression estimators. The asymptotic properties of the estimates are studied. Simulation studies are conducted to evaluate the performance of the method after a real data analysis.

Review of Statistical Methods for Biomarker-Driven Clinical Trials

The discovery of somatic driver mutations in kinases and receptors has stimulated the development of molecularly targeted treatments that require companion diagnostics and new approaches to clinical development. This article reviews some of the clinical trial designs that have been developed to address these opportunities, including phase II basket and platform trials as well as phase III enrichment and biomarker adaptive designs. It also re-examines some of the conventional wisdom that previously dominated clinical trial design and discusses development and internal validation of a predictive biomarker as a new paradigm for optimizing the intended-use subset for a treatment. Statistical methods now being used in adaptive biomarker-driven clinical trials are reviewed. Some previous paradigms for clinical trial design can limit the development of more effective methods on the basis of prospectively planned adaptive methods, but useful new methods have been developed for analysis of genome-wide data and for the design of adaptively enriched studies. In many cases, the heterogeneity of populations eligible for clinical trials as traditionally defined makes it unlikely that molecularly targeted treatments will be effective for a majority of the eligible patients. New methods for dealing with patient heterogeneity in therapeutic response should be used in the design of phase III clinical trials.

Designing precision medicine trials to yield a greater population impact

Traditionally, a clinical trial is conducted comparing treatment to standard care for all patients. However, it could be inefficient given patients' heterogeneous responses to treatments, and rapid advances in the molecular understanding of diseases have made biomarker-based clinical trials increasingly popular. We propose a new targeted clinical trial design, termed as Max-Impact design, which selects the appropriate subpopulation for a clinical trial and aims to optimize population impact once the trial is completed. The proposed design not only gains insights on the patients who would be included in the trial but also considers the benefit to the excluded patients. We develop novel algorithms to construct enrollment rules for optimizing population impact, which are fairly general and can be applied to various types of outcomes. Simulation studies and a data example from the SWOG Cancer Research Network demonstrate the competitive performance of our proposed method compared to traditional untargeted and targeted designs.

Phase III Precision Medicine Clinical Trial Designs That Integrate Treatment and Biomarker Evaluation

Recent advances in biotechnology and cancer genomics have afforded enormous opportunities for development of more effective anticancer therapies. A key thrust of this modern drug development paradigm is successful identification of predictive biomarkers that can distinguish patients who might be sensitive to new targeted therapies. To respond to this challenge, a number of phase III cancer trial designs integrating biomarker-based objectives have been proposed and implemented in oncology drug development. In this article, we provide an updated review of commonly used biomarker-based randomized clinical trial designs, with a particular focus on design efficiency. When the efficacy of a new therapy may be limited to a biomarker-defined subgroup, the choice of an appropriate randomized clinical trial design should be guided by the strength of the biomarker's credentials. If compelling evidence indicates that a targeted therapy is beneficial only in a particular biomarker-defined subgroup, an enrichment design should be used. If there is strong evidence that the treatment is likely to be more beneficial in the biomarker-positive patients but a meaningful benefit is also possible in the biomarker-negative patients, then a properly powered biomarker-stratified design (eg, a subgroup-specific or Marker Sequential Test strategy) would provide the most rigorous determination of the sensitive populations. If the evidence supporting the predictive value of the biomarker is weak and the treatment is expected to work in the overall population, then a fallback design could be used. Careful selection of an appropriate phase III design strategy that integrates evaluation of a new anticancer therapy and its companion diagnostic is critical to the success of precision medicine in oncology.

A Bayesian adaptive marker-stratified design for molecularly targeted agents with customized hierarchical modeling

