Working with medical doctors, we implemented novel data mining techniques to predict the Sustained Virological Response (SVR) to hepatitis C treatment. In order to make the models more interpretable, we used Probability Estimation Trees (PETs).
This document compares the data quality of nonprobability internet samples to low response rate probability samples. It finds that unweighted, nonprobability samples have substantially more bias than probability samples. Weighted nonprobability samples have significantly but not substantially less bias than unweighted. Probability samples also have less variability in biases. Matching nonprobability samples to probability samples reduces biases but biases are still higher than for probability samples. More work is needed to optimize matching to improve nonprobability sample quality. In conclusion, unweighted nonprobability samples are inferior to low response probability samples but weighting and matching can improve nonprobability sample quality.
3 data normalization (2014 lab tutorial)Dmitry Grapov
Get more information:
http://imdevsoftware.wordpress.com/2014/10/11/2014-metabolomic-data-analysis-and-visualization-workshop-and-tutorials/
Recently I had the pleasure of teaching statistical and multivariate data analysis and visualization at the annual Summer Sessions in Metabolomics 2014, organized by the NIH West Coast Metabolomics Center.
Enhancing Diagnostics for Invasive Aspergillosis using Machine LearningSimone Romano
Invasive Aspergillosis (IA) is a serious fungal infection and a major cause of mortality in patients undergoing
allogeneic stem cell transplantation or chemotherapy for acute leukaemia. Large amounts of data are collected during the treatment of high-risk haematology patients and we
propose leveraging such data to produce more accurate predictions of IA diagnosis. We describe here the
application of machine learning techniques to predict probability of IA, which can be used to enhance the
interpretation of biomarker results.
Developing high content image analysis software for biologistsClaire McQuin
ImageXD presentation 30 March 2017. Developing software for biological image analysis using classic compute vision techniques and looking forward to deep learning segmentation and classification.
Data analytics experts Metageni briefly explain how global information giant LexisNexis models user success from user analytics data using machine learning. A Moo.com tech talk for analysts and engineers with an interest in data science, covering the high level classifier method used in support of LexisNexis, working with their global digital team.
Evaluating the Impact of Literature Searching Services on Patient Care Throug...Jeff Mason
Hospital libraries must demonstrate the value and impact they have within their organizations. We created a short survey to assess the impact literature searches conducted by librarians have on patient care. This presentation was given at the 2014 Medical Library Association Annual Meeting in Chicago. Preliminary results are discussed.
We want you to use our survey to assess your own value! To view a copy please visit:
http://fluidsurveys.com/s/literature-searching-impact-survey-site/
Bioinformatics uses computational approaches and statistical methods to analyze large volumes of biological data and answer biological questions. It involves using computer technologies to manage and analyze biological data without expensive wet lab experiments. This allows biological research to be repeated many times without adverse effects while saving time.
Multiple Response Questions - Allowing for chance in authentic assessmentsMhairi Mcalpine
This document discusses multiple response questions (MRQs), which allow multiple correct answers from a list of options. The authors reviewed over 600 MRQs and found that most had moderate chance factors between 0.4-0.5, indicating a significant element of chance. Many MRQs had higher chance factors than true/false questions. Tests using MRQs also tended to have moderate chance factors above 0.3. The heavy weighting given to each MRQ response increased test chance factors in some cases. The authors recommend developing statistical methods to account for chance in MRQs and other computer-based assessments.
This document compares the data quality of nonprobability internet samples to low response rate probability samples. It finds that unweighted, nonprobability samples have substantially more bias than probability samples. Weighted nonprobability samples have significantly but not substantially less bias than unweighted. Probability samples also have less variability in biases. Matching nonprobability samples to probability samples reduces biases but biases are still higher than for probability samples. More work is needed to optimize matching to improve nonprobability sample quality. In conclusion, unweighted nonprobability samples are inferior to low response probability samples but weighting and matching can improve nonprobability sample quality.
3 data normalization (2014 lab tutorial)Dmitry Grapov
Get more information:
http://imdevsoftware.wordpress.com/2014/10/11/2014-metabolomic-data-analysis-and-visualization-workshop-and-tutorials/
Recently I had the pleasure of teaching statistical and multivariate data analysis and visualization at the annual Summer Sessions in Metabolomics 2014, organized by the NIH West Coast Metabolomics Center.
Enhancing Diagnostics for Invasive Aspergillosis using Machine LearningSimone Romano
Invasive Aspergillosis (IA) is a serious fungal infection and a major cause of mortality in patients undergoing
allogeneic stem cell transplantation or chemotherapy for acute leukaemia. Large amounts of data are collected during the treatment of high-risk haematology patients and we
propose leveraging such data to produce more accurate predictions of IA diagnosis. We describe here the
application of machine learning techniques to predict probability of IA, which can be used to enhance the
interpretation of biomarker results.
