Reinventing clinical trials: a review of innovative biomarker ...

Reinventing clinical trials: a review of innovative

biomarker trial designs in cancer therapies

Ja-An Lin1 and Pei He2,*

1Department of Biostatistics, The University of North Carolina at Chapel Hill, 170 Rosenau Hall, Chapel Hill,NC, USA, and 2Department of Statistics, Stanford University, Stanford, CA, USA

*Correspondence address. Department of Statistics, Stanford University, 390 Serra Mall, Stanford, CA 94305, USA.E-mail: [email protected]

Accepted 17 March 2015

Abstract

Introduction: Recently, new clinical trial designs involving biomarkers have

been studied and proposed in cancer clinical research, in the hope of incorp-

orating the rapid growing basic research into clinical practices.

Sources of data: Journal articles related to various biomarkers and their role

in cancer clinical trial, articles and books about statistical issues in trial

design, and regulatory website, documents, and guidance for submission of

targeted cancer therapies.

Areas of agreement: The drug development process involves four phases. The

confirmatory Phase III is essential in regulatory approval of a special treatment.

Areas of controversy: Regulatory agency has restrictions on confirmatory

trials ‘using adaptive designs’. No rule of thumb to pick the most appropriate

design for biomarker-related trials.

Growing points: Statistical issues to solve in new designs. Regulatory

acceptance of the ‘newly proposed trial designs’.

Areas timely for developing research: Biomarker-related trial designs that

can resolve the statistical issues and satisfy the regulatory requirement.

Key words: clinical trial, adaptive design, biomarker, personalized medicine

Introduction

Clinical trials, defined as ‘a prospective studies com-paring the effect and value of intervention(s) against‘control group’ in human beings, where the control

group can either receive placebo or active treatmentsby Friedman et al.1 In clinical trial, typically asubject will be randomly assigned to one of the treat-ment groups during the study, to reduce possible

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Advance Access Publication Date: 28 April 2015

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confounding effects as well as for the ethicalpurpose; also in some cases, the physicians or/andpatients are blinded to the treatment patient receivedto avoid problems of bias in data collection andassessment.1 On the other hand, another type ofstudy, in which individuals are observed or certainoutcomes are measured while ‘no attempt’ is madeto affect the outcome,2 is typically categorized as‘observational study’. The biggest difference betweenthe two types of studies is in their design: possiblerandomization and blindness in clinical trials, that inobservational study the treatment information arefully disclosed to both patients and physicians andassigned treatment to patients, are pre-determined.Since the concept of randomization was first appliedin 1931,1 methods to adequately conduct clinicaltrials and gather data from the trial have been widelydeveloped and utilized in both academia and indus-try for either scientific findings or medically relatedproduct development, especially over the last severaldecades. The government regulatory agencies (e.g.US Food and Drug Administration (US FDA) andEuropean Medicine Agency (EMA)) that regulate themarketing approval of medically related products(e.g. drugs, medical devices, vaccines)3,4 also con-stantly publish guidelines for industries to properlydesign and implement clinical trials.

Currently, clinical trials are commonly dividedinto four phases; each one has its specific aim. AtPhase I, the goal is to determine the proper doserange. In this phase, the subjects are usually healthyvolunteers; subjects may also be patients who havetried and failed to improve on the existing standardtherapies. More specifically, the tolerability is esti-mated and pharmacokinetics and pharmacodynamicsare also characterized at this stage. Besides thedevelopment of new designs, mainly for the area ofcancer research, most of the Phase I studies employsimple dose-escalation approaches. After a dose or arange of doses is determined in Phase I, Phase IIstudy focuses on the efficacy or biological activity ofdrug. The drug or a treatment is given to a largergroup of people to see whether it is effective and tofurther evaluate safety. At this stage, a concurrentcontrol group, or historical controls, may be involvedfor comparison. Alternatively, pre-treatment status

