Treffer: Sequential Parallel Comparison Design for Assessing Induction, Maintenance, Long-Term, and Other Treatment Effects on a Binary Endpoint.

Title:
Sequential Parallel Comparison Design for Assessing Induction, Maintenance, Long-Term, and Other Treatment Effects on a Binary Endpoint.
Authors:
Quan H; Evidence Generation and Decision Science, Sanofi, Morristown, New Jersey, USA., Xu Z; Evidence Generation and Decision Science, Sanofi, Morristown, New Jersey, USA., Chen X; Evidence Generation and Decision Science, Sanofi, Morristown, New Jersey, USA.
Source:
Statistics in medicine [Stat Med] 2026 Jan; Vol. 45 (1-2), pp. e70382.
Publication Type:
Journal Article; Comparative Study
Language:
English
Journal Info:
Publisher: Wiley Country of Publication: England NLM ID: 8215016 Publication Model: Print Cited Medium: Internet ISSN: 1097-0258 (Electronic) Linking ISSN: 02776715 NLM ISO Abbreviation: Stat Med Subsets: MEDLINE
Imprint Name(s):
Original Publication: Chichester ; New York : Wiley, c1982-
References:
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Contributed Indexing:
Keywords: combination test; induction; maintenance; missing data handling; randomized withdrawal; treat‐through
Entry Date(s):
Date Created: 20260122 Date Completed: 20260122 Latest Revision: 20260122
Update Code:
20260122
DOI:
10.1002/sim.70382
PMID:
41569397
Database:
MEDLINE

Weitere Informationen

For a chronic disease, besides the treatment induction effect, it is also important to demonstrate the maintenance effect of long-term treatment use. To fulfill these and other objectives for a clinical study, we often apply one of three designs: the active treatment lead-in followed by randomized maintenance design, the randomized induction followed by re-randomized withdrawal maintenance design and the treat-through design (FDA 2022). Separately, a two-stage sequential parallel comparison design (SPCD) is frequently used in therapeutic areas where placebo has a large effect. In this paper, we use a SPCD for a clinical trial with a binary endpoint for induction, maintenance, long-term and other treatment effect assessments. This SPCD can actually be treated as a hybrid of the above three designs and has some additional advantages. For example, compared to the re-randomized withdrawal maintenance design, the SPCD does not need a re-randomization to simplify trial operation and it also provides controlled data for formal long-term efficacy and safety analyses. To fully utilize all available data of the two stages for an overall treatment effect evaluation, a weighted combination test is considered with the incorporation of correlations of the components. Further, a multiple imputation approach is applied to handle missing not at random data. Simulations are conducted to evaluate the performances of the methods and a data example is employed to illustrate the applications of the methods.
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