Developing high content image analysis software for biologistsClaire McQuin
ImageXD presentation 30 March 2017. Developing software for biological image analysis using classic compute vision techniques and looking forward to deep learning segmentation and classification.
Data analytics experts Metageni briefly explain how global information giant LexisNexis models user success from user analytics data using machine learning. A Moo.com tech talk for analysts and engineers with an interest in data science, covering the high level classifier method used in support of LexisNexis, working with their global digital team.
Evaluating the Impact of Literature Searching Services on Patient Care Throug...Jeff Mason
Hospital libraries must demonstrate the value and impact they have within their organizations. We created a short survey to assess the impact literature searches conducted by librarians have on patient care. This presentation was given at the 2014 Medical Library Association Annual Meeting in Chicago. Preliminary results are discussed.
We want you to use our survey to assess your own value! To view a copy please visit:
http://fluidsurveys.com/s/literature-searching-impact-survey-site/
Bioinformatics uses computational approaches and statistical methods to analyze large volumes of biological data and answer biological questions. It involves using computer technologies to manage and analyze biological data without expensive wet lab experiments. This allows biological research to be repeated many times without adverse effects while saving time.
Multiple Response Questions - Allowing for chance in authentic assessmentsMhairi Mcalpine
This document discusses multiple response questions (MRQs), which allow multiple correct answers from a list of options. The authors reviewed over 600 MRQs and found that most had moderate chance factors between 0.4-0.5, indicating a significant element of chance. Many MRQs had higher chance factors than true/false questions. Tests using MRQs also tended to have moderate chance factors above 0.3. The heavy weighting given to each MRQ response increased test chance factors in some cases. The authors recommend developing statistical methods to account for chance in MRQs and other computer-based assessments.
Rachhpal Malhi has over 30 years of experience in manufacturing processes including as a process engineer, supervisor, and machine operator. He has extensive experience in foam production processes for automotive seating and has helped launch several new plants both domestically and internationally. His skills include process development, training, quality control, and problem solving.
A Framework to Adjust Dependency Measure Estimates for Chance Simone Romano
Winner of the best paper award at the SIAM International Conference on Data Mining.
Estimating the strength of dependency between two variables is fundamental for exploratory analysis and many other applications in data mining. For example: non-linear dependencies between two continuous variables can be explored with the Maximal Information Coefficient (MIC); and categorical variables that are dependent to the target class are selected using Gini gain in random forests. Nonetheless, because dependency measures are estimated on finite samples, the interpretability of their quantification and the accuracy when ranking dependencies become challenging. Dependency estimates are not equal to 0 when variables are independent, cannot be compared if computed on different sample size, and they are inflated by chance on variables with more categories. In this paper, we propose a framework to adjust dependency measure estimates on finite samples. Our adjustments, which are simple and applicable to any dependency measure, are helpful in improving interpretability when quantifying dependency and in improving accuracy on the task of ranking dependencies. In particular, we demonstrate that our approach enhances the interpretability of MIC when used as a proxy for the amount of noise between variables, and to gain accuracy when ranking variables during the splitting procedure in random forests.
Duch Group is a Chinese technological company founded in 1996 that specializes in one-stop services for industrial design, modular design, aerospace applications, military equipment, cultural products, 3D printing, and custom manufacturing. It operates a 3D printing base in Xiamen, China that exhibits various 3D printing equipment and products manufactured using these technologies. The base aims to provide customized low-volume production and prototyping services to customers.
In this presentation, I discuss the topics I covered during my PhD:
Dependency measures between variables are fundamental for a number of important applications in machine learning. They are ubiquitously used: for feature selection, as splitting criteria in random forest, for clustering comparison and validation, to infer biological networks, to list a few. Nonetheless there exist a number of problems when dependencies are estimated on finite data: detection, quantification, and ranking of dependencies are challenging.
This thesis proposes a series of contributions to improve performances on each of the 3 goals above. During the seminar I will demonstrate that:
- Adjusted measures can improve on the tasks of quantification and ranking. In particular, I will discuss some adjustments applied to the Maximal Information Coefficient (MIC), random forests, and clustering comparisons;
- A measure based on mutual information and randomisation we designed is competitive on the tasks of detection and ranking of relationships. We named this measure the Randomised Information Coefficient (RIC) and tested it on the applications of biological network inference and multi-variable feature selection.
Este documento detalla la planificación de una boda entre Anahí y Manuel Velazco. Contiene información sobre los comités organizadores, la agenda del evento, los gastos, la lista de invitados y otros detalles como la luna de miel. El documento proporciona una planificación minuciosa para el matrimonio programado para el 28 de noviembre.
