The alpha spending function approach to interim data analyses

DeMets, David L.; Lan, Gordon

doi:10.1007/978-1-4615-2009-2_1

Cited by 48 publications

(38 citation statements)

References 65 publications

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“…An O'Brien-Fleming stopping boundary for efficacy was calculated with the use of a Lan-DeMets alpha-spending function on the basis of the number of events observed at the data-cutoff date. 15,16 All reported P values are two-sided.…”

Section: Discussionmentioning

confidence: 98%

“…Randomization was stratified according to the β 2 -microglobulin level (<2.5 mg per liter vs. ≥2.5 mg per liter), previous therapy with bortezomib (no vs. yes), and previous therapy with lenalidomide (no vs. yes). Carfilzomib was administered as a 10-minute infusion on days 1,2,8,9,15, and 16 (starting dose, 20 mg per square meter on days 1 and 2 of cycle 1; target dose, 27 mg per square meter thereafter) during cycles 1 through 12 and on days 1, 2, 15, and 16 during cycles 13 through 18, after which carfilzomib was discontinued. Lenalidomide (25 mg) was given on days 1 through 21.…”

Section: Methodsmentioning

confidence: 99%

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Carfilzomib, Lenalidomide, and Dexamethasone for Relapsed Multiple Myeloma

2015

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Section: Discussionmentioning

confidence: 98%

Section: Methodsmentioning

confidence: 99%

Carfilzomib, Lenalidomide, and Dexamethasone for Relapsed Multiple Myeloma

2015

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“…For larger two-group designs, additional interim analyses are likely for safety assessments. The reviewers monitoring the trial must consider safety trade-offs, additional efficacy variables, and other conditions that might arise unexpectedly during the trial (DeMets and Lan 1995). Because of this complexity, the calculation of the planned decision criteria assumed conservative nonbinding stopping rules to reflect their role as efficient guidance rather than rigorously enforced rules.…”

Section: Discussionmentioning

confidence: 99%

Optimal Designs With Interim Analyses for Randomized Studies With Long-Term Time-Specific Endpoints

Huang¹,

Thomas²

2014

Statistics in Biopharmaceutical Research

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With the advent of novel cancer therapies targeting molecular pathways or the immune system, time-to-event variables evaluated at longer times (e.g., 6 or 12 months) are increasingly used as the primary endpoint in randomized Phase II trials. Two-stage "Simon" designs have often been used in single-group phase II oncology trials to reduce the expected sample size under the null hypothesis. We develop optimal two-stage and three-stage randomized two-group designs with analogous optimality criteria. Accurate implementation of the Simon design can be difficult, and the difficulty increases with a longer-term endpoint, because it requires complete information from each accrued patient. Earlier work of Case and Morgan generalized the Simon design so it can be implemented without an accrual pause. We derive optimal designs that allow a brief preplanned pause (≥ 0) in accrual before interim analyses to more efficiently use data from the accrued patients, and to allow focused data collection before the interim analyses. We provide software (R package OptInterim) that creates the optimal designs and also simulates their properties to check asymptotic approximations and the robustness of the designs to differing conditions. Various design options are illustrated using an example based on a recent colorectal cancer drug trial.

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“…We can then proceed to conduct interim analysis times at information fraction t j equal to the observed number of subjects divided by 210. Similar formulations can be developed for the comparison of means, repeated measures, and survival analysis [48]. However, for most applications, the standard sample size formulas with the new alpha spending function final critical value will be a very good approximation.…”

Section: Trial Designmentioning

confidence: 95%

Lan‐DeMetsAlpha‐Spending Function

DeMets

Lan

2005

Encyclopedia of Statistical Sciences

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Randomized clinical trials are typically monitored periodically for early evidence of convincing benefit or of potential harm for both scientific and ethical reasons by a data monitoring committee. After increments in the number of patients randomized or in the number of observed events, interim statistical analyses may be conducted to evaluate emerging results. This process may be referred to as a group sequential design. However, repeated analysis of accumulating data with conventional statistical testing can lead to substantially increased Type I or false positive error rates. The alpha spending function provides a flexible approach to control the Type I error while allowing the data monitoring committee to review the data as needed without having to specify a priori when and how many such interim analyses will be conducted. Other earlier group sequential methods had these somewhat restrictive requirements. This alpha spending function approach can be applied to several spending functions that approximate some of these earlier methods.

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The alpha spending function approach to interim data analyses

Cited by 48 publications

References 65 publications

Carfilzomib, Lenalidomide, and Dexamethasone for Relapsed Multiple Myeloma

Carfilzomib, Lenalidomide, and Dexamethasone for Relapsed Multiple Myeloma

Optimal Designs With Interim Analyses for Randomized Studies With Long-Term Time-Specific Endpoints

Lan‐DeMetsAlpha‐Spending Function

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