The document discusses Cochrane Collaboration, which involves over 28,000 volunteers in over 100 countries who systematically review randomized controlled trials and other studies on health care interventions. The goal is to help people make informed health care decisions. Key principles include collaboration, avoiding bias, and ensuring quality and accessibility. Forest plots and meta-analyses are discussed as methods to combine results from multiple studies. Meta-analysis can identify overall effects, variables that explain differences between studies, and assess for publication bias. Single subject designs are also reviewed as a type of study that can be included in meta-analyses, though challenges exist in interpreting these designs.
"Hierarchies of Evidence" is an important but problematic concept for medical professionals to understand as it underpins their capacity to be effective practitioners and researchers.
How to practice medicine ? to provide ordinary care or to provide the best available care? Cochrane systematic reviews help u in this issue. This talk illustrates how Cochrane reviews helps with special focus on reproductive medicine
"Hierarchies of Evidence" is an important but problematic concept for medical professionals to understand as it underpins their capacity to be effective practitioners and researchers.
How to practice medicine ? to provide ordinary care or to provide the best available care? Cochrane systematic reviews help u in this issue. This talk illustrates how Cochrane reviews helps with special focus on reproductive medicine
This workshop is meant to be an introduction to the systematic review process. Further information about systematic reviews was available through a research guide. http://libguides.ucalgary.ca/content.php?pid=593664
This workshop is meant to be an introduction to the systematic review process. Further information about systematic reviews was available through a research guide. http://libguides.ucalgary.ca/content.php?pid=593664
Engineering Assisted Surgery - Robots and NanobotsNinian Peckitt
Engineering Assisted Surgery is the application of Industrial Manufacturing Technology to the delivery of Healthcare. Professor Peckitt discusses the application of Robots and Nanobots in this presentation
The Origins and Failure of Health Reforms are presented in cartoon format with an analysis of System Failure in the NHS and dysfunctional management. The solutions are obvious.
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All the concepts related to research design are covered in this PPT Presentation.Research Design being an integral and crucial part of Research majorly deals with Parametric and non-parametric test, Type 1 and type 2 error, level of significance etc.It helps in ascertaining which research technique is used in which situation.
Systematic review and meta analysis is considered as the highest body of evidence in research evidence hierarchy. Often misunderstood or skipped over, this powerful tool can broaden our understanding on a specific topic and form basis of practicing evidence based medicine for us.
I presented systematic review and meta analysis as part of my PG seminar and got a good feedback. Now I wanted to share the presentation for a broader audience.
Any kind of constructive feedback is welcome.
Dr. Anik Chakraborty
JR3, Dept. Of Community Medicine
Pt. B. D. Sharma PGIMS, Rohtak
Topics:
Quantitative research
Characteristics of Quantitative Research
Strengths of Quantitative Research
Weaknesses of Quantitative Research
Importance of Quantitative Research Across Fields
TYPES OF QUANTITATIVE RESEARCH DESIGN
The Pathophysiology of Obesity and its medical management in detail with future strategies for Prohibitin binding and the destruction of the blood supply to Adipose Tissue
New Directions in Targeted Therapeutic Approaches for Older Adults With Mantl...i3 Health
i3 Health is pleased to make the speaker slides from this activity available for use as a non-accredited self-study or teaching resource.
This slide deck presented by Dr. Kami Maddocks, Professor-Clinical in the Division of Hematology and
Associate Division Director for Ambulatory Operations
The Ohio State University Comprehensive Cancer Center, will provide insight into new directions in targeted therapeutic approaches for older adults with mantle cell lymphoma.