It is well known that the treatment effect of a molecularly targeted agent (MTA) may vary dramatically, depending on each patient's biomarker profile. Therefore, for a clinical trial evaluating MTA, it is more reasonable to evaluate its treatment effect within different marker subgroups rather than evaluating the average treatment effect for the overall population. The marker-stratified design (MSD) provides a useful tool to evaluate the subgroup treatment effects of MTAs. Under the Bayesian framework, the beta-binomial model is conventionally used under the MSD to estimate the response rate and test the hypothesis. However, this conventional model ignores the fact that the biomarker used in the MSD is, in general, predictive only for the MTA. The response rates for the standard treatment can be approximately consistent across different subgroups stratified by the biomarker. In this paper, we proposed a Bayesian hierarchical model incorporating this biomarker information into consideration. The proposed model uses a hierarchical prior to borrow strength across different subgroups of patients receiving the standard treatment and, therefore, improve the efficiency of the design. Prior informativeness is determined by solving a "customized" equation reflecting the physician's professional opinion. We developed a Bayesian adaptive design based on the proposed hierarchical model to guide the treatment allocation and test the subgroup treatment effect as well as the predictive marker effect. Simulation studies and a real trial application demonstrate that the proposed design yields desirable operating characteristics and outperforms the existing designs.

Challenges and shifting paradigms in clinical trials in oncology: the case for immunological and targeted therapies

The advent of immunotherapy has undoubtedly changed the current standard for cancer treatment. Immunotherapy offers the possibility of achieving excellent results—a new alternative for patients with advanced-stage or relapsed disease. Nowadays, the progress made in tumour biology has led to multiple advances in clinical and translational cancer research. Many oncogenic pathways responsible for tumour growth and metastases have been described and, consequently, multiple new cancer therapeutic agents have been developed and are under current investigation. Due to this rapid increase in knowledge and pharmaceutical development, traditional clinical trials designs have encountered major limitations. The pharmacological differences (in toxicity profiles and effectiveness patterns) between immunotherapy and chemotherapy have caused traditional clinical trials to evolve in order to meet this emerging need. This review focuses on the different options pertaining to clinical trial design that have arisen in the field of immuno-oncology, as well as the challenges of accurately interpreting traditional survival analyses within this novel area of cancer medicine.

Bayesian adaptive basket trial design using model averaging

In this article, we develop a Bayesian adaptive design methodology for oncology basket trials with binary endpoints using a Bayesian model averaging framework. Most existing methods seek to borrow information based on the degree of homogeneity of estimated response rates across all baskets. In reality, an investigational product may only demonstrate activity for a subset of baskets, and the degree of activity may vary across the subset. A key benefit of our Bayesian model averaging approach is that it explicitly accounts for the possibility that any subset of baskets may have similar activity and that some may not. Our proposed approach performs inference on the basket-specific response rates by averaging over the complete model space for the response rates, which can include thousands of models. We present results that demonstrate that this computationally feasible Bayesian approach performs favorably compared to existing state-of-the-art approaches, even when held to stringent requirements regarding false positive rates.

Auxiliary variable-enriched biomarker-stratified design

Clinical trials in the era of precision medicine require assessment of biomarkers to identify appropriate subgroups of patients for targeted therapy. In a biomarker-stratified design (BSD), biomarkers are measured on all patients and used as stratification variables. However, such a trial can be both inefficient and costly, especially when the prevalence of the subgroup of primary interest is low and the cost of assessing the biomarkers is high. Efficiency can be improved and costs reduced by using enriched biomarker-stratified designs, in which patients of primary interest, typically the biomarker-positive patients, are oversampled. We consider a special type of enrichment design, an auxiliary variable-enriched design (AEBSD), in which enrichment is based on some inexpensive auxiliary variable that is positively correlated with the true biomarker. The proposed AEBSD reduces the total cost of the trial compared with a standard BSD when the prevalence rate of true biomarker positivity is small and the positive predictive value (PPV) of the auxiliary biomarker is larger than the prevalence rate. In addition, for an AEBSD, we can immediately randomize the patients selected in the screening process without waiting for the result of the true biomarker test, reducing the treatment waiting time. We propose an adaptive Bayesian method to adjust the assumed PPV while the trial is ongoing. Numerical studies and an example illustrate the approach. An R package is available.