versus post-treatment status for self-control withwashout period is sometimes used to assess the effi-cacy while monitoring for safety. With more infor-mation about efficacy and safety, as well as properdose range obtained from previous two phases,Phase III studies are used for confirmatory purposeof the drug for treatment in terms of its effectiveness,side effects and long-term safety. In Phase III studies,the treatment or drug is applied in a larger group ofsubjects targeting a certain population with clearlydefined exclusion and inclusion criteria. The treat-ment or drug is commonly compared with placeboor active control, such as standard care. Two inde-pendent pivotal trials, comparing the intervention toplacebo, are generally required by US FDA formarket approval, and labeling facts of the drug ortreatment are generally garnered from this phase aswell. The adequate and well-controlled traditionalPhase III studies (A&WC) are also called conven-tional studies in US FDA guidance. After agenciesapprove the treatment or drug, Phase IV study com-mences. The purpose of Phase IV trials are gatheringinformation on the drug or treatment’s effect ongeneral population and any side effect or safety issuerelating to long-term use of the drug or treatment.Typically, the Phase IV studies are observationalstudies, which are conducted utilizing public orgovernment-funded database. Since during Phase I–III, the drug or treatment is applied to a subject withclose monitoring by medical professions, certainadverse events may be potentially prevented andunseen when applied to a larger population. Thus,Phase IV studies monitoring the long-term safetyissues in reality are still necessary. Before Phase I–IVclinical trials, pre-phase trial (or sometimes beingcalled Phase 0 trials, pilot study, etc.) is usually con-ducted first in human for the purpose of proof ofconcept with smaller sample size and limited budget.

Why scientists are still developing new

trial methods?

It is widely recognized that cancer is one of the mostimportant public health issues we are facing now. Upto 2008, the annual cancer healthcare-related costsplus lost productivity measured in pure economic

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terms is as high as $228 billion in the USA alone and£18.3 billion in the UK.5 However, the drug develop-ment in the field of oncology remains stagnant, result-ing in exponentially increasing clinical trial costs,especially in Phase III confirmatory trials—whicheventually transferred to costly oncology healthcareexpense. Even with the skyrocketing cost, the rate atwhich the primary endpoint of oncology drug trialachieves its statistical significance is only 34% from2004 to 2010.6 Given the most common end point oftime to event in oncology trial, one reason for the lowsuccessful rate is due to a growing pipeline ofanti-cancer drugs with false promising outcomes inearly phase trials.6 Another important reason isbecause of the pathological nature of cancer. Thanksto the developments in biotechnology and advance-ment in our understanding of genomics, we nowunderstand that the cancers of the same primary sitesand stages are diverse in terms of pathogenesis, naturalhistory and responsiveness to therapy. The hetero-geneities of tumors of the same primary site and stagemake many cases, in fact different diseases. Thus,under traditional trial design, only small treatmenteffect is identified in large-scale trial because of‘heterogeneous’ patient cohort recruitment, that manypatients are actually treated with non-effective yettoxic and expensive therapies.5 The concept of bio-marker sheds a light to this issue. Biomarkers helpresearchers identify these different patient groups, inwhich the disease itself can be further defined by thespecific molecular, genetic or ‘immunological’ sub-types. The introduction and understanding of ‘biomar-ker’s effect’ on the response of treatment in differentpatient groups provide biostatisticians opportunities todevelop innovative trial designs, which not only iden-tify adequate populations that are most beneficial tothe treatment, but also have shorter trial turnaroundand smaller budgets to implement the trial. In the fol-lowing section, we will shortly review the categories ofbiomarkers commonly used in trials for those readerswho are not yet familiar with biomarkers.

Biomarkers

Biomarkers, or biological markers, are defined invarious ways. Some define it as ‘a biologic feature

that can be used to measure the presence or progressof disease or the effects of treatment’.7 For example,prostate-specific antigen (PSA) is a biomarker forprostate cancer under this definition. In 2011, Galloet al.8 proposed a definition as ‘any substance or bio-logical structure that can be measured in the humanbody and may influence, explain or predict the inci-dence or outcome of disease’. To address the limitedscope of this definition in ‘measured in human body’from other scholars, Gallo et al.8 further remarked‘any substance, structure or process that can be mea-sured in bio-specimen and which may be associatedwith health-related outcomes’. This definition is byfar the most commonly adopted one, which fitsseveral biomarker-related circumstances and context.In this review, we employ the definition provided byUS National Institute of Health (US NIH) ‘a charac-teristic that is objectively measured and evaluated asan indicator of normal biologic processes, pathogenicprocesses, or pharmacologic responses to a thera-peutic intervention’,9 for the reason that it gives widercoverage of current clinical needs.