STATEMENT OF NEED
Mantle cell lymphoma (MCL) is a rare, aggressive B-cell non-Hodgkin lymphoma (NHL) accounting for 5% to 7% of all lymphomas. Its prognosis ranges from indolent disease that does not require treatment for years to very aggressive disease, which is associated with poor survival (Silkenstedt et al, 2021). Typically, MCL is diagnosed at advanced stage and in older patients who cannot tolerate intensive therapy (NCCN, 2022). Although recent advances have slightly increased remission rates, recurrence and relapse remain very common, leading to a median overall survival between 3 and 6 years (LLS, 2021). Though there are several effective options, progress is still needed towards establishing an accepted frontline approach for MCL (Castellino et al, 2022). Treatment selection and management of MCL are complicated by the heterogeneity of prognosis, advanced age and comorbidities of patients, and lack of an established standard approach for treatment, making it vital that clinicians be familiar with the latest research and advances in this area. In this activity chaired by Michael Wang, MD, Professor in the Department of Lymphoma & Myeloma at MD Anderson Cancer Center, expert faculty will discuss prognostic factors informing treatment, the promising results of recent trials in new therapeutic approaches, and the implications of treatment resistance in therapeutic selection for MCL.
Target Audience
Hematology/oncology fellows, attending faculty, and other health care professionals involved in the treatment of patients with mantle cell lymphoma (MCL).
Learning Objectives
1.) Identify clinical and biological prognostic factors that can guide treatment decision making for older adults with MCL
2.) Evaluate emerging data on targeted therapeutic approaches for treatment-naive and relapsed/refractory MCL and their applicability to older adults
3.) Assess mechanisms of resistance to targeted therapies for MCL and their implications for treatment selection
Flu Vaccine Alert in Bangalore Karnatakaaddon Scans
As flu season approaches, health officials in Bangalore, Karnataka, are urging residents to get their flu vaccinations. The seasonal flu, while common, can lead to severe health complications, particularly for vulnerable populations such as young children, the elderly, and those with underlying health conditions.
Dr. Vidisha Kumari, a leading epidemiologist in Bangalore, emphasizes the importance of getting vaccinated. "The flu vaccine is our best defense against the influenza virus. It not only protects individuals but also helps prevent the spread of the virus in our communities," he says.
This year, the flu season is expected to coincide with a potential increase in other respiratory illnesses. The Karnataka Health Department has launched an awareness campaign highlighting the significance of flu vaccinations. They have set up multiple vaccination centers across Bangalore, making it convenient for residents to receive their shots.
To encourage widespread vaccination, the government is also collaborating with local schools, workplaces, and community centers to facilitate vaccination drives. Special attention is being given to ensuring that the vaccine is accessible to all, including marginalized communities who may have limited access to healthcare.
Residents are reminded that the flu vaccine is safe and effective. Common side effects are mild and may include soreness at the injection site, mild fever, or muscle aches. These side effects are generally short-lived and far less severe than the flu itself.
Healthcare providers are also stressing the importance of continuing COVID-19 precautions. Wearing masks, practicing good hand hygiene, and maintaining social distancing are still crucial, especially in crowded places.
Protect yourself and your loved ones by getting vaccinated. Together, we can help keep Bangalore healthy and safe this flu season. For more information on vaccination centers and schedules, residents can visit the Karnataka Health Department’s official website or follow their social media pages.
Stay informed, stay safe, and get your flu shot today!
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Ve...kevinkariuki227
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
ARTIFICIAL INTELLIGENCE IN HEALTHCARE.pdfAnujkumaranit
Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems. It encompasses tasks such as learning, reasoning, problem-solving, perception, and language understanding. AI technologies are revolutionizing various fields, from healthcare to finance, by enabling machines to perform tasks that typically require human intelligence.
NVBDCP.pptx Nation vector borne disease control programSapna Thakur
NVBDCP was launched in 2003-2004 . Vector-Borne Disease: Disease that results from an infection transmitted to humans and other animals by blood-feeding arthropods, such as mosquitoes, ticks, and fleas. Examples of vector-borne diseases include Dengue fever, West Nile Virus, Lyme disease, and malaria.
