***TASK*** the task is to summarize an input biomedical literature in six sentences 
    ***INPUT*** the input is a biomedical literature 
    ***OUTPUT*** the output is the summary of an input biomedical literature in six sentences 
    ***DOCUMENTATION*** 
    ***EXAMPLES*** 
    Input: Purpose : A new generation of molecularly targeted agents is entering the definitive stage of clinical evaluation . Many of these drugs benefit only a subset of treated patients and may be overlooked by the traditional , broad- eligibility approach to r and omized clinical trials . Thus , there is a need for development of novel statistical methodology for rapid evaluation of these agents . Experimental Design : We propose a new adaptive design for r and omized clinical trials of targeted agents in setting s where an assay or signature that identifies sensitive patients is not available at the outset of the study . The design combines prospect i ve development of a gene expression – based classifier to select sensitive patients with a properly powered test for overall effect . Results : Performance of the adaptive design , relative to the more traditional design , is evaluated in a simulation study . It is shown that when the proportion of patients  sensitive to the new drug is low , the adaptive design substantially reduces the chance of false rejection of effective new treatments . When the new treatment is broadly effective , the adaptive design has power to detect the overall effect similar to the traditional design . Formulas are provided to determine the situations in which the new design is advantageous . Conclusion : Development of a gene expression – based classifier to identify the subset of sensitive patients  can be prospect ively incorporated into a r and omized phase III design without compromising the ability to detect an overall effect PURPOSE Biomarkers can add substantial value to current medical practice by providing an integrated approach to prediction using the genetic makeup of the tumor and the genotype of the patient to guide patient-specific treatment selection . We discuss and evaluate various clinical trial design s for the validation of biomarker-guided therapy . METHODS Design s for predictive marker validation are broadly classified as retrospective ( ie , using data from previously well-conducted r and omized controlled trials [ RCTs ] ) versus prospect i ve ( enrichment , unselected , hybrid , or adaptive analysis ) . We discuss the salient features of each design in the context of real trials . RESULTS Well- design ed retrospective analysis from well-conducted prospect i ve RCTs can bring forward effective treatments to marker-defined subgroups of patients in a timely manner ( eg , KRAS and colorectal cancer ) . Enrichment design s are appropriate when preliminary evidence suggest that patients with or without that marker profile do not benefit from the treatments in question ; however , this may sometimes leave questions unanswered ( eg , trastuzumab and breast cancer ) . An unselected design is optimal where preliminary evidence regarding treatment benefit and assay reproducibility is uncertain ( eg , epidermal growth factor receptor and lung cancer ) . Hybrid design s are appropriate when preliminary evidence demonstrate the efficacy of certain treatments for a marker-defined subgroup , making it unethical to r and omly assign patients with that marker status to other treatments ( eg , multigene assay and breast cancer ) . Adaptive analysis design s allow for prespecified marker-defined subgroup analyses of data from an RCT . CONCLUSION The implementation of these design strategies will lead to a more rapid clinical validation of biomarker-guided therapy Purpose : Many anticancer therapies benefit only a subset of treated patients and may be overlooked by the traditional broad eligibility approach to design phase III clinical trials . New biotechnologies such as microarrays can be used to identify the patients that are most likely to benefit from anticancer therapies .  However , due to the high-dimensional nature of the genomic data , developing a reliable classifier by the time the definitive phase III trail is design ed may not be feasible . Experimental Design : Previously , Freidlin and Simon ( Clinical Cancer Research , 2005 ) introduced the adaptive signature design that combines a prospect i ve development of a sensitive patient classifier and a properly powered test for overall effect in a single pivotal trial . In this article , we propose a cross-validation extension of the adaptive signature design that optimizes the efficiency of both the classifier development and the validation components of the design . Results : The new design is evaluated through simulations and is applied to data from a r and omized breast cancer trial . Conclusion : The cross-validation approach is shown to considerably improve the performance of the adaptive signature design . We also describe approaches to the estimation of the treatment effect for the identified sensitive sub population . Clin Cancer Res ; 16(2 ) ; Background There is currently much interest in pharmacogenetics : determining variation in genes that regulate drug effects , with a particular emphasis on improving drug safety and efficacy . The ability to determine such variation motivates the application of personalized drug therapies that utilize a