In recent clinical studies, biomarkers can be gen-erally categorized into four categories: preventive,diagnostic, prognostic and predictive biomarkers. Bythe name, preventive biomarkers refer to those withprevention capability. The genes BRCA1 andBRCA2 on chromosome 17 and 13, respectively, arethe most typical preventive biomarkers. Patients withmutation in either of these two genes have higherrisk of 56 and 87% to develop breast cancer.10

Thus, US NIH recommends that women who carrymutations in these two genes receive bilateralprophylactic mastectomy as one of the preventativemeasures to reduce their lifetime risk of havingbreast cancer.11 Diagnostic biomarkers are the onesthat assist clinicians in making diagnostic decisions.The example of PSA mentioned above belongs tothis category. Prognostics and predictive biomarkersare two key players in the development of new trialdesign with biomarkers. Prognostic biomarkers indi-cate the biomarkers with disease outcome regardlessof treatment effect, whereas predictive biomarkersare the ones associated with drug/treatment response.12

Because of commercialized feasibility of humangenomic sequencing, it is much more practical to

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include the detection, confirmation and applicationof biomarkers in large clinical trials with a shortertrial period and smaller budget. In the scope of dis-cussing innovative trial approaches, we will focus onthe application of predictive biomarkers in currentclinical trial design.

In the following sections, we will introduce severalcontemporary trial designs utilizing predictive bio-markers. Subsequently, we will discuss some relevantissues, such as the necessity of randomization andaddress some statistical concerns. Then we will con-clude with the attitude of regulatory agencies to thesenew trial methods and provide our thoughts to somepossible extension.

Innovative clinical trial designs

Enrichment design

The purpose of enrichment design (Fig. 1)13 is to focuson treatment response at a certain sub-populationwith a specific type of biomarker. During the screen-ing period, subjects who satisfy all eligibility criteriawill be recruited with a diagnostic biomarker. Onlysubjects with predicted responsive biomarkers willbe enrolled in to the study. Then this group of sub-jects will be randomized, into either intervention orcontrol arms after enrollment. The pivotal study oftrastuzumab (a recombinant humanized monoclonalantibody) for breast cancer patients is one of theearliest trials using enrichment design.14 Patientswith HER2-positive breast adenocarcinoma (humanepidermal growth factor receptor2) are found tohave better survival and response rate using the com-bination therapy with chemotherapy, comparedwith trastuzumab alone. The enrichment design isusually selected when there is (i) strong evidenceshowing patients are likely to respond to the inter-vention treatment via a predicted treatment respon-sive biomarker or (ii) the counter group of patientswill undergo serious toxicity issues while onlyhaving modest benefit from treatment. If researchersselect traditional trial design (i.e. skip the step of bio-marker type selection in enrichment design), theeffect of intervention treatment may be diluted dueto the inclusion of subjects who are not likely to

respond to treatment. One disadvantage in the enrich-ment design lies in the fact that only biomarker posi-tive patients are recruited. Therefore, the treatmenteffect in the general population remains unclear.

All-comers design

The primary purpose of all-comers design is to inves-tigate the interaction between treatment effect andbiomarker status.15 The timing of introducing thebiomarker depends on study aim and end points.Mandrekar and Sargent divided the design into twogroups: sequential testing strategy design andmarker-based design.

Sequential testing strategy (Fig. 2a) design is fun-damentally the same as conventional trial method—all eligible subjects are directly randomized to inter-vention or control arm after enrollment. The sequen-tial testing strategy design typically has a singleprimary hypothesis about the interaction betweenbiomarker and treatment effect. Either the treatmenteffect will be tested first in the general populationand then in a subgroup analysis for pre-specified bio-markers, or the treatment effect will be first tested ina marker-defined subgroup and then tested for thewhole entire population (while sequentially control-ling type I errors). The selection of the order dependson the research team’s belief in the strength of the

Fig. 1 Enrichment design.

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interaction effect of biomarker by treatment in thegeneral population compared with the subgroup,based on historical information or preliminaryunderstanding. So the beforehand analysis is crucial:false positive information may lead to a failed trial,even though the treatment effect is truly significant ina certain cohort.

The marker-based design (Fig. 2b) includes themarker-by-treatment interaction design and marker-based strategy design. The marker-by-treatmentinteraction design detects the interaction betweenbiomarker and treatment effect by using biomarkerstatus as stratum (or strata) with the presumptionthat the entire population can be separated bymarker-defined subgroup(s). The subjects are thenrandomized to treatment arms within marker-defined groups. Statistical modeling including inter-action effect or statistical test for dependencybetween two factors, such as interaction term oftreatment by biomarker for continuous end point orχ2 for categorical end point, may then be implemen-ted. The marker-based strategy design randomlyassigns eligible subjects either based on or independ-ent of their marker status. One caveat to adapt thisdesign strategy is the distribution of biomarker in thegeneral population. If the distributions of marker-defined subgroups are greatly imbalanced in thegeneral population, then the subject recruitment maybe delayed or the inference from the study result maybe biased. For example, if one of the biomarkergroups with positive response to treatment has small

count (saying 3), then using statistical test, whichrequires relative large count (e.g. Cochran–Mantel–Haenszel test), will lead to inflated type I error.

