This document provides an overview of meta-analysis methods for time-to-event data. It discusses key concepts such as censoring, Kaplan-Meier curves, the log-rank test, hazard ratios, and how to estimate the log hazard ratio and its variance from individual trials for use in a meta-analysis. It presents different approximations that can be used to calculate the p-value when direct estimates of the log hazard ratio and its variance are reported.
2010 smg training_cardiff_day1_session2b (2 of 2) herbisonrgveroniki
This document discusses meta-analysis of count data. It notes that count data can vary in time scale and be complicated by different exposure times among study participants. Various analysis approaches for count data are described, including analyzing rates, dichotomizing counts, treating counts as continuous, and analyzing time to first event. A simulation study is presented comparing different analysis methods across data sets with varying means and levels of overdispersion. The conclusions are that lower mean counts allow combining analyses more flexibly, and that dichotomizing and time to first event analyses increasingly underestimate effects as means increase, while adjusting for overdispersion has only a small effect. Ratios of means and medians can also provide reasonable estimates.
IPOS10 T276 - Large Scale Validation of the Emotion Thermometers as a Screen...Alex J Mitchell
1. The study validated the Emotion Thermometers as a screening tool for mood disorders and distress in a diverse cancer population.
2. Results showed the Depression Thermometer had the best validity for detecting depression overall, while the Distress Thermometer was also good.
3. For detecting depression in ethnically diverse patients, the Distress Thermometer may have the best validity, achieving a sensitivity of 100%.
The document provides daily and weekly performance data for several stock market indexes (S&P 100, Nasdaq 100, S&P 500, Russell 1000, Russell 2000, Russell 3000) from November 25, 2008. It includes information on advances/declines, moving averages, high/low levels, and breakouts/levels for each index.
This document provides market index data for various US stock market indices as of Friday, December 12, 2008. It includes daily and weekly statistics on price movement, breakdowns of stocks within each index by moving averages, and high/low price levels for each of the S&P 100, Nasdaq 100, S&P 500, Russell 1000, Russell 2000, and Russell 3000 indices.
This document discusses potential sources of missing data in meta-analyses, including studies not being found, outcomes not being fully reported, missing standard deviations or other information needed for the meta-analysis, and missing participants. It also covers concepts related to missing data like whether it is missing completely at random, missing at random, or informatively missing. Strategies for dealing with missing data include simple or multiple imputation as well as sensitivity analyses. Specific examples discussed include imputing missing standard deviations or correlation coefficients.
This document discusses meta-analysis of ordinal data and some of the challenges involved. It notes that ordinal outcomes are common in Cochrane reviews of stroke interventions, but are typically analyzed as dichotomous or continuous data rather than using methods suited for ordinal scales. Dichotomizing or treating ordinal data as continuous can discard important information. The document recommends using proportional odds modeling for ordinal data, which makes no distributional assumptions and can provide a single odds ratio summarizing the treatment effect across the full ordinal scale. It provides examples of how this method can be applied and discusses some remaining challenges like assessing model assumptions.
This document outlines the concepts and methods of multiple-treatments meta-analysis (MTM). MTM allows for the simultaneous comparison of multiple interventions for a condition by combining both direct and indirect evidence from randomized controlled trials. Key advantages of MTM include the ability to rank treatments, comprehensively use all available data, and compare interventions not directly compared in trials. The document discusses MTM approaches using frequentist meta-regression and Bayesian statistics.
This document discusses investigating heterogeneity in meta-analyses through subgroup analysis and meta-regression. It outlines when and how to use these techniques to explore reasons for variability in study results. Key challenges include having enough studies, selecting explanatory variables carefully to avoid false positives, and accounting for confounding and aggregation bias in study-level data. Meta-regression allows for random effects but interpretation requires caution given observational relationships between study characteristics and effects.
