Literature DB >> 8735021

A computer program for sample size and power calculations in the design of multi-arm and factorial clinical trials with survival time endpoints.

R Natarajan1, B W Turnbull, E H Slate, L C Clark.   

Abstract

This paper presents a computer program for use in the design of long-term clinical trials with multiple treatment arms in which the primary outcome variables are censored survival times. The treatment arms may be structured as a one-way or multi-way factorial design. It is assumed that patients are entered and randomized to a treatment arm during an accrual period. The patients are then followed for a fixed period during which there may be dropouts. Various distributional assumptions can be used to model the survival times. These include an option in which there is an effect of treatment duly after a lag or delay time. The program then computes the power of various statistical tests of hypotheses concerning treatment differences, interactions and trends. The power computations are "exact" in that they use the Monte Carlo method to obtain Type I and II error probabilities. However the program also outputs the normal approximations for comparison, although they are typically not accurate in these situations. Fisher's LSD method is used to adjust for the multiple comparisons. By comparing the power for various sets of design parameters, such as sample size, numbers of factor levels, patient accrual rate, and length of follow-up, an appropriate design can be constructed. Two examples are provided. The first is a simple one-way layout with multiple treatment arms; the second a two-way factorial design for a proposed large scale cancer chemoprevention trial.

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Year:  1996        PMID: 8735021     DOI: 10.1016/0169-2607(96)01717-8

Source DB:  PubMed          Journal:  Comput Methods Programs Biomed        ISSN: 0169-2607            Impact factor:   5.428


  5 in total

1.  Sample size calculation for rank tests comparing K survival distributions.

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2.  A novel gene THSD7A is associated with obesity.

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Journal:  Int J Obes (Lond)       Date:  2015-08-04       Impact factor: 5.095

3.  Sample Size Requirements and Study Duration for Testing Main Effects and Interactions in Completely Randomized Factorial Designs When Time to Event is the Outcome.

Authors:  Barry Kurt Moser; Susan Halabi
Journal:  Commun Stat Theory Methods       Date:  2015       Impact factor: 0.893

4.  The maternal ITPK1 gene polymorphism is associated with neural tube defects in a high-risk Chinese population.

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Journal:  PLoS One       Date:  2014-01-20       Impact factor: 3.240

5.  A systematic approach to designing statistically powerful heteroscedastic 2 × 2 factorial studies while minimizing financial costs.

Authors:  Show-Li Jan; Gwowen Shieh
Journal:  BMC Med Res Methodol       Date:  2016-08-31       Impact factor: 4.615

  5 in total

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