Mixture (hybrid) design

Another group of design combines both marker-based design and conventional clinical trial method.This is usually termed ‘hybrid design’ 13 or sometimescalled ‘combination of biomarker trial design’.14,16

The most straightforward hybrid design is anextension from enrichment design (Fig. 3a): subjectswho do not have predicted responsive biomarkerwill stay in the study and receive standard care. Thisdesign compensates the disadvantage from enrich-ment design; in this hybrid design, the treatmenteffect of intervention therapy in predicted responsivebiomarker group can be compared with treatmenteffect of control arm in both the biomarker positiveand negative population.

Alternatively, another extension from all-comerdesign is sometimes adopted, called ‘biomarker strat-egy design’ (Fig. 3b). Biomarker strategy designrecruits eligible subjects regardless of their biomarkerstatus, just like all-comer design. The subjects will thenbe randomized to control arm (to receive placebo orstandard care) or experimental arm. For the subjects inthe experimental arm, their biomarker status will betested before they are assigned to intervention treat-ment group or control group depending on their bio-marker status. This design will be considered as first

Fig. 2 All-comers design. (a) Sequential testing; (b) Maker based.


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priority choice when there is sufficient informationsaying only patients with predicted responsive bio-marker will benefit from the interventional treatment;thus, it is unethical to randomize the other marker-defined subgroup into the intervention arm. Thedesign gives the advantage of possibly comparing thevalidity of the companion testing assay as well as iden-tifying putative prognostic biomarkers. One commoncritique for this design is that the patients in the cohortwithout predicted responsive biomarker are doubleenrolled in the study, which potentially increases cost.Another potential disadvantage of this biomarkerstrategy design, especially with smaller sample size, isthe potential imbalance in the sub-populations, as therandomization has been taken place before the bio-marker testing. However, such an effect would bediluted when the trial is on a large scale.

Adaptive design

Given the comprehensive development of Bayesianparadigm in recent decades,17,18 researchers alsoinvented a group of trials called adaptive design withmultiple trial stages. In the adaptive design trial,Bayesian approaches are utilized to incorporate priorinformation from early stage to modify the trialimplementation in later stage, to identify the cohortwhich is most benefited by intervention treatment.

For the innovative trial designs mentioned in theprevious section, researchers have to trade-off betweenthe cost, statistical power and additional information

gain. A method named ‘adaptive enrichment design’19

(Fig. 4a) provides a flexible solution to overcome therestriction. In this design, all of the eligible subjects arerecruited in the first stage, followed by an interim ana-lysis to determine the study design between enrich-ment design and all-comer design. The sample size,end points, randomization ratio or enrichment hypoth-esis may also be adjusted using interim data beforemoving forward to Stage 2. Bayesian methods areproposed for the adjustment of randomization schemeusing interim data.18 The adaptive feature providesthe advantage of reducing the risk to expose non-responsive patients under unnecessary hazard sideeffects, while maintaining sufficient statistical powerto detect treatment effect in adequate population.19

The other prominent advantage adaptive enrichmentdesign has is the minimum assumption the investiga-tor has to make before the trial starts: that the validityof the assumption can be justified using the interimdata from the first stage. One forewarning to applythe adaptive enrichment design is that the end pointfor interim analysis should be properly chosen, in thatthe end point should be measurable and that sufficientdata are attainable to give investigators reliable guid-ance to move forward into the next stage.

Another common adaptive design is called ‘groupsequential design’20 (Fig. 4b) which contains aninterim data analysis. The role of interim data ana-lysis is a traffic light, which tells the investigatorwhether the trial should be continued, depending onthe interim result showing potential efficacy or

Fig. 3 Hybrid design. (a) Enrichment extension; (b) Biomaker strategy.

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futility of the intervention treatment. Group sequen-tial design can also be combined with patient sub-population under the biomarker-directed trial. Laiet al.21 provides a group sequential design to testmultiple composite hypotheses in the overall popula-tion and the biomarker positive populations.

