LC-MS METABOLOMICS FROM STUDY DESIGN TO DATA-ANALYSIS – USING A VERSATILE PATHOGEN AS A TEST CASE

Background: The COVID-19 pandemic has made the world to come to a standstill. What started as on 16th March 2020, as 114 confirmed cases of COVID‑19 in the country has now reached worrisome figures. The latest world scenario as per WHO as on 30th November, 2020 is as under-World data: 62,509,444 cases, deaths: 1,458,782; USA: 13,082,877 cases, deaths: 263,946; India: 9,431,691 cases, deaths 137, 139. It is evident that worldwide India is number two in case load and there’s no reason to prevent India from becoming number one unless appropriate corrective steps are taken.Methods: The present study has looked into various data sources available in public domain. The study covered a period of almost nine months i.e., from March 2020 to November 2020. The study revealed a steady increase in the number of COVID-19 cases from March 2020 with peak of pandemic occurring in the mid of September and then a steady decline of cases from October.Results: The data analysis shows that after peaking of cases in September, the epidemic will decline in a phased manner by the end of March 2021. Even though there is a decline seen from the month of October, spike of COVID-19 cases was seen in November in some of the states of India. Therefore, we can’t deny the possibility of a second wave of pandemic to occur in the month of December 2020 and January 2021.Conclusions: Hence appropriate and strict control measures have to be put in place for effective control of the Pandemic and its resurgence.

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Massive Open Online Course Evaluation Methods: Systematic Review (Preprint)

10.2196/preprints.13851 ◽

2019 ◽

Cited By ~ 3

Author(s):

Abrar Alturkistani ◽

Ching Lam ◽

Kimberley Foley ◽

Terese Stenfors ◽

Elizabeth R Blum ◽

...

Keyword(s):

Data Analysis ◽

Data Collection ◽

Quality Assessment ◽

Study Design ◽

Evaluation Methods ◽

Course Evaluation ◽

Data Sources ◽

Massive Open Online ◽

Analysis Methods ◽

Data Analysis Methods

BACKGROUND Massive open online courses (MOOCs) have the potential to make a broader educational impact because many learners undertake these courses. Despite their reach, there is a lack of knowledge about which methods are used for evaluating these courses. OBJECTIVE The aim of this review was to identify current MOOC evaluation methods to inform future study designs. METHODS We systematically searched the following databases for studies published from January 2008 to October 2018: (1) Scopus, (2) Education Resources Information Center, (3) IEEE (Institute of Electrical and Electronic Engineers) Xplore, (4) PubMed, (5) Web of Science, (6) British Education Index, and (7) Google Scholar search engine. Two reviewers independently screened the abstracts and titles of the studies. Published studies in the English language that evaluated MOOCs were included. The study design of the evaluations, the underlying motivation for the evaluation studies, data collection, and data analysis methods were quantitatively and qualitatively analyzed. The quality of the included studies was appraised using the Cochrane Collaboration Risk of Bias Tool for randomized controlled trials (RCTs) and the National Institutes of Health—National Heart, Lung, and Blood Institute quality assessment tool for cohort observational studies and for before-after (pre-post) studies with no control group. RESULTS The initial search resulted in 3275 studies, and 33 eligible studies were included in this review. In total, 16 studies used a quantitative study design, 11 used a qualitative design, and 6 used a mixed methods study design. In all, 16 studies evaluated learner characteristics and behavior, and 20 studies evaluated learning outcomes and experiences. A total of 12 studies used 1 data source, 11 used 2 data sources, 7 used 3 data sources, 4 used 2 data sources, and 1 used 5 data sources. Overall, 3 studies used more than 3 data sources in their evaluation. In terms of the data analysis methods, quantitative methods were most prominent with descriptive and inferential statistics, which were the top 2 preferred methods. In all, 26 studies with a cross-sectional design had a low-quality assessment, whereas RCTs and quasi-experimental studies received a high-quality assessment. CONCLUSIONS The MOOC evaluation data collection and data analysis methods should be determined carefully on the basis of the aim of the evaluation. The MOOC evaluations are subject to bias, which could be reduced using pre-MOOC measures for comparison or by controlling for confounding variables. Future MOOC evaluations should consider using more diverse data sources and data analysis methods. INTERNATIONAL REGISTERED REPORT RR2-10.2196/12087

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Fluoride exposure and indicators of thyroid functioning: study design and data analysis considerations

Journal of Epidemiology & Community Health ◽

10.1136/jech-2017-209956 ◽

2017 ◽

pp. jech-2017-209956

Author(s):

Declan Timothy Waugh

Keyword(s):

Data Analysis ◽

Study Design ◽

Fluoride Exposure

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Confirmatory study design, data analysis, and results that matter.

Toward a more perfect psychology: Improving trust, accuracy, and transparency in research. ◽

10.1037/0000033-004 ◽

2017 ◽

pp. 59-78 ◽

Cited By ~ 4

Author(s):

Matthew T. McBee ◽

Samuel H. Field

Keyword(s):

Data Analysis ◽

Study Design ◽

Design Data

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A framework for clinical and translational research in the era of rigor and reproducibility

Journal of Clinical and Translational Science ◽

10.1017/cts.2020.523 ◽

2020 ◽

pp. 1-10

Author(s):

Chris Wichman ◽

Lynette M. Smith ◽

Fang Yu

Keyword(s):

Data Analysis ◽

Data Collection ◽

Translational Research ◽

Study Design ◽

Clinical Translation ◽

Multidisciplinary Teams ◽

Scientific Practices ◽

Design Data ◽

Anaplastic Lymphoma ◽

Clinical And Translational Research

Abstract Introduction: Rigor and reproducibility are two important cornerstones of medical and scientific advancement. Clinical and translational research (CTR) contains four phases (T1–T4), involving the translation of basic research to humans, then to clinical settings, practice, and the population, with the ultimate goal of improving public health. Here we provide a framework for rigorous and reproducible CTR. Methods: In this paper we define CTR, provide general and phase-specific recommendations for improving quality and reproducibility of CTR with emphases on study design, data collection and management, analyses and reporting. We present and discuss aspects of rigor and reproducibility following published examples of CTR from the literature, including one example that shows the development path of different treatments that address anaplastic lymphoma kinase-positive (ALK+) non-small cell lung cancer (NSCLC). Results: It is particularly important to consider robust and unbiased experimental design and methodology for analysis and interpretation for clinical translation studies to ensure reproducibility before taking the next translational step. There are both commonality and differences along the clinical translation research phases in terms of research focuses and considerations regarding study design, implementation, and data analysis approaches. Conclusions: Sound scientific practices, starting with rigorous study design, transparency, and team efforts can greatly enhance CTR. Investigators from multidisciplinary teams should work along the spectrum of CTR phases, and identify optimal practices for study design, data collection, data analysis, and results reporting to allow timely advances in the relevant field of research.

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