Lecture of Respected Sir Dr. L.M. BEHERA from N.I.H. KOLKATA in a workshop at G.D.M.H.M.C. - Patna in the Year 2011.
SUBJECT : BIOSTATISTICS
TOPIC : 'INTRODUCTION TO BIOSTATISTICS'.
Prelude
PART (A) TYPES OF GRAPHS
Line graphs
Pie charts
Bar graph
Scatter plot
Stem and plot
Histogram
Frequency polygon
Frequency curve
Cumulative frequency or ogives
PART (B) FLOW CHART
PART (C) LOG AND SEMILOG GRAPH
A presentation I have presented as a part of the Saudi Board of Community Medicine, Western Region. It simplifies the ideas behind hypothesis and hypothesis testing, also contains many different approaches of choosing the best statistical tests needed in any study.
Lecture of Respected Sir Dr. L.M. BEHERA from N.I.H. KOLKATA in a workshop at G.D.M.H.M.C. - Patna in the Year 2011.
SUBJECT : BIOSTATISTICS
TOPIC : 'INTRODUCTION TO BIOSTATISTICS'.
Prelude
PART (A) TYPES OF GRAPHS
Line graphs
Pie charts
Bar graph
Scatter plot
Stem and plot
Histogram
Frequency polygon
Frequency curve
Cumulative frequency or ogives
PART (B) FLOW CHART
PART (C) LOG AND SEMILOG GRAPH
A presentation I have presented as a part of the Saudi Board of Community Medicine, Western Region. It simplifies the ideas behind hypothesis and hypothesis testing, also contains many different approaches of choosing the best statistical tests needed in any study.
I. INTRODUCTION
DEFINITION
HISTORY
NEED TO STUDY BIOSTATISTICS
SAMPLING
METHODS OF PRESENTATION OF DATA
METHODS OF SUMMARIZING THE DATA
: Measures of Central Tendency
: Mean
: Median
: Mode
: Measures of Dispersion
: range
: Mean deviation
: Standard deviation
: Coefficient of variation
CORRELATION & REGRESSION
NORMAL DISTRIBUTION AND NORMAL CURVE.
METHODS OF ANALYZING THE DATA
SUMMARY & CONCLUSION
Researchers, as a whole, tend to underestimate the need for power. I'm just now starting to get it.
I recently gave a brief, easy-to-follow presentation on statistical power, it's importance, and how to go about getting it.
Hope you find it useful.
These annotated slides will help you interpret an OR or RR in clinical terms. Please download these slides and view them in PowerPoint so you can view the annotations describing each slide.
Normal Distribution
Properties of Normal Distribution
Empirical rule of normal distribution
Normality limits
Standard normal distribution(z-score/ SND)
Properties of SND
Use of z/normal table
Solved examples
I. INTRODUCTION
DEFINITION
HISTORY
NEED TO STUDY BIOSTATISTICS
SAMPLING
METHODS OF PRESENTATION OF DATA
METHODS OF SUMMARIZING THE DATA
: Measures of Central Tendency
: Mean
: Median
: Mode
: Measures of Dispersion
: range
: Mean deviation
: Standard deviation
: Coefficient of variation
CORRELATION & REGRESSION
NORMAL DISTRIBUTION AND NORMAL CURVE.
METHODS OF ANALYZING THE DATA
SUMMARY & CONCLUSION
Researchers, as a whole, tend to underestimate the need for power. I'm just now starting to get it.
I recently gave a brief, easy-to-follow presentation on statistical power, it's importance, and how to go about getting it.
Hope you find it useful.
These annotated slides will help you interpret an OR or RR in clinical terms. Please download these slides and view them in PowerPoint so you can view the annotations describing each slide.
Normal Distribution
Properties of Normal Distribution
Empirical rule of normal distribution
Normality limits
Standard normal distribution(z-score/ SND)
Properties of SND
Use of z/normal table
Solved examples
Statistics is the science of dealing with numbers.
It is used for collection, summarization, presentation and analysis of data.
Statistics provides a way of organizing data to get information on a wider and more formal (objective) basis than relying on personal experience (subjective).
