***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: The analytical effect of the number of events per variable ( EPV ) in a proportional hazards regression analysis was evaluated using Monte Carlo simulation techniques for data from a r and omized trial containing 673 patients and 252 deaths , in which seven predictor variables had an original significance level of p < 0.10 . The 252 deaths and 7 variables correspond to 36 events per variable analyzed in the full data set . Five hundred simulated analyses were conducted for these seven variables at EPVs of 2 , 5 , 10 , 15 , 20 , and 25 . For each simulation , a r and om exponential survival time was generated for each of the 673 patients , and the simulated results were compared with their original counterparts . As EPV decreased , the regression coefficients became more biased relative to the true value ; the 90 % confidence limits about the simulated values did not have a coverage of 90 % for the original value ; large sample properties did not hold for variance estimates from the proportional hazards model , and the Z statistics used to test the significance of the regression coefficients lost validity under the null hypothesis . Although a single boundary level for avoiding problems is not easy to choose , the value of EPV = 10 seems most prudent . Below this value for EPV , the results of proportional hazards regression analyses should be interpreted with caution because the statistical model may not be valid Multivariable methods of analysis can yield problematic results if method ological guidelines and mathematical assumptions are ignored . A problem arising from a too-small ratio of events per variable ( EPV ) can affect the accuracy and precision of regression coefficients and their tests of statistical significance . The problem occurs when a proportional hazards analysis contains too few " failure " events ( e.g. , deaths ) in relation to the number of included independent variables . In the current research , the impact of EPV was assessed for results of proportional hazards analysis done with Monte Carlo simulations in an empirical data set of 673 subjects enrolled in a multicenter trial of coronary artery bypass surgery .  The research is presented in two parts : Part I describes the data set and strategy used for the analyses , including the Monte Carlo simulation studies done to determine and compare the impact of various values of EPV in proportional hazards analytical results . Part II compares the output of regression models obtained from the simulations , and discusses the implication of the findings 
    Output:
    
Results demonstrate that previous well-known tests for detecting funnel plot asymmetry suffer from low power or excessive type-I error rates in meta- analysis of survival data , particularly when trials are affected by participant dropout .