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Clinical Trials Versus Health Outcomes Research: SAS/STAT Versus SAS Enterprise Miner by Patricia B. Cerrito
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Lecture on clinical decision making to the Jacobi Medical Center / AECOM Internal Medicine Residents using examples from the COVID-19 literature.
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Task: To build multiple regression and logistic regression models on appropriate data. Approach: A general topic was selected first after which the data was downloaded from the source keeping the restrictions in mind and then cleaned in R. Then the multiple regression and logistic regression models were built using IBM SPSS and the outputs were interpreted. The dependent variable was life expectancy and the independent variables were Age-standardized Mortality-Communicable”, “Age-standardized Mortality-Cardiovascular Disease and Diabetes". Findings: Multipleregression - analysis was conducted to make sure normality, linearity, multi-collinearity, independence of errors and homoscedasticity were not violated. Statistically, the score of Life expectancy at age 60, 퐹(2,102) = 39.474 푅2 = .436, 푝 < 0.0005 Logistic Regression: Result shows 58.9% (Cox & Snell R-Square) and 80.1% (Nagelkerke R-Square) of the variance and gives 92.4% of correctly classified countries. The two indicating factors made a remarkable commitment to the model. Also, the model predicts the increase in “Mortality-Cardiovascular/Diabetes” and “Mortality rate cause by Communicable” variables is the cause of a decrease in Life Expectancy in a country. Tools: IBM SPSS
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Lecture on clinical decision making to the Jacobi Medical Center / AECOM Internal Medicine Residents using examples from the COVID-19 literature.
Statistics in clinical and translational research common pitfalls
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Pavlos Msaouel, MD, PhD
Lecture on causal inference to the pediatric hematology/oncology fellows at Texas Children's hospital as part of their Biostatistics for Busy Clinicians lecture seriers.
Causal inference lecture to Texas Children's fellows
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Power Analysis and Sample Size Determination
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Sample size calculation for medical research - a brief overview
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Azmi Mohd Tamil
Task: To build multiple regression and logistic regression models on appropriate data. Approach: A general topic was selected first after which the data was downloaded from the source keeping the restrictions in mind and then cleaned in R. Then the multiple regression and logistic regression models were built using IBM SPSS and the outputs were interpreted. The dependent variable was life expectancy and the independent variables were Age-standardized Mortality-Communicable”, “Age-standardized Mortality-Cardiovascular Disease and Diabetes". Findings: Multipleregression - analysis was conducted to make sure normality, linearity, multi-collinearity, independence of errors and homoscedasticity were not violated. Statistically, the score of Life expectancy at age 60, 퐹(2,102) = 39.474 푅2 = .436, 푝 < 0.0005 Logistic Regression: Result shows 58.9% (Cox & Snell R-Square) and 80.1% (Nagelkerke R-Square) of the variance and gives 92.4% of correctly classified countries. The two indicating factors made a remarkable commitment to the model. Also, the model predicts the increase in “Mortality-Cardiovascular/Diabetes” and “Mortality rate cause by Communicable” variables is the cause of a decrease in Life Expectancy in a country. Tools: IBM SPSS
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Due to the advancements in various data acquisition and storage technologies, different disciplines have attained the ability to not only accumulate a wide variety of data but also to monitor observations over longer time periods. In many real-world applications, the primary objective of monitoring these observations is to estimate when a particular event of interest will occur in the future. One of the major difficulties in handling such problem is the presence of censoring, i.e., the event of interests is unobservable in some instance which is either because of time limitation or losing track. Due to censoring, standard statistical and machine learning based predictive models cannot readily be applied to analyze the data. An important subfield of statistics called survival analysis provides different mechanisms to handle such censored data problems. In addition to the presence of censoring, such time-to-event data also encounters several other research challenges such as instance/feature correlations, high-dimensionality, temporal dependencies, and difficulty in acquiring sufficient event data in a reasonable amount of time. To tackle such practical concerns, the data mining and machine learning communities have started to develop more sophisticated and effective algorithms that either complement or compete with the traditional statistical methods in survival analysis. In spite of the importance of this problem and relevance to real-world applications, this research topic is scattered across various disciplines. In this tutorial, we will provide a comprehensive and structured overview of both statistical and machine learning based survival analysis methods along with different applications. We will also discuss the commonly used evaluation metrics and other related topics. The material will be coherently organized and presented to help the audience get a clear picture of both the fundamentals and the state-of-the-art techniques.