Genotype-Based Recall Studies in Complex Cardiometabolic Traits

In genotype-based recall (GBR) studies, people (or their biological samples) who carry genotypes of special interest for a given hypothesis test are recalled from a larger cohort (or biobank) for more detailed investigations. There are several GBR study designs that offer a range of powerful options to elucidate (1) genotype-phenotype associations (by increasing the efficiency of genetic association studies, thereby allowing bespoke phenotyping in relatively small cohorts), (2) the effects of environmental exposures (within the Mendelian randomization framework), and (3) gene-treatment interactions (within the setting of GBR interventional trials). In this review, we overview the literature on GBR studies as applied to cardiometabolic health outcomes. We also review the GBR approaches used to date and outline new methods and study designs that might enhance the utility of GBR-focused studies. Specifically, we highlight how GBR methods have the potential to augment randomized controlled trials, providing an alternative application for the now increasingly accepted Mendelian randomization methods usually applied to large-scale population-based data sets. Further to this, we consider how functional and basic science approaches alongside GBR designs offer intellectually intriguing and potentially powerful ways to explore the implications of alterations to specific (and potentially druggable) biological pathways.

Temporal trends and characteristics of clinical trials for which only one racial or ethnic group is eligible

BACKGROUND

Increasing diversity in clinical trials may be worthwhile. We examined clinical trials that restricted eligibility to a single race or ethnicity.

METHODS

We reviewed 19,246 trials registered on ClinicalTrials.gov through January 2013. We mapped trial ZIP-codes to U.S. Census and American Community Survey data. The outcome was whether trials required participants to be from a single racial or ethnic group.

RESULTS

In adjusted analyses, the odds of trials restricting eligibility to a single race/ethnicity increased by 4% per year (95% CI 1.01-1.08, p = .024). Behavioral (5.79% with single race/ethnicity requirements), skin-related (4.49%), and Vitamin D (6.14%) studies had higher rates of single race/ethnicity requirements. Many other trial-specific characteristics, such as funding agency and region of the U.S. in which the trial opened, were associated with eligibility restrictions. In terms of neighborhood characteristics, studies with single race eligibility requirements were more likely to be located in ZIP-codes with greater percentages of those self-reporting the characteristic. For example, 35.2% (SD = 24.9%) of the population self-reported themselves as Black or African American in ZIP-codes with trials requiring participants to be Black/African American, but only 5.9% (SD = 6.9%) self-reported themselves as Black/African American in ZIP-codes with trials that required Asian ethnicity. In ZIP-codes with trials requiring Asian ethnicity, 24.6% (SD = 16.2%) self-reported as Asian. In ZIP-codes with trials requiring Hispanic/Latino ethnicity, 33.3% (SD = 28.5%) self-reported as Hispanic/Latino. Neighborhood level poverty rates and reduced English language ability were also associated with more single race eligibility requirements.

CONCLUSIONS

In selected fields, there has been a modest temporal increase in single race/ethnicity inclusion requirements. Some studies may not fall under regulatory purview and hence may be less likely to include diverse samples. Conversely, some eligibility requirements may be related to health disparities research. Future work should examine whether targeted enrollment criteria facilitates development of personalized medicine or reduces trial access.

Formalising recall by genotype as an efficient approach to detailed phenotyping and causal inference

Detailed phenotyping is required to deepen our understanding of the biological mechanisms behind genetic associations. In addition, the impact of potentially modifiable risk factors on disease requires analytical frameworks that allow causal inference. Here, we discuss the characteristics of Recall-by-Genotype (RbG) as a study design aimed at addressing both these needs. We describe two broad scenarios for the application of RbG: studies using single variants and those using multiple variants. We consider the efficacy and practicality of the RbG approach, provide a catalogue of UK-based resources for such studies and present an online RbG study planner.

Relative efficiency of precision medicine designs for clinical trials with predictive biomarkers

Prospective randomized clinical trials addressing biomarkers are time consuming and costly, but are necessary for regulatory agencies to approve new therapies with predictive biomarkers. For this reason, recently, there have been many discussions and proposals of various trial designs and comparisons of their efficiency in the literature. We compare statistical efficiencies between the marker-stratified design and the marker-based precision medicine design regarding testing/estimating 4 hypotheses/parameters of clinical interest, namely, treatment effects in each marker-positive and marker-negative cohorts, marker-by-treatment interaction, and the marker's clinical utility. As may be expected, the stratified design is more efficient than the precision medicine design. However, it is perhaps surprising to find out how low the relative efficiency can be for the precision medicine design. We quantify the relative efficiency as a function of design factors including the marker-positive prevalence rate, marker assay and classification sensitivity and specificity, and the treatment randomization ratio. It is interesting to examine the trends of the relative efficiency with these design parameters in testing different hypotheses. We advocate to use the stratified design over the precision medicine design in clinical trials with predictive biomarkers.

