***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 phase I/II trial to assess safety and efficacy of the combination bendamustine , rituximab , and lenalidomide ( BRL ) in patients with chronic lymphocytic leukemia ( CLL ) . PATIENTS AND METHODS Seventeen relapsed or refractory ( R/R ) and five previously untreated ( FL ) CLL patients were enrolled in the trial . In the R/R cohort , four different dose levels of lenalidomide ( maximum 15 mg/d ) were used . In the FL cohort , lenalidomide was dose escalated from 5 mg/d to 15 mg/d . Bendamustine was used at doses of 50 or 90 mg/m(2 ) for R/R or FL treatment , respectively . 375 mg/m(2 ) Rituximab were used for the first and 500 mg/m(2 ) for subsequent treatment courses . Treatment consisted of up to six courses of 28 d. RESULTS The maximal tolerable dose of lenalidomide was 5 mg/d . The response rate was 47.1 % in R/R and 60 % in FL patients . Median progression-free survival was 8.0 months . Median overall survival was 22.9 and 12.3 months , respectively , in R/R and FL patients . Grade 3/4 hematological toxicity was observed in 71.4 % , and severe infections in 47.6 % of patients . Due to high toxicity and low response rate of BRL , the trial was closed prematurely . CONCLUSION BRL was associated with a high toxicity rate , a high number of treatment interruptions , and a low remission rate . Therefore , BRL can not be considered an appropriate treatment option for patients with CLL N 1988 , THE National Cancer Institute-sponsored Working Group ( NCI-WC ) on chronic lymphocytic leukemia ( CLL ) published guidelines for the design and conduct of clinical trials in CLL with two major objectives : first , to facilitate comparisons of results of clinical trials in CLL by providing st and ardized eligibility , response , and toxicity criteria ; and , second , to encourage a framework on which to evaluate new scientific studies related to our increasing underst and ing of the biology and immunology of this disease . ' These guidelines were rapidly adopted by the majority of the clinical trials community , and were also used by the Food and Drug Administration during its evaluation process for the approval of fludarabine . The differences between these guidelines and those subsequently published by the International Working Group on CLL ( IWCLL ) , which were general- practice recommendations ' are listed in Table 1 . For diagnosis , the NCI-WC requires a lymphocyte count of 5 X loy & which is lower than the 10 X 109/L required by the IWCLL , unless the lymphocytes are B cells and the bone marrow is involved . To be considered a complete remission ( CR ) , the NCI-WC criteria specify that less than 30 % lymphocytes must be present in the bone marrow , with a recommendation that the clinical significance of lymphoid nodules be assessed prospect ively ( Table 1 ) ; the IWCLL allows focal infiltrates or nodules in the bone marrow aspirate and biopsy for CR . The IWCLL uses a shift in clinical stage as the sole index of partial remission ( PR ) , whereas the NCI-WC provides more specific criteria and recommends validation of the relevance of stage shift . The major differences were the well-defined criteria in the NCI guidelines regarding when to initiate therapy , hematologic toxicity , and other important components for clinical trials design . The purpose of this report is to present those revisions as considered necessary in view of advances in the past 8 years . Many of these revisions evolved as the guidelines were used in a systematic fashion in large clinical trials and , also , with the experience following the use of newer , more effective agents , such as fludarabine . " ' Although this report will focus on those changes recommended by the NCI-sponsored CLL Working Group , it will include sufficient details from the original guidelines so that the reader would find it a complete document by itself without having to refer to the older version PURPOSE Patients with relapsed or refractory chronic lymphocytic leukemia ( CLL ) have profound immune defects and limited treatment options . Given the dramatic activity of lenalidomide in other B-cell malignancies and its pleotropic immunomodulatory effects , we conducted a phase II trial of this agent in CLL . PATIENTS AND METHODS Patients with relapsed or refractory B-cell CLL ( B-CLL ) were eligible if they required treatment as per the National Cancer Institute Working Group 1996 guidelines .  Lenalidomide was administered orally at 25 mg on days 1 through 21 of a 28-day cycle . Response was assessed after each cycle . Patients were to continue treatment until disease progression , unacceptable toxicity , or complete remission .  