To arrange the data in such a way that it should create interest in the reader’s mind at the first sight.
To present the information in a compact and concise form without losing important details.
Unit 11. Interepreting the Research Findings.pptxshakirRahman10
INTERPRETING THE RESEARCH FINDINGS.
Objectives:
At the completion of this unit learners will be able to
Discuss the different means and interpretation of data presentation/displaying through, Graphs (pie, bar, line, histogram), Tables, Charts. (spot map)
Discuss the different inferences through inferential tests and their interpretation.
Methods of Data collection and Presentation:
Methods of data collection:
Source of Data:
Statistical data may be obtained from two sources, namely, primary and secondary.
Primary data:
data measured or collected by the investigator or the user directly from the source. Primary sources are sources that can supply first hand information for immediate user.
Secondary data:
When an investigator uses data, which have already been collected by others, such data are called secondary data. Data gathered or compiled from published and unpublished sources.
Two different methods of collecting data:
Extraction of data from self – administered questionnaire
Direct investigation-measurement (observation) of the subject and interviewing (face-to-face, telephone)
first step is to decide on which of these three methods to use.
Methods of data Presentation:
Textual Method: – a narrative description of the data gathered.
Tabular Method or frequency distribution :– a systematic arrangement of information into columns and rows.
Graphical Method :– an illustrative description of the data.
The frequency distribution table:
A statistical table showing the frequency or number of observations contained in each of the defined classes or categories.
Frequency distribution: is a basic techniques that provide rich insights into the data and lay the foundation for more advanced analysis.
A frequency distribution table: lists categories of scores along with their corresponding frequencies.
Frequency distribution:
It is a grouping of all the (numerical) observations into intervals or classes together with a count of the number of observations that fall in each interval or class.
A frequency distribution has two main parts:
The values of the variable (if quantitative) or the categories (if qualitative), and
The number of observations (frequency) corresponding to the values or categories.
There are two types of Frequency distributions:
Categorical (or qualitative) Numerical (or quantitative)
Categorical Frequency Distribution:
Data are classified according to non-numerical categories. Categories must be mutually exclusive.
Used to present nominal and ordinal data
Nominal data: Here the construction is straight forward: count the occurrences in each category and find the totals.
Example: The martial status of 60 adults classified as single, married, divorced and widowed is presented in a FD as below:
Ordinal data:The construction is identical to the nominal case. How ever, the categories should be put in an ordered manner
Example: Satisfaction of hospital admission in a hospital size of 80 is presented as.
Description of various ultrasound features of benign and suspicious thyroid nodules with multiple ultrasound systems for risk stratification of malignancy.
Description of different ultrasound features of carpal tunnel syndrome before and after carpal tunnel release including Doppler imaging and elastography
Doppler ultrasound of visceral arteriesSamir Haffar
Doppler ultrasound of different diseases of visceral arteries including arterial stenosis and occlusion, arterial aneurysm, artrial pseudoaneurysm, arterio-venous fistula, artrial dissection, and abdominal vascular compression syndromes
The prostate is an exocrine gland of the male mammalian reproductive system
It is a walnut-sized gland that forms part of the male reproductive system and is located in front of the rectum and just below the urinary bladder
Function is to store and secrete a clear, slightly alkaline fluid that constitutes 10-30% of the volume of the seminal fluid that along with the spermatozoa, constitutes semen
A healthy human prostate measures (4cm-vertical, by 3cm-horizontal, 2cm ant-post ).