Phase II cancer clinical trials for biomarker-guided treatments

The design and analysis of cancer clinical trials with biomarker depend on various factors, such as the phase of trials, the type of biomarker, whether the used biomarker is validated or not, and the study objectives. In this article, we demonstrate the design and analysis of two Phase II cancer clinical trials, one with a predictive biomarker and the other with an imaging prognostic biomarker. Statistical testing methods and their sample size calculation methods are presented for each trial. We assume that the primary endpoint of these trials is a time to event variable, but this concept can be used for any type of endpoint.

Precision oncology: neither a silver bullet nor a dream

Precision oncology is not an illusion, nor is it the magic bullet that will eradicate all cancers. Precision oncology is simply another weapon in our growing armament against cancer. Rather than honing in on the failures of a relatively young field, one should advocate for integrating its successes into widespread clinical practice, especially for indications, such as: ABL, ALK, BRAF, BRCA1, BRCA2, EGFR, KIT, KRAS, PDGFRA, PDGFRB, ROS1, BCR-ABL, FLT3 and ROS1, where aberrations have been shown to alter responses to US FDA approved drugs - that is, level 1 data. Moreover, to truly assess the promise of precision oncology, we must first begin by defining our expectations for this field. Importantly, we must recognize that the conception of precision oncology arose as an antithesis of the 'one-size fits all' cancer therapeutics approach. Consequently, tools used for evaluating these conventional, large-scale trials, are not directly transferable for assessing nonconventional, smaller-scale trials needed for evaluating precision oncology. Hence, a thorough vetting of precision oncology as another tool of the trade, must first begin by reassessing our expectations for this field, as well as current clinical trial designs and end point measurements. Importantly, we must recognize that most targeted therapy approaches are in their infancy, with only monotherapy approaches being assessed and combination therapies likely being necessary to fulfill the promise of precision oncology.

Critical Review of Umbrella, Basket, and Platform Designs for Oncology Clinical Trials

The successful development of new drugs with a companion diagnostic based on genomic alteration of an oncogene has led to rethinking of all phases on clinical development of cancer drugs. We critically review some of the new clinical trial designs for biomarker-based cancer drug development. We try to clarify the objectives of the new designs and examine completed trials using these designs to evaluate what has been learned about these designs.

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.

On study designs and hypotheses for clinical trials with predictive biomarkers

Recent interest in conducting clinical trials with predictive biomarkers has generated research in comparing relative efficiency of different trial designs. We find these comparisons of efficiency mostly misleading since they are based on different hypotheses. In this paper, we discuss several commonly used trial designs and consider the hypotheses that each design is capable to address. We first consider the ideal situation of no classification errors, then the more realistic situation where marker assay's sensitivity, specificity and the rule of classification are imperfect. We pay special attention to the differences between treatment utility versus absolute treatment effect, and marker by treatment interaction versus marker utility.

Beyond Correlations, Sensitivities, and Specificities: A Roadmap for Demonstrating Utility of Advanced Imaging in Oncology Treatment and Clinical Trial Design

Despite the widespread belief that advanced imaging should be very helpful in guiding oncology treatment decision and improving efficiency and success rates in treatment clinical trials, its acceptance has been slow. Part of this is likely attributable to gaps in study design and statistical methodology for these imaging studies. Also, results supporting the performance of the imaging in these roles have largely been insufficient to justify their use within the design of a clinical trial or in treatment decision making. Statistically significant correlations between the imaging results and clinical outcomes are often incorrectly taken as evidence of adequate performance. Assessments of whether the imaging can outperform standard techniques or meaningfully supplement them are also frequently neglected. This paper provides guidance on study designs and statistical analyses for evaluating the performance of advanced imaging in the various roles in treatment decision guidance and clinical trial conduct. Relevant methodology from the imaging literature is reviewed; gaps in the literature are addressed using related concepts from the more extensive genomic and in vitro biomarker literature.