The document appears to be 3 scanned pages from a magazine or newspaper article discussing the benefits of meditation for reducing stress and anxiety. It notes that regular meditation practice can calm the mind and help people feel more relaxed. Research studies cited in the article also found meditation can positively impact the brain and may lessen symptoms for those suffering from anxiety, depression and other mental health issues.
This resume summarizes the professional experience of Ayrat N. Shakirov, including over 33 years of experience in civil engineering and project management for oil and gas projects. Recent roles include Deputy General Director for Capital Construction Projects at Irkutsk Oil Company, managing over 100 projects, and Project Engineering Manager at Sakhalin Energy Investment Company, overseeing offshore and onshore oil and gas facilities. The resume lists extensive experience in engineering design, procurement, construction management, and project controls for pipelines, gas plants, and other oil and gas infrastructure projects in Russia and other countries.
My Entry to the Sportsbet/CIKM competitionSimone Romano
The Sportsbet/CIKM competition (http://sportsbetcikm15.com) is a data mining and machine learning challenge: use data about Australian Football League (AFL) matches already played to predict future ones. These slides are related to the entry I submitted to the competition.
Modelling radiation toxicity is challenging due to issues such as low event rates, the influence of radiation dose, and the need for large, high-quality datasets. Subjective evaluations of toxicity are difficult to analyze quantitatively. Different components of acute and late toxicity need to be considered separately. Choosing the appropriate measure of toxicity severity (e.g. peak grade vs. average grade) and follow-up length can impact results. Improving modelling may involve techniques like mixture models accounting for incomplete follow-up and graded response models avoiding cutoff choices. The patient perspective is also important to consider.
The document discusses the key stages in the drug discovery and development process including target selection, compound screening and hit optimization, selecting a drug candidate through further optimization of properties like absorption and metabolism, safety testing in animals and humans, proof of concept clinical trials in patients, large phase 3 clinical trials for registration and approval, and finally launch and life cycle management. It notes that the entire process from discovery to approval can take 12-16 years and cost over $1 billion.
Innovative Sample Size Methods For Clinical Trials nQuery
"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.
Extending A Trial’s Design Case Studies Of Dealing With Study Design IssuesnQuery
This document discusses several case studies of dealing with complex study design issues in clinical trials, including non-proportional hazards, cluster randomization, and three-armed trials. The agenda outlines topics on non-proportional hazards modeling and sample size considerations, cluster randomized and stepped-wedge designs, and methods for analyzing data from three-armed trials that include experimental, reference, and placebo groups. Worked examples are provided to illustrate sample size calculations and statistical approaches for each of these complex trial design scenarios.
This document provides an overview of research methods and biostatistics. It defines key terms like research, research methods, and statistical analysis. It describes different types of study designs including descriptive studies, analytical studies, experimental studies, and epidemiological study designs. It outlines the characteristics of observational studies like cross-sectional and case-control studies as well as experimental studies. It also discusses appropriate statistical tests to analyze different types of data and research problems. Finally, it lists some online resources and computer software that can be used in statistical analysis.
Power and sample size calculations for survival analysis webinar SlidesnQuery
This webinar presentation introduced sample size determination for survival analysis. It discussed how to estimate the appropriate sample size, key considerations for survival analysis including expected survival curves and handling dropouts. It demonstrated an example in nQuery software to calculate the sample size needed for a clinical trial to show a risk reduction in progression-free survival between treatment arms. The webinar concluded with plans to further enhance survival analysis capabilities in nQuery and addressed questions from participants.
1. Sample size calculation is an important part of ethical scientific research to avoid underpowered studies.
2. There are different approaches to sample size calculation depending on the study design and endpoints, such as comparing proportions, estimating confidence intervals, or analyzing time to event outcomes.
3. Key steps include defining the research hypothesis, primary and secondary endpoints, how and in whom the endpoints will be measured, and determining what difference is clinically meaningful to detect between study groups.
Laboratory Management With Constrains Iamm 2010PathKind Labs
Clinical laboratory services are a critical yet much neglected component of health systems in resource poor countries. They are crucial for public health, disease control and surveillance, and guide patient diagnosis and care, but their key role is often not recognized by governments or donors. Laboratory tests should be used to improve the outcome for individual patients or to provide public health information. However, if the quality of laboratory tests is poor, resources will be wasted on repeat tests or inappropriate management and the laboratory service will be inefficient.
The primary goal of Laboratory Medicine is to provide information that is useful to assist medical decision-making, allowing optimal health care. This can only be obtained by generating reliable analytical results on patient samples. Meaningful measurements are indeed essential for the diagnosis, monitoring, treatment, and risk assessment of patients. Inadequate laboratory performance may have extensive consequences for practical medicine, healthcare system, and, in conclusion, for the patient. Poor quality results may actually lead to incorrect interpretation by the clinician, impairing the patient’s
situation.
