Meta-analysis combines the results of multiple studies on a topic to increase power and improve estimates of the size of effects. A meta-meta-analysis examined over 7,500 primary studies and 54,000 effect sizes across 176 meta-analyses. It found small to medium cumulative effect sizes for factors like advertising, pricing, and consumer behavior, demonstrating how meta-analysis can advance knowledge by integrating vast amounts of empirical data. However, meta-analysis also faces challenges like heterogeneous studies, publication bias, and selecting only a non-random sample of a domain.
In this webinar, we talk about the risks associated with colorectal cancer – including everything from diet, lifestyle, age, family history and more. We review the risks of recurrence for colorectal cancer survivors. Join us to learn how to reduce your risk of colorectal cancer!
Presented by Harvey Murff, M.D, M.P.H. is an Associate Professor of Medicine in the Division of General Internal Medicine and Public Health at Vanderbilt University
Dr. Clayton Johnson - Why Are We Not Making More Progress to Decrease PRRS In...John Blue
Why Are We Not Making More Progress to Decrease PRRS Incidence? - Dr. Clayton Johnson, Director of Health at Carthage Veterinary Service, from the 2017 Allen D. Leman Swine Conference, September 16-19, 2017, St. Paul, Minnesota, USA.
More presentations at http://www.swinecast.com/2017-leman-swine-conference-material
MH Prediction Modeling and Validation -cleanMin-hyung Kim
Overfitting
The Bias-Variance Trade-Off
Sequestered (unseen) test dataset
K-fold cross-validation (within the training dataset)
Performance measures
regression: mean squared error (MSE)
classification: sensitivity, specificity, AUROC, etc.
Visualize the overfitting and the bias-variance trade-off versus the model complexity
Presentation by Prof. George Gray, Director of the Centre for Risk Science and Public Health, George Washington University, at the Workshop on Risk Assessment in Regulatory Policy Analysis (RIA), Session 9, Mexico, 9-11 June 2014. Further information is available at http://www.oecd.org/gov/regulatory-policy/
In this webinar, we talk about the risks associated with colorectal cancer – including everything from diet, lifestyle, age, family history and more. We review the risks of recurrence for colorectal cancer survivors. Join us to learn how to reduce your risk of colorectal cancer!
Presented by Harvey Murff, M.D, M.P.H. is an Associate Professor of Medicine in the Division of General Internal Medicine and Public Health at Vanderbilt University
Dr. Clayton Johnson - Why Are We Not Making More Progress to Decrease PRRS In...John Blue
Why Are We Not Making More Progress to Decrease PRRS Incidence? - Dr. Clayton Johnson, Director of Health at Carthage Veterinary Service, from the 2017 Allen D. Leman Swine Conference, September 16-19, 2017, St. Paul, Minnesota, USA.
More presentations at http://www.swinecast.com/2017-leman-swine-conference-material
MH Prediction Modeling and Validation -cleanMin-hyung Kim
Overfitting
The Bias-Variance Trade-Off
Sequestered (unseen) test dataset
K-fold cross-validation (within the training dataset)
Performance measures
regression: mean squared error (MSE)
classification: sensitivity, specificity, AUROC, etc.
Visualize the overfitting and the bias-variance trade-off versus the model complexity
Presentation by Prof. George Gray, Director of the Centre for Risk Science and Public Health, George Washington University, at the Workshop on Risk Assessment in Regulatory Policy Analysis (RIA), Session 9, Mexico, 9-11 June 2014. Further information is available at http://www.oecd.org/gov/regulatory-policy/
Presentation by Prof. George Gray, Director of the Centre for Risk Science and Public Health, George Washington University, at the Workshop on Risk Assessment in Regulatory Policy Analysis (RIA), Session 12, Mexico, 9-11 June 2014. Further information is available at http://www.oecd.org/gov/regulatory-policy/
Workshop on "Building Successful Pipelines for Predictive Analytics in Healthcare" delivered by Danielle Belgrave, PhD, Researcher at Microsoft Research, Cambridge, UK.
Designing Causal Inference Studies Using Real-World DataInsideScientific
In this webinar, experts provide an overview of causal inference, along with step-by-step guidance to designing these studies using real-world healthcare data.
Causal inference is used to answer cause and effect research questions and yield estimates of effect. Causal study design considerations and statistical methods address the effects of confounding variables and other potential biases and allow researchers to answer questions such as, “Does treatment A produce better patient outcomes compared to Treatment B?”