2010 smg training_cardiff_day1_session2b (2 of 2) herbisonrgveroniki
This document discusses meta-analysis of count data. It notes that count data can vary in time scale and be complicated by different exposure times among study participants. Various analysis approaches for count data are described, including analyzing rates, dichotomizing counts, treating counts as continuous, and analyzing time to first event. A simulation study is presented comparing different analysis methods across data sets with varying means and levels of overdispersion. The conclusions are that lower mean counts allow combining analyses more flexibly, and that dichotomizing and time to first event analyses increasingly underestimate effects as means increase, while adjusting for overdispersion has only a small effect. Ratios of means and medians can also provide reasonable estimates.
IPOS10 T276 - Large Scale Validation of the Emotion Thermometers as a Screen...Alex J Mitchell
1. The study validated the Emotion Thermometers as a screening tool for mood disorders and distress in a diverse cancer population.
2. Results showed the Depression Thermometer had the best validity for detecting depression overall, while the Distress Thermometer was also good.
3. For detecting depression in ethnically diverse patients, the Distress Thermometer may have the best validity, achieving a sensitivity of 100%.
The document provides daily and weekly performance data for several stock market indexes (S&P 100, Nasdaq 100, S&P 500, Russell 1000, Russell 2000, Russell 3000) from November 25, 2008. It includes information on advances/declines, moving averages, high/low levels, and breakouts/levels for each index.
This document provides market index data for various US stock market indices as of Friday, December 12, 2008. It includes daily and weekly statistics on price movement, breakdowns of stocks within each index by moving averages, and high/low price levels for each of the S&P 100, Nasdaq 100, S&P 500, Russell 1000, Russell 2000, and Russell 3000 indices.
This document discusses potential sources of missing data in meta-analyses, including studies not being found, outcomes not being fully reported, missing standard deviations or other information needed for the meta-analysis, and missing participants. It also covers concepts related to missing data like whether it is missing completely at random, missing at random, or informatively missing. Strategies for dealing with missing data include simple or multiple imputation as well as sensitivity analyses. Specific examples discussed include imputing missing standard deviations or correlation coefficients.
This document discusses meta-analysis of ordinal data and some of the challenges involved. It notes that ordinal outcomes are common in Cochrane reviews of stroke interventions, but are typically analyzed as dichotomous or continuous data rather than using methods suited for ordinal scales. Dichotomizing or treating ordinal data as continuous can discard important information. The document recommends using proportional odds modeling for ordinal data, which makes no distributional assumptions and can provide a single odds ratio summarizing the treatment effect across the full ordinal scale. It provides examples of how this method can be applied and discusses some remaining challenges like assessing model assumptions.
This document outlines the concepts and methods of multiple-treatments meta-analysis (MTM). MTM allows for the simultaneous comparison of multiple interventions for a condition by combining both direct and indirect evidence from randomized controlled trials. Key advantages of MTM include the ability to rank treatments, comprehensively use all available data, and compare interventions not directly compared in trials. The document discusses MTM approaches using frequentist meta-regression and Bayesian statistics.
This document discusses investigating heterogeneity in meta-analyses through subgroup analysis and meta-regression. It outlines when and how to use these techniques to explore reasons for variability in study results. Key challenges include having enough studies, selecting explanatory variables carefully to avoid false positives, and accounting for confounding and aggregation bias in study-level data. Meta-regression allows for random effects but interpretation requires caution given observational relationships between study characteristics and effects.
2010 smg training_cardiff_day1_session1 (1 of 3)_mckenziergveroniki
This document discusses different analytical methods for meta-analyzing continuous outcome data from randomized trials: final values, change scores, and analysis of covariance (ANCOVA). It presents an example comparing the properties of these estimators using observed and simulated trial data. Key findings include:
1) The three estimators can produce different intervention effect estimates depending on the correlation between baseline and follow-up scores; ANCOVA generally has the smallest standard error.
2) ANCOVA is preferred as it is unconditionally and conditionally unbiased, whereas final values and change scores can be conditionally biased.