Radiation-agent combinations for glioblastoma: challenges in drug development and future considerations

Glioblastoma is an aggressive disease characterized by moderate initial response rates to first-line radiation-chemotherapy intervention followed by low poor response rates to second-line intervention. This article discusses novel strategic platforms for the development of radiation-investigational agent combination clinical trials for primary and recurrent glioblastoma in a NCI-NCTN settings with simultaneous analysis of challenges in the drug development process.

Precision oncology: A new era of cancer clinical trials

Traditionally, site of disease and anatomic staging have been used to define patient populations to be studied in individual cancer clinical trials. In the past decade, however, oncology has become increasingly understood on a cellular and molecular level, with many cancer subtypes being described as a function of biomarkers or tumor genetic mutations. With these changes in the science of oncology have come changes to the way we design and perform clinical trials. Increasingly common are trials tailored to detect enhanced efficacy in a patient subpopulation, e.g. patients with a known biomarker value or whose tumors harbor a specific genetic mutation. Here, we provide an overview of traditional and newer biomarker-based trial designs, and highlight lessons learned through implementation of several ongoing and recently completed trials.

Biomarker-Guided Non-Adaptive Trial Designs in Phase II and Phase III: A Methodological Review

Biomarker-guided treatment is a rapidly developing area of medicine, where treatment choice is personalised according to one or more of an individual’s biomarker measurements. A number of biomarker-guided trial designs have been proposed in the past decade, including both adaptive and non-adaptive trial designs which test the effectiveness of a biomarker-guided approach to treatment with the aim of improving patient health. A better understanding of them is needed as challenges occur both in terms of trial design and analysis. We have undertaken a comprehensive literature review based on an in-depth search strategy with a view to providing the research community with clarity in definition, methodology and terminology of the various biomarker-guided trial designs (both adaptive and non-adaptive designs) from a total of 211 included papers. In the present paper, we focus on non-adaptive biomarker-guided trial designs for which we have identified five distinct main types mentioned in 100 papers. We have graphically displayed each non-adaptive trial design and provided an in-depth overview of their key characteristics. Substantial variability has been observed in terms of how trial designs are described and particularly in the terminology used by different authors. Our comprehensive review provides guidance for those designing biomarker-guided trials.

Statistical power considerations in genotype-based recall randomized controlled trials

Randomized controlled trials (RCT) are often underpowered for validating gene-treatment interactions. Using published data from the Diabetes Prevention Program (DPP), we examined power in conventional and genotype-based recall (GBR) trials. We calculated sample size and statistical power for gene-metformin interactions (vs. placebo) using incidence rates, gene-drug interaction effect estimates and allele frequencies reported in the DPP for the rs8065082 SLC47A1 variant, a metformin transported encoding locus. We then calculated statistical power for interactions between genetic risk scores (GRS), metformin treatment and intensive lifestyle intervention (ILI) given a range of sampling frames, clinical trial sample sizes, interaction effect estimates, and allele frequencies; outcomes were type 2 diabetes incidence (time-to-event) and change in small LDL particles (continuous outcome). Thereafter, we compared two recruitment frameworks: GBR (participants recruited from the extremes of a GRS distribution) and conventional sampling (participants recruited without explicit emphasis on genetic characteristics). We further examined the influence of outcome measurement error on statistical power. Under most simulated scenarios, GBR trials have substantially higher power to observe gene-drug and gene-lifestyle interactions than same-sized conventional RCTs. GBR trials are becoming popular for validation of gene-treatment interactions; our analyses illustrate the strengths and weaknesses of this design.

A subgroup cluster-based Bayesian adaptive design for precision medicine

In precision medicine, a patient is treated with targeted therapies that are predicted to be effective based on the patient's baseline characteristics such as biomarker profiles. Oftentimes, patient subgroups are unknown and must be learned through inference using observed data. We present SCUBA, a Subgroup ClUster-based Bayesian Adaptive design aiming to fulfill two simultaneous goals in a clinical trial, 1) to treatments enrich the allocation of each subgroup of patients to their precision and desirable treatments and 2) to report multiple subgroup-treatment pairs (STPs). Using random partitions and semiparametric Bayesian models, SCUBA provides coherent and probabilistic assessment of potential patient subgroups and their associated targeted therapies. Each STP can then be used for future confirmatory studies for regulatory approval. Through extensive simulation studies, we present an application of SCUBA to an innovative clinical trial in gastroesphogeal cancer.