patient 's genetic makeup to determine a safe and effective drug at the correct dose . To ascertain whether a genotype-guided drug therapy improves patient care , a personalized medicine intervention may be evaluated within the framework of a r and omized controlled trial . The statistical design of this type of personalized medicine intervention requires special considerations : the distribution of relevant allelic variants in the study population ; and whether the pharmacogenetic intervention is equally effective across sub population s defined by allelic variants . Methods The statistical design of the Clarification of Optimal Anticoagulation through Genetics ( COAG ) trial serves as an illustrative example of a personalized medicine intervention that uses each subject 's genotype information . The COAG trial is a multicenter , double blind , r and omized clinical trial that will compare two approaches to initiation of warfarin therapy : genotype-guided dosing , the initiation of warfarin therapy based on algorithms using clinical information and genotypes for polymorphisms in CYP2C9 and VKORC1 ; and clinical -guided dosing , the initiation of warfarin therapy based on algorithms using only clinical information . Results We determine an absolute minimum detectable difference of 5.49 % based on an assumed 60 % population prevalence of zero or multiple genetic variants in either CYP2C9 or VKORC1 and an assumed 15 % relative effectiveness of genotype-guided warfarin initiation for those with zero or multiple genetic variants . Thus we calculate a sample size of 1238 to achieve a power level of 80 % for the primary outcome . We show that reasonable departures from these assumptions may decrease statistical power to 65 % . Conclusions In a personalized medicine intervention , the minimum detectable difference used in sample size calculations is not a known quantity , but rather an unknown quantity that depends on the genetic makeup of the subjects enrolled . Given the possible sensitivity of sample size and power calculations to these key assumptions , we recommend that they be monitored during the conduct of a personalized medicine intervention . Trial Registration clinical trials.gov : The optimal design of phase II studies continues to be the subject of vigorous debate , especially studies of newer molecularly targeted agents . The observations that many new therapeutics “ fail ” in definitive phase III studies , coupled with the numbers of new agents to be tested as well as the increasing costs and complexity of clinical trials , further emphasize the critical importance of robust and efficient phase II design . The Clinical Trial Design Task Force ( CTD-TF ) of the National Cancer Institute ( NCI ) Investigational Drug Steering Committee ( IDSC ) has published a series of discussion papers on phase II trial design in Clinical Cancer Research . The IDSC has developed formal recommendations about aspects of phase II trial design that are the subject of frequent debate , such as endpoints ( response versus progression-free survival ) , r and omization ( single-arm design s versus r and omization ) , inclusion of biomarkers , biomarker-based patient enrichment strategies , and statistical design ( e.g. , two-stage design s versus multiple-group adaptive design s ) . Although these recommendations in general encourage the use of progression-free survival as the primary endpoint , r and omization , inclusion of biomarkers , and incorporation of newer design s , we acknowledge that objective response as an endpoint and single-arm design s remain relevant in certain situations . The design of any clinical trial should always be carefully evaluated and justified based on characteristic specific to the situation . Clin Cancer Res ; 16(6 ) ; Pharmacogenomics is the science of determining how the benefits and adverse effects of a drug vary among a target population of patients based on genomic features of the patient 's germ line and diseased tissue . By identifying those patients who are most likely to respond while eliminating serious adverse effects , the therapeutic index of a drug can be substantially increased . This may facilitate demonstrating the effectiveness of the drug and may avoid subsequent problems due to serious adverse events . Our objective here is to provide clinical trial design s and analysis strategies for the utilization of genomic signatures as classifiers for patient stratification or patient selection in therapeutics development . We review methods for the development of genomic signature classifiers of treatment outcome in high-dimensional setting s , where the number of variables available for prediction far exceeds the number of cases . The split- sample and crossvalidation methods for obtaining estimates of prediction accuracy in developmental studies are described . We present clinical trial design s for utilizing genomic signature classifiers in therapeutics development . The purpose of the classifier is to facilitate the identification of groups of patients with a high probability of benefiting from it and avoiding serious adverse events . We distinguish exploratory analysis during the development of the genomic classifier from prospect i ve planning and rigorous testing of therapeutic hypotheses in studies that utilize the genomic classifier in therapeutics development . We discuss a variety of clinical