It surrounds the urethra just below the urinary bladder. It has anterior, median, posterior and two lateral lobes
It’s work is regulated by androgens which are responsible for male sex characteristics
Generalised disease of the prostate due to hormonal derangement which leads to non malignant enlargement of the gland (increase in the number of epithelial cells and stromal tissue)to cause compression of the urethra leading to symptoms (LUTS
Lung Cancer: Artificial Intelligence, Synergetics, Complex System Analysis, S...Oleg Kshivets
RESULTS: Overall life span (LS) was 2252.1±1742.5 days and cumulative 5-year survival (5YS) reached 73.2%, 10 years – 64.8%, 20 years – 42.5%. 513 LCP lived more than 5 years (LS=3124.6±1525.6 days), 148 LCP – more than 10 years (LS=5054.4±1504.1 days).199 LCP died because of LC (LS=562.7±374.5 days). 5YS of LCP after bi/lobectomies was significantly superior in comparison with LCP after pneumonectomies (78.1% vs.63.7%, P=0.00001 by log-rank test). AT significantly improved 5YS (66.3% vs. 34.8%) (P=0.00000 by log-rank test) only for LCP with N1-2. Cox modeling displayed that 5YS of LCP significantly depended on: phase transition (PT) early-invasive LC in terms of synergetics, PT N0—N12, cell ratio factors (ratio between cancer cells- CC and blood cells subpopulations), G1-3, histology, glucose, AT, blood cell circuit, prothrombin index, heparin tolerance, recalcification time (P=0.000-0.038). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and PT early-invasive LC (rank=1), PT N0—N12 (rank=2), thrombocytes/CC (3), erythrocytes/CC (4), eosinophils/CC (5), healthy cells/CC (6), lymphocytes/CC (7), segmented neutrophils/CC (8), stick neutrophils/CC (9), monocytes/CC (10); leucocytes/CC (11). Correct prediction of 5YS was 100% by neural networks computing (area under ROC curve=1.0; error=0.0).
CONCLUSIONS: 5YS of LCP after radical procedures significantly depended on: 1) PT early-invasive cancer; 2) PT N0--N12; 3) cell ratio factors; 4) blood cell circuit; 5) biochemical factors; 6) hemostasis system; 7) AT; 8) LC characteristics; 9) LC cell dynamics; 10) surgery type: lobectomy/pneumonectomy; 11) anthropometric data. Optimal diagnosis and treatment strategies for LC are: 1) screening and early detection of LC; 2) availability of experienced thoracic surgeons because of complexity of radical procedures; 3) aggressive en block surgery and adequate lymph node dissection for completeness; 4) precise prediction; 5) adjuvant chemoimmunoradiotherapy for LCP with unfavorable prognosis.
Couples presenting to the infertility clinic- Do they really have infertility...Sujoy Dasgupta
Dr Sujoy Dasgupta presented the study on "Couples presenting to the infertility clinic- Do they really have infertility? – The unexplored stories of non-consummation" in the 13th Congress of the Asia Pacific Initiative on Reproduction (ASPIRE 2024) at Manila on 24 May, 2024.
Anti ulcer drugs and their Advance pharmacology ||
Anti-ulcer drugs are medications used to prevent and treat ulcers in the stomach and upper part of the small intestine (duodenal ulcers). These ulcers are often caused by an imbalance between stomach acid and the mucosal lining, which protects the stomach lining.
||Scope: Overview of various classes of anti-ulcer drugs, their mechanisms of action, indications, side effects, and clinical considerations.
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
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
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
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
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
Ocular injury ppt Upendra pal optometrist upums saifai etawah
Types of graphs used in medicine
1. Type of graphs
Samir Haffar M.D.
Assistant Professor of Gastroenterology
2. Who might benefit?
• Researchers who want to display results of
their studies for publication in a journal
• Readers of research literature who wish to do a
critical appraisal of a piece of work
• People who have to deliver a presentation
4. The best advice that a statistician can give a
researcher is to first plot the data
Conventional statistics textbooks give only brief
details on how to draw figures & display data
Freeman JV, Walters SJ, Campbell MJ. How to display data.
Blackwell Publishing, Massachusetts, USA, 1st edition, 2008.
6. Table or graph?
Choice between using a table or a figure not easy
Nor is it easy to offer much general guidance
Altman D & Bland M. Presentation of numerical data. BMJ 1996 ; 312 : 572.
7. Table or graph?
Graph Table
Better in presentations Better in papers
Can only show summaries Can often show all the data
Show only a few variables Better for multiple variables
Trend better illustrated Trend badly illustrated
Freeman JV, Walters SJ, Campbell MJ. How to display data.
Blackwell Publishing, Massachusetts, USA, 1st edition, 2008.
