sample size

Linear-regression analysis is a well-known statistical technique that serves as a basis for understanding the relationships between variables. Its simplicity and interpretability render it the preferred choice in healthcare research, including vision science, as it enables researchers and practitioners to model and predict outcomes effectively. This article presents the fundamentals of linear-regression modeling and reviews the applications and interpretations of the main linear-regression analysis.

The primary objective of linear regression is to fit a linear equation to observed data, thus allowing one to predict and interpret the effects of predictor variables. A simple linear regression involves a single independent variable, whereas multiple linear regression includes multiple predictors. A linear-regression model is used to identify the general underlying pattern connecting independent and dependent variables, prove the relationship between these variables, and predict the dependent variables for a specified value of the independent variables. This review demonstrates the appropriate interpretation of linear-regression results using examples from publications in the field of vision science.

Simple and multiple linear regressions are performed, with emphasis on the correct interpretation of standardized and unstandardized regression coefficients, the coefficient of determination, the method for variable selection, assumptions in linear regression, dummy variables, and sample size, along with common mistakes in reporting linear-regression analysis. Finally, a checklist is presented to the editors and peer reviewers for a systematic assessment of submissions that used linear-regression models.

Medical practitioners and researchers should acquire basic knowledge of linear-regression such that they can contribute meaningfully to the development of technology by accurately interpreting research outcomes. Incorrect use or interpretation of appropriate linear-regression models may result in inaccurate results. Appointing an expert statistician in an interdisciplinary research team may offer added value to the study design by preventing overstated results.

Sample sizes that are too small can produce inconclusive results, while sample sizes that are too large may raise ethical concerns and produce trivial outcomes.  Ethical considerations of sample size calculation in animal studies are essential and researchers should consider the 3R approach. Therefore, accurately calculating the sample size is essential to ensure adequate statistical power and avoid wastage of resources.

The paper presents several innovative approaches for conducting small-scale sampling in animal studies. It includes a comprehensive review of relevant literature, discussing various proposed methods for determining sample size in animal studies.

In this study, various formulas are available for preparing sample size calculations that are relevant to the research design. These include t-tests (for one sample and two independent/paired samples), ANOVA, ANCOVA, simple/multiple linear regression, as well as proportion studies and studies utilizing correlation coefficients.

Our aims to equip researchers with formulas for reliable findings and adherence to ethical principles for sample size calculating in animal studies.

In statistical quality control, inspecting complete products is very hard due to its cost and time. This research uses a time-truncated single acceptance sampling plan to investigate bladder cancer data for the exponentiated moment exponential distribution. 

A single acceptance sampling plan is used to investigate bladder cancer data when a patient’s remission times following an exponentiated moment exponential distribution. Minimum sample sizes, operating characteristic function, and the associated producer’s risks are obtained and calculated.

Minimum sample sizes necessary to ensure a certain mean lifetime for selected acceptance numbers and consumer confidence levels are obtained. The operating characteristic function and the associated producer’s risks are provided. We also analyzed the minimum ratios of the mean life to the specified life.

The results show optimal sample sizes decrease when the time to pre-determined mean lifetime ratio increases. Besides, the operating characteristic values of the proposed single sampling plan increase when the ratios of the mean life to the specified life increase.

Scientific papers usually contain information and data that we call them statistics. We expect statistics to provide us with a suitable description of the data by summarizing.
Scientific journals have specific frameworks for this work in mention to writers and readers can understand statistical concepts with a common terminology.
In this paper we guide reader to write a scientific issue without any confusion and crowding and we propose some notes to report a scientific descriptive statistics, and make a table properly and draw a visual graph.

Statistical methods as a complement to biomedical research have a major role to play in the design, management, analysis, andinterpretation of scientific data. The present study aimed to determine the statistical methods, time-to-acceptance, and the associated factors in articlespublished in the Iranian Red Crescent Medical Journal (IRCMJ).

Original articles in the period 2014 to 2021 from volumes 16 to 23 and issues 1 to 12 were assessed(1,300 articles). Each article was assessed by a two-member team consisting of a statistician and an expert researcher in the field of medical research. Statistical methods, sample size, sampling method, statistical population, study design, and software were extracted. Frequency, Percentage, Median, Interquartile range, Multiple response analysis (MRA), Kruskal–Wallis test, and Spearman correlation coefficient were used for data description and analysis, respectively. All analyzes were performed in SPSS software (version 26) at a significance level of 5%.

The statistical population of most published articles was related to patients (n=547; 41.2%).Most studies (n=565; 43.5%) had a sample size between 100 and 500 people. The majority of them were analytical interventions (n=535; 41.2%).The median (IQR) of the acceptance period was 94(58-153.75)days.The results of MRA demonstrated that both among the total tests and the articles, the highest rate of statistical methods was related to the T-test, Chi-square test, and descriptive statistics. There was no statistically significant factor influencing the acceptance period (P>0.05), and no significantcorrelation was detected between the acceptance period and the sample size of published articles (r=-0.04;P=0.625).

The acceptance period is a key factor academicresearchersshould consider whenselectingan academicjournalfor theirresearchpaper. Contrary to some novice researchers' beliefs, the acceptance period of the article was not affected by the design, statistical methods, and sample size of the study.