Causal study interpretations have traditionally been restricted to randomized controlled trials; however, causal inference applied to observational healthcare data is growing in importance, driven by the need for generalizable and rapidly delivered real-world evidence to inform regulatory, payer, and patient/provider decision making. The application of causal inference methods leads to stronger and more powerful evidence. When these techniques are applied to observational data, the results generated are both from and for the real world.
Presenters walk through several real-world case studies including the PCORI-funded BESTMED study and a collaborative study with a prominent pharmacy payer.
BioVariance - Pediatric Pharmacogenomics in Drug DiscoveryJosef Scheiber
This slideset gives an overview of pharmacogenomic and pediatric dosing knowledge and various influence factors. Finally it shows an example on how to use this kind of Data within predictive approaches.
Presentation "The Impact of All Data on Healthcare"
Keith Perry
Associate VP & Deputy CIO
UT MD Anderson Cancer Center
With continuing advancement in both technology and medicine, the drive is on to make all data meaningful to drive medical discovery and create actionable outcomes. With tools and capabilities to capture more data than ever before, the challenge becomes linking existing structured and unstructured clinical data with genomic data to increase the industry’s analytical footprint.
Learning Objectives:
∙ Discuss the need to make all data meaningful in order to speed discovery of new knowledge
∙ Provide examples of an analytical direction that supports evolution in medicine
∙ Expose the challenges facing the industry with respect to ~omits
"Getting men to talk when they won't even listen" - insights from qualitative research based on over 25 informants concludes that getting men to talk requires being (more) available to men who want to talk, at the right time and place, and with the "right care"
Presentation by Prof. George Gray, Director of the Centre for Risk Science and Public Health, George Washington University, at the Workshop on Risk Assessment in Regulatory Policy Analysis (RIA), Session 12, Mexico, 9-11 June 2014. Further information is available at http://www.oecd.org/gov/regulatory-policy/
Workshop on "Building Successful Pipelines for Predictive Analytics in Healthcare" delivered by Danielle Belgrave, PhD, Researcher at Microsoft Research, Cambridge, UK.
Designing Causal Inference Studies Using Real-World DataInsideScientific
In this webinar, experts provide an overview of causal inference, along with step-by-step guidance to designing these studies using real-world healthcare data.
Causal inference is used to answer cause and effect research questions and yield estimates of effect. Causal study design considerations and statistical methods address the effects of confounding variables and other potential biases and allow researchers to answer questions such as, “Does treatment A produce better patient outcomes compared to Treatment B?”
Causal study interpretations have traditionally been restricted to randomized controlled trials; however, causal inference applied to observational healthcare data is growing in importance, driven by the need for generalizable and rapidly delivered real-world evidence to inform regulatory, payer, and patient/provider decision making. The application of causal inference methods leads to stronger and more powerful evidence. When these techniques are applied to observational data, the results generated are both from and for the real world.
Presenters walk through several real-world case studies including the PCORI-funded BESTMED study and a collaborative study with a prominent pharmacy payer.
BioVariance - Pediatric Pharmacogenomics in Drug DiscoveryJosef Scheiber
This slideset gives an overview of pharmacogenomic and pediatric dosing knowledge and various influence factors. Finally it shows an example on how to use this kind of Data within predictive approaches.
Presentation "The Impact of All Data on Healthcare"
Keith Perry
Associate VP & Deputy CIO
UT MD Anderson Cancer Center
With continuing advancement in both technology and medicine, the drive is on to make all data meaningful to drive medical discovery and create actionable outcomes. With tools and capabilities to capture more data than ever before, the challenge becomes linking existing structured and unstructured clinical data with genomic data to increase the industry’s analytical footprint.