Rituximab was added to lenalidomide on disease progression . RESULTS Forty-five patients were enrolled , with a median age of 64 years . Sixty-four percent of the patients had Rai stage III or IV disease , and 51 % were refractory to fludarabine . The overall response rate was 47 % , with 9 % of the patients attaining a complete remission . Fatigue , thrombocytopenia , and neutropenia were the most common adverse effects noted in 83 % , 78 % , and 78 % of the patients , respectively . CONCLUSION Lenalidomide is clinical ly active in patients with relapsed or refractory B-CLL . These findings are encouraging and warrant further investigation of this agent in the treatment of this disorder Patients with chronic lymphocytic lymphoma ( CLL ) with high-risk cytogenetics [ del(11q)(q22.3 ) or del(17p)(p13.1 ) ] have limited therapeutic options and their prognosis remains poor . This analysis was conducted to determine the clinical activity of lenalidomide in patients with high-risk disease . Relapsed/refractory patients with CLL enrolled in a phase II clinical trial who had del(11q)(q22.3 ) or del(17p)(p13.1 ) were included in this analysis . Patients received single agent lenalidomide for 21 days of the 4 week treatment cycle . The overall response rate among patients with high-risk cytogenetics was 38 % , with 19 % of patients achieving a complete response . Median progression-free survival was 12.1 months , which is higher than demonstrated with other agents in comparable patient population s. In addition , the estimated 2-year survival probability was 58 % , demonstrating that the responses achieved with lenalidomide are durable , even in patients with CLL with high-risk disease with poor risk cytogenetics Adequate dosing of lenalidomide in Chronic Lymphocytic Leukemia ( CLL ) remains unclear . This study determined maximum tolerated dose ( MTD ) in relapsed CLL patients ( Cohort A ) and patients achieving a partial response ( PR ) or better to recent therapy ( Cohort B ) . Thirty-seven patients were enrolled . MTD was 2.5 mg followed by 5.0 mg continuous . In Cohort A , tumor flare grade 1 - 2 occurred in 15 patients ( 50 % ) and grade 3 in 1 patient ( 3 % ) . Cohort A had 19 of 23 evaluable ( 83 % ) patients , 4 PR ( 17 % ) and 15 ( 65 % ) stable disease ( SD ) , Cohort B had 6 of 7 patients ( 86 % ) with SD . Despite overall response rate not being high , many patients remained on therapy several months with SD A quantitative systematic review , or meta- analysis , uses statistical methods to combine the results of multiple studies . Meta-analyses have been done for systematic review s of therapeutic trials , diagnostic test evaluations , and epidemiologic studies . Although the statistical methods involved may at first appear to be mathematically complex , their purpose is simple : They are trying to answer four basic questions . Are the results of the different studies similar ? To the extent that they are similar , what is the best overall estimate ? How precise and robust is this estimate ? Finally , can dissimilarities be explained ? This article provides some guidance in underst and ing the key technical aspects of the quantitative approach to these questions . We have avoided using equations and statistical notations ; interested readers will find implementations of the described methods in the listed references . We focus here on the quantitative synthesis of reports of r and omized , controlled , therapeutic trials because far more meta-analyses on therapeutic studies than on other types of studies have been published . For practical reasons , we present a stepwise description of the tasks that are performed when statistical methods are used to combine data . These tasks are 1 ) deciding whether to combine data and defining what to combine , 2 ) evaluating the statistical heterogeneity of the data , 3 ) estimating a common effect , 4 ) exploring and explaining heterogeneity , 5 ) assessing the potential for bias , and 6 ) presenting the results . Deciding Whether To Combine Data and Defining What To Combine By the time one performs a quantitative synthesis , certain decisions should already have been made about the formulation of the question and the selection of included studies . These topics were discussed in two previous articles in this series [ 1 , 2 ] . Statistical tests can not compensate for lack of common sense , clinical acumen , and biological plausibility in the design of the protocol of a meta- analysis . Thus , a reader of a systematic review should always address these issues before evaluating the statistical methods that have been used and the results that have been generated . Combining poor- quality data , overly biased data , or data that do not make sense can easily produce unreliable results . The data to be combined in a meta- analysis are usually either binary or continuous . Binary data involve a yes/no categorization ( for example , death or survival ) . Continuous data take a range of values ( for example , change in diastolic blood pressure after antihypertensive