Accreditation authorities have identified twelve quality system essentials that need to be in place for a laboratory to perform clinical tests adequately and in a quality assured manner. Along with each laboratory performing tests that are in its scope, it is essential that duplication and excess capacity is addressed by forging and operating a network of laboratories leading to consolidation and integration of clinical testing. A network would have collection centers at places convenient to the patients, supported by frequent transfer of samples in appropriate conditions to the laboratory. In the laboratory there is a need for increased automation and relevant training of personnel and the setting up of centralized accessioning, pneumontic chutes for transport of samples to the work bench and for bidirectional interphased equipment to transfer results to desk top of laboratory physicians and after validation of results for the results to be electronically transferred by SMS and/or PDF files via email and/or becoming available online for clients, supplemented by delivery of hard copies of the results.
The challenge in the next decade for laboratory medicine is to accomplish these major changes in organization to meet fiscal restraint and shortage of adequately trained laboratory personnel. Collaborative networks, constructive use of point of care devices, and the development of rapport between laboratories and their clients leading to cost effective utilization of limited resources, are some of the strategies that will maximize patient benefit
演講-Meta analysis in medical research-張偉豪Beckett Hsieh
This document provides an overview of meta-analysis. It defines meta-analysis as a quantitative approach to systematically combining results from previous studies to arrive at conclusions about the body of research. It discusses key aspects of planning and conducting a meta-analysis such as defining the research question, searching for relevant literature, determining study eligibility, extracting data, analyzing effect sizes, assessing heterogeneity, and addressing publication bias. Software for performing meta-analyses and specific effect sizes like risk ratio and odds ratio are also mentioned.
Melanoma Nancy Shum And Anne Marcy Intro To Clinical Data Managementcunniffe6
This summarizes a clinical trial studying the effectiveness of a melanoma vaccine compared to standard of care for patients with unresectable melanoma. The randomized, double-blinded, placebo-controlled trial assigns patients to either receive the melanoma vaccine or a placebo by direct injection into a tumor site. The primary objective is to evaluate tumor response rates between the two groups. Secondary objectives include assessing safety, progression-free survival, and overall survival.
Rachhpal Malhi has over 30 years of experience in manufacturing processes including as a process engineer, supervisor, and machine operator. He has extensive experience in foam production processes for automotive seating and has helped launch several new plants both domestically and internationally. His skills include process development, training, quality control, and problem solving.
A Framework to Adjust Dependency Measure Estimates for Chance Simone Romano
Winner of the best paper award at the SIAM International Conference on Data Mining.
Estimating the strength of dependency between two variables is fundamental for exploratory analysis and many other applications in data mining. For example: non-linear dependencies between two continuous variables can be explored with the Maximal Information Coefficient (MIC); and categorical variables that are dependent to the target class are selected using Gini gain in random forests. Nonetheless, because dependency measures are estimated on finite samples, the interpretability of their quantification and the accuracy when ranking dependencies become challenging. Dependency estimates are not equal to 0 when variables are independent, cannot be compared if computed on different sample size, and they are inflated by chance on variables with more categories. In this paper, we propose a framework to adjust dependency measure estimates on finite samples. Our adjustments, which are simple and applicable to any dependency measure, are helpful in improving interpretability when quantifying dependency and in improving accuracy on the task of ranking dependencies. In particular, we demonstrate that our approach enhances the interpretability of MIC when used as a proxy for the amount of noise between variables, and to gain accuracy when ranking variables during the splitting procedure in random forests.
Duch Group is a Chinese technological company founded in 1996 that specializes in one-stop services for industrial design, modular design, aerospace applications, military equipment, cultural products, 3D printing, and custom manufacturing. It operates a 3D printing base in Xiamen, China that exhibits various 3D printing equipment and products manufactured using these technologies. The base aims to provide customized low-volume production and prototyping services to customers.
In this presentation, I discuss the topics I covered during my PhD:
Dependency measures between variables are fundamental for a number of important applications in machine learning. They are ubiquitously used: for feature selection, as splitting criteria in random forest, for clustering comparison and validation, to infer biological networks, to list a few. Nonetheless there exist a number of problems when dependencies are estimated on finite data: detection, quantification, and ranking of dependencies are challenging.
This thesis proposes a series of contributions to improve performances on each of the 3 goals above. During the seminar I will demonstrate that:
- Adjusted measures can improve on the tasks of quantification and ranking. In particular, I will discuss some adjustments applied to the Maximal Information Coefficient (MIC), random forests, and clustering comparisons;
- A measure based on mutual information and randomisation we designed is competitive on the tasks of detection and ranking of relationships. We named this measure the Randomised Information Coefficient (RIC) and tested it on the applications of biological network inference and multi-variable feature selection.