Acute scrotum is a general term referring to an emergency condition affecting the contents or the wall of the scrotum.
There are a number of conditions that present acutely, predominantly with pain and/or swelling
A careful and detailed history and examination, and in some cases, investigations allow differentiation between these diagnoses. A prompt diagnosis is essential as the patient may require urgent surgical intervention
Testicular torsion refers to twisting of the spermatic cord, causing ischaemia of the testicle.
Testicular torsion results from inadequate fixation of the testis to the tunica vaginalis producing ischemia from reduced arterial inflow and venous outflow obstruction.
The prevalence of testicular torsion in adult patients hospitalized with acute scrotal pain is approximately 25 to 50 percent
- Video recording of this lecture in English language: https://youtu.be/lK81BzxMqdo
- Video recording of this lecture in Arabic language: https://youtu.be/Ve4P0COk9OI
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
- Link to NephroTube website: www.NephroTube.com
- Link to NephroTube social media accounts: https://nephrotube.blogspot.com/p/join-nephrotube-on-social-media.html
Ozempic: Preoperative Management of Patients on GLP-1 Receptor Agonists Saeid Safari
Preoperative Management of Patients on GLP-1 Receptor Agonists like Ozempic and Semiglutide
ASA GUIDELINE
NYSORA Guideline
2 Case Reports of Gastric Ultrasound
These simplified slides by Dr. Sidra Arshad present an overview of the non-respiratory functions of the respiratory tract.
Learning objectives:
1. Enlist the non-respiratory functions of the respiratory tract
2. Briefly explain how these functions are carried out
3. Discuss the significance of dead space
4. Differentiate between minute ventilation and alveolar ventilation
5. Describe the cough and sneeze reflexes
Study Resources:
1. Chapter 39, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 34, Ganong’s Review of Medical Physiology, 26th edition
3. Chapter 17, Human Physiology by Lauralee Sherwood, 9th edition
4. Non-respiratory functions of the lungs https://academic.oup.com/bjaed/article/13/3/98/278874
micro teaching on communication m.sc nursing.pdfAnurag Sharma
Microteaching is a unique model of practice teaching. It is a viable instrument for the. desired change in the teaching behavior or the behavior potential which, in specified types of real. classroom situations, tends to facilitate the achievement of specified types of objectives.
Report Back from SGO 2024: What’s the Latest in Cervical Cancer?bkling
Are you curious about what’s new in cervical cancer research or unsure what the findings mean? Join Dr. Emily Ko, a gynecologic oncologist at Penn Medicine, to learn about the latest updates from the Society of Gynecologic Oncology (SGO) 2024 Annual Meeting on Women’s Cancer. Dr. Ko will discuss what the research presented at the conference means for you and answer your questions about the new developments.
Report Back from SGO 2024: What’s the Latest in Cervical Cancer?
Cochrane Collaboration
1. Ninian Peckitt
FRCS FFD RCS FDS RCS FACCS
Oral and Maxillofacial Surgeon / Facial Plastic Surgeon
Adjunct Associate Professor of Engineering Assisted Surgery
Massey University, New Zealand
2. Cochrane Collaboration
• group >28,000 volunteers
• >100 countries
• Review health care interventions tested in biomedical
randomized controlled trials.[3]
• + more non-randomized, observational studies.
• systematic reviews published as "Cochrane Reviews"
• in the Cochrane Library.
3. Goals and Prinicples
The goal of the collaboration is to help people make well informed
decisions about health care by preparing, maintaining and ensuring the
accessibility of systematic reviews of the effects of health care
interventions. The principles of the Cochrane Collaboration are:
• collaboration
• building on the enthusiasm of individuals
• avoiding duplication
• minimizing bias
• keeping up to date
• striving for relevance
• promoting access
• ensuring quality
• continuity
• enabling wide participation
4. Forest Plot - (Blobbogram)
• Common - 2 columns
• The left-hand column lists:
– the names of the studies
(e.g. RCTs / epidemiol studies)
– chronological order
– from the top downwards.