trial design s including those utilizing specimen collection and assay prospect ively for newly accrued patients and those involving a prospect ively planned analysis of archived specimens from a previously conducted clinical trial . Our discussion of the development and use of classifiers of efficacy is mostly focused on applications in oncology using classifiers based on biomarkers measured in tumors . Some of the same considerations apply , however , to development of efficacy and safety classifiers in nononcologic diseases based on single-nucleotide germline polymorphisms Advancements made in molecularly targeted therapeutics have focussed increasing attention on biomarker-adaptive clinical trial design s as a means of improving the efficiency of drug development and advancing medical care . Here , we focus on r and omized clinical trial ( RCT ) design s aim ed at developing/validating clinical ly relevant pharmacogenomic biomarkers capable of identifying patients sensitive to a given therapy(s ) , and thereby , selecting the best therapy for a given patient . The term ‘ biomarker-adaptive ’ refers to biomarker trials that incorporate adaptive clinical trial method ology to modify the trial according to the accumulating outcome data . While some adaptive methods ( e.g. , interim monitoring ) have been an integral part of traditional clinical trial design for decades , these methods can be particularly advantageous in biomarker studies that evaluate molecularly targeted therapies . Here , we briefly review the main clinical trial methods that are used in design ing adaptive trials . We then discuss how these methods can be used to improve the efficiency of biomarker RCT design The decade since the publication of the Human Genome Project draft has ended with the discovery of hundreds of genomic markers related to diseases and phenotypes . However , the project has not yet delivered on its promise to tailor treatments for individuals . The number of genomic markers in clinical practice is very small . The number of markers to guide treatment decisions is even smaller . In order to speed up discovery and validation of genomic treatment selection markers , we call for considering the brilliant potential of r and omized clinical trials . If biomedical research community can collaborate in organizing large-scale consortium of clinical trials associated with well- design ed biobanks , these studies would soon act as huge laboratories for investigating genomic medicine ; a big step forward towards personalizing medicine BACKGROUND We present the combined results of two trials that compared adjuvant chemotherapy with or without concurrent trastuzumab in women with surgically removed HER2-positive breast cancer . METHODS The National Surgical Adjuvant Breast and Bowel Project trial B-31 compared doxorubicin and cyclophosphamide followed by paclitaxel every 3 weeks ( group 1 ) with the same regimen plus 52 weeks of trastuzumab beginning with the first dose of paclitaxel ( group 2 ) . The North Central Cancer Treatment Group trial N9831 compared three regimens : doxorubicin and cyclophosphamide followed by weekly paclitaxel ( group A ) , the same regimen followed by 52 weeks of trastuzumab after paclitaxel ( group B ) , and the same regimen plus 52 weeks of trastuzumab initiated concomitantly with paclitaxel ( group C ) . The studies were amended to include a joint analysis comparing groups 1 and A ( the control group ) with groups 2 and C ( the trastuzumab group ) . Group B was excluded because trastuzumab was not given concurrently with paclitaxel . RESULTS By March 15 , 2005 , 394 events ( recurrent , second primary cancer , or death before recurrence ) had been reported , triggering the first scheduled interim analysis . Of these , 133 were in the trastuzumab group and 261 in the control group ( hazard ratio , 0.48 ; P<0.0001 ) . This result crossed the early stopping boundary . The absolute difference in disease-free survival between the trastuzumab group and the control group was 12 percent at three years . Trastuzumab therapy was associated with a 33 percent reduction in the risk of death ( P=0.015 ) . The three-year cumulative incidence of class III or IV congestive heart failure or death from cardiac causes in the trastuzumab group was 4.1 percent in trial B-31 and 2.9 percent in trial N9831 . CONCLUSIONS Trastuzumab combined with paclitaxel after doxorubicin and cyclophosphamide improves outcomes among women with surgically removed HER2-positive breast cancer . ( Clinical Trials.gov numbers , NCT00004067 and NCT00005970 . The developmental pathway from discovery to clinical practice for biomarkers and biomarker-directed therapies is complex . While several issues need careful consideration , two critical issues that surround the validation of biomarkers are the choice of clinical trial design ( which is based on the strength of the preliminary evidence and marker prevalence ) and the biomarker assay related issues surrounding the marker assessment methods such as the reliability and reproducibility of the assay . This review focuses on trial design s for marker validation , both in 
    Output:
    
Our review showed that a considerable number of labels has been proposed for trial design s evaluating prognostic and predictive biomarkers which , based on patient flow elements , can be classified into five basic categories .