Abdullah Al Masud has over 10 years of experience in biostatistics and clinical research, currently working as a senior research statistician at AbbVie. He has extensive experience analyzing oncology clinical trial data and developing statistical analysis plans. His qualifications include published research, programming skills in SAS and R, and a PhD in Biostatistics from Indiana University.
This workshop is meant to be an introduction to the systematic review process. Further information about systematic reviews was available through a research guide. http://libguides.ucalgary.ca/content.php?pid=593664
2016_The exchangeability of self-reports and administrative health care resou...Cindy Noben
The document summarizes a systematic review that assessed the methodological reporting quality (MeRQ) of studies comparing self-reported and administrative methods of measuring health care resource use. Key findings include:
1. Only a few studies (n=6) reported adequate information according to the MeRQ criteria to cautiously indicate exchangeability of self-reported and administrative measurement methods.
2. Most studies had fair reporting quality but lacked essential details regarding statistical methods, validation estimates, and interpretation of results.
3. Studies that met minimum MeRQ criteria found varying levels of agreement between data sources, with correlations ranging from 0.14 to 0.93, indicating both random and systematic errors.
In conclusion
A well recognised form of research is called systematic reviews on specific point. Why do we need them and How they can be done?? this talk is trying to answer these questions in a simple way
For a School of Information class on medical librarianship, this presentation was created to provide a very basic introduction and overview of the concepts, expectations, and experience of the librarian portion of working in a systematic review team.
The document provides an introduction to systematic reviews and meta-analyses. It discusses that systematic reviews aim to reduce bias by comprehensively identifying, appraising, and synthesizing all relevant studies on a topic. They often include a meta-analysis to statistically synthesize data from multiple studies. Systematic reviews use a predefined protocol and search strategy to find all studies, whereas traditional reviews may not consider study quality or report how conclusions follow from evidence. The key steps in a systematic review are developing a protocol, conducting a comprehensive literature search, assessing study eligibility, extracting data, critically appraising studies, and synthesizing results.
This paper reviews recent literature on exploratory factor analysis (EFA) and assesses its current use in nursing research. The review finds that while EFA is commonly used, researchers often rely on outdated heuristics rather than evidence-based recommendations when making key decisions. An assessment of 54 EFA solutions in nursing journals found researchers commonly used participants-to-items ratios to determine sample sizes, used PCA instead of EFA, relied on eigenvalues >1 and scree tests to determine factors, used PCA for extraction and Varimax rotation. The paper recommends researchers draw on simulation studies to determine sample sizes and make informed choices aligned with the goals and models of EFA and PCA.
This workshop is meant to be an introduction to the systematic review process. Further information about systematic reviews was available through a research guide. http://libguides.ucalgary.ca/content.php?pid=593664
2016_The exchangeability of self-reports and administrative health care resou...Cindy Noben
The document summarizes a systematic review that assessed the methodological reporting quality (MeRQ) of studies comparing self-reported and administrative methods of measuring health care resource use. Key findings include:
1. Only a few studies (n=6) reported adequate information according to the MeRQ criteria to cautiously indicate exchangeability of self-reported and administrative measurement methods.
2. Most studies had fair reporting quality but lacked essential details regarding statistical methods, validation estimates, and interpretation of results.
3. Studies that met minimum MeRQ criteria found varying levels of agreement between data sources, with correlations ranging from 0.14 to 0.93, indicating both random and systematic errors.
In conclusion
A well recognised form of research is called systematic reviews on specific point. Why do we need them and How they can be done?? this talk is trying to answer these questions in a simple way
For a School of Information class on medical librarianship, this presentation was created to provide a very basic introduction and overview of the concepts, expectations, and experience of the librarian portion of working in a systematic review team.
The document provides an introduction to systematic reviews and meta-analyses. It discusses that systematic reviews aim to reduce bias by comprehensively identifying, appraising, and synthesizing all relevant studies on a topic. They often include a meta-analysis to statistically synthesize data from multiple studies. Systematic reviews use a predefined protocol and search strategy to find all studies, whereas traditional reviews may not consider study quality or report how conclusions follow from evidence. The key steps in a systematic review are developing a protocol, conducting a comprehensive literature search, assessing study eligibility, extracting data, critically appraising studies, and synthesizing results.
This paper reviews recent literature on exploratory factor analysis (EFA) and assesses its current use in nursing research. The review finds that while EFA is commonly used, researchers often rely on outdated heuristics rather than evidence-based recommendations when making key decisions. An assessment of 54 EFA solutions in nursing journals found researchers commonly used participants-to-items ratios to determine sample sizes, used PCA instead of EFA, relied on eigenvalues >1 and scree tests to determine factors, used PCA for extraction and Varimax rotation. The paper recommends researchers draw on simulation studies to determine sample sizes and make informed choices aligned with the goals and models of EFA and PCA.
Health economics analyzes efficiency and equity in healthcare systems. Systematic reviews comprehensively search literature to answer a focused question using explicit methods. They involve developing a protocol and search strategy, screening studies, extracting data, and synthesizing results. Meta-analysis statistically combines results from multiple studies on the same topic. It allows for analysis of effect sizes across studies but must account for biases like publication and language biases.
This document provides an overview of systematic reviews, including their definition, purpose, history, levels of evidence, and the steps involved in conducting a systematic review. It defines systematic reviews as objective, secondary research that integrates and synthesizes findings from multiple research studies on a topic. The goals are to use evidence to guide effective, outcome-improving patient care decisions. Key figures and developments discussed include Archie Cochrane's call for randomized controlled trial registration and the formation of the Cochrane Collaboration. The document outlines the various stages of a systematic review process from developing a question to searching, organizing, analyzing data, and reporting results. It also reviews the role librarians can play in supporting systematic reviews.
