This document discusses statistics used in meta-analyses. It explains that meta-analyses statistically combine results from multiple studies on a topic. Effect measures are calculated for individual studies and then combined to find an overall effect. For dichotomous outcomes, common effect measures are risk ratio, odds ratio, and absolute risk reduction. Random effects models account for heterogeneity between studies, while fixed effect models assume one true effect. Forest plots visually display individual study results and the overall effect, allowing readers to assess consistency and precision.
This deals with the current paradigm of treatment of osteosarcoma. It is an honest effort to clear the prevailing confusion in the treatment of osteosarcoma. I would be happy to get anyone
Study of the distribution and determinants of
health-related states or events in specified populations and the application of this study to control health problems.
John M. Last, Dictionary of Epidemiology
This deals with the current paradigm of treatment of osteosarcoma. It is an honest effort to clear the prevailing confusion in the treatment of osteosarcoma. I would be happy to get anyone
Study of the distribution and determinants of
health-related states or events in specified populations and the application of this study to control health problems.
John M. Last, Dictionary of Epidemiology
In this presentation , we explain what is study design ,
why we need study design,
types of study design,
case control study design,
what is sample and how to estimate sample size
Mathematics in Epidemiology and Biostatistics (Medical Booklet Series by Dr. ...Dr. Aryan (Anish Dhakal)
Basic mathematics needed for epidemiology and bio statistics. Slides include formulas and conceptual understanding of sensitivity, specificity, predictive values, likelihood ratios, odds, probability and many more.
This powerpoint presentation gives a brief explanation about the biostatic data .this is quite helpful to individuals to understand the basic research methodology terminologys
A sample design is a definite plan for obtaining a sample from a given population. Researcher must select/prepare a sample design which should be reliable and appropriate for his research study.
Similar to Imran rizvi statistics in meta analysis (20)
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.
- 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
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
New Drug Discovery and Development .....NEHA GUPTA
The "New Drug Discovery and Development" process involves the identification, design, testing, and manufacturing of novel pharmaceutical compounds with the aim of introducing new and improved treatments for various medical conditions. This comprehensive endeavor encompasses various stages, including target identification, preclinical studies, clinical trials, regulatory approval, and post-market surveillance. It involves multidisciplinary collaboration among scientists, researchers, clinicians, regulatory experts, and pharmaceutical companies to bring innovative therapies to market and address unmet medical needs.
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!
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.
Title: Sense of Taste
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the structure and function of taste buds.
Describe the relationship between the taste threshold and taste index of common substances.
Explain the chemical basis and signal transduction of taste perception for each type of primary taste sensation.
Recognize different abnormalities of taste perception and their causes.
Key Topics:
Significance of Taste Sensation:
Differentiation between pleasant and harmful food
Influence on behavior
Selection of food based on metabolic needs
Receptors of Taste:
Taste buds on the tongue
Influence of sense of smell, texture of food, and pain stimulation (e.g., by pepper)
Primary and Secondary Taste Sensations:
Primary taste sensations: Sweet, Sour, Salty, Bitter, Umami
Chemical basis and signal transduction mechanisms for each taste
Taste Threshold and Index:
Taste threshold values for Sweet (sucrose), Salty (NaCl), Sour (HCl), and Bitter (Quinine)
Taste index relationship: Inversely proportional to taste threshold
Taste Blindness:
Inability to taste certain substances, particularly thiourea compounds
Example: Phenylthiocarbamide
Structure and Function of Taste Buds:
Composition: Epithelial cells, Sustentacular/Supporting cells, Taste cells, Basal cells
Features: Taste pores, Taste hairs/microvilli, and Taste nerve fibers
Location of Taste Buds:
Found in papillae of the tongue (Fungiform, Circumvallate, Foliate)
Also present on the palate, tonsillar pillars, epiglottis, and proximal esophagus
Mechanism of Taste Stimulation:
Interaction of taste substances with receptors on microvilli
Signal transduction pathways for Umami, Sweet, Bitter, Sour, and Salty tastes
Taste Sensitivity and Adaptation:
Decrease in sensitivity with age
Rapid adaptation of taste sensation
Role of Saliva in Taste:
Dissolution of tastants to reach receptors
Washing away the stimulus
Taste Preferences and Aversions:
Mechanisms behind taste preference and aversion
Influence of receptors and neural pathways
Impact of Sensory Nerve Damage:
Degeneration of taste buds if the sensory nerve fiber is cut
Abnormalities of Taste Detection:
Conditions: Ageusia, Hypogeusia, Dysgeusia (parageusia)
Causes: Nerve damage, neurological disorders, infections, poor oral hygiene, adverse drug effects, deficiencies, aging, tobacco use, altered neurotransmitter levels
Neurotransmitters and Taste Threshold:
Effects of serotonin (5-HT) and norepinephrine (NE) on taste sensitivity
Supertasters:
25% of the population with heightened sensitivity to taste, especially bitterness
Increased number of fungiform papillae
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 lecture slides, by Dr Sidra Arshad, offer a quick overview of physiological basis of a normal electrocardiogram.