Designing Issues in Confirmatory Adaptive Population Enrichment Trials

Adaptive population enrichment designs enable the data-driven selection of one or more pre-specified subpopulations in an interim analysis, and the confirmatory proof of efficacy in the selected subset at the end of the trial. Sample size reassessment and other adaptive design changes can be performed as well. Strong control of the experimentwise Type I error rate is guaranteed by use of the combination testing principle together with the closed testing argument. In this paper the general methodology and designing issues when planning such a design are reviewed. It is shown how to derive overall confidence intervals and p-values. Criteria for assessing the operating characteristics of these designs are given, and the application is illustrated by examples.

Subgroup analyses in confirmatory clinical trials: time to be specific about their purposes

Background

It is well recognized that treatment effects may not be homogeneous across the study population. Subgroup analyses constitute a fundamental step in the assessment of evidence from confirmatory (Phase III) clinical trials, where conclusions for the overall study population might not hold. Subgroup analyses can have different and distinct purposes, requiring specific design and analysis solutions. It is relevant to evaluate methodological developments in subgroup analyses against these purposes to guide health care professionals and regulators as well as to identify gaps in current methodology.

Methods

We defined four purposes for subgroup analyses: (1) Investigate the consistency of treatment effects across subgroups of clinical importance, (2) Explore the treatment effect across different subgroups within an overall non-significant trial, (3) Evaluate safety profiles limited to one or a few subgroup(s), (4) Establish efficacy in the targeted subgroup when included in a confirmatory testing strategy of a single trial. We reviewed the methodology in line with this “purpose-based” framework. The review covered papers published between January 2005 and April 2015 and aimed to classify them in none, one or more of the aforementioned purposes.

Results

In total 1857 potentially eligible papers were identified. Forty-eight papers were selected and 20 additional relevant papers were identified from their references, leading to 68 papers in total. Nineteen were dedicated to purpose 1, 16 to purpose 4, one to purpose 2 and none to purpose 3. Seven papers were dedicated to more than one purpose, the 25 remaining could not be classified unambiguously. Purposes of the methods were often not specifically indicated, methods for subgroup analysis for safety purposes were almost absent and a multitude of diverse methods were developed for purpose (1).

Conclusions

It is important that researchers developing methodology for subgroup analysis explicitly clarify the objectives of their methods in terms that can be understood from a patient’s, health care provider’s and/or regulator’s perspective. A clear operational definition for consistency of treatment effects across subgroups is lacking, but is needed to improve the usability of subgroup analyses in this setting. Finally, methods to particularly explore benefit-risk systematically across subgroups need more research.

Electronic supplementary material

The online version of this article (doi:10.1186/s12874-016-0122-6) contains supplementary material, which is available to authorized users.

Clinical trial designs incorporating predictive biomarkers

Development of oncologic therapies has traditionally been performed in a sequence of clinical trials intended to assess safety (phase I), preliminary efficacy (phase II), and improvement over the standard of care (phase III) in homogeneous (in terms of tumor type and disease stage) patient populations. As cancer has become increasingly understood on the molecular level, newer "targeted" drugs that inhibit specific cancer cell growth and survival mechanisms have increased the need for new clinical trial designs, wherein pertinent questions on the relationship between patient biomarkers and response to treatment can be answered. Herein, we review the clinical trial design literature from initial to more recently proposed designs for targeted agents or those treatments hypothesized to have enhanced effectiveness within patient subgroups (e.g., those with a certain biomarker value or who harbor a certain genetic tumor mutation). We also describe a number of real clinical trials where biomarker-based designs have been utilized, including a discussion of their respective advantages and challenges. As cancers become further categorized and/or reclassified according to individual patient and tumor features, we anticipate a continued need for novel trial designs to keep pace with the changing frontier of clinical cancer research.