5. Forest Plot - (Blobbogram)
• The right-hand column
• a plot of the measure of effect
– e.g. an odds ratio for each of these studies
– (often represented by a square)
– incorporating confidence intervals
– represented by horizontal lines.
• The graph may be plotted on a natural logarithmic
scale when using odds ratios or other ratio-based
effect measures
• confidence intervals are symmetrical about the means
from each study
• ensures undue emphasis is not given to odds ratios
greater than 1 when compared to those less than 1
7. Meta Analysis
• combines the results of several studies
• that address a set of related research hypotheses
• In its simplest form, this is normally by identification of a common measure of
effect size,
• for which a weighted average might be the output of a meta-analyses.
• Here the weighting might be related to sample sizes within the individual studies.
• More generally there are other differences between the studies that need to be
allowed for, but the general aim of a meta-analysis is to more powerfully estimate
the true "effect size" as opposed to a smaller "effect size" derived in a single study
under a given single set of assumptions and conditions.
8. Advantages of Meta Analysis
• Shows if the results are more varied than expected from sample diversity
• Derivation and statistical testing of overall factors / effect size parameters in related studies
• Generalization to the population of studies
• Ability to control for between-study variation
• Including moderators to explain variation
• Higher statistical power to detect an effect than in ‘n=1 sized study sample’
• Deal with information overload: the high number of articles published each year.
• combines several studies less influenced by local findings than single studies will be.
• May show if a publication bias exists.
9. Steps in Meta Analysis
1. Formulation of the problem
2. Search of literature
3. Selection of studies (‘incorporation criteria’)
– Based on quality criteria, e.g. the requirement of randomization and blinding in a clinical trial
– Selection of specific studies on a well-specified subject, e.g. the treatment of breast cancer.
– Decide whether unpublished studies are included to avoid publication bias
4. Decide which dependent variables or summary measures are allowed.
– Differences (discrete data)
– Means (continuous data)
10. Steps in Meta Analysis
• Hedges' g
– is a popular summary measure for continuous data
– that is standardized in order to eliminate scale differences, but
incorporates an index of variation between groups:
μt is the treatment mean, μc is the control mean, σ2 the pooled
variance
• For reporting guidelines, see QUOROM statement [6][7]
11. Metaregression Models
5. Model selection
• simple regression
• fixed effect meta-regression
• random effects meta-regression
12. Simple Regression
Where yj is the effect size in study j and
β0 (intercept) the estimated overall effect size
The variables specify
• different characteristics of the study,
• specifies the between study variation
• Note that this model does not allow specification of within study variation.
13. Fixed Effect Meta-regression
• assumes that the true effect size θ
• is normally distributed - within study variance of the effect size
• allows for within study variability
• but no between study variability
• because all studies have the identical expected fixed effect size θ,
• i.e.***Note that for the "fixed-effect" no plural is used (in contrast
to "random-effects") as only ONE true effect across all datasets is
assumed.***
14. Fixed Effect Meta-regression
• variance of the effect size in study j
• Fixed effect meta-regression ignores between study variation
• parameter estimates biased if between study variation can not be ignored
• Generalizations to the population are not possible
15. Random effects meta-regression
• assumption that θ in is a random variable
• following a (hyper-)distribution
• Here is the variance of the effect size in study j.
• Between study variance is estimated using common
estimation procedures for random effects models
(restricted maximum likelihood (REML) estimators).
16. Applications in Modern Science
• Modern statistical meta-analysis
• does more than just combine the effect sizes of a set of studies.
• test if studies show more variation than expected (e.g. sampling different research participants)
• study characteristics such as :
– Measurement
– instrument used
– population sampled
– or aspects of the studies' design are coded.