Meta analysis: Made Easy with Example from RevManGaurav Kamboj
This document provides an overview of meta-analysis, including:
1) Meta-analysis allows researchers to quantitatively combine the results of multiple studies on a topic to arrive at overall conclusions about the body of research.
2) The key steps of conducting a meta-analysis include developing a research protocol, performing a comprehensive literature search, selecting studies, assessing study quality, extracting data, analyzing data, and addressing heterogeneity and publication bias.
3) Funnel plots and statistical tests can be used to examine potential biases like publication bias in a meta-analysis. Addressing these biases helps ensure the meta-analysis provides an accurate summary of the evidence.
The document discusses various statistical methodologies that can be applied to Ayurveda research, including experimentation, surveys, case-control studies, meta-analysis, survival studies, and time series analysis. It provides an overview of how these methods are currently used in Ayurveda research and highlights some areas that could be improved, such as employing stratification and larger sample sizes. Logistic regression and decision trees are presented as effective analytical techniques for case-control studies.
Systematic Reviews in the Health SciencesBecky Morin
This document provides an overview of systematic reviews, including what they are, why they are important in health sciences research, and the steps involved in conducting one. It defines a systematic review as a comprehensive study that collects and analyzes data from multiple research studies to answer a specific question. It notes that systematic reviews use explicit and rigorous methods to minimize bias and provide reliable conclusions. The key steps outlined include developing a protocol and research question, conducting comprehensive searches, selecting and assessing studies, extracting and synthesizing data, and disseminating findings.
Clinical Research Informatics (CRI) Year-in-Review 2014Peter Embi
Peter Embi's review of notable publications and events in the field of Clinical Research Informatics (CRI) that took place in 2013+. This was presented as the closing keynote presentation of the 2014 AMIA CRI Summit in San Francisco, CA on April 11, 2014.
A systematic review is a review of a clearly formulated question that uses systematic and explicit methods to identify, select, and critically appraise relevant research studies. It collects and analyzes data from the included studies. Statistical methods like meta-analysis may be used to analyze and summarize the results. The gold standard is an individual participant data meta-analysis that models both study and individual characteristics.
The document is a quiz on quantitative synthesis and meta-analysis. It asks questions about which effect size metric is more likely to be heterogeneous, the appropriate reasons for conducting a meta-analysis, the key difference between fixed and random effects models, assessing study conclusions and methods, and critically appraising included studies. The quiz was prepared by members of Tufts Medical Center to teach systematic review methods based on their methods guide.
Statistical tests for data involving quantitative dataRizwan S A
This document discusses statistical tests for quantitative data. It begins by defining quantitative variables and different types of quantitative scales. It then discusses prerequisites for choosing a statistical test, including the number of variables, nature of dependent and independent variables, and whether variables are normally distributed. The document outlines various parametric and non-parametric statistical tests for both paired and unpaired quantitative data, including t-tests, ANOVA, correlation, regression, Mann-Whitney U test, Kruskal-Wallis test, and Wilcoxon signed-rank test. Examples of applying some of these tests are provided.
Validity of Instruments, Appropriateness of Designs and Statistics in Article...iosrjce
The main purpose of the study is to appraise the validity of research instruments, the appropriateness
of the research designs and the statistics used for data analyses in articles published in education journals in
Nigeria. Currently, higher institutions in Nigeria tend to prefer articles published in foreign/international
journals. Some researchers in the country also question the validity of some of the articles published in local
journals. Appropriate research designs, valid instrument and appropriate use of statisticals tools are some of
the indices that make research results credible and dependable. To assess these important variables, three
questions were posed. Journal articles published in Nigeria education Journals for last five years were selected
through accidental sampling technique. Then purposive sampling technique was used to select 132 empirical
studies. Empirical studies were selected because they are the studies that lend themselves to use of designs, data
collection with instruments and statistical analysis of data. Appraisal guides for instrument, design and
statistics were used to assess the articles. The results showed that 67% of the articles were carried out with
appropriate research designs, and in 78% of the articles, appropriate statistics were applied in data analyses.
However, only 36% of the instruments used for the study would generate data that can lead to valid
interpretation of the results. Programmes that will enhance knowledge and stills of researchers to improve the
quality of research based publications are recommended. Institutions of higher learning can help their staff in
this direction
An excellent article that uses predictive and optimization methods to reduce hospital readmissions.
Another great article, "Reducing hospital readmissions by integrating empirical prediction with resource optimization" (Helm, Alaeddini, Stauffer, Bretthaur, and Skolarus, 2016) describes how Machine Learning modeling tools were used to determine the root-causes and individualized estimation of readmissions. The post-discharge monitoring schedule and workplans were then optimized to patient changes in health states.
Great article on how to integrate machine learning and optimization technique.
One group of researchers was able to reduce heart failure readmissions by 35% by combining machine learning and decision science technique, see "Data-driven decisions for reducing readmissions for heart failure: general methodology and case study" (Bayati, et. al., 2014).
Systematic Reviews: the researcher's perspective and the research question. E...healthlibaust2012
The document discusses the perspectives of researchers conducting systematic reviews and the importance of developing a clear research question. It emphasizes that a well-constructed question using the PICO/PICo framework is fundamental to guiding the review process, including developing inclusion criteria and an effective search strategy. Researchers may have different levels of experience and understanding of systematic reviews, so librarians play an important role in helping them conceptualize the question and properly carry out the various review steps, such as developing a reproducible search strategy.