Optimal two-stage enrichment design correcting for biomarker misclassification

The enrichment design is an important clinical trial design to detect the treatment effect of the molecularly targeted agent (MTA) in personalized medicine. Under this design, patients are stratified into marker-positive and marker-negative subgroups based on their biomarker statuses and only the marker-positive patients are enrolled into the trial and randomized to receive either the MTA or a standard treatment. As the biomarker plays a key role in determining the enrollment of the trial, a misclassification of the biomarker can induce substantial bias, undermine the integrity of the trial, and seriously affect the treatment evaluation. In this paper, we propose a two-stage optimal enrichment design that utilizes the surrogate marker to correct for the biomarker misclassification. The proposed design is optimal in the sense that it maximizes the probability of correctly classifying each patient's biomarker status based on the surrogate marker information. In addition, after analytically deriving the bias caused by the biomarker misclassification, we develop a likelihood ratio test based on the EM algorithm to correct for such bias. We conduct comprehensive simulation studies to investigate the operating characteristics of the optimal design and the results confirm the desirable performance of the proposed design.

Bayesian predictive modeling for genomic based personalized treatment selection

Efforts to personalize medicine in oncology have been limited by reductive characterizations of the intrinsically complex underlying biological phenomena. Future advances in personalized medicine will rely on molecular signatures that derive from synthesis of multifarious interdependent molecular quantities requiring robust quantitative methods. However, highly parameterized statistical models when applied in these settings often require a prohibitively large database and are sensitive to proper characterizations of the treatment-by-covariate interactions, which in practice are difficult to specify and may be limited by generalized linear models. In this article, we present a Bayesian predictive framework that enables the integration of a high-dimensional set of genomic features with clinical responses and treatment histories of historical patients, providing a probabilistic basis for using the clinical and molecular information to personalize therapy for future patients. Our work represents one of the first attempts to define personalized treatment assignment rules based on large-scale genomic data. We use actual gene expression data acquired from The Cancer Genome Atlas in the settings of leukemia and glioma to explore the statistical properties of our proposed Bayesian approach for personalizing treatment selection. The method is shown to yield considerable improvements in predictive accuracy when compared to penalized regression approaches.

Complement pathway biomarkers and age-related macular degeneration

In the age-related macular degeneration (AMD) 'inflammation model', local inflammation plus complement activation contributes to the pathogenesis and progression of the disease. Multiple genetic associations have now been established correlating the risk of development or progression of AMD. Stratifying patients by their AMD genetic profile may facilitate future AMD therapeutic trials resulting in meaningful clinical trial end points with smaller sample sizes and study duration.

Knowledge and attitudes regarding clinical trials and willingness to participate among prostate cancer patients

BACKGROUND

Enrollment of minorities in clinical trials remains low. Through a California population-based study of men with early stage prostate cancer, we examined the relationships between race/ethnicity and 1) attitudes, 2) knowledge and 3) willingness to participate in clinical trials.

METHODS

From November 2011-November 2012, we identified all incident cases of prostate cancer in African American, Latino, and Asian American men ages 18-75 years, and a random sample of white men diagnosed in 2008, through the California Cancer Registry, living within 60 miles of a site offering ≥ 1 clinical trial. Participants completed a 30-min telephone interview in English, Spanish, or Chinese. In this cross-sectional population-based study, multivariable logistic regression was used to estimate associations between race/ethnicity and 1) attitudes, 2) knowledge and 3) willingness to participate.

RESULTS

Of 855 participants, 52% were ≥ 65 years, 42% were white, 24% Latino, 19% African American and 15% Asian American. The majority (81%) had medium-to-high health literacy. Compared to non-Latino white men, African American men were less likely to have above average knowledge of clinical trials (OR=0.55; CI=0.35-0.86), as were Asian American (OR=0.55; CI=0.33-0.93) and Latino men (OR=0.30; CI=0.18-0.48). There were no racial/ethnic differences in willingness to participate. The attitude that "researchers are the main beneficiaries" was negatively associated with willingness (OR=0.63; CI=0.43-0.93); the attitude that "patients are the main beneficiaries" was positively associated with willingness to participate (OR=1.57; CI=1.07-2.29).

CONCLUSIONS

Men with early stage prostate cancer are willing to take part in clinical trials and this willingness does not vary by race/ethnicity.