3) In meta-analysis, when trials have adequate allocation concealment, pooled baseline imbalance is usually not problematic; however
2010 smg training_cardiff_day1_session1(2 of 3) altmanrgveroniki
This document discusses approaches for conducting meta-analyses of skewed continuous data where studies report outcomes using different metrics such as means, medians, and ranges. It recommends converting outcomes reported as medians or ranges into means and standard deviations on the log scale before combining, assuming the data are approximately lognormal. It also describes using the method of Higgins et al. (2008) to combine statistics reported on both raw and log-transformed scales. References are provided for methods converting ranges to means and standard deviations and for an example application in a Cochrane review.
This document discusses biases that can arise in randomized controlled trials and meta-analyses. It notes that biases can be introduced in the design, conduct, analysis, and reporting of trials. Various empirical studies are presented that demonstrate biases from lack of allocation concealment and blinding in trials. Risk of bias assessments are recommended over quality scores for evaluating biases in individual trials and meta-analyses.
This document discusses selective outcome reporting bias (ORB), which occurs when researchers select a subset of original outcomes to report based on statistical significance. ORB threatens the validity of systematic reviews and meta-analyses. The document describes different types of ORB and methods to assess risk of bias. It proposes the ORBIT classification system to code incomplete outcome reporting in trials. Sensitivity analyses can estimate the potential impact of ORB on review conclusions. While awareness of ORB is growing, more needs to be done to address this issue through improved trial registration, reporting and access to protocols and outcomes.
2010 smg training_cardiff_day1_session1(3 of 3)beyenergveroniki
This document summarizes a presentation on using the Ratio of Means (RoM) as an alternative effect measure for meta-analyzing continuous outcomes. Through simulation studies and an empirical analysis of Cochrane reviews, the RoM was found to have statistical performance comparable to the Mean Difference and Standardized Mean Difference. Specifically:
1) Simulation studies found the RoM to have similar bias, coverage, power, and ability to estimate heterogeneity as the Mean Difference and Standardized Mean Difference in most scenarios.
2) An empirical analysis of over 200 Cochrane reviews found no significant differences in treatment effect sizes or heterogeneity between the RoM, Mean Difference, and Standardized Mean Difference.
3) The RoM was proposed
This document provides an overview of multiple treatment meta-analysis (MTMA) or network meta-analysis. It discusses Bayesian pairwise meta-analysis models as well as extensions to multiple treatments. Key assumptions of MTMA including consistency are explained. Computational details using Markov Chain Monte Carlo are covered. Measures of model fit such as residual deviance and model comparison using Deviance Information Criteria are also summarized. Examples from cardiovascular treatment meta-analyses are provided.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
-------------------------------------------------------------------------------
Find out more about ISO training and certification services
Training: ISO/IEC 27001 Information Security Management System - EN | PECB
ISO/IEC 42001 Artificial Intelligence Management System - EN | PECB
General Data Protection Regulation (GDPR) - Training Courses - EN | PECB
Webinars: https://pecb.com/webinars
Article: https://pecb.com/article
-------------------------------------------------------------------------------
For more information about PECB:
Website: https://pecb.com/
LinkedIn: https://www.linkedin.com/company/pecb/
Facebook: https://www.facebook.com/PECBInternational/
Slideshare: http://www.slideshare.net/PECBCERTIFICATION
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
2010 smg training_cardiff_day1_session1 (1 of 3)_mckenziergveroniki
This document discusses different analytical methods for meta-analyzing continuous outcome data from randomized trials: final values, change scores, and analysis of covariance (ANCOVA). It presents an example comparing the properties of these estimators using observed and simulated trial data. Key findings include:
1) The three estimators can produce different intervention effect estimates depending on the correlation between baseline and follow-up scores; ANCOVA generally has the smallest standard error.
2) ANCOVA is preferred as it is unconditionally and conditionally unbiased, whereas final values and change scores can be conditionally biased.
