***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 R and omized Controlled Trials ( RCTs ) on time-to-event outcomes that are usually reported are median time to events and Cox Hazard Ratio . These do not constitute the sufficient statistics required for meta- analysis or cost-effectiveness analysis , and their use in secondary analyses requires strong assumptions that may not have been adequately tested . In order to enhance the quality of secondary data analyses , we propose a method which derives from the published Kaplan Meier survival curves a close approximation to the original individual patient time-to-event data from which they were generated . Methods We develop an algorithm that maps from digitised curves back to KM data by finding numerical solutions to the inverted KM equations , using where available information on number of events and numbers at risk . The reproducibility and accuracy of survival probabilities , median survival times and hazard ratios based on reconstructed KM data was assessed by comparing published statistics ( survival probabilities , medians and hazard ratios ) with statistics based on repeated reconstructions by multiple observers . Results The validation exercise established there was no material systematic error and that there was a high degree of reproducibility for all statistics . Accuracy was excellent for survival probabilities and medians , for hazard ratios reasonable accuracy can only be obtained if at least numbers at risk or total number of events are reported . Conclusion The algorithm is a reliable tool for meta- analysis and cost-effectiveness analyses of RCTs reporting time-to-event data . It is recommended that all RCTs should report information on numbers at risk and total number of events alongside KM curves OBJECTIVE : To test the feasibility of creating a valid and reliable checklist with the following features : appropriate for assessing both r and omised and non-r and omised studies ; provision of both an overall score for study quality and a profile of scores not only for the quality of reporting , internal validity ( bias and confounding ) and power , but also for external validity . DESIGN : A pilot version was first developed , based on epidemiological principles , review s , and existing checklists for r and omised studies . Face and content validity were assessed by three experienced review ers and reliability was determined using two raters assessing 10 r and omised and 10 non-r and omised studies . Using different raters , the checklist was revised and tested for internal consistency ( Kuder-Richardson 20 ) , test-retest and inter-rater reliability ( Spearman correlation coefficient and sign rank test ; kappa statistics ) , criterion validity , and respondent burden . MAIN RESULTS : The performance of the checklist improved considerably after revision of a pilot version . The Quality Index had high internal consistency ( KR-20 : 0.89 ) as did the subscales apart from external validity ( KR-20 : 0.54 ) . Test-retest ( r 0.88 ) and inter-rater ( r 0.75 ) reliability of the Quality Index were good . Reliability of the subscales varied from good ( bias ) to poor ( external validity ) . The Quality Index correlated highly with an existing , established instrument for assessing r and omised studies ( r 0.90 ) . There was little difference between its performance with non-r and omised and with r and omised studies . Raters took about 20 minutes to assess each paper ( range 10 to 45 minutes ) . CONCLUSIONS : This study has shown that it is feasible to develop a checklist that can be used to assess the method ological quality not only of r and omised controlled trials but also non-r and omised studies . It has also shown that it is possible to produce a checklist that provides a profile of the paper , alerting review ers to its particular method ological strengths and weaknesses . Further work is required to improve the checklist and the training of raters in the assessment of external validity The use of r and om effects modeling in statistics has increased greatly in recent years . The introduction of such modeling into event-time analysis has proceeded more slowly , however . Previously , r and om effects models for survival data have either required assumptions regarding the form of the baseline hazard function or restrictions on the classes of models that can be fit . In this paper , we develop a method of r and om effect analysis of  survival data  , the hierarchical Cox model , that is an extension of Cox 's original formulation in that the baseline hazard function remains unspecified . This method also allows an arbitrary distribution for the r and om effects . We accomplish this using Markov chain Monte Carlo methods in a Bayesian setting . The method is illustrated with three models for a data set with times to multiple occurrences of mammory tumors for 48 rats treated with a carcinogen and then r and omized to either treatment or control . This analysis is more satisfying than st and ard approaches , such as study ing the first event for each subject , which does not fully use the data , or assuming independence , which in this case would overestimate the precision Background Patients with advanced urothelial carcinoma that progresses after platinum‐based chemotherapy have a poor prognosis and limited treatment options . Methods In this open‐label , international , phase 3 trial , we r and omly assigned 542 patients with advanced urothelial cancer that recurred or progressed after platinum‐based chemotherapy  to receive pembrolizumab ( a highly selective , humanized monoclonal IgG4κ isotype antibody against programmed death 1 [ PD‐1 ] ) at a dose of 200 mg every 3 weeks or the investigator 's choice of chemotherapy with paclitaxel , docetaxel , or vinflunine . The co primary end points were overall survival and progression‐free survival , which were assessed among all patients and among patients who had a tumor PD‐1 lig and ( PD‐L1 ) combined positive score ( the percentage of PD‐L1–expressing tumor and infiltrating immune cells relative to the total number of tumor cells ) of 10 % or more . Results The median overall survival in the total population was 10.3 months ( 95 % confidence interval [ CI ] , 8.0 to 11.8 ) in the pembrolizumab group , as compared with 7.4 months ( 95 % CI , 6.1 to 8.3 ) in the chemotherapy group ( hazard ratio for death , 0.73 ; 95 % CI , 0.59 to 0.91 ; P=0.002 ) . The median overall survival among patients who had a tumor PD‐L1 combined positive score of 10 % or more was 8.0 months ( 95 % CI , 5.0 to 12.3 ) in the pembrolizumab group , as compared with 5.2 months ( 95 % CI , 4.0 to 7.4 ) in the chemotherapy group ( hazard ratio , 0.57 ; 95 % CI , 0.37 to 0.88 ; P=0.005 ) . There was no significant between‐group difference in the duration of progression‐free survival in the total population ( hazard ratio for death or disease progression , 0.98 ; 95 % CI , 0.81 to 1.19 ; P=0.42 ) or among patients who had a tumor PD‐L1 combined positive score of 10 % or more ( hazard ratio , 0.89 ; 95 % CI , 0.61 to 1.28 ; P=0.24 ) . Fewer treatment‐related adverse events of any grade were reported in the pembrolizumab group than in the chemotherapy group ( 60.9 % vs. 90.2 % ) ; there were also fewer events of grade 3 , 4 , or 5 severity reported in the pembrolizumab group than in the chemotherapy group ( 15.0 % vs. 49.4 % ) . Conclusions Pembrolizumab was associated with significantly longer overall survival ( by approximately 3 months ) and with a lower rate of treatment‐related adverse events than chemotherapy as second‐line therapy for platinum‐refractory advanced urothelial carcinoma . ( Funded by Merck ; KEYNOTE‐045 Clinical Trials.gov number , NCT02256436 . 
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Conclusions ICI therapy for metastatic UC post platinum therapy has a higher overall response rate , OS and PFS in patients who are biomarker positive compared to those who are negative .
However , some patients who are biomarker negative do achieve complete responses .