8. Table or graph?
Trend badly illustrated with a table
Urinary excretion of PG metabolite after indomethacin administration
Urinary prostaglandin metabolite (mg/24 h)
Subject Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
I 4.8 4.8 1.8* 1.1* 1.5* 2 .7 4.1
II 3.9 4.4 0.7* 0.7* 0.7* 3.1 6.5
III 3.8 3.0 0.5* 0.3* 0.3* 0.8 1.1
Subject taking indomethacin 4 x 50 mg/24 h
Hamberg 1972
9. Urinary excretion of a prostaglandin metabolite decreased
following indomethacin administration in three humans
Hamberg 1972
Table or graph?
Trend better illustrated with a graph
10. Why use of graphs in presentation?
• You need to get your audience’s attention
• Many people respond better to visual cues
than to straight text or lists of numbers
• Effective graph can help drive home your point
11. Software for graphs
• No single package can draw all graphs to display data
• Simple graphs can be drawn in Microsoft Excel
• More complex graphs
Major statistical packages: SPSS, STATA, SAS
R for superimposing several graphs into single figure
• Packages change regularly
12. Types of graph
• Bar/column graph & variants
• Pie graph
• Dot plot
• Stem & leaf plot
• Histogram
• Box-whisker plot
• Line graph
• Spider or radar plot
• Pictogram
• Venn diagram
13. Types of data
Qualitative
(Categorical)
Quantitative
(Numerical)
Ordinal
Ordered
Rate pain
None
Mild
Moderate
Severe
Continuous
Range of values
No gap
Blood glucose
BP (mmHg)
Counted
Certain values
Gaps
Days stick/year
Nominal
Unordered
Blood type
A
B
AB
O
Petrie A & Sabin C. Medical statistics at a glance.
Dichotomous
Only 2 values
Present/absent
Alive/dead
Blackwell Publishing, Massachusetts, USA, 2nd edition , 2005.
14. Types of data
• Qualitative (categorical)
Dichotomous Only 2 values
Nominal Unordered
Ordinal Ordered
• Quantitative (numerical)
Counted Gaps
Continuous No gaps
16. Recommendations for construction of graph
• Tufte’s principle
• Clear title with sample size
• Labeled axes
• Gridlines kept to a minimum
• Categories ordered by size
• No three-dimensional graphs
17. Tufte’s principle for graph & table
Maximum amount of information for
minimum amount of ink
Tufte ER. The visual display of quantitative information.
Cheshire, Connecticut: Graphics Press; 1983
18. Column chart
Marital status for 226 patients in leg ulcer study
Columns wider than spaces between them
Bars have gray tone which is more pleasing to the eye
Vertical axis doesn’t extend beyond what the graph demands
BMJ 1998 ; 316 : 1487 – 91.
19. Column chart
Marital status for 226 patients in leg ulcer study
Only the height of columns presents the data of interest
BMJ 1998 ; 316 : 1487 – 91.
20. Column chart
Marital status for 226 patients in leg ulcer study
Tufte’s principle
BMJ 1998 ; 316 : 1487 – 91.
21. Column chart
Marital status for 226 patients in leg ulcer study
Clear title with sample size
BMJ 1998 ; 316 : 1487 – 91.
22. Column chart
Marital status for 226 patients in leg ulcer study
Labeled axes
BMJ 1998 ; 316 : 1487 – 91.
23. Column chart
Marital status for 226 patients in leg ulcer study
No gridlines
BMJ 1998 ; 316 : 1487 – 91.
24. Column chart
Marital status for 226 patients in leg ulcer study
Categories ordered by size
BMJ 1998 ; 316 : 1487 – 91.
25. Column chart
Marital status for 226 patients in leg ulcer study
No three-dimensional graph
BMJ 1998 ; 316 : 1487 – 91.
26. Bar chart ordered alphabetically
Population for 20 European countries in 2004
The most populous country, Germany, can be readily seen
It’s not obvious for France, Italy, & UK which has largest population
Schott B. Schott’s almanac. London: Bloomsbury; 2006.
27. Bar chart ordered by size
Population for 20 European countries in 2004
It is clear now how each country relates to others for population size
Schott B. Schott’s almanac. London: Bloomsbury; 2006.