Learning Objectives:
∙ Discuss the need to make all data meaningful in order to speed discovery of new knowledge
∙ Provide examples of an analytical direction that supports evolution in medicine
∙ Expose the challenges facing the industry with respect to ~omits
Similar to How meta-analysis advances knowledge (20)
"Getting men to talk when they won't even listen" - insights from qualitative research based on over 25 informants concludes that getting men to talk requires being (more) available to men who want to talk, at the right time and place, and with the "right care"
Obesity research agenda: a reflection & a refreshStephen S Holden
Despite volumes of research on obesity, we seem little closer to a solution. A review of existing research suggests that research utility might be improved by focusing on (1) the ultimate outcomes (weight and health), (2) employing natural, quasi-experimental designs, (3) effect sizes and replication
Deakin University,
Overcoming obesity: small, slow and steady stepsStephen S Holden
We want to know which interventions might reduce obesity. However, existing research focuses on only some part of the chain of effects between intervention & better health, and many of those effects tend to be small. On the bright side, the association between BMI and health is also smaller than many might fear. Ultimately, the solution is likely to be small, slow, steady steps.
Marketing has a strong influence on people's consumer behavior. Is that true? And whether strong or weak, is it right (ethical)?
Influence is an essential component of human nature: humans are social animals.
In this presentation, various forms of influence are explored, how marketing and social marketing uses them is examined, and the question of whether the use of such influences is powerful and ethical is addressed.
Practical wisdom - developed through experience (not age), and self-reflection. So be foolish - but not fatally so. And share your wisdom - not just for others, but for selfish (self-reflection) reasons.
Sleeping with the enemy: learning from and working with food marketers
What are the costs of demonizing food marketers? We engage in misdirection, minimise the effectiveness of our own efforts, and miss an opportunity to cooperate (use their knowledge & resources)
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
12. Effect of attitudes on behaviour?
• r=.5
• k=128, N=4,598
• Glasman & Albarracin (2006)
• stronger when
• attitudes are more accessible
• direct experience and/or reported frequently
• attitudes are more stable over time
• confident, based on bhvr relevant information, one-sided info
13. Effect of anti-depressants?
• Cohen’s d < .2
• for HDRS < 23 (mild or moderate), placebo vs ADM (anti-depressant
medication)
• HDRS scale runs 0 to 52
• pax treated with ADMs average 2 points higher on HDRS than
placebo!
• statistical significance vs clinical significance
• https://www.scientificamerican.com/article/antidepressants-do-
they-work-or-dont-they/
19. Meta-meta-analysis of advance in
knowledge
• 176 meta-analyses
• >7,500 primary studies (43 studies per meta-analysis)
published between 1918 and 2012
• >54,000 effect sizes (307 effect sizes per meta-analysis)
• sample of 8,337,096 subjects (based on primary studies of 131
meta-analyses)
• Eisend 2015 JM
26. • research designs
• preferential treatment of experiments (RCT)
• selection bias
• file drawer
• publication bias
• heterogeneity / moderator analyses
• non-random sample of domain
• fixed vs random effects
• collinearity
Contentious issues
Editor's Notes
10 cohort studies
5.8% risk background risk vs 6.9% risk if eating 50g of bacon a day !
+17% risk / 100g of red meat / day
cf men who smoke have 20x risk of lung cancer
SIMULATED 95% CIs of +/- 0.5%
what does it tell us if the CIs are smaller? larger?
statistically significant vs clinically (or practical) significance
statistical significance is a function of sample size, p-value, and effect-size (bigger effects are more likely to be “statistically significant”)
Tversky & Kahneman (1971) “Belief in the law of small numbers” Psych Bull
expt n=20 generated a significant result, z=2.23, p<.05
Well (1991) “The perils of N=1” JCR
Bass (1995) “Empirical generalizations in marketing science: a personal view” Mktg Science
McShane & Gal (2015) “Blinding Us to the Obvious? The Effect of Statistical Training on the Evaluation of Evidence” Mgt Science
NHST encouraging dichotomous view
Original study effect size versus replication effect size (correlation coefficients). Diagonal line represents replication effect size equal to original effect size. Dotted line represents replication effect size of 0. Points below the dotted line were effects in the opposite direction of the original. Density plots are separated by significant (blue) and nonsignificant (red) effects.
the stage is set and the play requires hypotheses, test, conclusions
so we create hypotheses to explain the data - after they are collected !
Hamilton Depression Rating Scale (HDRS)
k=6, 718 patients
breakage rate of condoms is 2%
you have more chance of a condom failing than someone clicking on your banner ad!
highlighting that mktrs don’t control powerful effects, but are playing in the ultimate numbers game
the stage is set and the play requires hypotheses, test, conclusions
so we create hypotheses to explain the data - after they are collected !
Go fishing, let your suspicions run wild, choose those which have evidence, accept that there may be some false alarms