3) In meta-analysis, when trials have adequate allocation concealment, pooled baseline imbalance is usually not problematic; however
2010 smg training_cardiff_day1_session1(2 of 3) altmanrgveroniki
This document discusses approaches for conducting meta-analyses of skewed continuous data where studies report outcomes using different metrics such as means, medians, and ranges. It recommends converting outcomes reported as medians or ranges into means and standard deviations on the log scale before combining, assuming the data are approximately lognormal. It also describes using the method of Higgins et al. (2008) to combine statistics reported on both raw and log-transformed scales. References are provided for methods converting ranges to means and standard deviations and for an example application in a Cochrane review.
This document discusses biases that can arise in randomized controlled trials and meta-analyses. It notes that biases can be introduced in the design, conduct, analysis, and reporting of trials. Various empirical studies are presented that demonstrate biases from lack of allocation concealment and blinding in trials. Risk of bias assessments are recommended over quality scores for evaluating biases in individual trials and meta-analyses.
This document discusses selective outcome reporting bias (ORB), which occurs when researchers select a subset of original outcomes to report based on statistical significance. ORB threatens the validity of systematic reviews and meta-analyses. The document describes different types of ORB and methods to assess risk of bias. It proposes the ORBIT classification system to code incomplete outcome reporting in trials. Sensitivity analyses can estimate the potential impact of ORB on review conclusions. While awareness of ORB is growing, more needs to be done to address this issue through improved trial registration, reporting and access to protocols and outcomes.
2010 smg training_cardiff_day1_session1(3 of 3)beyenergveroniki
This document summarizes a presentation on using the Ratio of Means (RoM) as an alternative effect measure for meta-analyzing continuous outcomes. Through simulation studies and an empirical analysis of Cochrane reviews, the RoM was found to have statistical performance comparable to the Mean Difference and Standardized Mean Difference. Specifically:
1) Simulation studies found the RoM to have similar bias, coverage, power, and ability to estimate heterogeneity as the Mean Difference and Standardized Mean Difference in most scenarios.
2) An empirical analysis of over 200 Cochrane reviews found no significant differences in treatment effect sizes or heterogeneity between the RoM, Mean Difference, and Standardized Mean Difference.
3) The RoM was proposed
This document provides an overview of multiple treatment meta-analysis (MTMA) or network meta-analysis. It discusses Bayesian pairwise meta-analysis models as well as extensions to multiple treatments. Key assumptions of MTMA including consistency are explained. Computational details using Markov Chain Monte Carlo are covered. Measures of model fit such as residual deviance and model comparison using Deviance Information Criteria are also summarized. Examples from cardiovascular treatment meta-analyses are provided.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
-------------------------------------------------------------------------------
Find out more about ISO training and certification services
Training: ISO/IEC 27001 Information Security Management System - EN | PECB
ISO/IEC 42001 Artificial Intelligence Management System - EN | PECB
General Data Protection Regulation (GDPR) - Training Courses - EN | PECB
Webinars: https://pecb.com/webinars
Article: https://pecb.com/article
-------------------------------------------------------------------------------
For more information about PECB:
Website: https://pecb.com/
LinkedIn: https://www.linkedin.com/company/pecb/
Facebook: https://www.facebook.com/PECBInternational/
Slideshare: http://www.slideshare.net/PECBCERTIFICATION
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
हिंदी वर्णमाला पीपीटी, hindi alphabet PPT presentation, hindi varnamala PPT, Hindi Varnamala pdf, हिंदी स्वर, हिंदी व्यंजन, sikhiye hindi varnmala, dr. mulla adam ali, hindi language and literature, hindi alphabet with drawing, hindi alphabet pdf, hindi varnamala for childrens, hindi language, hindi varnamala practice for kids, https://www.drmullaadamali.com
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
A wound is a break in the integrity of the skin or tissues, which may be associated with disruption of the structure and function.
