Sample size calculation for logrank test and prediction of number of events over time

2020 ◽  
Author(s):  
Kaifeng Lu
Keyword(s):  
Sample Size ◽  
Sample Size Calculation ◽  
Logrank Test ◽  
Over Time

scholarly journals A Menu-driven Facility for Complex Sample Size Calculation in Randomized Controlled Trials with a Survival or a Binary Outcome

2002 ◽  
Vol 2 (2) ◽  
pp. 151-163 ◽  
Author(s):  
Patrick Royston ◽  
Abdel Babiker
Keyword(s):  
Sample Size ◽  
Sample Size Calculation ◽  
Binary Outcome ◽  
Test Statistic ◽  
Logrank Test ◽  
Loss To Follow Up ◽  
Patient Allocation ◽  
Hazard Ratios ◽  
Event Distribution

We present a menu-driven Stata program for the calculation of sample size or power for complex clinical trials with a survival time or a binary outcome. The features supported include up to six treatment arms, an arbitrary time-to-event distribution, fixed or time-varying hazard ratios, unequal patient allocation, loss to follow-up, staggered patient entry, and crossover of patients from their allocated treatment to an alternative treatment. The computations of sample size and power are based on the logrank test and are done according to the asymptotic distribution of the logrank test statistic, adjusted appropriately for the design features.

Quality of Split-Mouth Trials in Dentistry: 1998, 2008, and 2018

2020 ◽  
Vol 99 (13) ◽  
pp. 1453-1460
Author(s):  
D. Qin ◽  
F. Hua ◽  
H. He ◽  
S. Liang ◽  
H. Worthington ◽  
...  
Keyword(s):  
Sample Size ◽  
Methodological Quality ◽  
Linear Models ◽  
Sample Size Calculation ◽  
Reporting Quality ◽  
Treatment Groups ◽  
Item Checklist ◽  
The Mean ◽  
Over Time

The objectives of this study were to assess the reporting quality and methodological quality of split-mouth trials (SMTs) published during the past 2 decades and to determine whether there has been an improvement in their quality over time. We searched the MEDLINE database via PubMed to identify SMTs published in 1998, 2008, and 2018. For each included SMT, we used the CONsolidated Standards Of Reporting Trials (CONSORT) 2010 guideline, CONSORT for within-person trial (WPT) extension, and a new 3-item checklist to assess its trial reporting quality (TRQ), WPT-specific reporting quality (WRQ), and SMT-specific methodological quality (SMQ), respectively. Multivariable generalized linear models were performed to analyze the quality of SMTs over time, adjusting for potential confounding factors. A total of 119 SMTs were included. The mean overall score for the TRQ (score range, 0 to 32), WRQ (0 to 15), and SMQ (0 to 3) was 15.77 (SD 4.51), 6.06 (2.06), and 1.12 (0.70), respectively. The primary outcome was clearly defined in only 28 SMTs (23.5%), and only 27 (22.7%) presented a replicable sample size calculation. Only 45 SMTs (37.8%) provided the rationale for using a split-mouth design. The correlation between body sites was reported in only 5 studies (4.2%) for sample size calculation and 4 studies (3.4%) for statistical results. Only 2 studies (1.7%) performed an appropriate sample size calculation, and 46 (38.7%) chose appropriate statistical methods, both accounting for the correlation among treatment groups and the clustering/multiplicity of measurements within an individual. Results of regression analyses suggested that the TRQ of SMTs improved significantly with time ( P < 0.001), while there was no evidence of improvement in WRQ or SMQ. Both the reporting quality and methodological quality of SMTs still have much room for improvement. Concerted efforts are needed to improve the execution and reporting of SMTs.

scholarly journals Power and sample-size calculations for trials that compare slopes over time: Introducing the slopepower command

2021 ◽  
Vol 21 (3) ◽  
pp. 575-601
Author(s):  
Stephen Nash ◽  
Katy E. Morgan ◽  
Chris Frost ◽  
Amy Mulick
Keyword(s):  
Sample Size ◽  
Statistical Power ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Sample Size Calculation ◽  
Model Fit ◽  
User Input ◽  
Sample Size Calculations ◽  
User Friendly ◽  
Over Time