Este documento detalla la planificación de una boda entre Anahí y Manuel Velazco. Contiene información sobre los comités organizadores, la agenda del evento, los gastos, la lista de invitados y otros detalles como la luna de miel. El documento proporciona una planificación minuciosa para el matrimonio programado para el 28 de noviembre.
The document appears to be 3 scanned pages from a magazine or newspaper article discussing the benefits of meditation for reducing stress and anxiety. It notes that regular meditation practice can calm the mind and help people feel more relaxed. Research studies cited in the article also found meditation can positively impact the brain and may lessen symptoms for those suffering from anxiety, depression and other mental health issues.
This resume summarizes the professional experience of Ayrat N. Shakirov, including over 33 years of experience in civil engineering and project management for oil and gas projects. Recent roles include Deputy General Director for Capital Construction Projects at Irkutsk Oil Company, managing over 100 projects, and Project Engineering Manager at Sakhalin Energy Investment Company, overseeing offshore and onshore oil and gas facilities. The resume lists extensive experience in engineering design, procurement, construction management, and project controls for pipelines, gas plants, and other oil and gas infrastructure projects in Russia and other countries.
My Entry to the Sportsbet/CIKM competitionSimone Romano
The Sportsbet/CIKM competition (http://sportsbetcikm15.com) is a data mining and machine learning challenge: use data about Australian Football League (AFL) matches already played to predict future ones. These slides are related to the entry I submitted to the competition.
Modelling radiation toxicity is challenging due to issues such as low event rates, the influence of radiation dose, and the need for large, high-quality datasets. Subjective evaluations of toxicity are difficult to analyze quantitatively. Different components of acute and late toxicity need to be considered separately. Choosing the appropriate measure of toxicity severity (e.g. peak grade vs. average grade) and follow-up length can impact results. Improving modelling may involve techniques like mixture models accounting for incomplete follow-up and graded response models avoiding cutoff choices. The patient perspective is also important to consider.
The document discusses the key stages in the drug discovery and development process including target selection, compound screening and hit optimization, selecting a drug candidate through further optimization of properties like absorption and metabolism, safety testing in animals and humans, proof of concept clinical trials in patients, large phase 3 clinical trials for registration and approval, and finally launch and life cycle management. It notes that the entire process from discovery to approval can take 12-16 years and cost over $1 billion.
Innovative Sample Size Methods For Clinical Trials nQuery
"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.
Extending A Trial’s Design Case Studies Of Dealing With Study Design IssuesnQuery
This document discusses several case studies of dealing with complex study design issues in clinical trials, including non-proportional hazards, cluster randomization, and three-armed trials. The agenda outlines topics on non-proportional hazards modeling and sample size considerations, cluster randomized and stepped-wedge designs, and methods for analyzing data from three-armed trials that include experimental, reference, and placebo groups. Worked examples are provided to illustrate sample size calculations and statistical approaches for each of these complex trial design scenarios.
This document provides an overview of research methods and biostatistics. It defines key terms like research, research methods, and statistical analysis. It describes different types of study designs including descriptive studies, analytical studies, experimental studies, and epidemiological study designs. It outlines the characteristics of observational studies like cross-sectional and case-control studies as well as experimental studies. It also discusses appropriate statistical tests to analyze different types of data and research problems. Finally, it lists some online resources and computer software that can be used in statistical analysis.
Power and sample size calculations for survival analysis webinar SlidesnQuery
This webinar presentation introduced sample size determination for survival analysis. It discussed how to estimate the appropriate sample size, key considerations for survival analysis including expected survival curves and handling dropouts. It demonstrated an example in nQuery software to calculate the sample size needed for a clinical trial to show a risk reduction in progression-free survival between treatment arms. The webinar concluded with plans to further enhance survival analysis capabilities in nQuery and addressed questions from participants.
1. Sample size calculation is an important part of ethical scientific research to avoid underpowered studies.
2. There are different approaches to sample size calculation depending on the study design and endpoints, such as comparing proportions, estimating confidence intervals, or analyzing time to event outcomes.
3. Key steps include defining the research hypothesis, primary and secondary endpoints, how and in whom the endpoints will be measured, and determining what difference is clinically meaningful to detect between study groups.
Laboratory Management With Constrains Iamm 2010PathKind Labs
Clinical laboratory services are a critical yet much neglected component of health systems in resource poor countries. They are crucial for public health, disease control and surveillance, and guide patient diagnosis and care, but their key role is often not recognized by governments or donors. Laboratory tests should be used to improve the outcome for individual patients or to provide public health information. However, if the quality of laboratory tests is poor, resources will be wasted on repeat tests or inappropriate management and the laboratory service will be inefficient.