Healing is the body’s response to injury in an attempt to restore normal structure and functions.
Healing can occur in two ways: Regeneration and Repair
There are 4 phases of wound healing: hemostasis, inflammation, proliferation, and remodeling. This document also describes the mechanism of wound healing. Factors that affect healing include infection, uncontrolled diabetes, poor nutrition, age, anemia, the presence of foreign bodies, etc.
Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.
2010 smg training_cardiff_day1_session2a (1 of 1) tudur-smith
1. Meta‐analysis of
Time‐to‐event data
Catrin Tudur Smith
cat1@liv.ac.uk
MRC North West Hub for Trials
Methodology Research
University of Liverpool
SMG course Cardiff 2010
2. Contents
• Introduction to time‐to‐event data
• Meta‐analysis of time‐to‐event data
• Estimating log(HR) and its variance
• Practical
• Other issues
SMG course Cardiff 2010
3. Time‐to‐event data
● Data arising when we measure the length of time
taken for an event to occur
● The event might be
discharge from hospital
recurrence of tumour
remission of a disease
● The time starting point might be
time of diagnosis
time of surgery
time of randomisation
SMG course Cardiff 2010
5. Examples of censoring
• A patient with cancer is still alive at the time of
analysis. Time to death would be censored.
• A patient with epilepsy who has had no seizures
since entry moves GP and is lost to follow‐up.
Time to first seizure would be censored.
• A patient with epilepsy who has had no seizures
since entry dies from a non‐epilepsy related
cause. Time to first seizure would be censored.
SMG course Cardiff 2010
7. Why special methods of analysis?
• Why not analyse the time to event as a binary response variable?
– May be reasonable if...
event is likely to occur very early on (e.g. acute liver failure)
event is rare
lengths of follow up are similar between patients
interested in whether event occurs at all rather than time to event
– But if…
an appreciable proportion of the patients do die
death may take a considerable time
.. looking not only at how many patients died, but also at how long after
treatment they died, gives a more sensitive assessment
SMG course Cardiff 2010
8. Kaplan‐Meier curves
• A Kaplan‐Meier plot
gives a graphical
display of the survival
function estimated
from a set of data
• The curve starts at 1
(or 100%) at time 0.
All patients are 'alive'
• The curve steps down
each time an event
occurs, and so tails off
towards 0
9. Plotting the results
Survival Number Survival
Patient Died?
Time at Risk Proportion
6 No 118 1
10 Yes 152 11 0.91
11 No 297
4 No 336
8 No 549
7 Yes 614 7 0.78
12 Yes 641 6 0.65
2 No 752
9 No 854
1 Yes 910 3 0.43
5 No 923
3 Yes 1006 1 0
Time 0 152 614 641 910 1006
No. at risk 12 11 7 6 3 1
10. Interpreting the results
Median survival:
• The median survival time is time
beyond which 50% of the
individuals in the population
under study are expected to
survive
• Read off the survival time
corresponding to a cumulative
survival of 0.5.
Shape of the curve:
• Poor survival is reflected by a
curve that drops relatively
Time 0 152 614 641 910 1006
rapidly towards 0.
No. at risk 12 11 7 6 3 1 • The curve will only reach 0 if the
patient with longest follow‐up
was not censored.
11. Accuracy of the K‐M estimates
• The curve is only an estimate of
'true' survival.
Kaplan-Meier Curve with 95% Confidence Intervals • We can calculate confidence
1.0
intervals for the curve.
0.9
0.8
• These give an area in which the true
survival curve lies.
Survival Probability
0.7
•
0.6
0.5
Note that the confidence intervals
0.4 get wider towards the right‐hand‐
0.3 side of the graph.
0.2
0.1
• This is because the sample size gets
0.0 smaller as subjects either die or
0 500 1000
Survival Time (Days) drop‐out.
• The larger the sample size, the more
accurate the K‐M curve will be of
true survival.
