Use of appropriate statistical tools in biomedical research: Current trend & status

Background & objectives: Due to lack of appropriate statistical knowledge, published research articles contain various errors related to the design, analysis and interpretation of results in the area of biomedical research. If research contains statistical error, however, costly, it may be of no use and the purpose of the investigation gets defeated. Many biomedical research articles published in different peer reviewed journals may retain several statistical errors and flaws in them. This study aimed to examine the trend and status of application of statistics in biomedical research articles. Study design, sample size estimation and statistical measures are crucial components of a study. These points were evaluated in published original research articles to understand the use or misuse of statistical tools. Methods: Three hundred original research articles from the latest issues of selected 37 journals were reviewed. These journals were from the five internationally recognized publication groups (CLINICAL KEY, BMJ Group, WILEY, CAMBRIDGE and OXFORD) accessible through the online library of SGPGI, Lucknow, India. Results: Among articles assessed under present investigation, 85.3 per cent (n=256) were observational, and 14.7 per cent (n=44) were interventional studies. In 93 per cent (n=279) of research articles, sample size estimation was not reproducible. The simple random sampling was encountered rarely in biomedical studies even though none of the articles was adjusted by design effect and, only five articles had used randomized test. The testing of assumption of normality was mentioned in only four studies before applying parametric tests. Interpretation & conclusions: In order to present biomedical research results with reliable and precise estimates based on data, the role of engaging statistical experts need to be appreciated. Journals must have standard rules for reporting study design, sample size and data analysis tools. Careful attention is needed while applying any statistical procedure as, it will not only help readers to trust in the published articles, but also rely on the inferences the published articles draw.

Metodologías cuantitativas: Cálculo del tamaño de muestra con STATA y R / Quantitative methods: Sample size calculation with STATA and R

El cálculo de tamaño de muestra es un aspecto esencial del diseño de estudios cuantitativos. Un adecuado tamaño de muestra nos permite determinar cuál es la mínima cantidad de participantes necesarios para probar nuestra hipótesis de interés. De esta manera, podemos reducir costos, maximizar el uso de nuestros recursos de investigación y garantizar la factibilidad del estudio. Contradictoriamente, a pesar de su relevancia muy pocos investigadores dominan esta habilidad. Esta revisión tiene por objeto revisar los conceptos básicos para realizar un cálculo de tamaño de muestra y compartir códigos de Stata y R específicamente diseñados para facilitar estos cálculos.

The calculation of sample size is an essential aspect of the design of quantitative studies. An adequate sample size allows us to determine the minimum number of participants necessary to test our hypothesis of interest. Hence, we can reduce costs, maximize the use of our research resources and guarantee the feasibility of the study. Contradictorily, despite its relevance, very few researchers dominate this skill. This review aims to review the basics of sample size calculation and share Stata and R codes specifically designed to facilitate these calculations.

Preparation process and in vitro evaluation of Shufei in situ gel optimized by central composite design-response surface methodology / 中草药

objective To optimize the preparation technology of sulfuric in situ gel and study the infiltration experiment of different dosage forms. Methods Shufei in situ gel was prepared by cold cut method with poloxamer 188 (P188) and poloxamer 407 (P407) as gel base. Using gelling temperature index, the dosage range of gel matrix P407 and P188 in Shufei in situ gel was determinated by the single factor and star design-response surface methods to get the best prescription of Shufei in situ gel. The transdermal diffusion process of Shufei in situ gel was carried out in Franz diffusion cells to explore the permeation mechanism. Results The optimized prescription of Shufei in situ gel was as follow: The ratio of gel matrix to drug at 1:3, P188 dosage of 4%, P407 dosage of 22.5%, and the phase transition temperature within 32-36 ℃ to form gel. The releases of sinapine thiocyanate and genkwanin in Shufei in situ gel were all in line with the Higuchi release model. Conclusion The preparation process of Shufei in situ gel is stable and feasible with reliable product quality and good application prospect, which is suitable for industrial production and clinical application.

Parainfluenza-3 and bovine respiratory syncytial virus: intraherd correlation adjusted for sensitivity and specificity / Parainfluenza-3 y virus respiratorio bovino sincitial: correlación intrahato ajustado por la sensibilidad y especificidad

Objective. The purpose of this study was to compare the intra-class correlation coefficients (ICC) and design effects (D) estimates adjusted or unadjusted for sensibility (Se) and specificity (Sp) of the diagnostic tests using a Bayesian procedure. Materials and methods. Sera from 232 animals from 44 randomly selected herds, to detect antibodies against parainfluenza-3 virus (PIV3) from non-vaccinated dual-purpose cattle from Colima Mexico, were used. Only 176 animals from 33 herds were used to evaluate the presence of the bovine respiratory syncytial virus (BRSV). Results. The ICC and D values adjusted and unadjusted for PIV3 were 0.33, 2.73, 0.32, and 2.71, respectively. For BRSV the values were 0.31, 2.64, 0.28 and 2.49. Conclusions. The adjusted or unadjusted ICC and D estimates were similar because of the high Se and Sp of the diagnostic tests and the relatively high prevalence of the diseases here studied.

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Comparison of the Designing Effects (DE) among different designs related to complex sampling methods / 中华流行病学杂志

To compare the designing effects (DE) among different complex sampling designing programs.Data from the ‘2002 Chinese Nutrition and Health Survey' was used as an example to generate the sampling population,and statistical simulation method was used to estimate the values of DEs from six complex sampling designing programs.It was found that the values of DEs varied among the six complex sampling designing programs.The values of the DEs were associated with the sample sizes in a positive way,with more sample stages and less stratified categories.Reduction of the numbers of sample stages and detailing stratified categories could decrease the DE values so as to improve the DE.

Complex sample design effects and inference for Korea National Health and Nutrition Examination Survey data

Nutritional researchers world-wide are using large-scale sample survey methods to study nutritional health epidemiology and services utilization in general, non-clinical populations. This article provides a review of important statistical methods and software that apply to descriptive and multivariate analysis of data collected in sample surveys, such as national health and nutrition examination survey. A comparative data analysis of the Korea National Health and Nutrition Examination Survey (KNHANES) was used to illustrate analytical procedures and design effects for survey estimates of population statistics, model parameters, and test statistics. This article focused on the following points, method of approach to analyze of the sample survey data, right software tools available to perform these analyses, and correct survey analysis methods important to interpretation of survey data. It addresses the question of approaches to analysis of complex sample survey data. The latest developments in software tools for analysis of complex sample survey data are covered, and empirical examples are presented that illustrate the impact of survey sample design effects on the parameter estimates, test statistics, and significance probabilities (p values) for univariate and multivariate analyses.