• These characteristics are then used as predictor variables :
– to analyze the excess variation in the effect sizes
– Studiesweaknesses can be corrected statistically (e.g. bias in size or codings)
17. Applications in Modern Science
• Meta-analysis can be done with
– single-subject design
– group research designs
• much research on low incidents populations
– single-subject research designs
– Considerable dispute exists for the most appropriate
meta-analytic technique for single subject research.[8]
18. Single Subject Design
Continuous assessment:
• Individual behaviour observed repeatedly over the intervention.
• Insures Rx effects observed long enough to convince that Rx produces a lasting effect.
Baseline assessment:
• Before Rx is implemented, look for behavioral trends.
• If Rx reverses a baseline trend (e.g., getting worse [baseline ]vx [Rx] reversed this trend)
• powerful evidence suggesting (though not proving) a treatment effect.
Variability in data:
• Ability to observe how Rx changes behavior from day-to-day
• Large-group statistical designs do not typically provide this information
• because repeated assessments are not usually not taken
• and the behavior of individuals in the groups are not scrutinized - group means are reported
19. Phases within Single Subject Design
Phases within single-subject design
• Baseline:
– data on the dependent variable without any intervention in place
• Intervention:
– introduce independent variable (the intervention)
– collect data on dependent variable
• Reversal:
– removes the independent variable (reversal)
– collects data on the dependent variable
• Data must be Stable (steady trend / low variability) before move to the next phase
• Single-subject designs produce or approximate three levels of knowledge:
– (1) descriptive
– (2) correlational
– (3) causal[4]
20. Flexibility of Single Subject Design
• highly flexible
• highlight individual differences
• in response to intervention effects[5]
• In general
– reduce interpretation bias
– for counselors when doing therapy[6]
21. Data Interpretation
Independent variable on the dependent variable,
• graph the data collected / visually inspect the differences between phases
• If there is a clear distinction between baseline and intervention, and then the data returns to the
same trends/level during reversal, a functional relation between the variables is inferred.[7]
• Sometimes, visual inspection of the data demonstrates results that statistical tests fail to find[8][9]
• Begin with Graphic analysis
– During the baseline, data are repeatedly collected and then graphed on the behavior of interest.
– Visual representation of the subject’s behavior before application of the intervention
– Several (e.g. 3-5 ) baseline data points for description of effects on the target behavior during intervention
• Subject as their own control
• Baseline behavior would match its behavior in the intervention phase unless the intervention does
something to change it.
• This logic then holds to rule out confound
23. Meta-analysis of single subject research
• Meta-analysis, like all research, has the ability to change a profession.
• functional analysis < effect sizes than contingency management
• Debate meta-analysis of single-subject designs.
• The two choices being debated are
– the percentage nonoverlapping data(PND) vs.
– data points exceeding the median(PEM) method.[13][14][15]
– Noorgate and colleagues have argued that meta-analyses that analyze all
linear trends in data don’t work since they don’t distinguish between effects
on level and slope.[14][16]
24. Limitations Single Subject Designs
• Preplanning of Designs often made as the data are collected.[17]
• No widely agreed upon rules for: altering phases / conflicting ideas / conduct
• Major criticisms of single-subject designs are:
– Carry-over effects: results from the previous phase carry-over into the next phase,
– Order effects: the ordering (sequence) of the intervention / treatment affects what results
– Irreversibility:
• once a change in the independent variable occurs, the dependent variable is affected
• This cannot be undone by simply removing the independent variable.
– Ethical problems:
• Withdrawal of treatment - ethical and feasibility problems
25. Statistical Significance
• unlikely to have occurred by chance
• Stat hypothesis tests that answer the question
– Assuming that the null hypothesis is true
– what is the probability of a value as extreme as
the value observed?
26.
27.
28. • Systematic Review: takes 23 months from protocol to publication
• Timespan: Hundreds / Thousands of hours.