The primary goal of Laboratory Medicine is to provide information that is useful to assist medical decision-making, allowing optimal health care. This can only be obtained by generating reliable analytical results on patient samples. Meaningful measurements are indeed essential for the diagnosis, monitoring, treatment, and risk assessment of patients. Inadequate laboratory performance may have extensive consequences for practical medicine, healthcare system, and, in conclusion, for the patient. Poor quality results may actually lead to incorrect interpretation by the clinician, impairing the patient’s
situation.
Accreditation authorities have identified twelve quality system essentials that need to be in place for a laboratory to perform clinical tests adequately and in a quality assured manner. Along with each laboratory performing tests that are in its scope, it is essential that duplication and excess capacity is addressed by forging and operating a network of laboratories leading to consolidation and integration of clinical testing. A network would have collection centers at places convenient to the patients, supported by frequent transfer of samples in appropriate conditions to the laboratory. In the laboratory there is a need for increased automation and relevant training of personnel and the setting up of centralized accessioning, pneumontic chutes for transport of samples to the work bench and for bidirectional interphased equipment to transfer results to desk top of laboratory physicians and after validation of results for the results to be electronically transferred by SMS and/or PDF files via email and/or becoming available online for clients, supplemented by delivery of hard copies of the results.
The challenge in the next decade for laboratory medicine is to accomplish these major changes in organization to meet fiscal restraint and shortage of adequately trained laboratory personnel. Collaborative networks, constructive use of point of care devices, and the development of rapport between laboratories and their clients leading to cost effective utilization of limited resources, are some of the strategies that will maximize patient benefit
演講-Meta analysis in medical research-張偉豪Beckett Hsieh
This document provides an overview of meta-analysis. It defines meta-analysis as a quantitative approach to systematically combining results from previous studies to arrive at conclusions about the body of research. It discusses key aspects of planning and conducting a meta-analysis such as defining the research question, searching for relevant literature, determining study eligibility, extracting data, analyzing effect sizes, assessing heterogeneity, and addressing publication bias. Software for performing meta-analyses and specific effect sizes like risk ratio and odds ratio are also mentioned.
Melanoma Nancy Shum And Anne Marcy Intro To Clinical Data Managementcunniffe6
This summarizes a clinical trial studying the effectiveness of a melanoma vaccine compared to standard of care for patients with unresectable melanoma. The randomized, double-blinded, placebo-controlled trial assigns patients to either receive the melanoma vaccine or a placebo by direct injection into a tumor site. The primary objective is to evaluate tumor response rates between the two groups. Secondary objectives include assessing safety, progression-free survival, and overall survival.
1. The document provides an overview of statistical analysis methods for clinical research trials.
2. It discusses key concepts like randomization, intention-to-treat analysis, multiplicity, and mixed effects models.
3. Mixed effects models that treat subjects as random effects are recommended for analyzing longitudinal or repeated measures data as they properly account for within- and between-subject variation.
This document provides a summary of a meta-analysis presented by Preethi Rai on November 12, 2013. It defines meta-analysis as a quantitative approach that systematically combines the results of previous research studies in order to arrive at conclusions about the body of research. The summary explains that meta-analysis increases the overall sample size and statistical power to better understand treatment effects. It also addresses how meta-analysis can help resolve controversies, identify areas needing more research, and generalize study results. Limitations including publication bias and inability to improve original study quality are also noted.
This document discusses and compares case-control and cohort studies in epidemiology. It defines epidemiology as the study of health-related states in populations and applying this to control health problems. Analytical epidemiology focuses on testing hypotheses about individuals within populations. Both case-control and cohort studies are described as types of analytical epidemiology. Case-control studies are retrospective while cohort studies are prospective. The key differences and advantages/disadvantages of each study type are outlined.
2014-10-22 EUGM | WEI | Moving Beyond the Comfort Zone in Practicing Translat...Cytel USA
1. The document discusses moving beyond conventional practices in translational statistics to obtain more robust and clinically meaningful results from clinical studies.
2. Several methodology issues are discussed, including how to define primary endpoints when there are multiple outcomes, how to handle dropouts and competing risks, and how to quantify treatment contrasts in a model-free way.
3. Alternative approaches are proposed for various types of studies, such as using restricted mean survival times instead of hazard ratios for survival analyses and performing meta-analyses for evaluating safety issues using large amounts of data.
Have you ever wondered why two brands of pharmaceuticals with similar efficacy and safety measures are prescribed differently? Often this is more influenced by emotion than rational thinking.
Find out more https://goo.gl/e3yMcl
This document discusses research methodology and design. It covers key aspects of research design including selecting subjects, controlling variables, establishing evaluation criteria, and ensuring internal and external validity. Factors to consider in research design are the objectives, feasibility, ethics, efficiency, and validity. The document also outlines steps in the research process such as developing data collection tools, planning analysis, collecting and processing data, conducting analysis and interpretation. Statistical tests are matched to different research designs and levels of measurement.
