***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: BACKGROUND The results of FASTACT , a r and omised , placebo-controlled , phase 2 study , showed that intercalated chemotherapy and erlotinib significantly prolonged progression-free survival ( PFS ) in patients with advanced non-small-cell lung cancer . We undertook FASTACT-2 , a phase 3 study in a similar patient population . METHODS In this phase 3 trial , patients with untreated stage IIIB/IV non-small-cell lung cancer were r and omly assigned in a 1:1 ratio by use of an interactive internet response system with minimisation algorithm ( stratified by disease stage , tumour histology , smoking status , and chemotherapy regimen ) to receive six cycles of gemcitabine ( 1250 mg/m(2 ) on days 1 and 8 , intravenously ) plus platinum ( carboplatin 5 × area under the curve or cisplatin 75 mg/m(2 ) on day 1 , intravenously ) with intercalated erlotinib ( 150 mg/day on days 15 - 28 , orally ; chemotherapy plus erlotinib ) or placebo orally ( chemotherapy plus placebo ) every 4 weeks . With the exception of an independent group responsible for monitoring data and safety monitoring board , everyone outside the interactive internet response system company was masked to treatment allocation . Patients continued to receive erlotinib or placebo until progression or unacceptable toxicity or death , and all patients in the placebo group were offered second-line erlotinib at the time of progression . The primary endpoint was PFS in the intention-to-treat population . This trial is registered with Clinical Trials.gov , number NCT00883779 . FINDINGS From April 29 , 2009 , to Sept 9 , 2010 , 451 patients were r and omly assigned to chemotherapy plus erlotinib ( n=226 ) or chemotherapy plus placebo ( n=225 ) . PFS was significantly prolonged with chemotherapy plus erlotinib versus chemotherapy plus placebo ( median PFS 7·6 months [ 95 % CI 7·2 - 8·3 ] , vs 6·0 months [ 5·6 - 7·1 ] , hazard ratio [ HR ] 0·57 [ 0·47 - 0·69 ] ; p<0·0001 ) . Median overall survival for patients in the chemotherapy plus erlotinib and chemotherapy plus placebo groups was 18·3 months ( 16·3 - 20·8 ) and 15·2 months ( 12·7 - 17·5 ) , respectively ( HR 0·79 [ 0·64 - 0·99 ] ; p=0·0420 ) . Treatment benefit was noted only in patients with an activating EGFR gene mutation ( median PFS 16·8 months [ 12·9 - 20·4 ] vs 6·9 months [ 5·3 - 7·6 ] , HR 0·25 [ 0·16 - 0·39 ] ; p<0·0001 ; median overall survival 31·4 months [ 22·2-undefined ] , vs 20·6 months [ 14·2 - 26·9 ] , HR 0·48 [ 0·27 - 0·84 ] ; p=0·0092 ) . Serious adverse events were reported by 76 ( 34 % ) of 222 patients in the chemotherapy plus placebo group and 69 ( 31 % ) of 226 in the chemotherapy plus erlotinib group . The most common grade 3 or greater adverse events were neutropenia ( 65 [ 29 % ] patients and 55 [ 25 % ] , respectively ) , thrombocytopenia ( 32 [ 14 % ] and 31 [ 14 % ] , respectively ) , and anaemia ( 26 [ 12 % ] and 21 [ 9 % ] , respectively ) . INTERPRETATION Intercalated chemotherapy and erlotinib is a viable first-line option for patients with non-small-cell lung cancer with EGFR mutation-positive disease or selected patients with unknown EGFR mutation status . FUNDING F Hoffmann-La Roche 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 dichotomous data . The Peto method has been particularly popular in the past . It has the advantage of simple calculation ; however , although it is appropriate in most cases , it may introduce large biases if the data are unbalanced [ 10 , 11 ] . On the other h and , r and om-effects models also add the between- study variance to the within- study variance of each individual study when the pooled mean of the r and om effects is calculated . The r and om-effects model most commonly used for dichotomous data is the DerSimonian and Laird estimate of the between- study variance [ 12 ] . Fixed- and r and om-effects models for continuous data have also been described [ 13 ] . Pooled results are generally reported as a point estimate and CI , typically a 95 % CI . Other quantitative techniques for combining data , such as the Confidence Profile Method [ 14 ] , use Bayesian methods to calculate posterior probability distributions for effects of interest . Bayesian statistics are based on the principle that each observation or set of observations should be viewed in conjunction with a prior probability describing the prior knowledge about the phenomenon of interest [ 15 ] . The new observations alter this prior probability to generate a posterior probability . Traditional meta- analysis assumes that nothing is known about the magnitude of the treatment effect before r and omized trials are performed . In Bayesian terms , the prior probability distribution is noninformative . Bayesian approaches may also allow the incorporation of indirect evidence in generating prior distributions [ 14 ] and may be particularly helpful in situations in which few data from r and omized studies exist [ 16 ] . Bayesian analyses may also be used to account for the uncertainty introduced by estimating the between- study variance in the r and om-effects model , leading to more appropriate estimates and predictions of treatment efficacy [ 17 ] . Exploring and Explaining Heterogeneity The next important issue is whether the common estimate obtained in the previous step is robust . Sensitivity analyses determine whether the common estimate is influenced by changes in the assumptions and in the protocol for combining the data . A comparison of the results of fixed- and r and om-effects models is one such sensitivity analysis [ 18 ] . Generally , the r and om-effects model produces wider CIs than does the fixed-effects model , and the level of statistical significance may therefore be different depending on the model used . The pooled point estimate per se is less likely to be affected , although exceptions are possible [ 19 ] . Other sensitivity analyses may include the examination of the residuals and the chi-square components [ 13 ] and assessment of the effect of deleting each study in turn . Statistically significant results that depend on a single study may require further exploration . Cumulative Meta- Analysis PURPOSE To determine the proportion of lung adenocarcinomas from East Asian never-smokers who harbor known oncogenic driver mutations . PATIENTS AND METHODS In this surgical series , 52 resected lung adenocarcinomas from never-smokers ( < 100 cigarettes in a lifetime ) at a single institution ( Fudan University , Shanghai , China ) were analyzed concurrently for mutations in EGFR , KRAS , NRAS , HRAS , HER2 , BRAF , ALK , PIK3CA , TP53 and LKB1 . RESULTS Forty-one tumors harbored EGFR mutations , three harbored EML4-ALK fusions , two harbored HER2 insertions , and one harbored a KRAS mutation . All mutations were mutually exclusive . Thus , 90 % ( 47 of 52 ; 95 % CI , 0.7896 to 0.9625 ) of lung adenocarcinomas from never-smokers were found to harbor well-known oncogenic mutations in just four genes . No BRAF , NRAS , HRAS , or LKB1 mutations were detected , while 
    Output:
    
Conclusions This meta- analysis suggests that EGFR mutation is an important predictive factor linked to improved OS for NSCLC patients with brain metastases .
It can serve as a useful index in the prognostic assessment of NSCLC patients with brain metastases