• Problems: Production and Updating of review
• <40% data is up-to-date
Out of Date Systematic Reviews
• 2009 - 2,383
• 2012 - 3,149
• Terrible figures made worse by increase in
spending on Cochrane
http://blog.tripdatabase.com/2013/04/a-critique-of-cochrane-collaboration.html#sthash.vioz56f0.dpuf
29. Funding Cochrane
• £100 million over last 7 years
• >£150 million – $250 billion US Dollars over 20 years of its
existence
http://blog.tripdatabase.com/2013/04/a-critique-of-cochrane-collaboration.html#sthash.vioz56f0.dpuf
30. • Current system is unsustainable
• Current System is not fit for purpose.
• The methodology
• has reduced some bias,
• but resulted in a huge financial cost increase
• a huge cost in opportunity
• The Tamiflu fiasco highlights a flawed methodology
http://blog.tripdatabase.com/2013/04/a-critique-of-cochrane-collaboration.html#sthash.vioz56f0.dpuf
31. Tamiflu - Tom Jefferson Review Leader stated:
“…I personally believe and my colleagues believe with me that Cochrane
Reviews based on publications should really be a thing of the past…”
Cochrane systematic review relied on published journal articles
• Large amounts of data were missed, most of which was made available
for the regulatory agencies e.g. EMA, FDA.
• The updated, 2012, review was a huge undertaking
• Jack Cuzick - Evidence Live 2013 made a general call for reviews to be based
on individual patient data (IPD)
http://blog.tripdatabase.com/2013/04/a-critique-of-cochrane-collaboration.html#sthash.vioz56f0.dpuf
32. Peer opinion Cochrane Methodology
• Incapable of making an accurate assessment of an interventions ‘worth’
• The seriousness of this challenge should not be underestimated,
• This challenge attacks at the very heart of the Cochrane Collaboration.
http://blog.tripdatabase.com/2013/04/a-critique-of-cochrane-collaboration.html#sthash.vioz56f0.dpuf
33. Doing things more quickly can give you the same or similar results to the
Cochrane methodology.
1) Can we rely on the best trial?
A comparison of individual trials and systematic reviews
• Random sample of Cochrane systematic reviews
• Was largest RCT in agreement with the subsequent meta-analysis?
• Yes - 81% of the meta-analyses examined and if the largest RCT was
positive and significant it was around 95%.
• In other words, using the largest RCT can give a broad hint as to the likely
result of a subsequent meta-analysis
Is Systematic Review Really Required?
http://blog.tripdatabase.com/2013/04/a-critique-of-cochrane-collaboration.html#sthash.vioz56f0.dpuf
34. 2) McMaster Premium LiteratUre Service (PLUS) performed well for identifying
new studies for updated Cochrane reviews.
• Authors compared the performance of McMaster Premium LiteratUre Service
(PLUS) and Clinical Queries (CQs) to that of the Cochrane Controlled Trials
Register, MEDLINE, and EMBASE for locating studies added during an update of
reviews.
• They concluded that PLUS included less than a quarter of the new studies in
Cochrane updates, but most reviews appeared unaffected by the omission of
these studies.
• In other words, you do not necessarily need to get all articles to arrive at an
accurate effect size (compared to the Cochrane systematic review).
Is Systematic Review Really Required?
http://blog.tripdatabase.com/2013/04/a-critique-of-cochrane-collaboration.html#sthash.vioz56f0.dpuf
35. Is Systematic Review Really Required?
3) A pragmatic strategy for the review of clinical evidence.
• Authors compared a research strategy based on the review of a selected number
of core journals, with that derived by an SR in estimating the efficacy of
treatments.
• Conclusion: “We verified in a sample of SRs that the conclusion of a research
strategy based on a pre-defined set of general and specialist medical journals is
able to replicate almost all the clinical recommendations of a formal SR.
http://blog.tripdatabase.com/2013/04/a-critique-of-cochrane-collaboration.html#sthash.vioz56f0.dpuf
36. The Future
• Reduce the cost per review.
