scholarly journals A systematic review of the quality of conduct and reporting of survival analyses of tuberculosis outcomes in Africa

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Moses M. Ngari ◽  
Susanne Schmitz ◽  
Christopher Maronga ◽  
Lazarus K. Mramba ◽  
Michel Vaillant
Keyword(s):  
Systematic Review ◽  
Sample Size ◽  
Survival Function ◽  
Sample Size Estimation ◽  
Size Estimation ◽  
Survival Curves ◽  
Survival Analyses ◽  
Survival Regression

Abstract Background Survival analyses methods (SAMs) are central to analysing time-to-event outcomes. Appropriate application and reporting of such methods are important to ensure correct interpretation of the data. In this study, we systematically review the application and reporting of SAMs in studies of tuberculosis (TB) patients in Africa. It is the first review to assess the application and reporting of SAMs in this context. Methods Systematic review of studies involving TB patients from Africa published between January 2010 and April 2020 in English language. Studies were eligible if they reported use of SAMs. Application and reporting of SAMs were evaluated based on seven author-defined criteria. Results Seventy-six studies were included with patient numbers ranging from 56 to 182,890. Forty-three (57%) studies involved a statistician/epidemiologist. The number of published papers per year applying SAMs increased from two in 2010 to 18 in 2019 (P = 0.004). Sample size estimation was not reported by 67 (88%) studies. A total of 22 (29%) studies did not report summary follow-up time. The survival function was commonly presented using Kaplan-Meier survival curves (n = 51, (67%) studies) and group comparisons were performed using log-rank tests (n = 44, (58%) studies). Sixty seven (91%), 3 (4.1%) and 4 (5.4%) studies reported Cox proportional hazard, competing risk and parametric survival regression models, respectively. A total of 37 (49%) studies had hierarchical clustering, of which 28 (76%) did not adjust for the clustering in the analysis. Reporting was adequate among 4.0, 1.3 and 6.6% studies for sample size estimation, plotting of survival curves and test of survival regression underlying assumptions, respectively. Forty-five (59%), 52 (68%) and 73 (96%) studies adequately reported comparison of survival curves, follow-up time and measures of effect, respectively. Conclusion The quality of reporting survival analyses remains inadequate despite its increasing application. Because similar reporting deficiencies may be common in other diseases in low- and middle-income countries, reporting guidelines, additional training, and more capacity building are needed along with more vigilance by reviewers and journal editors.

scholarly journals Tamaño óptimo de la muestra

2017 ◽  
Vol 5 (9) ◽  
Author(s):  
M. H. Badii ◽  
J. Castillo ◽  
A. Guillen
Keyword(s):  
Sample Size ◽  
Normal Distribution ◽  
Key Words ◽  
Sample Size Estimation ◽  
Size Estimation ◽  
Bias Estimation ◽  
Population Variance ◽  
Population Proportion ◽  
Estimation Process ◽  
The Mean

Key words: Bias, estimation, population, sampleAbstract. The basics of sample size estimation process are described. Assuming the normal distribution, the procedures for estimation of sample size for the mean; with and without knowledge of the population variance, and population proportion are noted. Sample size for more than one population feature is also given.Palabras clave: Estimación, muestra, población, sesgoResumen. Se describen los fundamentos del proceso de la estimación del tamaño óptimo de la muestra. Suponiendo una distribución normal para una población, se notan los procedimientos de la estimación del tamaño óptimo de la muestra para la media muestral con y sin el conocimiento de la varianza poblacional. Se presenta el tamaño óptimo de la muestra con más de una característica poblacional.

A comparison of methods for sample size estimation for non-inferiority studies with binary outcomes

2010 ◽  
Vol 20 (6) ◽  
pp. 595-612 ◽  
Author(s):  
Steven A Julious ◽  
Roger J Owen
Keyword(s):  
Sample Size ◽  
Bayesian Methods ◽  
Sensitivity Analyses ◽  
Binary Outcomes ◽  
Sample Size Estimation ◽  
Controlled Trials ◽  
Comparison Of Methods ◽  
Size Estimation ◽  
Control Response ◽  
Established Treatment

Non-inferiority trials are motivated in the context of clinical research where a proven active treatment exists and placebo-controlled trials are no longer acceptable for ethical reasons. Instead, active-controlled trials are conducted where a treatment is compared to an established treatment with the objective of demonstrating that it is non-inferior to this treatment. We review and compare the methodologies for calculating sample sizes and suggest appropriate methods to use. We demonstrate how the simplest method of using the anticipated response is predominantly consistent with simulations. In the context of trials with binary outcomes with expected high proportions of positive responses, we show how the sample size is quite sensitive to assumptions about the control response. We recommend when designing such a study that sensitivity analyses be performed with respect to the underlying assumptions and that the Bayesian methods described in this article be adopted to assess sample size.

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