Learning objectives:
1. Define an electrocardiogram (ECG) and electrocardiography
2. Describe how dipoles generated by the heart produce the waveforms of the ECG
3. Describe the components of a normal electrocardiogram of a typical bipolar leads (limb II)
4. Differentiate between intervals and segments
5. Enlist some common indications for obtaining an ECG
Study Resources:
1. Chapter 11, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 9, Human Physiology - From Cells to Systems, Lauralee Sherwood, 9th edition
3. Chapter 29, Ganong’s Review of Medical Physiology, 26th edition
4. Electrocardiogram, StatPearls - https://www.ncbi.nlm.nih.gov/books/NBK549803/
5. ECG in Medical Practice by ABM Abdullah, 4th edition
6. ECG Basics, http://www.nataliescasebook.com/tag/e-c-g-basics
Pulmonary Thromboembolism - etilogy, types, medical- Surgical and nursing man...VarunMahajani
Disruption of blood supply to lung alveoli due to blockage of one or more pulmonary blood vessels is called as Pulmonary thromboembolism. In this presentation we will discuss its causes, types and its management in depth.
6. Meta-analysis answers the following
questions
• What is the direction of effect?
• What is the size of effect?
• Is the effect consistent across studies?
7. Statistics in meta-analysis
Meta-analysis is a 2 stage process.
1. Calculate an effect estimate (summary statistics) for
each study (RR, OR, HR etc for each study)
2. Calculate the overall treatment effect (usually as a
weighted average of these summary statistics).
8. Calculating effect measure of individual
studies
• Calculation of effect measure depends upon the type of
outcome data.
• Outcome data can be of following 4 types.
Dichotomous (Binary data)
Continuous data
Ordinal data
Time to event (survival rate)
9. Types of outcome Definition and example
Dichotomous (Binary) When the outcome for every participant
falls in one of two possibilities.
Example: Dead/Living
Clinical Improvement/No clinical
improvement.
Continuous Data that can take any value in a specified
range.
Example: BP reduction following T/t
Blood sugar reduction following T/t
Ordinal Ordinal outcome data arise when each
participant is classified in a category and
when the categories have a natural order.
Mild/moderate/severe
Pain scales/ disability scales
Time to event Time elapsed before an event is
experienced.
Time to death after cancer/MI
10. Effect measures used according to the type of outcome
Type of Outcome Effect measure (Summary)
commonly used
Dichotomous (Binary) Risk Ratio= Relative Risk= RR
Odds ratio
Risk difference (ARR)
NNT
Continuous The mean difference and the
standardized mean difference.
Ordinal Proportional Odds ratio
Longer ordinal scales: continuous data
Shorter ordinal scales: can be clubbed
into 2 categories to be analysed as
dichotomous data
Time to event Hazard ratio
11. Risk Ratio/ Relative Risk
• Most commonly used measure when the outcome is
dichotomous.
• Ratio of risk of event in the experimental group to Risk of
event in the control group.
• RR= Risk in exposed/ Risk in non exposed
14. Example
• 545 TBM patients were randomly assigned to receive
dexamethasone (N=274) Or placebo (N=271).
• The primary outcome measure was death.
• 87 deaths occurred in the dexamethasone arm, 112
deaths occurred in the placebo arm.
• What is the RR ?
15. RR calculation
Treatm
ent
Death Alive Total
Dexa 87 187 274
Placebo 112 159 271
545
• Risk of event in
exposed=
87/274=31.75%
• Risk of event in
control=112/271=41.33
%
• RR=31.75/41.33=0.77
16. Interpretation
• RR=1: Risk of event in the exposed is equal to the risk of
event in the non exposed (No association).
• RR<1: Risk of event in exposed is less than risk of event
in the non exposed.
• RR>1: Risk of event in exposed is more than risk of event
in the non exposed.
17. Odds ratio
• Ratio of the odds of an event in the Treatment group to
the odds of an event in the control group.
• The odds of an event is the number of events divided by
the number of non-events.
• Odds ratios are most commonly used in case-control
studies.
19. Interpretation
• OR=1 Exposure does not affect odds of outcome.
• OR>1 Exposure associated with higher odds of outcome.
• OR<1 Exposure associated with lower odds of outcome.
20. Absolute Risk Reduction
• ARR is also called as Risk difference
• The difference between the risk of an event in the control
group and the risk of an event in the treated group.