Data mining is a powerful method to extract knowledge from data. Raw data faces various challenges that make traditional method improper for knowledge extraction.
Data mining is supposed to be able to handle various data types in all formats.
Medical data mining is a multidisciplinary field with contribution of medicine and data mining.
each paper is studied based on the six medical tasks: screening, diagnosis, treatment, prognosis, monitoring and management.
Systematic Reviews: the process, quantitative, qualitative and mixed methods ...healthlibaust2012
This document discusses systematic reviews and their process. It defines systematic reviews as attempts to identify, appraise, and synthesize all empirical evidence that meets pre-specified eligibility criteria to answer a given research question. It notes there are quantitative, qualitative, and mixed methods reviews. The document outlines the steps in conducting a systematic review, including developing a protocol and research question, locating studies, assessing studies, extracting and analyzing data, and presenting results.
This paper describes the methods of the Treatment In Morning versus Evening (TIME) study, a large prospective randomized open-label blinded endpoint study comparing morning versus evening dosing of antihypertensive medications. The TIME study recruits participants through advertising, primary and secondary care, and patient databases in the UK. Participants self-enroll and consent on a secure website, and are randomized to morning or evening dosing. Follow-ups are conducted by automated email at 1 month and every 3 months thereafter. The study uses a prospective randomized open-label blinded endpoint design to establish if evening dosing is more cardioprotective than morning dosing.
Narayana P provides a professional summary highlighting over 3 years of experience as an Associate Statistician Programmer in clinical research. He demonstrates strong problem solving, teamwork, and technical skills including proficiency in SAS, clinical trial data like demographics and adverse events. Narayana holds an MBA and BSc and has worked on projects evaluating treatments for conditions like diabetes, infections, dementia, and pain management. His responsibilities involve creating SAS datasets, programs for analysis and reporting, validating outputs, and implementing statistical analyses according to protocols.
Xiao Wang is a biostatistician with over 5 years of experience in clinical research. She has led statistical analysis for multiple phase I-III clinical studies in various therapeutic areas. Her skills include SAS programming, statistical modeling, clinical study report writing, and data analysis. She holds an MS in Statistics from Arizona State University where she conducted research comparing bootstrap methods for longitudinal data analysis.
Health economics analyzes efficiency and equity in healthcare systems. Systematic reviews comprehensively search literature to answer a focused question using explicit methods. They involve developing a protocol and search strategy, screening studies, extracting data, and synthesizing results. Meta-analysis statistically combines results from multiple studies on the same topic. It allows for analysis of effect sizes across studies but must account for biases like publication and language biases.
This document provides an overview of systematic reviews, including their definition, purpose, history, levels of evidence, and the steps involved in conducting a systematic review. It defines systematic reviews as objective, secondary research that integrates and synthesizes findings from multiple research studies on a topic. The goals are to use evidence to guide effective, outcome-improving patient care decisions. Key figures and developments discussed include Archie Cochrane's call for randomized controlled trial registration and the formation of the Cochrane Collaboration. The document outlines the various stages of a systematic review process from developing a question to searching, organizing, analyzing data, and reporting results. It also reviews the role librarians can play in supporting systematic reviews.
Meta analysis: Made Easy with Example from RevManGaurav Kamboj
This document provides an overview of meta-analysis, including:
1) Meta-analysis allows researchers to quantitatively combine the results of multiple studies on a topic to arrive at overall conclusions about the body of research.
2) The key steps of conducting a meta-analysis include developing a research protocol, performing a comprehensive literature search, selecting studies, assessing study quality, extracting data, analyzing data, and addressing heterogeneity and publication bias.
3) Funnel plots and statistical tests can be used to examine potential biases like publication bias in a meta-analysis. Addressing these biases helps ensure the meta-analysis provides an accurate summary of the evidence.
The document discusses various statistical methodologies that can be applied to Ayurveda research, including experimentation, surveys, case-control studies, meta-analysis, survival studies, and time series analysis. It provides an overview of how these methods are currently used in Ayurveda research and highlights some areas that could be improved, such as employing stratification and larger sample sizes. Logistic regression and decision trees are presented as effective analytical techniques for case-control studies.
Systematic Reviews in the Health SciencesBecky Morin
This document provides an overview of systematic reviews, including what they are, why they are important in health sciences research, and the steps involved in conducting one. It defines a systematic review as a comprehensive study that collects and analyzes data from multiple research studies to answer a specific question. It notes that systematic reviews use explicit and rigorous methods to minimize bias and provide reliable conclusions. The key steps outlined include developing a protocol and research question, conducting comprehensive searches, selecting and assessing studies, extracting and synthesizing data, and disseminating findings.
Clinical Research Informatics (CRI) Year-in-Review 2014Peter Embi
Peter Embi's review of notable publications and events in the field of Clinical Research Informatics (CRI) that took place in 2013+. This was presented as the closing keynote presentation of the 2014 AMIA CRI Summit in San Francisco, CA on April 11, 2014.
A systematic review is a review of a clearly formulated question that uses systematic and explicit methods to identify, select, and critically appraise relevant research studies. It collects and analyzes data from the included studies. Statistical methods like meta-analysis may be used to analyze and summarize the results. The gold standard is an individual participant data meta-analysis that models both study and individual characteristics.
The document is a quiz on quantitative synthesis and meta-analysis. It asks questions about which effect size metric is more likely to be heterogeneous, the appropriate reasons for conducting a meta-analysis, the key difference between fixed and random effects models, assessing study conclusions and methods, and critically appraising included studies. The quiz was prepared by members of Tufts Medical Center to teach systematic review methods based on their methods guide.