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Webinar slides how to reduce sample size ethically and responsibly
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Due to the advancements in various data acquisition and storage technologies, different disciplines have attained the ability to not only accumulate a wide variety of data but also to monitor observations over longer time periods. In many real-world applications, the primary objective of monitoring these observations is to estimate when a particular event of interest will occur in the future. One of the major difficulties in handling such problem is the presence of censoring, i.e., the event of interests is unobservable in some instance which is either because of time limitation or losing track. Due to censoring, standard statistical and machine learning based predictive models cannot readily be applied to analyze the data. An important subfield of statistics called survival analysis provides different mechanisms to handle such censored data problems. In addition to the presence of censoring, such time-to-event data also encounters several other research challenges such as instance/feature correlations, high-dimensionality, temporal dependencies, and difficulty in acquiring sufficient event data in a reasonable amount of time. To tackle such practical concerns, the data mining and machine learning communities have started to develop more sophisticated and effective algorithms that either complement or compete with the traditional statistical methods in survival analysis. In spite of the importance of this problem and relevance to real-world applications, this research topic is scattered across various disciplines. In this tutorial, we will provide a comprehensive and structured overview of both statistical and machine learning based survival analysis methods along with different applications. We will also discuss the commonly used evaluation metrics and other related topics. The material will be coherently organized and presented to help the audience get a clear picture of both the fundamentals and the state-of-the-art techniques.
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[ Watch Webinar: http://bit.ly/2thIgmi ]. In this free webinar, Head of Statistics at Statsols, Ronan Fitzpatrick, addresses the issues of reducing risk in Phase II/III sample size calculations. Topics covered will include: Sample Size Determination For Different Trial Designs Bayesian Sample Size Determination Sample Size For Survival Analysis & more
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About the webinar This webinar examines the role of non-inferiority and equivalence in study design In this free webinar, you will learn about: -Regulatory information on this type of study design -Considerations for study design and your sample size -Practical worked examples of --Non-inferiority Testing --Equivalence Testing Duration - 60 minutes Speaker: Ronan Fitzpatrick, Head of Statistics, Statsols Watch the video at: https://www.statsols.com/webinars
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Distribuição Amostral
Brmedj00047 0038
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Waleska Monteiro
Determining the appropriate number of events needed for survival analysis is a complex task as study planners try to predict what sample size will be needed after accounting for the complications of unequal follow-up, drop-out and treatment crossover. The statistical, logistical and ethical considerations all complicate life for biostatisticians as issues to balance in planning a survival analysis. However, this complexity has created a need for new analyses and procedures to help the planning process for survival analysis trials. The wider move from fixed to flexible designs has opened up opportunities for advanced methods such as adaptive design and Bayesian analysis to help deal with the unique complications of planning for survival data but these methods have their own complications that need to be explored too.
Sample size for survival analysis - a guide to planning successful clinical t...