12. The Logrank test
• The Logrank Test is a simple
1.0
0.9
statistical test to compare the
0.8 time to event of two groups.
Survival Probability
0.7
0.6
0.5 • It takes censoring into account, is
0.4
0.3
non‐parametric, and compares
0.2
the groups over the whole time‐
0.1 period.
0.0
0 500 1000
Survival Time (days)
13. The logrank test cont’d…
• The logrank test compares the total number of deaths observed with
the number of deaths we would expect assuming that there is no
group effect.
• If deaths occur in the sample at the time‐points t1,…,tk, then the
expected number of deaths ej at time tj in group A is:
no. of deaths in sample at t j
e j no. at risk in group A at t j
no. at risk in sample at t j
• Then the total number of deaths expected for group A, EA, is:
E A e1 e2 ... ek
• The logrank test looks at whether EA is significantly different to the
observed number of deaths in group A. If it is, this provides evidence
that group is associated with survival.
SMG course Cardiff 2010
14. The hazard ratio
• The hazard is the chance that at any given moment, the event will
occur, given that it hasn’t already done so.
• The hazard ratio (HR) is a measure of the relative survival in two
groups.
• It is a ratio of the hazard for one group compared to another.
Suppose that we wish to compare group B relative to group A:
0 < HR < 1 Group B are at a decreased hazard compared to A.
HR = 1 The hazard is the same for both groups.
HR > 1 Group B are at an increased hazard compared to group A.
• Note that since HR is a ratio, a HR of 0.5 means a halving of risk, and a
HR of 2 means a doubling of risk.
SMG course Cardiff 2010
15. More on the hazard ratio
• Cox proportional hazards (PH) regression model ‐
most commonly used regression model
• The hazard is modelled with the equation:
h(t ) h0 (t ) expb1x1 b2 x2 ... bk xk
Underlying Parameters to be estimated Risk Factors (Covariates)
hazard – related to effect sizes
• So, we assume that the hazard function is partly
described by an underlying hazard, and partly by
the contribution of certain risk factors.
16. Interpretation of b1, b2 …
• Find the hazard of death for a person on Treatment (x1 = 1) compared to
Control (x1 = 0), assuming they are alike for all other covariates (x2, x3,
etc.).
– Hazard rate (risk of death) in Treatment group at time t:
ht ho t exp (b1 1) ho t exp (b1 )
– Hazard rate (risk of death) in Control group at time t:
ht ho t exp (b1 0 ) ho t 1
– Hazard ratio is:
h0 (t ) exp(b1 )
HR exp(b1 )
h0 (t ) 1
24. p‐value (balanced randomisation)
(3)
pi is the reported (two sided) p‐value associated with
the Mantel‐Haenszel version of the logrank statistic
is the cumulative distribution function of the Normal
distribution
is the total observed number of events across both
groups
SMG course Cardiff 2010
28. Choice of Vri
• Approximation (3) and (4) are identical when number of
events are equal in both arms
• Approximation (3) and (5) are identical when number
randomised are equal in both arms
• Approximation (4) requires number of events in each group
which may not always be given
• Collette et al (1998) compared three approximations by
simulation
All 3 provide very close estimates to IPD
Approximation (4) most precise for trial with low % of censoring
Approximation (5) preferred for trials with unequal sample sizes
SMG course Cardiff 2010
31. Survival curves – Parmar et al
Step 1 ‐ For each trial split the time‐axis into T non‐
overlapping time intervals – chosen to limit number of
events within any time interval
Step 2 ‐ For each arm and each time point, read off the
corresponding survival probability
SMG course Cardiff 2010
33. Survival curves – Parmar et al
Step 3
From reading the manuscript, estimate the minimum ( )