Statistical tests for data involving quantitative dataRizwan S A
This document discusses statistical tests for quantitative data. It begins by defining quantitative variables and different types of quantitative scales. It then discusses prerequisites for choosing a statistical test, including the number of variables, nature of dependent and independent variables, and whether variables are normally distributed. The document outlines various parametric and non-parametric statistical tests for both paired and unpaired quantitative data, including t-tests, ANOVA, correlation, regression, Mann-Whitney U test, Kruskal-Wallis test, and Wilcoxon signed-rank test. Examples of applying some of these tests are provided.
Validity of Instruments, Appropriateness of Designs and Statistics in Article...iosrjce
The main purpose of the study is to appraise the validity of research instruments, the appropriateness
of the research designs and the statistics used for data analyses in articles published in education journals in
Nigeria. Currently, higher institutions in Nigeria tend to prefer articles published in foreign/international
journals. Some researchers in the country also question the validity of some of the articles published in local
journals. Appropriate research designs, valid instrument and appropriate use of statisticals tools are some of
the indices that make research results credible and dependable. To assess these important variables, three
questions were posed. Journal articles published in Nigeria education Journals for last five years were selected
through accidental sampling technique. Then purposive sampling technique was used to select 132 empirical
studies. Empirical studies were selected because they are the studies that lend themselves to use of designs, data
collection with instruments and statistical analysis of data. Appraisal guides for instrument, design and
statistics were used to assess the articles. The results showed that 67% of the articles were carried out with
appropriate research designs, and in 78% of the articles, appropriate statistics were applied in data analyses.
However, only 36% of the instruments used for the study would generate data that can lead to valid
interpretation of the results. Programmes that will enhance knowledge and stills of researchers to improve the
quality of research based publications are recommended. Institutions of higher learning can help their staff in
this direction
An excellent article that uses predictive and optimization methods to reduce hospital readmissions.
Another great article, "Reducing hospital readmissions by integrating empirical prediction with resource optimization" (Helm, Alaeddini, Stauffer, Bretthaur, and Skolarus, 2016) describes how Machine Learning modeling tools were used to determine the root-causes and individualized estimation of readmissions. The post-discharge monitoring schedule and workplans were then optimized to patient changes in health states.
Great article on how to integrate machine learning and optimization technique.
One group of researchers was able to reduce heart failure readmissions by 35% by combining machine learning and decision science technique, see "Data-driven decisions for reducing readmissions for heart failure: general methodology and case study" (Bayati, et. al., 2014).
Systematic Reviews: the researcher's perspective and the research question. E...healthlibaust2012
The document discusses the perspectives of researchers conducting systematic reviews and the importance of developing a clear research question. It emphasizes that a well-constructed question using the PICO/PICo framework is fundamental to guiding the review process, including developing inclusion criteria and an effective search strategy. Researchers may have different levels of experience and understanding of systematic reviews, so librarians play an important role in helping them conceptualize the question and properly carry out the various review steps, such as developing a reproducible search strategy.
Data mining is a powerful method to extract knowledge from data. Raw data faces various challenges that make traditional method improper for knowledge extraction.
Data mining is supposed to be able to handle various data types in all formats.
Medical data mining is a multidisciplinary field with contribution of medicine and data mining.
each paper is studied based on the six medical tasks: screening, diagnosis, treatment, prognosis, monitoring and management.
Systematic Reviews: the process, quantitative, qualitative and mixed methods ...healthlibaust2012
This document discusses systematic reviews and their process. It defines systematic reviews as attempts to identify, appraise, and synthesize all empirical evidence that meets pre-specified eligibility criteria to answer a given research question. It notes there are quantitative, qualitative, and mixed methods reviews. The document outlines the steps in conducting a systematic review, including developing a protocol and research question, locating studies, assessing studies, extracting and analyzing data, and presenting results.
This paper describes the methods of the Treatment In Morning versus Evening (TIME) study, a large prospective randomized open-label blinded endpoint study comparing morning versus evening dosing of antihypertensive medications. The TIME study recruits participants through advertising, primary and secondary care, and patient databases in the UK. Participants self-enroll and consent on a secure website, and are randomized to morning or evening dosing. Follow-ups are conducted by automated email at 1 month and every 3 months thereafter. The study uses a prospective randomized open-label blinded endpoint design to establish if evening dosing is more cardioprotective than morning dosing.
Narayana P provides a professional summary highlighting over 3 years of experience as an Associate Statistician Programmer in clinical research. He demonstrates strong problem solving, teamwork, and technical skills including proficiency in SAS, clinical trial data like demographics and adverse events. Narayana holds an MBA and BSc and has worked on projects evaluating treatments for conditions like diabetes, infections, dementia, and pain management. His responsibilities involve creating SAS datasets, programs for analysis and reporting, validating outputs, and implementing statistical analyses according to protocols.
Xiao Wang is a biostatistician with over 5 years of experience in clinical research. She has led statistical analysis for multiple phase I-III clinical studies in various therapeutic areas. Her skills include SAS programming, statistical modeling, clinical study report writing, and data analysis. She holds an MS in Statistics from Arizona State University where she conducted research comparing bootstrap methods for longitudinal data analysis.
The document discusses statistical packages and their applications. It defines statistics and statistical packages. It describes several commonly used statistical packages, including Excel, SPSS, SAS, Minitab, and others. It outlines their key features and how they are used in nursing and healthcare, such as for data analysis, clinical decision support, and quality improvement. The conclusion states that statistical packages are important for nursing as they help interpret clinical trials, communicate findings to patients, and manage data.
