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10.1177/1740774515625990 http://scihub22266oqcxt.onion/10.1177/1740774515625990 C4995068!4995068!26908538     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Clin+Trials 2016 ; 13 (3): 352-7 Nephropedia Template TP {{tp|p=26908538|t=2016. Assessing Survival Benefit When Treatment Delays Disease Progression |pdf=|usr=}}{{26908538}} gab.com Text Assessing Survival Benefit When Treatment Delays Disease Progression JO: Clin Trials http://www.kidney.de/pget.php?&tw=1&pmid=26908538 #FOAvip Background: For a potentially lethal chronic disease like cancer, it is often infeasible to compare treatments on the basis of overall survival, so a combined outcome such as progression-free survival (which is the time from randomization to progression or death) has become an acceptable primary endpoint. The rationale of using an efficacy measure that is dominated by the time to progression is that an effective treatment will delay progression and when treatment is stopped at progression, the effect of treatment after this time is small. However often trials that show a significant benefit for delaying progression but not on overall survival are not universally viewed as convincing evidence that the drug should become the standard of care. Methods: We propose that when there is a significant treatment effect of delaying progression, a Bayesian analysis of overall survival should be undertaken. We suggest using a joint piecewise exponential model, where the treatment effect on the hazard for progression and for death after progression are captured through two distinct parameters. We develop a plot of the overall survival advantage of the new therapy versus the relative hazard for death after progression. This plot can augment the discussion about whether the new treatment is beneficial on survival. Results: In the example of an early breast cancer trial for which a new treatment significantly delayed disease recurrence, our Bayesian analysis showed that with very reasonable assumptions on the effects of treatment after recurrence, there is a high probability that the new treatment improves overall survival. Conclusions: For a clinical trial for which treatment delays progression, the proposed method can improve the interpretability of the survival comparison using data from the study. Twit Text FOAVip Assessing Survival Benefit When Treatment Delays Disease Progression ????Clin Trials http://www.kidney.de/pget.php?&tw=1&pmid=26908538 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26908538 #FOAvip English Wikipedia <ref>{{Cite pmid|26908538}}</ref> Assessing Survival Benefit When Treatment Delays Disease Progression #MMPMID26908538 Schoenfeld DA; Finkelstein DM Clin Trials 2016[Jun]; 13 (3): 352-7 PMID26908538show ga Background: For a potentially lethal chronic disease like cancer, it is often infeasible to compare treatments on the basis of overall survival, so a combined outcome such as progression-free survival (which is the time from randomization to progression or death) has become an acceptable primary endpoint. The rationale of using an efficacy measure that is dominated by the time to progression is that an effective treatment will delay progression and when treatment is stopped at progression, the effect of treatment after this time is small. However often trials that show a significant benefit for delaying progression but not on overall survival are not universally viewed as convincing evidence that the drug should become the standard of care. Methods: We propose that when there is a significant treatment effect of delaying progression, a Bayesian analysis of overall survival should be undertaken. We suggest using a joint piecewise exponential model, where the treatment effect on the hazard for progression and for death after progression are captured through two distinct parameters. We develop a plot of the overall survival advantage of the new therapy versus the relative hazard for death after progression. This plot can augment the discussion about whether the new treatment is beneficial on survival. Results: In the example of an early breast cancer trial for which a new treatment significantly delayed disease recurrence, our Bayesian analysis showed that with very reasonable assumptions on the effects of treatment after recurrence, there is a high probability that the new treatment improves overall survival. Conclusions: For a clinical trial for which treatment delays progression, the proposed method can improve the interpretability of the survival comparison using data from the study. äAssessing Survival Benefit When Treatment Delays Disease Progression (epmc).pdf DeepDyve Pubget Overpricing