Semantic MEDLINE applies automatic summarization techniques to manage the semantic predications extracted from the biomedical literature by SemRep. It does so by selecting salient predications based on several criteria. In this study, we investigated a new technique to automatically summarize SemRep predications. Our technique leverages hierarchical relations from the UMLS Metathesaurus for aggregating the semantic predications. We also generated new inferences from the aggregated semantic predications. Several quantitative measures are dened to evaluate the system. We applied our method to summarize medications used to treat diseases and also adverse drug events reported in the biomedical literature. Our preliminary experimental results are promising in terms of summarization rate. They also indicate that less than half of the newly generated inferences correspond to existing relations. Further work is needed to evaluate the rest of the inferences.
This document discusses sources of bias and error in epidemiological studies. It defines random and systematic errors, and describes the main types of each. Random errors are due to chance and include sampling variability. Systematic or bias errors are due to flaws in study design, implementation or analysis. The key types of bias discussed are selection bias, information bias, and confounding. Selection bias results from non-representative samples. Information bias stems from errors in measuring or recording exposures and outcomes. Confounding occurs when a third variable influences the exposure-outcome relationship. The document also provides examples and ways to reduce biases, such as increasing sample size and using statistical controls.
Similar to Predicting the Response to Hepatitis C Therapy (20)
(June 12, 2024) Webinar: Development of PET theranostics targeting the molecu...Scintica Instrumentation
Targeting Hsp90 and its pathogen Orthologs with Tethered Inhibitors as a Diagnostic and Therapeutic Strategy for cancer and infectious diseases with Dr. Timothy Haystead.
The technology uses reclaimed CO₂ as the dyeing medium in a closed loop process. When pressurized, CO₂ becomes supercritical (SC-CO₂). In this state CO₂ has a very high solvent power, allowing the dye to dissolve easily.
ESR spectroscopy in liquid food and beverages.pptxPRIYANKA PATEL
With increasing population, people need to rely on packaged food stuffs. Packaging of food materials requires the preservation of food. There are various methods for the treatment of food to preserve them and irradiation treatment of food is one of them. It is the most common and the most harmless method for the food preservation as it does not alter the necessary micronutrients of food materials. Although irradiated food doesn’t cause any harm to the human health but still the quality assessment of food is required to provide consumers with necessary information about the food. ESR spectroscopy is the most sophisticated way to investigate the quality of the food and the free radicals induced during the processing of the food. ESR spin trapping technique is useful for the detection of highly unstable radicals in the food. The antioxidant capability of liquid food and beverages in mainly performed by spin trapping technique.
EWOCS-I: The catalog of X-ray sources in Westerlund 1 from the Extended Weste...Sérgio Sacani
Context. With a mass exceeding several 104 M⊙ and a rich and dense population of massive stars, supermassive young star clusters
represent the most massive star-forming environment that is dominated by the feedback from massive stars and gravitational interactions
among stars.
Aims. In this paper we present the Extended Westerlund 1 and 2 Open Clusters Survey (EWOCS) project, which aims to investigate
the influence of the starburst environment on the formation of stars and planets, and on the evolution of both low and high mass stars.
The primary targets of this project are Westerlund 1 and 2, the closest supermassive star clusters to the Sun.
Methods. The project is based primarily on recent observations conducted with the Chandra and JWST observatories. Specifically,
the Chandra survey of Westerlund 1 consists of 36 new ACIS-I observations, nearly co-pointed, for a total exposure time of 1 Msec.
Additionally, we included 8 archival Chandra/ACIS-S observations. This paper presents the resulting catalog of X-ray sources within
and around Westerlund 1. Sources were detected by combining various existing methods, and photon extraction and source validation
were carried out using the ACIS-Extract software.
Results. The EWOCS X-ray catalog comprises 5963 validated sources out of the 9420 initially provided to ACIS-Extract, reaching a
photon flux threshold of approximately 2 × 10−8 photons cm−2
s
−1
. The X-ray sources exhibit a highly concentrated spatial distribution,
with 1075 sources located within the central 1 arcmin. We have successfully detected X-ray emissions from 126 out of the 166 known
massive stars of the cluster, and we have collected over 71 000 photons from the magnetar CXO J164710.20-455217.
Current Ms word generated power point presentation covers major details about the micronuclei test. It's significance and assays to conduct it. It is used to detect the micronuclei formation inside the cells of nearly every multicellular organism. It's formation takes place during chromosomal sepration at metaphase.
The cost of acquiring information by natural selectionCarl Bergstrom
This is a short talk that I gave at the Banff International Research Station workshop on Modeling and Theory in Population Biology. The idea is to try to understand how the burden of natural selection relates to the amount of information that selection puts into the genome.
