Trends in study design and the statistical methods employed in a leading general medicine journal

2017 ◽  
Vol 43 (1) ◽  
pp. 36-44 ◽  
Author(s):  
M. Gosho ◽  
Y. Sato ◽  
K. Nagashima ◽  
S. Takahashi
Keyword(s):  
Statistical Methods ◽  
Study Design ◽  
General Medicine ◽  
Medicine Journal

scholarly journals A Review of COVID-19-Related Publications and Lag Times During the First Six Months of the Year 2020

2021 ◽  
Vol 22 (4) ◽  
pp. 958-962
Author(s):  
Christopher Carvalho ◽  
Matthew Fuller ◽  
Emmanuel Quaidoo ◽  
Ahson Haider ◽  
Jonathan Rodriguez ◽  
...  
Keyword(s):  
Medical Information ◽  
General Medicine ◽  
World Health ◽  
Secondary Outcome ◽  
Original Research ◽  
Journal Articles ◽  
Medicine Journal ◽  
Time Period ◽  
Lag Times ◽  
Health Organization

Introduction: Considering the need for information regarding approaches to prevention and treatment of coronavirus disease 2019 (COVID-19), we sought to determine publication lag times of COVID-19-related original research articles published in top general medicine and emergency medicine (EM) journals. We further sought to characterize the types of COVID-19 publications within these journals. Methods: We reviewed 125 top-ranked general medicine journals and 20 top-ranked EM-specific journals for COVID-19-related publications. We abstracted article titles and manuscript details for each COVID-19-related article published between January 1–June 30, 2020, and categorized articles as one of the following: original research; case report; review; or commentary. We abstracted data for preprint publications over the same time period and determined whether articles from the general medicine and EM journals had been previously published as preprint articles. Our primary outcomes were the following: 1) lag time (days) between global cumulative World Health Organization (WHO)-confirmed cases of COVID-19 and publications; 2) lag times between preprint article publication and peer-reviewed journal publication; and 3) lag times between submission and publication in peer-reviewed journals. Our secondary outcome was to characterize COVID-19-related publications. Results: The first original research publications appeared in a general medicine journal 20 days and in an EM journal 58 days after the first WHO-confirmed case of COVID-19. We found median and mean lag times between preprint publications and journal publications of 32 days (19, 49) and 36 days (22) for general medicine journals, and 26 days (16, 36) and 25 days (13) for EM journals. Median and mean lag times between submission and publication were 30 days (19, 45) and 35 days (13) for general medicine journals, and 23 days (11, 39) and 27 days (19) for EM journals. Of 2530 general medicine journal articles and 351 EM journal articles, 28% and 23.6% were original research. We noted substantial closing of the preprint to peer-reviewed publication (160 days pre-pandemic) and peer-reviewed journal submission to publication (194 days pre-pandemic) lag times for COVID-19 manuscripts. Conclusion: We found a rapid and robust response with shortened publication lag times to meet the need for the publication of original research and other vital medical information related to COVID-19 during the first six months of 2020.

The psychology of experimental psychologists: Overcoming cognitive constraints to improve research

2019 ◽  
Author(s):  
Dorothy Vera Margaret Bishop
Keyword(s):  
Statistical Methods ◽  
Study Design ◽  
Experimental Psychology ◽  
Critical Reflection ◽  
Cognitive Biases ◽  
Research Process ◽  
Cognitive Constraints ◽  
Cognitive Schemata ◽  
Funding Agencies ◽  
Replication Crisis

Experimental psychology is affected by a "replication crisis" that is causing concern in many areas of science. Approaches to tackling this crisis include better training in statistical methods, greater transparency and openness, and changes to the incentives created by funding agencies, journals and institutions. Here I argue that if proposed solutions are to be effective, we need also to take into account people's cognitive constraints that can distort all stages of the research process: designing and executing experiments, analysing data, and writing up findings for publication. I focus specifically on cognitive schemata in perception and memory, confirmation bias, systematic misunderstanding of statistics, and asymmetry in moral judgements of errors of commission and omission. Finally, I consider methods that may help mitigate the effects of cognitive constraints: better training, including use of simulations to overcome statistical misunderstanding, specific programs directed at inoculating against cognitive biases, adoption of Registered Reports to encourage more critical reflection in planning studies, and using methods such as triangulation and "pre mortem" evaluation of study design to make a culture of criticism more acceptable.

scholarly journals A Study Design For The Assessment Of Fish Tumour Prevalence In The Lower St. Clair River

2021 ◽  
Author(s):  
Liliya Baranova
Keyword(s):  
Sample Size ◽  
Statistical Methods ◽  
Study Design ◽  
Histopathological Examination ◽  
Sentinel Species ◽  
Sampling Location ◽  
Special Focus ◽  
Anecdotal Evidence ◽  
Sampling Locations ◽  
Area Of Concern

A conclusive fish tumour prevalence assessment has never been conducted in the lower part of the St. Clair River Area of Concern, despite possible re-contamination of the river and anecdotal evidence of fish abnormalities. This paper provides a study design for a comprehensive fish tumour prevalence assessment of the Lower St. Clair River with special focus on Walpole Island First Nation and surrounding waters. Study details such as area of focus, sentinel species, suggested sampling locations, sample size, field protocols and statistical methods are identified. A brief guide for histopathological examination and interpretation is provided. An alternate method of sampling location siting is suggested. This study design is intended to provide a guide and background reference for the implementation of a future full scale fish tumour assessment in the Lower St. Clair River.

Guidelines for the Development of Research Proposals following a Structured, Holistic Approach for a Research Proposal (SHARP)

1998 ◽  
Vol 19 (3) ◽  
pp. 268-282 ◽  
Author(s):  
Rainer Gross ◽  
Darwin Karyadi ◽  
Soemilah Sastroamidjojo ◽  
Werner Schultink
Keyword(s):  
Sample Size ◽  
Statistical Methods ◽  
Study Design ◽  
Causal Model ◽  
Holistic Approach ◽  
Sampling Procedure ◽  
Research Proposal ◽  
Ethical Aspects ◽  
Indicator Method ◽  
Operational Plan

SHARP (a Structured, Holistic Approach for a Research Proposal) is a structured method for developing a research proposal that can be used either by individuals or by teams of researchers. The eight steps in SHARP are (1) setting up a causal model, (2) establishing a fact–hypothesis matrix (FaHM), (3) developing a variable–indicator–method matrix (VIM), (4) selecting the study design, (5) defining the sampling procedure and calculating the sample size, (6) selecting the statistical methods, (7) considering the ethical aspects, and (8) setting up an operational plan. The objectives of the research proposal are to help the researcher to define the contents and to plan and execute a research project, and to inform potential collaborators and supporters about the topic. The proposal that is produced during the process can be submitted to agencies for possible funding.

Oxford Handbook of Medical Statistics

2010 ◽  
Author(s):  
Janet Peacock ◽  
Philip Peacock
Keyword(s):  
Medical Students ◽  
Sample Size ◽  
Statistical Methods ◽  
Study Design ◽  
Medical Statistics ◽  
Wide Range ◽  
Chi Squared ◽  
Published Research ◽  
Difficult Subject

Written in an easily accessible style, the Oxford Handbook of Medical Statistics provides doctors and medical students with a concise and thorough account of this often difficult subject. It promotes understanding and interpretation of statistical methods across a wide range of topics, from study design and sample size considerations, through t- and chi-squared tests, to complex multifactorial analyses, using examples from published research.

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