and maximum ( ) follow‐up of patients
– May be given directly
– Censoring tick marks on curves
– Estimated from dates of accrual and date of submission, or
perhaps publication of the manuscript
SMG course Cardiff 2010
34. Survival curves – Parmar et al
Time point t s t e
NAR at start of interval R(t s)
Step 4 Research Group
Calculate Number at risk at start of interval
For first interval R(0) = number of patients analysed in the relevant
treatment group
SMG course Cardiff 2010
35. Survival curves – Parmar et al
Time point t s t e
NAR at start of interval R(t s)
Censored during the interval
Step 5 Research Group
Calculate Number censored during first interval
SMG course Cardiff 2010
36. Survival curves – Parmar et al
Time point t s t e
NAR at start of interval R(t s)
Censored during the interval
NAR during interval
Step 6 Research Group
Calculate Number at Risk during first interval
SMG course Cardiff 2010
37. Survival curves – Parmar et al
Time point t s t e
NAR at start of interval R(t s)
Censored during the interval
NAR during interval
Number of deaths during interval
Survival probability
Step 7 Research Group
Calculate Number of deaths during first interval
SMG course Cardiff 2010
38. Survival curves – Parmar et al
Time point t s t e
NAR at start of interval R(t s)
Censored during the interval
NAR during interval
Number of deaths during interval
Survival probability
Step 8 Control Group
Repeat step 4 ‐7 for the control group
SMG course Cardiff 2010
41. Survival curves – Parmar et al
Step 11
Calculate pooled log(HR) and its variance for the trial by combining
estimates across all intervals
SMG course Cardiff 2010
42. Zero deaths
• Difficulties with calculating logHR and its variance
will arise whenever estimated number of deaths
within an interval on either arms is zero
• Replace zero by a small number of deaths 10‐6 in
that interval
• Provides the best estimate of the total number of
deaths and overall variance in each arm
• Preferable to concatenating time intervals such
that there is none with zero deaths in it
SMG course Cardiff 2010
44. Practical
• For the trial of Gemcitabine in combination with Oxaliplatin
for pancreatic cancer (Louvet et al 2005), please complete the
following as far as possible for the outcome Overall Survival.
• If you have time, please complete a separate form for the
outcome Progression Free Survival.
P‐value 1‐p/2
0.13 0.935 1.51
0.15 0.925 1.44
0.04 0.98 2.05
0.05 0.975 1.96
0.22 0.89 1.23
SMG course Cardiff 2010
47. HR calculations spreadsheet
• Spreadsheet to facilitate the estimation of
hazard ratios from published summary
statistics or data extracted from Kaplan‐Meier
curves.
http://www.biomedcentral.com/content/supplementary/1745‐6215‐8‐16‐S1.xls
Tierney JF, Stewart LA, Ghersi D, Burdett S, Sydes MR. Practical
methods for incorporating summary time‐to‐event data into
meta‐analysis. Trials 2007 8:16.
SMG course Cardiff 2010
48. Empirical comparison
• Parmar et al studied 209 randomized controlled trials comparing the
survival of women treated for advanced breast cancer contrasting the
estimates of the log hazard ratio directly or indirectly taken from the
manuscript with those derived from survival curves.
• Overall no evidence of systematic bias for the survival curve approach
• There was no evidence of a systematic bias although the survival curve
estimate tended to underestimate the variance of treatment effect
provided directly from the papers.
SMG course Cardiff 2010
49. Empirical comparison
• Tudur C, Williamson PR, Khan S, Best L: The value of the aggregate data
approach in meta‐analysis with time‐to‐event outcomes. Journal of the
Royal Statistical Society A 2001, 164:357‐70.