Narrative review | Prisma systematic review | Medical writingPubrica
At Pubrica, we collect data from a wide range of sources and perform semantic annotation based on the research questions that you wanted to solve. Pubrica has the vast majority of the data in doctor’s notes; electronic medical records, prescriptions, and similar information are available. Although therein lies the golden possibility of big data in medical care, it’s challenging to yield valuable insights due to complex, unstructured, longitudinal, and voluminous data.
Visit us @ https://pubrica.com/academy/systematic-review/variables-used-in-data-extraction-for-prospective-cohort-studies-in-a-systematic-review/
Biostatistics_Unit_II_Research Methodology & Biostatistics_M. Pharm (Pharmace...RAHUL PAL
This document provides an overview of biostatistics topics including parametric and non-parametric statistical tests, sample size calculation, and factors influencing sample size. It discusses commonly used parametric tests like the t-test, ANOVA, correlation coefficient, and regression analysis. Non-parametric tests like the Wilcoxon rank-sum test are also covered. The importance of considering sample size, factors that can impact it, and how dropouts are handled are summarized as well.
The document outlines an evaluation plan to assess the usability of a hospital's new electronic health record (EHR) system from the nurses' perspective. The evaluation will use qualitative methods, including interviews and observations of nurses, to identify any areas where the EHR's documentation screens are inconsistent with nursing workflow and clinical practice. The goals are to understand usability issues and offer solutions to better align the EHR with nursing work. Ethical considerations around participant consent and data confidentiality will be addressed. The analysis will provide a narrative to illustrate nurses' experiences using the EHR and how it impacts their work.
Retrospective versus | Meta analysis | Systematic literature reviewPubrica
Systematic review for prospective studies is a meticulous and essential process ensuring research findings’ reliability and validity. The key to success lies in adhering to a well-structured methodology that includes defining the research question, developing a comprehensive search strategy, screening studies based on pre-defined criteria, and critically appraising the selected articles.
https://pubrica.com/academy/manuscript-editing/conduct-a-systematic-review-for-prospective-studies/
N 599 Aspen Pandemic Preparedness Results and Recommendations Paper.pdfbkbk37
This document outlines the methodology used in a study examining pandemic preparedness in acute healthcare settings. A cohort study design was used to assess preparedness levels through a structured questionnaire distributed to local hospital emergency and intensive care coordinators. Primary data was collected through questionnaires and interviews, while secondary data involved a qualitative case study review of past pandemic preparedness research. Limitations of the cohort study method are discussed, as well as ethical considerations to ensure informed and voluntary participation and protect confidentiality.
· Reflect on the four peer-reviewed articles you critically apprai.docxVannaJoy20
· Reflect on the four peer-reviewed articles you critically appraised in Module 4, related to your clinical topic of interest and PICOT.
· Reflect on your current healthcare organization and think about potential opportunities for evidence-based change, using your topic of interest and PICOT as the basis for your reflection.
· Consider the best method of disseminating the results of your presentation to an audience.
The Assignment: (Evidence-Based Project)
Part 4: Recommending an Evidence-Based Practice Change
Create an 8- to 9-slide
narrated PowerPoint presentation in which you do the following:
· Briefly describe your healthcare organization, including its culture and readiness for change. (You may opt to keep various elements of this anonymous, such as your company name.)
· Describe the current problem or opportunity for change. Include in this description the circumstances surrounding the need for change, the scope of the issue, the stakeholders involved, and the risks associated with change implementation in general.
· Propose an evidence-based idea for a change in practice using an EBP approach to decision making. Note that you may find further research needs to be conducted if sufficient evidence is not discovered.
· Describe your plan for knowledge transfer of this change, including knowledge creation, dissemination, and organizational adoption and implementation.
· Explain how you would disseminate the results of your project to an audience. Provide a rationale for why you selected this dissemination strategy.
· Describe the measurable outcomes you hope to achieve with the implementation of this evidence-based change.
· Be sure to provide APA citations of the supporting evidence-based peer reviewed articles you selected to support your thinking.
· Add a lessons learned section that includes the following:
· A summary of the critical appraisal of the peer-reviewed articles you previously submitted
· An explanation about what you learned from completing the Evaluation Table within the Critical Appraisal Tool Worksheet Template (1-3 slides)
Zeinab Hazime
Nurs 6052
10/16/2022
Evaluation Table
Use this document to complete the
evaluation table requirement of the Module 4 Assessment,
Evidence-Based Project, Part 3A: Critical Appraisal of Research
Full
APA formatted citation of selected article.
Article #1
Article #2
Article #3
Article #4
Abraham, J., Kitsiou, S., Meng, A., Burton, S., Vatani, H., & Kannampallil, T.
(2020). Effects of CPOE-based medication ordering on outcomes: an overview of systematic reviews.
BMJ Quality & Safety, 29(10), 1-2.
Alanazi, A. (2020). The effect of computerized physician order entry on mortality rates in pediatric and neonatal care setting: Meta-analysis.
Informatics in Medicine
Unlocked, 19, 100308. https.
This document provides guidelines for publishing manuscripts in medical/dental journals. It discusses various types of manuscripts like case reports, case series, research articles, and systematic reviews. It explains guidelines for each type like CARE guidelines for case reports and CONSORT guidelines for clinical trials. It also discusses the peer review process, impact factor, indexing/abstracting of journals, and tips for manuscript acceptance. Overall, the document serves as a useful reference for authors to understand the publishing process and guidelines for improving the quality of their manuscripts.