It's based on the first part of this research paper:
The cost of information acquisition by natural selection
Ryan Seamus McGee, Olivia Kosterlitz, Artem Kaznatcheev, Benjamin Kerr, Carl T. Bergstrom
bioRxiv 2022.07.02.498577; doi: https://doi.org/10.1101/2022.07.02.498577
Travis Hills of MN is Making Clean Water Accessible to All Through High Flux ...Travis Hills MN
By harnessing the power of High Flux Vacuum Membrane Distillation, Travis Hills from MN envisions a future where clean and safe drinking water is accessible to all, regardless of geographical location or economic status.
Describing and Interpreting an Immersive Learning Case with the Immersion Cub...Leonel Morgado
Current descriptions of immersive learning cases are often difficult or impossible to compare. This is due to a myriad of different options on what details to include, which aspects are relevant, and on the descriptive approaches employed. Also, these aspects often combine very specific details with more general guidelines or indicate intents and rationales without clarifying their implementation. In this paper we provide a method to describe immersive learning cases that is structured to enable comparisons, yet flexible enough to allow researchers and practitioners to decide which aspects to include. This method leverages a taxonomy that classifies educational aspects at three levels (uses, practices, and strategies) and then utilizes two frameworks, the Immersive Learning Brain and the Immersion Cube, to enable a structured description and interpretation of immersive learning cases. The method is then demonstrated on a published immersive learning case on training for wind turbine maintenance using virtual reality. Applying the method results in a structured artifact, the Immersive Learning Case Sheet, that tags the case with its proximal uses, practices, and strategies, and refines the free text case description to ensure that matching details are included. This contribution is thus a case description method in support of future comparative research of immersive learning cases. We then discuss how the resulting description and interpretation can be leveraged to change immersion learning cases, by enriching them (considering low-effort changes or additions) or innovating (exploring more challenging avenues of transformation). The method holds significant promise to support better-grounded research in immersive learning.
Describing and Interpreting an Immersive Learning Case with the Immersion Cub...
Predicting the Response to Hepatitis C Therapy
1. Simone Romano’s Research Activity
Formerly:
Research Assistant at Department of
Information Engineering (DEI)
Padova, Italy
Now:
PhD student at Department of
Computing and Information Systems (CIS)
Melbourne, Australia
Simone Romano (University of Melbourne) Simone Romano’s Research Activity March 21st 2012 1 / 8
2. 1 Problem Statement
2 Proposed Solutions
Probability Estimation Trees
Cost-sensitive classification
3 Results
4 Conclusions
Simone Romano (University of Melbourne) Simone Romano’s Research Activity March 21st 2012 2 / 8
3. Problem Statement
Problem: Interferon (IFN) and Ribavirin (RBV) therapy for Hepatitis C
is successful only in the 60% of cases. Moreover, this
combined treatment has many side effects.
Data: 606 Padova patients + 592 external patients already treated
with IFN and RBV with known outcome.
Objective: Predict the Sustained Virological Response (SVR) as earlier
as possible for future subjects.
Simone Romano (University of Melbourne) Simone Romano’s Research Activity March 21st 2012 3 / 8
4. Proposed Solutions
Proposed solutions:
Probability Estimation Trees (PETs);
Cost-Sensitive Classification;
PETs with future doses.
Simone Romano (University of Melbourne) Simone Romano’s Research Activity March 21st 2012 4 / 8
5. Proposed Solutions Probability Estimation Trees
Gender
Age
Male Female
40%
30% 50%
60% 30%
[years]
100
49 51
40 11
Simone Romano (University of Melbourne) Simone Romano’s Research Activity March 21st 2012 5 / 8
6. Proposed Solutions Cost-sensitive classification
Which is worse?
Exclude from therapy a patient that can get better?
Treat a patient with no result?
Simone Romano (University of Melbourne) Simone Romano’s Research Activity March 21st 2012 6 / 8
7. Results
PET example, with future doses:
HCV-RNA 1st
month
379
63%
91
5%
≤ 3.97 > 3.97
[log IU/mL]
subgroup 1
38
0%
53
9%
54
0%
37
14%
RBV dose percentage
[%]
IFN dose percentage
[%]
≤ 90 > 90
≤ 99 > 99
p = 0.051 p = 0.005
New stopping criteria:
Criterion Recall Precision
Standard criterion
EVR (3rd
month) 35.3 100.0
New Criteria
HCV-RNA 1st
m. > 4.90 40.3 100.0
HCV-RNA 1st
m. > 3.97 and
(IFN ≤ 99% or RBV ≤ 90%)
48.2 100.0
...
Simone Romano (University of Melbourne) Simone Romano’s Research Activity March 21st 2012 7 / 8