The change of <i>n</i>, <i>D</i> and <i>E</i>(<i>D</i>) for varied <i>γ</i> with fixed hazard ratio under the Weibull distribution (<i>HR</i> = 1.333).

<p>The change of <i>n</i>, <i>D</i> and <i>E</i>(<i>D</i>) for varied <i>γ</i> with fixed hazard ratio under the Weibull distribution (<i>HR</i> = 1.333).</p

A Practical Simulation Method to Calculate Sample Size of Group Sequential Trials for Time-to-Event Data under Exponential and Weibull Distribution

<div><p>Group sequential design has been widely applied in clinical trials in the past few decades. The sample size estimation is a vital concern of sponsors and investigators. Especially in the survival group sequential trials, it is a thorny question because of its ambiguous distributional form, censored data and different definition of information time. A practical and easy-to-use simulation-based method is proposed for multi-stage two-arm survival group sequential design in the article and its SAS program is available. Besides the exponential distribution, which is usually assumed for survival data, the Weibull distribution is considered here. The incorporation of the probability of discontinuation in the simulation leads to the more accurate estimate. The assessment indexes calculated in the simulation are helpful to the determination of number and timing of the interim analysis. The use of the method in the survival group sequential trials is illustrated and the effects of the varied shape parameter on the sample size under the Weibull distribution are explored by employing an example. According to the simulation results, a method to estimate the shape parameter of the Weibull distribution is proposed based on the median survival time of the test drug and the hazard ratio, which are prespecified by the investigators and other participants. 10+ simulations are recommended to achieve the robust estimate of the sample size. Furthermore, the method is still applicable in adaptive design if the strategy of sample size scheme determination is adopted when designing or the minor modifications on the program are made.</p></div

The comparison of results for varied <i>γ</i> with fixed hazard ratio under the Weibull distribution (HR = 1.333).

<p>The comparison of results for varied <i>γ</i> with fixed hazard ratio under the Weibull distribution (HR = 1.333).</p

Program flow of the SAS macro.

<p>Program flow of the SAS macro.</p

'Insecta exotica'

<p>The change of <i>n</i>, <i>D</i> and <i>E</i> (<i>D</i>) for varied <i>γ</i> with fixed <i>M_T</i> and <i>M_C</i> under the Weibull distribution (<i>M_T</i> = 6, <i>M_C</i> = 4.5).</p

The comparison of the results for different interim monitoring plans under the exponential distribution.

<p>The comparison of the results for different interim monitoring plans under the exponential distribution.</p

The input macro parameters in the SAS macro %n_gssur.

<p>The input macro parameters in the SAS macro %n_gssur.</p

MOESM1 of A novel cold-adapted esterase from Enterobacter cloacae: Characterization and improvement of its activity and thermostability via the site of Tyr193Cys

Additional file 1: Figure S1. Substrate specificity of purified Y193C. The activity towards p-NP acetate (C2) as 100%

Average Total Sample Size and Percentage Reduction.

<p>Note: The first row is average total sample size in our proposed design; numbers in parentheses are percentages reduction of sample size as compared to the XJT and the three-stage designs.</p

Additional file 1: of Assessment of a block curriculum design on medical postgraduates’ perception towards biostatistics: a cohort study

The self-administered questionnaire concerning perceptions towards biostatistics (DOCX 24 kb