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nQuery
2020 Trends In Biostatistics - What you should know about study design. In this free webinar you will learn about: -Adaptive designs in confirmatory trials -Using external data in study planning -Innovative designs in early-stage trials To watch the full webinar: https://www.statsols.com/webinar/2020-trends-in-biostatistics-what-you-should-know-about-study-design
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"Innovative Sample Size Methods for Clinical Trials" is hosted to coincide with the Spring 2018 update to nQuery - The leading Sample Size Software. Hosted by Ronan Fitzpatrick - Head of Statistics and nQuery Lead Researcher at Statsols - you'll learn about the benefits of a range of procedures and how you can implement them into your work: 1) Dose-escalation with the Bayesian Continual Reassessment Method CRM is a growing alternative to the 3+3 method for Phase I trials finding the Maximum Tolerated Dose (MTD). See how researchers can overcome 3+3 drawbacks to easily find the required sample size for this beneficial alternative for finding the MTD. 2) Bayesian Assurance with Survival Example This Bayesian alternative to power has experienced a rapid rise in interest and application from researchers. See how Assurance is being used by researchers to discover the true “probability of success” of a trial. 3) Mendelian Randomization Mendelian randomization (MR) is a method that allows testing of a causal effect from observational data in the presence of confounding factors. However, in order to design efficient Mendelian randomization studies, it is essential to calculate the appropriate sample sizes required. We demonstrate what to do to achieve this. 4) Negative Binomial Distribution Negative binomial model has been increasingly used to model the count data. One of the challenges of applying negative binomial model in clinical trial design is the sample size estimation. We demonstrate how best to determine the appropriate sample size in the presence of challenges such as unequal follow-up or dispersion.
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Watch the video at: https://www.statsols.com/webinars/practical-methods-to-overcome-sample-size-challenges In this webinar hosted by Ronan Fitzpatrick - Head of Statistics and nQuery Lead Researcher at Statsols - we will examine some of the most common practical challenges you will experience while calculating sample size for your study. These challenges will be split into two categories: 1. Overcoming Sample Size Calculation Challenges (Survival Analysis Example) We will examine practical methods to overcome common sample size calculation issues by focusing in on one of the more complex areas for sample size determination; Survival analysis. We will cover difficulties and potential issues surrounding challenges such as: Drop Out: How to deal with expected dropouts or censoring. We compare the simple loss-to-follow-up method and integrating a dropout process into the sample size model? Planning Uncertainty: How best to deal with the inevitable uncertainty at the planning stage? We examine how best to apply a sensitivity analysis and Bayesian approaches to explore the uncertainty in your sample size calculations. Choosing the Effect Size: Various approaches and interpretations exist for how to find the effect size value. We examine those contrasting interpretations and determine the best method and also how to deal with parameterization options. 2. Overcoming Study Design Challenges (Vaccine Efficacy Example) The Randomised Controlled Trial (RCT) is considered the gold standard in trial design in drug development. However, there are often practical impediments which mean that adjustments or pragmatic approaches are needed for some trials and studies. We will examine practical methods how to overcome common study design challenges and how these affect your sample size calculations. In this webinar, we will use common issues in vaccine study design to examine difficulties surrounding issues such as: Case-Control Analysis: We will examine how to deal with study constraints and how to deal with analyses done during an observational study. Alternative Randomization Methods: How best to address randomization in your vaccine trial design when full randomization is difficult, expensive or impractical. We examine how sample size calculations are affected with cluster or Mendelian randomization. Rare Events: How does an outcome being rare affect the types of study design and statistical methods chosen in your study.
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This talk gives an overview and illustration of various sources of uncertainty when developing clinical risk prediction models: aleatory, approximation, model, modeler, data, and population uncertainty. Presented at the International Conference of Clinical Biostatistics, 29th of August 2023, Milan, Italy.
ISCB 2023 Sources of uncertainty b.pptx
ISCB 2023 Sources of uncertainty b.pptx
BenVanCalster
Social statistics, sociology
Hypothesis testing: A single sample test
Hypothesis testing: A single sample test
Umme Salma Tuli
Discussing, using many Bobs, how a monte carlo simulation works for a Journal Club paper regarding the modality used for detection of infectious endocarditis.
Journal Club - Discussion of Heriot et al. Criteria for identifying patients ...
Journal Club - Discussion of Heriot et al. Criteria for identifying patients ...
Salpy Kelian
Oac guidelines
Oac guidelines
Jonas Ranstam PhD
1 statistical analysis notes
1 statistical analysis notes
cartlidge
Common statistical tests useful for biomedical scientists and research physicians.