There is also growing interest to develop a type ofcontinuous trial process by ‘connecting’ differenttrial phases, especially from Phase II to Phase III,called seamless design22 (Fig. 4c). Phase III trial istypically the most costly and risky phase during thewhole treatment development process; the successfullikelihood of Phase III usually depends on howaccurate is the conclusion drawn from Phase II. Con-versely, the Phase II trial tends to have much smallersample size (such as 10) to shorten drug developmentturnaround time. Thus, scientists consider the seam-less approach to combine the learning stage of PhaseII and confirmatory stage of Phase III. In the begin-ning of Phase II, subjects are randomized into thetreatment arms of A, B, combined therapy of A andB, or control. An interim analysis is then performedto determine which active arm should be dropped. Inthe confirmatory stage of Phase III study, the treat-ment groups with only one active arm and controlarm will be investigated. Berry6 systematicallycategorizes the seamless design into two groups:‘inference seamless’ and ‘operational seamless’. Inthe inference seamless approach, the subjects willcarry their treatment arm from learning phase toconfirmatory phase, and the data in both phases will

be analyzed together. For the operational seamless,the data in two phases are still analyzed separately.A good example of seamless design is proposed inNass et al.,23 which has two stages in Phase II andone stage in Phase III, looking at the effect of com-bined drug agents. Table 1 provides a summary ofthe pros and cons of each above-mentioned design.

Issues with the new designs

In this section, we discuss three issues: control groupselection in conventional and innovative trial methods,statistical consideration in new trial methods and regu-latory agencies’ attitude to the innovative trialmethods. We conclude with our future expectations.

Dowewant a control group?

Typically in learning stage of trial, the control groupis not mandated since the purpose of learning phase isto guide for confirmatory stage. The inclusion ofcontrol arm in confirmatory phase is commonly sug-gested by agencies such that the treatment effect,including efficacy and safety, of new drug can bebetter understood. The main constraint for this designis the difficulty to distinguish among treatment effect,placebo effect and the effect of natural history. Abiomarker-guided oncology trial may consider single-arm design when the targeted population is too smallthat a randomized trial is not feasible. In such trials,tumor response is often the primary end point, as it

Fig. 4 Adaptive design. (a) Adaptive enrichment; (b) Group sequential; (c) Example of seamiess.


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can reveal the direct impact of the treatment in theabsence of a placebo. Early phase trials can be singlearm to estimate response rate only, but Phase III trialhas to involve a hypothesis testing procedure that analternative hypothesis based on historical control isessential in trial design. The challenge then lies in thejustification of the historical control. Often time, thenatural history of such disease is not well studied toprovide a valid historical control that is acceptable forregulatory submissions. In 2009, FDA granted anaccelerated approval of orfatumumab for previouslytreated chronic lymphocytic leukemia based on single-arm trials with tumor response rate.24 However, laterin an Oncologic Drugs Advisory Committee (ODAC)meeting in 2011, FDA suggested the need of two con-trolled confirmatory trials. The only exception theymentioned is in the case where patient population issmall and strong treatment effect is observed.25

Since the new trial methods are more

complicated, are there any statistical

concerns?

With long-time development in advanced statisticalmodels to analyze various type of data, such as sur-vival data,26,27 longitudinal data28–30 and high-dimensional data,28,31,32 statisticians now have moretools to adequately analyze the clinical trial data.However, data quality still dominates the final result.

The rule of thumb to conduct the conventionaltrial still applies in innovative trial methods. Chenet al.33 have reviewed a series of related issues in bio-marker adaptive designs. Although the review ismainly focused on two types of adaptive design pro-posed by Jiang et al.34 and Freidlin and Simon,35 thestatistical issues are applicable to other biomarker-related designs. Essentially, the main statistical issuesin trial design are raised from the following sources:(i) type I error is inflated, such that the conclusion isless credible; (ii) blindness is disrupted while interimdata analysis is involved, which leads to potentialbiased result; (iii) insufficient sample size or durationdue to wrong assumption when designing the study,which results in potential failure of the trial byexposing subjects in unnecessary trial risk.