22. Treatm
ent
Death Alive Total
Dexa 87 187 274
Placebo 112 159 271
545
• Risk of death in
controls=112/271=
41.33%
• Risk of death in
intervention=87/274=
31.75
• ARR=41.33-
31.75=9.58%
If 100 TBM cases are given dexa
about 10 will be prevented from
dying
23. Number needed to treat
• The NNT is the average number of patients who need to
be treated to prevent one additional bad outcome.
• NNT is the reciprocal of the absolute risk reduction (i.e.,
1/absolute risk reduction).
• The higher the NNT, the less effective is the treatment
25. NNT
Treatm
ent
Death Alive Total
Dexa 87 187 274
Placebo 112 159 271
545
• Risk of death in
controls=112/271= 41.33%
• Risk of death in
intervention=87/274=
31.75
• ARR=41.33-31.75=9.58%
• NNT=100/9.58=10.45
About 10 TBM cases needs
to be given dexa to
prevent 1 death.
26. Effect estimate Definition Formula Advantage
Odds ratio Ratio of odds of event
in intervention group
to odds of event in the
control group.
A/B÷C/D=AD/BC Most useful measure
for case-control study.
Risk ratio Ratio of risk of event
in intervention group
to risk of event in the
control group.
A/A+B÷C/C+D Useful in clinical
trials.
Absolute risk reduction Difference between
risk of event in control
and risk of event in
intervention group.
C/C+D-A/A+B Useful in clinical
trials.
Number needed to treat Average number of
patients that needs to
be treated to prevent
one additional poor
outcome
1/ARR Useful for clinical
trials and to convey
information to the
patient
27. Confidence interval
• The results of any experiment are an estimate of the truth.
• The true effect of treatment may actually be greater or
less than what we observed.
• CI Is the range of values that is likely to include the true
population value .
• CIs give us an idea of how confident we are about a
studies estimate of treatment effect.
• The narrower the range, the more precise the study’s
estimates.
• 95% CI represents the range within which we can be 95
%certain that the true answer lies.
30. Step 2: Calculation of overall t/t effect
• Overall effect is calculated by a weighted averaged of
individual studies.
• Weight of individual study= inverse of variance
Two models are commonly used to combine the effects of
individual studies
1. Fixed Effect model
2. Random Effects model
31. Fixed Effect model
• We assume that there is one true effect size that underlies
all the studies in the analysis.
• All the studies are evaluating the same true effect.
• Any difference observed in the effect size is due to
random error.
• Fixed effect= common effect.
• There is only one source of variance i.e sampling error.
32.
33. Random effects model
• We assume that Effect size may differ from study to
study.
• For example effect size may vary according to the age of
participants, dose of drug, timing of outcome assessment.
• Random effects model allows for heterogeneity.
• Consider there are 2 sources of variance
1. Within study variance (sampling error)
2. Between study variance (Age, dose, outcome
assesment)
34.
35. • To calculate the overall mean the weighted average of
individual studies is calculated.
• Weighted average = Sum of(estimate X Weight)/sum of W
• Weight = 1/Variance
37. Assumption Heterogeneity Confidence
interval
Fixed effect model •One true effect
underlies all
studies.
•Differences are
due to chance.
Ignored Narrow
Random effects
mode
•Effect size varies
amongst studies.
•Difference are not
due to chance
Taken into account Wider
38. Which model to use ?
• If significant clinical or statistical heterogeneity is present
use the random effects model. Otherwise use fixed effect
model.
• Clinical heterogeneity: Age, population, dose of
intervention, method of outcome assessment.
• Statistical heterogeneity.
40. Forest plot
• The typical graph used for displaying results of a meta-
analysis: forest plot
• Blobbogram
• At one glance we can see the effect of individual studies
as well as the overall effect.
41.
42.
43. • The horizontal axis usually represents the scale of
statistics. (OR, RR, ARR, SMD etc).
• The vertical line is known as the “line of null effect.”
• This line is placed at the value where there is no
association between an exposure and outcome.
• For OR, RR the vertical line passes through 1.
• For ARR, MD the vertical line passes through 0.
47. • Effect estimates of individual studies are represented by
boxes.
• Size of the box represents the weight of the study.
• The horizontal line represents the 95% confidence
intervals of the study.
• Any study line which crosses the line of null effect does
not illustrate a significant result.
51. • The diamond represent the overall effect estimate.
• A vertical line through the vertical points of the diamond,
represents the overall effect.
• The horizontal points of the diamond represent the 95%
confidence interval of this combined point estimate.
• If the horizontal tips of the diamond cross the vertical line,
the combined result is not significant.
57. Hypothetical Meta-analysis
• Intensified ATT regimen Vs Standard ATT regimen for
management of TBM.
• P= TBM patients >18 years
• I= intensified ATT (Levofloxacin plus HRZS)
• C= standard ATT (HRZS)
• O= death at 6 months
• Search Literature
• Narrow down the articles
• Data extraction