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10S Intention-to treat versus treatment received analyses in randomized clinical trials: A simulation study
38S
Abstracts
number of active coordinating centers and resource centers in North America using the Internet telephone, and maiL The results of this survey will be summarized by: • type of center(s) involved in use; • type of information utility used and reasons for that choice; • category of use, e.g., mail, edit messages, inventory; • st~n,~ of use, e.g., planned, current, abandoned; • volume and duration of use; • cautions and difficulties encountered in use.
10S INTENTION-TO TREAT VERSUS TREATMENT RECEIVED ANALYSES IN RANDOMIZED CLINICAL TRIALS: A SIMULATION STUDY Cynthia B. Saiontz and Steven Piantadosi
The Johns Hopkins Univers~ School of Hygiene and Publ~ Health Baltimore,Maryland Intention-to-Tre.at (ITr) is the principle of analyzingpatients according to the treatment group to which they were assigned regardless of the U~ttment they actually received. An a l t e m ~ v e procedure is Treatment Received OR) which analyzes patients according to treatment actually received. To compare estimates of treatment effect from these two analyses, we performed shnulafions of clinicaltrialsunder various known assumlZ/ons and incorporating plausible complications. These complications included non-compliance with assigned treatment, dropouts, and treatment crossovers. Data were analyzed according to both the ITY and TR principles. The estimated treatment effect from each type of analysis was compared to the true treatment effect which was used to create the dlt,. We have outlined the conditions under which each type of analysis performs best. Under the null hypothesis, our results show that r I ' r analyses are generally less biased than TR analyses. This result does not depend on the particular model used for analysis or ff adjustments are made for a covariate that affects crossover. When treatment crossover is random and not related to outcome. TR analysis yields estimates closer to the Wae treatment effeot than IT]" analysis. Under the alternalive hypothesis, TR analyses can give more a~urate estimates of treatment effect. This is particularly true when adjusting for a covariate related to the crossover probability. The bias in estimated ~ effect depends on the amount of censoring, covariate adjustment, and the strength of the covariate on both odds of crossover and outcome. We discuss quantitatively the conditions under which a trial might best be analyzed by each type of approach.
Abstracts
number of active coordinating centers and resource centers in North America using the Internet telephone, and maiL The results of this survey will be summarized by: • type of center(s) involved in use; • type of information utility used and reasons for that choice; • category of use, e.g., mail, edit messages, inventory; • st~n,~ of use, e.g., planned, current, abandoned; • volume and duration of use; • cautions and difficulties encountered in use.
10S INTENTION-TO TREAT VERSUS TREATMENT RECEIVED ANALYSES IN RANDOMIZED CLINICAL TRIALS: A SIMULATION STUDY Cynthia B. Saiontz and Steven Piantadosi
The Johns Hopkins Univers~ School of Hygiene and Publ~ Health Baltimore,Maryland Intention-to-Tre.at (ITr) is the principle of analyzingpatients according to the treatment group to which they were assigned regardless of the U~ttment they actually received. An a l t e m ~ v e procedure is Treatment Received OR) which analyzes patients according to treatment actually received. To compare estimates of treatment effect from these two analyses, we performed shnulafions of clinicaltrialsunder various known assumlZ/ons and incorporating plausible complications. These complications included non-compliance with assigned treatment, dropouts, and treatment crossovers. Data were analyzed according to both the ITY and TR principles. The estimated treatment effect from each type of analysis was compared to the true treatment effect which was used to create the dlt,. We have outlined the conditions under which each type of analysis performs best. Under the null hypothesis, our results show that r I ' r analyses are generally less biased than TR analyses. This result does not depend on the particular model used for analysis or ff adjustments are made for a covariate that affects crossover. When treatment crossover is random and not related to outcome. TR analysis yields estimates closer to the Wae treatment effeot than IT]" analysis. Under the alternalive hypothesis, TR analyses can give more a~urate estimates of treatment effect. This is particularly true when adjusting for a covariate related to the crossover probability. The bias in estimated ~ effect depends on the amount of censoring, covariate adjustment, and the strength of the covariate on both odds of crossover and outcome. We discuss quantitatively the conditions under which a trial might best be analyzed by each type of approach.