Pandemic Preparedness Results and Recommendations.pdfbkbk37
This chapter discusses the findings and recommendations from a study on pandemic preparedness. The study used a cohort study design to assess preparedness levels in local hospitals. A questionnaire was administered to emergency management coordinators to collect data on facility planning, workforce capacity, and surge capacity. Qualitative data was also collected through interviews. The results showed both strengths and limitations in pandemic plans and capacity. Recommendations include continued planning and identification of gaps to improve readiness for future pandemics.
THE POWER OF DATA SCIENCE and ANALYTICS IN CLINICAL LABORATORYChelsea Osayande
The diagnostic laboratory has always been a key source of data that informs clinical decisions.
Clinical pathology tests generate discrete results with numeric or coded values that can be classified as normal or abnormal.
Anatomic pathology analysis results in a report based on visual analysis of tissues.
The emerging discipline of data science offers a valuable toolkit to maximize the value of all modalities of laboratory data and to improve the diagnostic and operational functions of a modern lab
Operations research within UK healthcare: A reviewHarender Singh
The paper "Operations research within UK healthcare: a review" provides an overview of the application of operations research (OR) in the UK healthcare sector. The review highlights the contribution of OR in improving efficiency, reducing costs, and enhancing patient outcomes in various areas of healthcare, such as hospital management, patient flow, resource allocation, and scheduling. The paper also discusses the challenges and opportunities in applying OR in healthcare, such as data availability, ethical considerations, and stakeholder engagement. Overall, the review provides insights into the potential of OR to drive innovation and improve healthcare delivery in the UK.
What is the formulation of the research question in systematic review | pubricaPubrica
• Formulating a research question is the challenging task for a researcher while initiating a systematic review.
• This article explains the different frameworks available for formulating a high-quality research question which includes PICO, SPIDER, SPICE, ECLIPSE.
• A well-formulated research question needs to have extreme specificity and preciseness that guides the implementation of the systematic review while keeping in mind the identification of variables and population of interest.
Reference: https://pubrica.com/services/research-services/systematic-review/
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IASCT Announcement: Registrations open for SDE "Statistical Applications in C...Korak Datta
Clinical trials are the cornerstone of new drug approvals. Effective planning of clinical studies in terms of design, sample size, analysis and reporting is essential for a successful clinical trial. This workshop will focus on such critical statistical aspects which support in better, faster and cheaper drug development. Participants will learn from seasoned statisticians about various statistical considerations to be applied at planning, conduct and post-study stages of a clinical trial with case studies.
Workshop will be conducted by experienced statisticians Dr. Shashidhar Savanur - Senior Director (Chiltern International Limited) and Dr. Vishwanath (Mahesh) Iyer, Head - Oncology Biometrics (Novartis Healthcare Private Limited).
Michael Ward has over 15 years of experience in biomedical research and drug development. He currently works as a Senior Clinical Science Specialist at Genentech, where he helps develop clinical trial protocols and analyzes data from Alzheimer's disease studies. Prior to this role, he worked as a Senior Manager leading statistical analysis teams. Dr. Ward has extensive experience across multiple disciplines including neuropsychology, bioinformatics, data management, and clinical research.
Shaun Cooley has extensive experience in clinical research and data management. He has worked on numerous clinical trials related to conditions such as PTSD, maternal immunology, neurology, and oncology. Currently he works as a research coordinator at a VA medical center, ensuring quality and compliance in clinical trials. He has also held positions as a biostatistician, laboratory contractor, and database manager. Cooley received his MPH in Epidemiology from USF and is currently a PhD candidate in nursing research at USF. He has expertise in statistical software programs including SAS and SPSS.
Hairong Shi has over 12 years of experience as a biostatistician working on clinical trials at Hines VA hospital. She is currently pursuing her PhD in Biostatistics from the University of Illinois at Chicago, which she plans to complete in the fall of 2016. Her role involves study design, statistical analysis, reporting, and collaborating with research teams on projects related to lung cancer, hearing loss, PTSD, and other areas. She has strong skills in SAS, R, and other statistical programming software.
This curriculum vitae summarizes the qualifications and experience of Weiliang Qiu. Qiu has over 12 years of experience in data analysis, especially of clinical trial and observational data. He has published over 70 peer-reviewed papers and edited two academic journals. Qiu has a Ph.D. in Statistics and is currently an Associate Biostatistician and Assistant Professor at Brigham and Women's Hospital, where he provides statistical support for clinical trials and develops novel statistical methods.