Biostatistics
Biostatistics
Larry Baum
A short introduction to sample size estimation for Research methodology workshop at Dr. BVP RMC, Pravara Institute of Medical Sciences(DU), Loni by Dr. Mandar Baviskar
Basics of Sample Size Estimation
Basics of Sample Size Estimation
Mandar Baviskar
NHS Education Scotland Produced in collaboration with the Association of Scottish Medicines Information Pharmacists Group
Critical Appriaisal Skills Basic 1 | May 4th 2011
Critical Appriaisal Skills Basic 1 | May 4th 2011
NES
Calculation of Sample Size Analysis and Interpretation of descriptive and inferential statistics. Reporting results
Advanced Biostatistics and Data Analysis abdul ghafoor sajjad
Advanced Biostatistics and Data Analysis abdul ghafoor sajjad
HeadDPT
Displaying your results
Displaying your results
Tarek Tawfik Amin
This was a research work done at the KATH (hospital) in Ghana.
Undergraduate Research work
Undergraduate Research work
Peter M Addo
Similaire à Clinical Trials Versus Health Outcomes Research: SAS/STAT Versus SAS Enterprise Miner by Patricia B. Cerrito
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Practical Methods To Overcome Sample Size Challenges
Practical Methods To Overcome Sample Size Challenges
Common statistical pitfalls & errors in biomedical research (a top-5 list)
Common statistical pitfalls & errors in biomedical research (a top-5 list)
Extending A Trial’s Design Case Studies Of Dealing With Study Design Issues
Extending A Trial’s Design Case Studies Of Dealing With Study Design Issues
Lemeshow samplesize
Lemeshow samplesize
Analytic Methods and Issues in CER from Observational Data
Analytic Methods and Issues in CER from Observational Data
Probability Models for Estimating Haplotype Frequencies and Bayesian Survival...
Probability Models for Estimating Haplotype Frequencies and Bayesian Survival...
Soni_Biostatistics.ppt
Soni_Biostatistics.ppt
Sampling distributions
Sampling distributions
Quantitative Synthesis I
Quantitative Synthesis I
ISCB 2023 Sources of uncertainty b.pptx
ISCB 2023 Sources of uncertainty b.pptx
Hypothesis testing: A single sample test
Hypothesis testing: A single sample test
Journal Club - Discussion of Heriot et al. Criteria for identifying patients ...
Journal Club - Discussion of Heriot et al. Criteria for identifying patients ...
Oac guidelines
Oac guidelines
1 statistical analysis notes
1 statistical analysis notes
Biostatistics
Biostatistics
Basics of Sample Size Estimation
Basics of Sample Size Estimation
Critical Appriaisal Skills Basic 1 | May 4th 2011
Critical Appriaisal Skills Basic 1 | May 4th 2011
Advanced Biostatistics and Data Analysis abdul ghafoor sajjad
Advanced Biostatistics and Data Analysis abdul ghafoor sajjad
Displaying your results
Displaying your results
Undergraduate Research work
Undergraduate Research work
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Final results from a phase III randomized study comparing DVd with VAd in fir...
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FlowKeepers®: A Device for Preventing Circulatory Ailments
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5-year survival of GCP after radical procedures significantly depended on: 1) PT “early-invasive cancer”; 2) PT N0--N12; 3) Cell Ratio Factors; 4) blood cell circuit; 5) biochemical factors; 6) hemostasis system; 7) AT; 8) GC cell dynamics; 9) GC characteristics; 10) tumor localization; 11) anthropometric data; 12) surgery type. Optimal diagnosis and treatment strategies for GC are: 1) screening and early detection of GC; 2) availability of sufficient quantity of experienced abdominal surgeons because of complexity of radical procedures; 3) aggressive en block surgery and adequate lymph node dissection for completeness; 4) precise prediction; 5) adjuvant chemoimmunotherapy for GCP with unfavorable prognosis.
Gastric Cancer: Сlinical Implementation of Artificial Intelligence, Synergeti...