• Recognise laws of diminishing returns
• Major challenge to do a modification of a systematic review in a month (or less)
rapid systematic reviews
A more detailed/costly sytematic review including regulatory data and/or IPD.
• Reduce cost of Review by 90%
Is Systematic Review Really Required?
http://blog.tripdatabase.com/2013/04/a-critique-of-cochrane-collaboration.html#sthash.vioz56f0.dpuf
37. But might we get the wrong answer with streamlining?
Yes !
But detailed Cochrane systematic reviews also have given us the wrong answer !
Is Systematic Review Really Required?
38. References
1. The Cochrane Oversight Committee. Measuring the performance of The Cochrane Library. 2012
2. Allen IE, Olkin I. Estimating time to conduct a meta-analysis from number of citations retrieved. JAMA. 1999 Aug
18;282(7):634-5.
3. Cochrane Collaboration Annual Report & Financial Statements 2010/11
4. Payne D. Tamiflu: the battle for secret drug data. BMJ 2012;345:e7303
5. HAI Europe - Dr. Tom Jefferson on lack of access to Tamiflu clinical trials
6. Jefferson TO, Demicheli V, Di Pietrantonj C, Jones M, Rivetti D. Neuraminidase inhibitors for preventing and treating influenza
in healthy adults. Cochrane Database Syst Rev. 2006 Jul 19;(3):CD001265
7. Jefferson T, Jones MA, Doshi P, Del Mar CB, Heneghan CJ, Hama R, Thompson MJ. Neuraminidase inhibitors for preventing
and treating influenza in healthy adults and children. Cochrane Database Syst Rev. 2012 Jan 18;1:CD008965. doi:
10.1002/14651858.CD008965.pub3
8. Glasziou PP, Shepperd S, Brassey J. Can we rely on the best trial? A comparison of individual trials and systematic reviews.
BMC Med Res Methodol. 2010 Mar 18;10:23. doi: 10.1186/1471-2288-10-23
9. Hemens BJ, Haynes RB. McMaster Premium LiteratUre Service (PLUS) performed well for identifying new studies for updated
Cochrane reviews. J Clin Epidemiol. 2012 Jan;65(1):62-72.e1
10.Sagliocca L, De Masi S, Ferrigno L, Mele A, Traversa G. A pragmatic strategy for the review of clinical evidence. J Eval Clin
Pract. 2013 Jan 15. doi: 10.1111/jep.1202
11.Greenhalgh T. Why do we always end up here? Evidence-based medicine's conceptual cul-de-sacs and some off-road
alternative routes. J Prim Health Care. 2012 Jun 1;4(2):92-7.J Prim Health Care. 2012 Jun 1;4(2):92-7.
Editor's Notes
The odds ratio [1][2][3] is a measure of effect size, describing the strength of association or non-independence between two binary data values. It is used as a descriptive statistic, and plays an important role in logistic regression. Unlike other measures of association for paired binary data such as the relative risk, the odds ratio treats the two variables being compared symmetrically, and can be estimated using some types of non-random samples.
An example forest plot of five odds ratios (squares, proportional to weights used in meta-analysis), with the summary measure (centre line of diamond) and associated confidence intervals (lateral tips of diamond), and solid vertical line of no effect. Names of (fictional) studies are shown on the left, odds ratios and confidence intervals on the right.
First meta-analysis performed by Karl Pearson 1904
to overcome the problem of reduced statistical power in studies with small sample sizes;
Analyzing the results from a group of studies can allow more accurate data analysis.[2][3]
Single Subject Design
subject serves as his/her own control
rather than using another individual/group.
These designs are sensitive to individual organism differences
vs group designs which are sensitive to averages of groups.
Often there will be large numbers of subjects in a research study using single-subject design, however—because the subject serves as their own control, this is still a single-subject design.[1]
These designs are used primarily to evaluate the effect of a variety of interventions in applied research.[2]