Sample size, power and effect size revisited: simplified and practical approaches in pre-clinical, clinical and laboratory studies

Calculating the sample size in scientific studies is one of the critical issues as regards the scientific contribution of the study. The sample size critically affects the hypothesis and the study design, and there is no straightforward way of calculating the effective sample size for reaching an accurate conclusion. Use of a statistically incorrect sample size may lead to inadequate results in both clinical and laboratory studies as well as resulting in time loss, cost, and ethical problems. This review holds two main aims. The first aim is to explain the importance of sample size and its relationship to effect size (ES) and statistical significance. The second aim is to assist researchers planning to perform sample size estimations by suggesting and elucidating available alternative software, guidelines and references that will serve different scientific purposes.

Pitfalls in scientific research: critical appraisal of articles published in one of the international journals in Egypt

BACKGROUND

To identify and report flaws of Internet-published articles in the Journal of the Egyptian National Cancer Institute (JENCI), Cairo University, through a retrospective documentary study on articles published during the period from 2011 to 2016. All sections were reviewed against a collective checklist. Articles were grouped by publication year into 2 intervals: early (from 2011 to 2013) and recent (from 2014 to 2016) to identify changes in study characteristics over time.

RESULTS

The study included 139 original articles. Half of the titles represented aim and 9.4% represented study design. Abstracts were concise, clear, with structured writing format in 98.6%, 93.5%, and 35.3%, respectively. Most introductions included the study aim, while 41% had a rationale. Study timing was reported in 59.0%, while the study design was reported in 25.9%. Inclusion and exclusion criteria were clearly reported in 43.1% and 40.1%, respectively. Statistical methods were mentioned in 80.6%, complete in 30.4%, and appropriate in 85.7%. Four studies reported sample size estimation. Only 52.5% and 58.3% of results were exhaustive and answer the research question, respectively. Incorrect statistical calculations occurred in 41.0%, inappropriate statistical tests or descriptive parameter selection in 26.6%, while inappropriate test application occurred in 49.1%. About 60% of discussions did not completely cover results, 31.7% fully justified the findings, 56.1% followed a logical flow, and 36.7% had contradiction within the text. Conclusions were mostly linked to aim, imprecise, and extrapolating beyond results. On comparing both periods, only a significant less misuse of statistical terms, more reporting conflict of interest, more missing references for cited texts in the recent period, and more participation of NCI over other institutes in the early period were found.

CONCLUSION

Articles published in JENCI (from 2011 to 2016) had many methodological and reporting defects and some points of strength. Using the collective checklist developed by this study, continuous training of researchers, involving epidemiologists throughout the whole research process, and applying strict journal reporting and publication rules should be encouraged.

Narrative Review of Statistical Reporting Checklists, Mandatory Statistical Editing, and Rectifying Common Problems in the Reporting of Scientific Articles

Considerable attention has been drawn to poor reproducibility in the biomedical literature. One explanation is inadequate reporting of statistical methods by authors and inadequate assessment of statistical reporting and methods during peer review. In this narrative review, we examine scientific studies of several well-publicized efforts to improve statistical reporting. We also review several retrospective assessments of the impact of these efforts. These studies show that instructions to authors and statistical checklists are not sufficient; no findings suggested that either improves the quality of statistical methods and reporting. Second, even basic statistics, such as power analyses, are frequently missing or incorrectly performed. Third, statistical review is needed for all papers that involve data analysis. A consistent finding in the studies was that nonstatistical reviewers (eg, "scientific reviewers") and journal editors generally poorly assess statistical quality. We finish by discussing our experience with statistical review at Anesthesia & Analgesia from 2006 to 2016.

Examination of published articles with respect to statistical errors in veterinary sciences

The aim of this present work was to examine statistical errors in published veterinary science articles. A total of 204 published articles (SCI or SCI-Exp) were used in this study. The articles were chosen from among those indexed in PubMed database between the years 2010 and 2014, inclusive. A total of 199 articles had at least one statistical error. The most frequently encountered statistical error among the articles published in journals indexed in SCI and in SCI-E was “errors in summarizing data”. No statistical error was found in 2.45% (n=5) of 204 (SCI: 0.98% (1/102), SCI-E: 3.92% (4/102)) articles. To reduce and prevent statistical errors in publications, the researchers must have a basic knowledge of statistics and during the study process they must consult field experts. While reviewing, the reviewers have to redirect the publications to statistical editors when needed and most importantly during the process of editing, the editors have to direct the publications to a statistics reviewer.

Putting Meaning Back Into the Mean: A Comment on the Misuse of Elementary Statistics in a Sample of Manuscripts Submitted to Clinical Therapeutics

PURPOSE

Errors in the statistical presentation and analyses of data in the medical literature remain common despite efforts to improve the review process, including the creation of guidelines for authors and the use of statistical reviewers. This article discusses common elementary statistical errors seen in manuscripts recently submitted to Clinical Therapeutics and describes some ways in which authors and reviewers can identify errors and thus correct them before publication.

METHODS

A nonsystematic sample of manuscripts submitted to Clinical Therapeutics over the past year was examined for elementary statistical errors.

FINDINGS

Clinical Therapeutics has many of the same errors that reportedly exist in other journals.

IMPLICATIONS

Authors require additional guidance to avoid elementary statistical errors and incentives to use the guidance. Implementation of reporting guidelines for authors and reviewers by journals such as Clinical Therapeutics may be a good approach to reduce the rate of statistical errors.

Increasing value and reducing waste: addressing inaccessible research

The methods and results of health research are documented in study protocols, full study reports (detailing all analyses), journal reports, and participant-level datasets. However, protocols, full study reports, and participant-level datasets are rarely available, and journal reports are available for only half of all studies and are plagued by selective reporting of methods and results. Furthermore, information provided in study protocols and reports varies in quality and is often incomplete. When full information about studies is inaccessible, billions of dollars in investment are wasted, bias is introduced, and research and care of patients are detrimentally affected. To help to improve this situation at a systemic level, three main actions are warranted. First, academic institutions and funders should reward investigators who fully disseminate their research protocols, reports, and participant-level datasets. Second, standards for the content of protocols and full study reports and for data sharing practices should be rigorously developed and adopted for all types of health research. Finally, journals, funders, sponsors, research ethics committees, regulators, and legislators should endorse and enforce policies supporting study registration and wide availability of journal reports, full study reports, and participant-level datasets.

Methodological issues of genetic association studies

Genetic association studies explore the association between genetic polymorphisms and a certain trait, disease or predisposition to disease. It has long been acknowledged that many genetic association studies fail to replicate their initial positive findings. This raises concern about the methodological quality of these reports. Case-control genetic association studies often suffer from various methodological flaws in study design and data analysis, and are often reported poorly. Flawed methodology and poor reporting leads to distorted results and incorrect conclusions. Many journals have adopted guidelines for reporting genetic association studies. In this review, some major methodological determinants of genetic association studies will be discussed.