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Explore the fundamentals of the human reproductive system in this concise presentation, suitable for medical students and professionals alike. Covering anatomy, physiology, and Pregnancy, it offers essential knowledge for understanding reproductive health.
ANATOMY AND PHYSIOLOGY OF REPRODUCTIVE SYSTEM.pptx
ANATOMY AND PHYSIOLOGY OF REPRODUCTIVE SYSTEM.pptx
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Delve into valuable content elucidating the anatomy and physiology of the respiratory system, in line with the PCI syllabus for pharmacy and PharmD students.
ANATOMY AND PHYSIOLOGY OF RESPIRATORY SYSTEM.pptx
ANATOMY AND PHYSIOLOGY OF RESPIRATORY SYSTEM.pptx
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In this lecture on circulatory shock, Dr. Faiza, an Assistant Professor of Physiology, delves into the profound implications of inadequate blood flow throughout the body, leading to tissue damage. The session begins by defining circulatory shock and elucidating its physiological causes, including decreased cardiac output, diminished blood volume, decreased vascular tone, obstruction to blood flow, excess metabolic rate, and abnormal perfusion patterns. Dr. Faiza categorizes shock into various types, such as cardiogenic shock, hypovolemic shock, neurogenic shock, obstructive shock, anaphylactic shock, and septic shock, and explores the pathophysiological basis of each. Furthermore, the lecture examines the stages of shock, from the non-progressive phase where compensatory mechanisms aim for full recovery to the progressive phase where shock worsens steadily without intervention, potentially leading to irreversible damage. Dr. Faiza discusses therapeutic interventions for shock, including replacement therapy, blood and plasma transfusion, sympathomimetic drugs, and other adjunctive treatments like head-down positioning, oxygen therapy, and glucocorticoids.
Circulatory Shock, types and stages, compensatory mechanisms
Circulatory Shock, types and stages, compensatory mechanisms
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These are simplified slides discussing the regulation of cardiac output and venous return. Learning objectives: 1. Comprehend the determinants of cardiac output and factors affecting cardiac output 2. Comprehend the factors affecting stroke volume and heart rate and total peripheral resistance 3. Identify the factors regulating venous return 4. Discuss the causes of high and low output cardiac failure 5. Enlist the functions of veins and recognise the significance of venous reservoirs Study Resources: 1. Chapter 20, Guyton and Hall Textbook of Medical Physiology, 14th edition 2. Chapter 30 and 32, Ganong’s Review of Medical Physiology, 26th edition 3. Chapter 10, Human Physiology by Lauralee Sherwood, 9th edition 4. Physiology, Cardiac Output - StatPearls https://www.ncbi.nlm.nih.gov/books/NBK470455/
Cardiac Output, Venous Return, and Their Regulation
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Circulatory Shock, types and stages, compensatory mechanisms
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Cardiac Output, Venous Return, and Their Regulation
Cardiac Output, Venous Return, and Their Regulation
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Clinical Trials Versus Health Outcomes Research: SAS/STAT Versus SAS Enterprise Miner by Patricia B. Cerrito
1.
Clinical Trials Versus
Health Outcomes Research: SAS/STAT Versus SAS Enterprise Miner Patricia B. Cerrito [email_address] University of Louisville
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Distribution of Patient
Stays
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Normal Estimate
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Kernel Density Estimation
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Kernel Estimate of
Length of Stay
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Sample Size=5
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Sample Size=30
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Sample Size=100
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Sample Size=1000
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Confidence Limit The
confidence limit excludes much of the actual population distribution
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Confidence Limit With
Larger n
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Regression Equation
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Simple Regression
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Classification Table
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Classification With 3
Variables continued...
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Classification With 3
Variables
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50/50 Split in
the Data Filter data to mortality outcome Filter data to non-mortality outcome Use PROC SURVEYSELECT to extract a subsample of non-mortality outcome Append the mortality outcome data to subsample
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75/25 Split in
the Data
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90/10 Split in
the Data
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Sampling Node
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Misclassification in Regression
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ROC Curves
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Rule Induction Results
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Variable Selection
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ROC Curves
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