treatment , measured in mm Hg ) . When one is comparing groups of patients , binary data can be summarized by using several measures of treatment effect that were discussed earlier in this series [ 3 ] . These measures include the risk ratio ; the odds ratio ; the risk difference ; and , when study duration is important , the incidence rate . Another useful clinical measure , the number needed to treat ( NNT ) , is derived from the inverse of the risk difference [ 3 ] . Treatment effect measures , such as the risk ratio and the odds ratio , provide an estimate of the relative efficacy of an intervention , whereas the risk difference describes the intervention 's absolute benefit . The various measures of treatment effect offer complementary information , and all should be examined [ 4 ] . Continuous data can be summarized by the raw mean difference between the treatment and control groups when the treatment effect is measured on the same scale ( for example , diastolic blood pressure in mm Hg ) , by the st and ardized mean difference when different scales are used to measure the same treatment effect ( for example , different pain scales being combined ) , or by the correlation coefficients between two continuous variables [ 5 ] . The st and ardized mean difference , also called the effect size , is obtained by dividing the difference between the mean in the treatment group and the mean in the control group by the SD in the control group . Evaluating the Statistical Heterogeneity of the Data This step is intended to answer the question , Are the results of the different studies similar ( homogeneous ) ? It is important to answer this question before combining any data . To do this , one must calculate the magnitude of the statistical diversity ( heterogeneity ) of the treatment effect that exists among the different sets of data . Statistical diversity can be thought of as attributable to one or both of two causes . First , study results can differ because of r and om sampling error . Even if the true effect is the same in each study , the results of different studies would be expected to vary r and omly around the true common fixed effect . This diversity is called the within- study variance . Second , each study may have been drawn from a different population , depending on the particular patients chosen and the interventions and conditions unique to the study . Therefore , even if each study enrolled a large patient sample , the treatment effect would be expected to differ . These differences , called r and om effects , describe the between- study variation with regard to an overall mean of the effects of all of the studies that could be undertaken . The test most commonly used to assess the statistical significance of between- study heterogeneity is based on the chi-square distribution [ 6 ] . It provides a measure of the sum of the squared differences between the results observed and the results expected in each study , under the assumption that each study estimates the same common treatment effect . A large total deviation indicates that a single common treatment effect is unlikely . Any pooled estimate calculated must account for the between- study heterogeneity . In practice , this test has low sensitivity for detecting heterogeneity , and it has been suggested that a liberal significance level , such as 0.1 , should be used [ 6 ] . Estimating a Common Effect The questions that this step tries to answers are , 1 ) To the extent that data are similar , what is their best common point estimate of a therapeutic effect , and 2 ) how precise is this estimate ? The mathematical process involved in this step generally involves combining ( pooling ) the results of different studies into an overall estimate . Compared with the results of individual studies , pooled results can increase statistical power and lead to more precise estimates of treatment effect . Each study is given a weight according to the precision of its results . The rationale is that studies with narrow CIs should be weighted more heavily than studies with greater uncertainty . The precision is generally expressed by the inverse of the variance of the estimate of each study . The variance has two components : the variance of the individual study and the variance between different studies . When the between- study variance is found to be or assumed to be zero , each study is simply weighted by the inverse of its own variance , which is a function of the study size and the number of events in the study . This approach characterizes a fixed-effects model , as exemplified by the Mantel-Haenszel method [ 7 , 8 ] or the Peto method [ 9 ] for dichotom 
    Output:
    
Our finding demonstrated that lenalidomide plus anti-CD20 mAbs could be an efficient therapy regimen for relapsed/refractory CLL patients , especially for those with high-risk cytogenetic factor