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A review and re-interpretation of a group-sequential approach to sample size re-estimation in two-stage trials.

Bowden J, Mander A - Pharm Stat (2014)

Bottom Line: In this paper, we review the adaptive design methodology of Li et al. (Biostatistics 3:277-287) for two-stage trials with mid-trial sample size adjustment.We argue that it is closer in principle to a group sequential design, in spite of its obvious adaptive element.Several extensions are proposed that aim to make it even more attractive and transparent alternative to a standard (fixed sample size) trial for funding bodies to consider.

View Article: PubMed Central - PubMed

Affiliation: MRC Biostatistics Unit, Cambridge, UK.

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Expected sample size (left) and overall power (right) of the LSW methods under designs 1–4, as a function of δ.
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fig05: Expected sample size (left) and overall power (right) of the LSW methods under designs 1–4, as a function of δ.

Mentions: Figure 5 compares the operating characteristics four designs featured. Designs 1 and 2 have a far smaller expected sample size than 3 and 4 but because of this, do not control the overall power at at the original desired level of 80%. As well as being identical to the fixed design at δ = , the unconditional power curves of designs 3 and 4 are very close to that of the fixed design across all values of δ.


A review and re-interpretation of a group-sequential approach to sample size re-estimation in two-stage trials.

Bowden J, Mander A - Pharm Stat (2014)

Expected sample size (left) and overall power (right) of the LSW methods under designs 1–4, as a function of δ.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4288989&req=5

fig05: Expected sample size (left) and overall power (right) of the LSW methods under designs 1–4, as a function of δ.
Mentions: Figure 5 compares the operating characteristics four designs featured. Designs 1 and 2 have a far smaller expected sample size than 3 and 4 but because of this, do not control the overall power at at the original desired level of 80%. As well as being identical to the fixed design at δ = , the unconditional power curves of designs 3 and 4 are very close to that of the fixed design across all values of δ.

Bottom Line: In this paper, we review the adaptive design methodology of Li et al. (Biostatistics 3:277-287) for two-stage trials with mid-trial sample size adjustment.We argue that it is closer in principle to a group sequential design, in spite of its obvious adaptive element.Several extensions are proposed that aim to make it even more attractive and transparent alternative to a standard (fixed sample size) trial for funding bodies to consider.

View Article: PubMed Central - PubMed

Affiliation: MRC Biostatistics Unit, Cambridge, UK.

Show MeSH