2024 HIPAA Compliance Training Guide to the Compliance OfficersConference Panel
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1. Abdullah Al Masud
900 Mark Lane#302,
Wheeling, Illinois 60090
317-712-1627 (Cell)
tomal22@hotmail.com
abdullah.masud@abbvie.com
Overall Career Goal
To be a proactive member and active contribution in an organization or a novel drug
development in statistics, biostatistics, bioinformatics, and epidemiology
Summary of Qualifications
• Working experience in oncology study, interim analysis, survival data analysis, lon-
gitudinal data analysis, study design and sample size calculation, linear and non-
linear modeling, predictive modeling and variable selection, developing algorithms
and statistical programming, simulation study and bootstrap method, clinical trials,
Bayesian analysis and hierarchical modeling, nonparametric analysis, meta analysis,
sensitivity analysis, cluster analysis, machine learning, data mining, data visualiza-
tion, genomics data analysis and sequencing, and economic data analysis
• Novel ideas and successful delivery within project timelines
• Experience defining standard analysis practices and implementing clinical data anal-
ysis workflows
• Developing protocol and statistical analysis plan (SAP) for oncology clinical trial
• Contributing to Case Report Form (CRF) development and reviewing edit check
specification
• Leverage collaborations with internal clinical development teams gaining novel in-
sights into disease indication and response data to enhance oncology study pipeline
• Participation in collaborative research with manager and colleagues
Work Experience
AbbVie Inc January 2017 – Present
Senior Research Statistician Chicago, Illinois
1. Work as a lead clinical statistician for cancer study with Chronic Lymphocytic
Leukemia (CLL) and Non-Hodgkin Lymphoma (NHL) disease indications
2. Provide lead statistical inputs on cancer study for designing, conducting, and report-
ing
3. Develop protocol and statistical analysis plan (SAP)
4. Support clinical team for conducting interim analyses and reviewing safety data
5. Create TLG template and derived analysis datasets specification, perform QC of
TLG
6. Support cross functional team to develop case report form (CRF) and electronic data
capture (EDC) system
7. Design necessary statistical edit check specification for EDC and SAS database
8. Collaborate with cross functional team members to provide statistical outputs for
clinical meetings and publications
9. Create mocked TLG for interim review and clinical data reporting to support pro-
gramming team
10. Use quantitative skills for analyzing safety lab data to guide a dose during the ramp-
up schedules
Research and Internship Experiences
Bristol-Myers Squibb May 2016 –August 2016
Biostatistician Intern Wallingford, Connecticut
1. Reviewed protocols and statistical analysis plans (SAP) for oncology clinical trials
2. Analyzed immuno-oncology study data
3. Supported the clinical and regulatory teams with providing statistical outputs
4. Introduced a novel endpoint of a randomized phase 3 trial
5. Evaluated the significant tests in the interim stage
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2. Abdullah Al Masud
6. Assessed a varieties of weighted log-rank tests for the analysis of time to treatment
failure
7. Conducted a sensitivity analysis of the study
8. Wrote R and SAS programming scripts for simulation and the lung cancer study data
Pfizer Inc May 2015 – August 2015
Statistical Intern New York, New York
1. Derived a novel statistical F-test for composite (multiple) endpoints of cardiovascular
therapeutic study in phase 3 trial
2. Developed and implemented non-parametric bootstrap technique for composite end-
points of cardiovascular therapeutic study in phase 3 trial
3. Assessed the new statistical tests using simulation studies and HIV-AIDS dataset
4. Wrote R programming codes and algorithms for clinical trial study with composite
endpoints
5. Reported the analysis document to managers for publication
Pinnacle Solutions Inc May 2014 – August 2014
Statistical Intern Indianapolis, Indiana
1. Analyzed and modeled repeated measurement dataset
2. Conducted cluster analysis and factor analysis on insurance claim data
3. Supported clients to conduct statistical analysis
4. Developed SAS macro codes for team
5. Reported and visualized statistical analysis to managers
Indiana University-School of Medicine August 2012 – December 2016
Department of Biostatistics Indianapolis, Indiana
Research Assistant
1. Survival Analysis And Observational Study:
• Collaborated with physicians and researchers at Regenstrief institute
• Analyzed the survival data and the hospital cost data from the electronic health
records (EHR)
• Utilized the Cox model for time-to-mortality analysis from a longitudinal study for
cardiovascular problem–Mitral regurgitation
• Analyzed correlated survival data for an oral examination study
• Developed SAS macro code and R code for analysis
• Reported and presented the analysis results to supervisors and collaborators
2. Model Selection And Specification in Survival Data with Long-Term Survivors:
• Analyzed the Cox model for survival or failure time data in SAS and R
• Applied predictive modeling technique such as LASSO and adaptive LASSO in sur-
vival models with long-term survivors
• Constructed a data-driven model building procedure to select the structure of cure
rate models or survival models with long-term survivors
• Developed model selection procedure to discover nonlinear variables in the mixture
models for recurrent survival events
• Derived expectation-maximization (EM) algorithm to analyze survival data
• Used B-spline function to approximate nonlinear functions in survival analysis
• Implemented and wrote R programming scripts for computation
Statistical Consult August 2009 –December 2014
3. Conducted observational study:
• Applied mixed effect model and linear regression model to air quality data
• Utilized piecewise spline technique in mixed effect model
• Analyzed missing data in an observational study
Utah State University August 2009 –May 2012
Graduate Assistant Logan, Utah
1. Taught undergraduate level Statistics and Mathematics courses
2. Conducted statistical analysis on genetics and financial data:
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3. Abdullah Al Masud
• Applied multiple hypothesis testing to control false discovery rate (FDR), and family-
wise error rate (FWER) in gene expression data
• Assessed statistical power of test for multiple testing using Monte Carlo simulation
• Applied time series regression analysis on stock returns and economic data
Skills
Statistical languages: SAS, SAS macro, SAS SQL, JMP, R, and WinBUGS
Programming languages: Python, and C++
Document preparation: LaTex, MS Office
Operating system: Linux, and Microsoft Windows
Education
PhD, Major in Biostatistics August 2012–December, 2016
(Minor in Epidemiology) Advisor: Dr. Wanzhu Tu
School of Medicine and Public Health, and Dr. Zhangsheng Yu
Indiana University, Indianapolis, Indiana
MS, Statistics and Financial Economics August 2009–July 2012
Utah State University, Logan, Utah
Bachelor of Science, Statistics Graduated 2008
University of Dhaka, Bangladesh, Dhaka
Theses
PhD Thesis
• Masud, A A (2016). Determination of The Composition of Failure Time Mod-
els with Long-Term Survivors. IUPUI ScholarWorks. https://doi.org/10.7912/
C2ZS36.