Due to complexity of design of new trial methods,beyond all the issues mentioned above, more statis-tical consideration should be taken into account. Forthe trials involved with biomarker, especially whentreatment assignment depends on marker status, thereproducibility and accuracy of biomarker test isextremely crucial. If the biomarker test has high false-classification rate, potentially the type I error willbe inflated (enrichment design, adaptive enrichmentdesign, all-comer design) or the statistical power isnot sufficient (all-comer design). Besides, if the bio-marker detection for the assay used is not sensitive,the trial duration will be longer in the screening

Table 1 Summary of different designs in cancer clinical trials with biomarkers

Type ofdesign

Pros Cons

Enrichmentdesign

Would best evaluate the treatment effect in abiomarker selected subgroup, avoiding thedecrease of power in All-comer design

Only recruit the sub-population and therefore lackthe understanding for the treatment effect in restof the population

All-comerdesign

Provide the study for the treatment effect in theoverall population

High cost; potential long recruitment and screeningtime; potential dilution of the treatment effect ifonly a small subgroup is benefiting fromtreatment

Hybrid design Have a comprehensive investigation for treatmenteffect in either biomarker positive or negativetrials

Logistically might be hard to implement due to theintricate nature; may subject to biased sample orrandomization without proper adjustment

Adaptivedesign

Flexible in choosing different patient populations andhypothesis; may be time efficient and cost saving

The sample size calculation relies heavily onsimulations; regulatory agencies may pose doubton the foundation of designs

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phase resulting in increasing trial cost. The otherconcern regarding biomarkers is how to select theproper predictive biomarker due to linkage disequi-librium nature in genomics. Suggested by Thangue,5

retrospective identification for biomarker fromstudies, which do not depend on marker status fortreatment group assignment, may be considered fordrug approval first, while using the identified bio-marker in prospective hypothesis-driven trial later toassure statistical power.

How do the regulatory agencies see the

contemporary trial methods?

In guidelines (or drafted guideline) published by USFDA and EMA, they point out that the trial conduc-tion using innovative design should still followgeneral guideline published for conventional clinicaltrials. And researchers should be aware of the guide-line from International Conference on Harmonisation(ICH).3,4 The agencies express their encouragement toconduct trials using new trial methods for explora-tory purpose first before having it for drug approval.A closer collaboration and communication withagencies before implementing the trial is specificallyaddressed as a necessity in US FDA guideline.3 Somevery important issues, such as potential unblindingin adaptive design or the design such that the interimanalysis will impact the subsequent stage, should becarefully and clearly described in protocol. An exter-nal and independent committee may be involved tomake decisions using unblinding data. Moreover, itis still less preferable to change end point during thestudy even with pre-specified adaptive plan. In brief,stronger evidence is still in demand for agencies togive more credibility in innovative trial design fordrug effect confirmatory in Phase III studies, while it ismore widely accepted in Phase II and Phase I studies.

Discussion

No matter what type of trial designs, there are alwayspros and cons for different innovative trial design.Unlike traditional trials, the biomarker-guided cancertrials may need to explore the subgroup behavior ofthe treatment and therefore involve more intricacies.

Here we summarize the four major types of innova-tive designs that help identify and/or validate the bio-marker’s impact, while at the same time test forefficacies. Enrichment design has the advantages of abetter-targeted population and would be mostapplicable when a well-established subgroup effect isvalidated. All-comer designs would be best toexplore the treatment behavior, but at the same timecompromise the cost and time. Both hybrid andadaptive designs provide flexibilities to either havemultiple hypotheses testing or interim adjustment.However, logistically they may involve more intrica-cies to implement and may subject to more regula-tory challenges than the other types of designs.

We expect more and more trials involving biomar-kers in the near future, given the affordable cost toidentify and test biomarkers. However, even withfruitful statistical methods36,37 to analyze data fromcomplex trials, there are still some remaining ques-tions. How does one implement a trial with compositeagents aimed for personalized medicine? How doesone design a trial with more than one biomarker?How does one properly quantify type I error whilemaintaining sufficient statistical power in a more com-plicated design? Despite these questions, we see atrend to use contemporary trial designs. Due to betterunderstanding of genetic effect in disease or diseasecaused by different molecular features, contemporarytrial designs are used not only in oncology but also inpsychiatry and neurology and might be extended toall other medical field. The development in medicalresearch motivated the invention of new clinical trialdesigns, and the revolutionary trials can help themedical research move forward to better understandthe disease mechanism and eventually lead to bettertreatment to serve the patients.

Acknowledgements

We thank anonymous reviewers Dr Mihye Ahn, Miss EdenHuang and Dr Yu-Cheng Ku for their insightful suggestionsto make this paper more comprehensive.

Conflict of Interest statement

The authors have no potential conflicts of interest.


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