Trials of interventions that aim to slow disease progression may analyze a continuous outcome by comparing its change over time—its slope—between the treated and the untreated group using a linear mixed model. To perform a sample-size calculation for such a trial, one must have estimates of the parameters that govern the between- and within-subject variability in the outcome, which are often unknown. The algebra needed for the sample-size calculation can also be complex for such trial designs. We have written a new user-friendly command, slopepower, that performs sample-size or power calculations for trials that compare slope outcomes. The package is based on linear mixed-model methodology, described for this setting by Frost, Kenward, and Fox (2008, Statistics in Medicine 27: 3717–3731). In the first stage of this approach, slopepower obtains estimates of mean slopes together with variances and covariances from a linear mixed model fit to previously collected user-supplied data. In the second stage, these estimates are combined with user input about the target effectiveness of the treatment and design of the future trial to give an estimate of either a sample size or a statistical power. In this article, we present the slopepower command, briefly explain the methodology behind it, and demonstrate how it can be used to help plan a trial and compare the sample sizes needed for different trial designs.

scholarly journals Using the Geometric Average Hazard Ratio in Sample Size Calculation for Time-to-event Data With Composite Endpoints

2021 ◽  
Author(s):  
Jordi Cortés Martínez ◽  
Ronald B Geskus ◽  
KyungMann Kim ◽  
Guadalupe Gómez Melis
Keyword(s):  
Sample Size ◽  
Proportional Hazards ◽  
Sample Size Calculation ◽  
Composite Endpoint ◽  
Hazard Ratio ◽  
Time To Event ◽  
Logrank Test ◽  
Geometric Average ◽  
Effect Measure ◽  
Average Hazard Ratio

Abstract Background: Sample size calculation is a key point in the design of a randomized controlled trial. With time-to-event outcomes, it’s often based on the logrank test. We provide a sample size calculation method for a composite endpoint (CE) based on the geometric average hazard ratio (gAHR) in case the proportional hazards assumption can be assumed to hold for the components, but not for the CE. Methods: The required number of events, sample size and power formulae are based on the non-centrality parameter of the logrank test under the alternative hypothesis which is a function of the gAHR. We use the web platform, CompARE, for the sample size computations. A simulation study evaluates the empirical power of the logrank test for the CE based on the sample size in terms of the gAHR. We consider different values of the component hazard ratios, the probabilities of observing the events in the control group and the degrees of association between the components. We illustrate the sample size computations using two published randomized controlled trials. Their primary CEs are, respectively, progression-free survival (time to progression of disease or death) and the composite of bacteriologically confirmed treatment failure or Staphilococcus aureus related death by 12 weeks. Results: For a target power of 0.80, the simulation study provided mean (± SE) empirical powers equal to 0.799 (±0.004) and 0.798 (±0.004) in the exponential and non-exponential settings, respectively. The power was attained in more than 95% of the simulated scenarios and was always above 0.78, regardless of compliance with the proportional-hazard assumption.Conclusions: The geometric average hazard ratio as an effect measure for a composite endpoint has a meaningful interpretation in the case of non-proportional hazards. Furthermore it is the natural effect measure when using the logrank test to compare the hazard rates of two groups and should be used instead of the standard hazard ratio.

The most frequently asked question to a statistician: The sample size

2017 ◽  
Vol 23 (5) ◽  
pp. 644-646 ◽  
Author(s):  
Maria Pia Sormani
Keyword(s):  
Clinical Study ◽  
Sample Size ◽  
Study Design ◽  
Treatment Effect ◽  
Sample Size Calculation ◽  
Ethical Implications ◽  
Number Of Patients ◽  
Reducing Costs ◽  
Correct Size

The calculation of the sample size needed for a clinical study is the challenge most frequently put to statisticians, and it is one of the most relevant issues in the study design. The correct size of the study sample optimizes the number of patients needed to get the result, that is, to detect the minimum treatment effect that is clinically relevant. Minimizing the sample size of a study has the advantage of reducing costs, enhancing feasibility, and also has ethical implications. In this brief report, I will explore the main concepts on which the sample size calculation is based.

Sample size calculation for clinical trials in which entry criteria and outcomes are counts of events

1994 ◽  
Vol 13 (8) ◽  
pp. 859-870 ◽  
Author(s):  
Robert P. McMahon ◽  
Michael Proschan ◽  
Nancy L. Geller ◽  
Peter H. Stone ◽  
George Sopko
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Sample Size Calculation
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