Considerations for Design and Data Analysis of Noninferiority/Superiority Cardiovascular Trials

Quan, Hui; Li, Mingyu; Zhao, Peng-Liang; Cho, Meehyung; Zhang, Ji; Wu, Yujun

doi:10.1080/10543406.2013.735788

Cited by 5 publications

(2 citation statements)

References 15 publications

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“…Asymptotically, the estimated log hazard ratio

{\overset{δ}{true}}_{i}^{^{'}} = \log ((), {\overset{λ}{true}}_{1 i} (/, {\overset{λ}{true}}_{0 i})) \sim N (δ, 1 / E_{1 i} + 1 / E_{0 i}),

where δ is the true log hazard ratio. If E 1 i and E 0 i are similar (particularly for a balanced design), becomes

{\overset{δ}{true}}_{i}^{^{'}} = \log ((), {\overset{λ}{true}}_{1 i} (/, {\overset{λ}{true}}_{0 i})) \sim N (δ, 4 / E_{i}),

where E i is the total expected number of events for the two treatment groups combined for the i ‐th study . We then use the prespecified weights

w_{i} = \sqrt{(E_{1 i} E_{0 i}) / (E_{1 i} + E_{0 i}) / {falsefalse}_{\sum}^{j = 1} (E_{1 j} E_{0 j}) / (E_{1 j} + E_{0 j})}

w_{i} = \sqrt{E_{i} (/, {falsefalse}_{\sum}^{j = 1} E_{j})}

if all studies use a balanced design.…”

Section: Hypothesis Testing and Estimationmentioning

confidence: 99%

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Adaptive and repeated cumulative meta‐analyses of safety data during a new drug development process

Quan

Zheng

et al. 2015

Pharmaceutical Statistics

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During a new drug development process, it is desirable to timely detect potential safety signals. For this purpose, repeated meta-analyses may be performed sequentially on accumulating safety data. Moreover, if the amount of safety data from the originally planned program is not enough to ensure adequate power to test a specific hypothesis (e.g., the noninferiority hypothesis of an event of interest), the total sample size may be increased by adding new studies to the program. Without appropriate adjustment, it is well known that the type I error rate will be inflated because of repeated analyses and sample size adjustment. In this paper, we discuss potential issues associated with adaptive and repeated cumulative meta-analyses of safety data conducted during a drug development process. We consider both frequentist and Bayesian approaches. A new drug development example is used to demonstrate the application of the methods.

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“…Asymptotically, the estimated log hazard ratio

{\overset{δ}{true}}_{i}^{^{'}} = \log ((), {\overset{λ}{true}}_{1 i} (/, {\overset{λ}{true}}_{0 i})) \sim N (δ, 1 / E_{1 i} + 1 / E_{0 i}),

where δ is the true log hazard ratio. If E 1 i and E 0 i are similar (particularly for a balanced design), becomes

{\overset{δ}{true}}_{i}^{^{'}} = \log ((), {\overset{λ}{true}}_{1 i} (/, {\overset{λ}{true}}_{0 i})) \sim N (δ, 4 / E_{i}),

where E i is the total expected number of events for the two treatment groups combined for the i ‐th study . We then use the prespecified weights

w_{i} = \sqrt{(E_{1 i} E_{0 i}) / (E_{1 i} + E_{0 i}) / {falsefalse}_{\sum}^{j = 1} (E_{1 j} E_{0 j}) / (E_{1 j} + E_{0 j})}

w_{i} = \sqrt{E_{i} (/, {falsefalse}_{\sum}^{j = 1} E_{j})}

if all studies use a balanced design.…”

Section: Hypothesis Testing and Estimationmentioning

confidence: 99%

“…where E i is the total expected number of events for the two treatment groups combined for the i-th study [24]. We then use the prespecified weights…”

Section: Binary and Time-to-event Endpointsmentioning

confidence: 99%