• We compared as many methods as possible across 24 trials from 2
systematic reviews – one in cancer and another in chronic liver disease
• AD and IPD were available for one review in cancer
SMG course Cardiff 2010
52. Empirical comparison
• Conclusions of Tudur et al 2001
– Good agreement between non‐survival curve indirect
methods and IPD where available
– Good agreement between different indirect methods
based on p‐values
– Survival curve approach is generally less reliable especially
when event rate is low
– Recommend looking at sensitivity analysis with at least 2
sets of Fmin and Fmax (if not given directly)
– Indirect estimates generally robust to different
assumptions about accuracy of p‐value and Fmin Fmax
– Not always easy to identify direction of effect
SMG course Cardiff 2010
53. Empirical comparison
• D'Amico et al (2000) assessed the performance of the indirect estimate of
HR when estimated from survival curves (Parmar approach)
• Examined the effect of a) maximum and minimum LFU; b) rate of
censoring at various time‐points and c) numbers of time intervals to be
considered
• Simulated data and calculated several HRs a) using the individual data and
b) using the indirect method under different assumptions
• Distributions of the logHRs obtained by the indirect methods were
compared to the distribution of the logHRs obtained by using the
individual data
• Preliminary results indicate that means and variances of the distribution
of the logHRs estimates were similar regardless of the number of time
intervals and the assumption of the maximum and minimum
SMG course Cardiff 2010
54. Median survival or survival rate at
time point
• The median survival and survival rates at specific time points are
frequently presented
• These are sometimes used for meta‐analysis
• Potential bias could arise if time points are subjectively chosen by the
reviewer or selectively reported by the trialist at times of maximal or
minimal difference between groups
• Also, requires that all trials report data at same time point
• Michiels et al 2005 found that both the MR and OR method may result in
serious under‐ or overestimation of the treatment effect and major loss of
statistical power
• Conclude that MR and OR are not reasonable surrogate measures for
meta‐analyses of survival outcomes
• Wherever possible, HRs should be calculated
– Contact authors if sufficient data not available to estimate log(HR) and its variance
SMG course Cardiff 2010
55. Individual patient data
• Meta‐analysis of TTE outcomes often use individual
patient data (IPD)
• Many advantages including
more thorough analysis
more thorough investigation of potential causes of
heterogeneity
• Two‐stage analysis – analyse each trial separately
and obtain estimate of logHR and its variance
• One‐stage analysis –analyse IPD from each trial in
one model with appropriate recognition for trial
e.g. Cox model stratified by trial
• See Tudur Smith et al 2005 for further details
SMG course Cardiff 2010
56. Conclusions
• Aggregate Data meta‐analysis of time‐to‐event
data is possible
• Estimates based on survival curve may not be
reliable – specify how logHR and its variance have
been estimated in the review publication
• Always contact author for further details if possible
• Avoid using median survival time or survival rate at
a particular time point
• IPD has many advantages which should be
considered carefully when planning meta‐analysis
of TTE data
SMG course Cardiff 2010
57. References
1. Parmar MKB, Torri V, Stewart L: Extracting summary statistics to perform meta‐analyses
of the published literature for survival endpoints. Statistics in Medicine 1998, 17:2815‐34.
2. Williamson PR, Tudur Smith C, Hutton JL, Marson AG: Aggregate data meta‐analysis with
time‐to‐event outcomes. Statistics in Medicine 2002, 21:3337‐51.
3. Tudur C, Williamson PR, Khan S, Best L: The value of the aggregate data approach in
meta‐analysis with time‐to‐event outcomes. Journal of the Royal Statistical Society A
2001, 164:357‐70.
4. Tierney JF, Stewart LA, Ghersi D, Burdett S, Sydes MR. Practical methods for incorporating
summary time‐to‐event data into meta‐analysis. Trials 2007 8:16.
5. Tudur Smith C, Williamson PR, Marson AG. Investigating heterogeneity in an individual
patient data meta‐analysis of time to event outcomes. Statistics in Medicine 2005;
24:1307–1319
6. Michiels S, Piedbois P, Burdett S, Syz N, Stewart L, Pignon JP. Meta‐analysis when only the
median survival times are known: A comparison with individual patient data results.
International Journal of Technology Assessment in Health Care 2005; 21:1 119–125
SMG course Cardiff 2010