tetano
Editor, Senior Moderator
Contemp Clin Trials Commun. 2019 Jun 21;15:100401. doi: 10.1016/j.conctc.2019.100401. eCollection 2019 Sep.
[h=1]Comparison of an ordinal endpoint to time-to-event, longitudinal, and binary endpoints for use in evaluating treatments for severe influenza requiring hospitalization.[/h] Peterson RL[SUP]1[/SUP], Vock DM[SUP]1[/SUP], Babiker A[SUP]2[/SUP], Powers JH 3rd[SUP]3[/SUP], Hunsberger S[SUP]4[/SUP], Angus B[SUP]5[/SUP], Paez A[SUP]6[/SUP], Neaton JD[SUP]1[/SUP]; INSIGHT FLU-IVIG study group.
[h=3]Author information[/h]
[h=3]Abstract[/h] [h=4]Background/aims:[/h] The Food and Drug Administration recommends research into developing well-defined and reliable endpoints to evaluate treatments for severe influenza requiring hospitalization. A novel 6-category ordinal endpoint of patient health status after 7 days that ranges from death to hospital discharge with resumption of normal activities is being used in a randomized placebo-controlled trial of intravenous immunoglobulin (IVIG) for severe influenza (FLU-IVIG). We compare the power of the ordinal endpoint under a proportional odds model to other types of endpoints as a function of various trial parameters.
[h=4]Methods:[/h] We used closed-form analysis and empirical simulation to compare the power of the ordinal endpoint to time-to-event, longitudinal, and binary endpoints. In the simulation setting, we varied the treatment effect and the distribution of the placebo group across the follow-up period with consideration of adjustment for baseline health status.
[h=4]Results:[/h] In the analytic setting, ordinal endpoints of high granularity provided greater power than time-to-event endpoints when most patients in the placebo group had either naturally progressed to the category of hospital discharge by day 7 or were far from hospital discharge on day 7. In the simulation setting, adjustment for baseline health status universally raised power for the proportional odds model. Across different placebo group distributions of the ordinal endpoint regardless of adjustment for baseline health status, only time-to-event endpoints yielded higher power than the ordinal endpoint for certain treatment effects.
[h=4]Conclusions:[/h] In this case study, the FLU-IVIG ordinal endpoint provided greater power than time-to-event, binary, and longitudinal endpoints for most scenarios of the treatment effect and placebo group distribution, including the target population studied for FLU-IVIG. The ordinal endpoint was only surpassed by the time-to-event endpoint when many patients in the placebo group were on the cusp of hospital discharge on day 7 and the follow-up period for the time-to-event endpoint was extended to allow for additional events. Our general approach for evaluating the power of several potential endpoints for an influenza trial can be used for designing other influenza trials with different target populations and for other trials in other disease areas.
[h=4]KEYWORDS:[/h] Clinical trials; Endpoints; Outcome assessments; Proportional odds model; Statistical power
PMID: 31312748 PMCID: PMC6609815 DOI: 10.1016/j.conctc.2019.100401
Free PMC Article
[h=1]Comparison of an ordinal endpoint to time-to-event, longitudinal, and binary endpoints for use in evaluating treatments for severe influenza requiring hospitalization.[/h] Peterson RL[SUP]1[/SUP], Vock DM[SUP]1[/SUP], Babiker A[SUP]2[/SUP], Powers JH 3rd[SUP]3[/SUP], Hunsberger S[SUP]4[/SUP], Angus B[SUP]5[/SUP], Paez A[SUP]6[/SUP], Neaton JD[SUP]1[/SUP]; INSIGHT FLU-IVIG study group.
[h=3]Author information[/h]
[h=3]Abstract[/h] [h=4]Background/aims:[/h] The Food and Drug Administration recommends research into developing well-defined and reliable endpoints to evaluate treatments for severe influenza requiring hospitalization. A novel 6-category ordinal endpoint of patient health status after 7 days that ranges from death to hospital discharge with resumption of normal activities is being used in a randomized placebo-controlled trial of intravenous immunoglobulin (IVIG) for severe influenza (FLU-IVIG). We compare the power of the ordinal endpoint under a proportional odds model to other types of endpoints as a function of various trial parameters.
[h=4]Methods:[/h] We used closed-form analysis and empirical simulation to compare the power of the ordinal endpoint to time-to-event, longitudinal, and binary endpoints. In the simulation setting, we varied the treatment effect and the distribution of the placebo group across the follow-up period with consideration of adjustment for baseline health status.
[h=4]Results:[/h] In the analytic setting, ordinal endpoints of high granularity provided greater power than time-to-event endpoints when most patients in the placebo group had either naturally progressed to the category of hospital discharge by day 7 or were far from hospital discharge on day 7. In the simulation setting, adjustment for baseline health status universally raised power for the proportional odds model. Across different placebo group distributions of the ordinal endpoint regardless of adjustment for baseline health status, only time-to-event endpoints yielded higher power than the ordinal endpoint for certain treatment effects.
[h=4]Conclusions:[/h] In this case study, the FLU-IVIG ordinal endpoint provided greater power than time-to-event, binary, and longitudinal endpoints for most scenarios of the treatment effect and placebo group distribution, including the target population studied for FLU-IVIG. The ordinal endpoint was only surpassed by the time-to-event endpoint when many patients in the placebo group were on the cusp of hospital discharge on day 7 and the follow-up period for the time-to-event endpoint was extended to allow for additional events. Our general approach for evaluating the power of several potential endpoints for an influenza trial can be used for designing other influenza trials with different target populations and for other trials in other disease areas.
[h=4]KEYWORDS:[/h] Clinical trials; Endpoints; Outcome assessments; Proportional odds model; Statistical power
PMID: 31312748 PMCID: PMC6609815 DOI: 10.1016/j.conctc.2019.100401
Free PMC Article