MS Thesis
• Masud, A A (2011).Controlling Error Rates with Multiple Positively-Dependent
Tests. All Graduate Reports and Creative Projects. Paper 30.
http://digitalcommons.usu.edu/gradreports/30.
• Masud, A A (2012).The Effect of Kurtosis on the Cross-Section of Stock Returns.
All Graduate Plan B and other Reports. Paper 180.
http://digitalcommons.usu.edu/gradreports/180.
Published Paper
• Khemka, A, Gradus-Pizlo, I, Kovacs, R, Tu, W, Hayden, R, Masud, A A , Eckert,
G, and Tierney, W (2017). Using Clinical Data Repositories to Assess the Clinical
and Financial Burden of Disease: The Example of Mitral Regurgitation. Journal of
Health and Medical Informatics, 8 (3): 266. doi: 10.4172/2157-7420.1000266
• Stevens, J R, Masud, A A, and Suyundikov, A. (2017). A Comparison of Multi-
ple Testing Adjustment Methods with Block-Correlation Positively-Dependent Tests.
PLoS ONE 12(4): e0176124. https://doi.org/10.1371/journal.pone.0176124
• Masud A A, Tu, Wanzhu, and Yu, Zhangsheng (2016). Variable Selection for
Mixture and Promotion Time Cure Rate Models. Statistical Methods in Medical
Research, 0(0):1–15.
http://journals.sagepub.com/doi/pdf/10.1177/0962280216677748
• Kwag, A, Masud, A A (2013). Modeling and Predicting Stock Returns: The Rule
of Parsimony. POSRI Business Economic Research Article, 13(2): 149–187.
• Chowdhury, Z, Leah, T L, Karen C C, Masud, A A, Alauddin, M, Hossain, M,
Zakaria, ABM, Hopke, P H (2012). Quantification of Indoor Air Pollution from Using
Cook Stoves and Estimating its Health Effects in Northwest Bangladesh. Aerosol and
Air Quality Research, 12: 463–475.
• Chowdhury, Z, Campanella, L, Gray, C., Masud, A A, Pennise, D.,Zhuzhang, X
(2012). Evaluation and Modeling of Indoor Air Pollution in Rural Households with
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4. Abdullah Al Masud
Multiple Stove Interventions in Yunnan, China. Atmospheric Environment, 67: 161–
169.
Working Paper
• Masud, A A,Yu, Z, and Tu, W. (2017). Variable Selection And Nonlinear Effect
Discovery in Partially Linear Mixture Cure Rate Models, submitted to Biostatistics
and Epidemiology.
• Masud, A A,Weerahandi, S, and Yu, C. (2018). Evaluating Treatment Efficacy by
Combining Multiple Measures in Clinical Trial Applications, submitted to Pharma-
ceutical Statistics.
• Gradus-Pizlo, I, Khemka, A, Kovacs, R, Tu, W, Hayden, R, Eckert, G, Masud, A A,
and Tierney, W (2018). Understanding the Health Burden of Moderate Functional
Mitral Regurgitation through Clinical Data Repositories, submitted to Journal of
General internal Medicine.
• Masud, A A, Tu, W, and Yu, Z (2016). Variable Selection in Semi-parametric
Linear Mixture Survival Models for correlated failure-time data, In Progress.
Presentation
• Masud, A A, Yu, Z., and Tu, W (2017). Variable Selection And Nonlinear Ef-
fect Discovery in Partially Linear Mixture Cure Rate Models. Oral Presentation at
International Chinese Statistical Association (ICSA) Midwest Chapter and North
Illinois Chapter Meeting; October 2017; Glenview, Illinois. http://midwest-icsa.
org/wp-content/uploads/2017/10
• Masud, A A, Yu, Z., and Tu, W (2017). Variable Selection And Nonlinear Effect
Discovery in Partially Linear Mixture Cure Rate Models. Poster Presentation at
Joint Statistical Meeting (JSM); August 2017; Baltimore, Maryland.
• Gradus-Pizlo, I, Khemka, A, Kovacs, R, Tu, W, Hayden, R, Masud, A A, Eckert,
G, and Tierney, W. Impact of Moderate Mitral Regurgitation on Patients: Mortality
May Be Underestimated-Analysis of Clinical Data Repositories. Poster Presenta-
tion at American College of Cardiology, 66th Annual Scientific Session; March 2017;
Washington, D.C.
• Khemka, A, Kovacs, R, Tu, W, Hayden, R, Eckert, G, Masud, A A, Tierney, W, and
Gradus-Pizlo, I. Understanding the Health Burden of Mitral Regurgitation through
Clinical Data Repositories. Paper Presented at The 20th Annual Scientific Meeting-
Heart Failure Society of America (HFSA), September 2016; Kissimmee, Florida.
Abstract retrieved from https://doi.org/10.1016/j.cardfail.2016.06.228
• Masud AA. Improved Finkelstein-Schoenfeld (FS) Test in Clinical Trial applica-
tions. Oral Presentation at Global Innovative Pharma Business (GIPB), Pfizer Inc;
August 2015; Manhattan, New York.
• Masud A A and Yu, Zhangsheng. Variable Selection for Mixture And Promo-
tion Time Cure Rate Models. Oral Presentation at International Chinese Statistical
Association (ICSA) Applied Statistics Symposium; June 2016; Atlanta, Georgia.
• Masud A A and Stevens, J R. A Comparison of Weighted P-Values And Multi-Stage
Analyses in Multiple Hypothesis Testing. Poster Presentation at Applied Statistics
in Agriculture; April 2010; Manhattan, Kansas.
Professional Membership
American Statistical Association, International Biometric Society, and International
Chinese Statistical Association
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