28. Three-dimensional column charts
Self-reported type of delivery for all new mothers (N: 3 321)
Data have only two dimensions
A third dimension is falsely introduced
BMJ 2002 ; 324 : 643 – 6.
29. Two-dimensional column charts
Self-reported type of delivery for all new mothers (N: 3 321)
BMJ 2002 ; 324 : 643 – 6.
30. Two-dimensional column charts
Self-reported type of delivery for all new mothers (N: 3 321)
2 separate texts nearly
run into each other
Space of a journal column (8 cm)
Chart on the verge of being overcrowded
Problem overcome with use of bar (horizontal) chart
BMJ 2002 ; 324 : 643 – 6.
31. Bar chart
Annual alcohol consumption per inhabitant in Europe
Gustavii B. How to write & illustrate scientific papers.
Cambridge University Press, Cambridge, UK, 2nd edition, 2008.
32. Two-dimensional column charts
Self-reported type of delivery for all new mothers (N: 3 321)
BMJ 2002 ; 324 : 643 – 6.
33. Two-dimensional column charts
Self-reported type of delivery for all new mothers (N: 3 321)
BMJ 2002 ; 324 : 643 – 6.
34. Grouped column graph
Self-reported type of delivery for all new mothers (N: 3 321)
BMJ 2002 ; 324 : 643 – 6.
35. Grouped column graph
Probability of dying in ICU after admission with AMI
2 – 3 categories in each group should be the maximum
Remove the keys
Gustavii B. How to write & illustrate scientific papers.
Cambridge University Press, Cambridge, UK, 2nd edition, 2008.
36. Grouped bar chart
Probability of dying in ICU after admission with AMI
Remove the keys
One way to remove the keys is to label first group directly
Gustavii B. How to write & illustrate scientific papers.
Cambridge University Press, Cambridge, UK, 2nd edition, 2008.
37. Segmented column charts
Feeding method by maternal age for all women
BMJ 2002 ; 324 : 643 – 6.
38. Pie chart
Appropriate usage in a magazine article
Large segment begins at 12 o’clock
Proceed in clockwise direction
Ordered by size
No of observations & percentages
Number of segments ≤ 5
Color employed with caution
Freeman JV, Walters SJ, Campbell MJ. How to display data.
Blackwell Publishing, Massachusetts, USA, 1st edition, 2008.
43. “The only worse design than a pie
chart is several of them”
Tufte ER. The visual display of quantitative information.
Cheshire, Connecticut: Graphics Press; 1983
44. Types of data
• Qualitative (categorical)
Dichotomous Only 2 values
Nominal Unordered
Ordinal Ordered
• Quantitative (numerical)
Counted Gaps
Continuous No gaps
45. Display quantitative data
• Counted (gaps) Bar chart
• Continuous (no gaps) Dot plot
Stem & leaf plot
Histograms
Box-whisker plot
46. Dot plot
BAO in normal subjects & PU patients
Use dot plot if sample size per group is low (<100)
Each point represents a value for a single individual
Horizontal lines indicate mean values
Blair AJ et al. J Clin Invest 1987 ; 79 : 582.
Mean values
47. Dot plot
BAO & PAO in normal subjects & PU patients
Substantial overlap in values among individuals in the groups
Blair AJ et al. J Clin Invest 1987 ; 79 : 582.
48. Stem and leaf plot
Height of male in leg ulcer patients (n: 77)
Frequency Stem Leaf
1 1.55- 7
3 1.60- 333
4 1.65- 5588
18 1.70- 000000333333333333
24 1.75- 555558888888888888888888
15 1.80- 000000003333333
10 1.85- 5555888888
1 1.90- 13
Each data is divided into 2 parts: leaf (last digit) & stem (other part)
Separate line for each different stem value
Stem on left of plot & leaves on right
Median
49. Histogram
Serum albumin in 481 white men aged over 20
No gaps between columns (continuous data)
Keep same width of each group (bin width)
Columns labeled by using midpoint, or better start or end of interval
BMJ 1999 ; 318 : 1667.
50. Histogram – Normal distribution
Serum albumin in 481 white men aged over 20
Mean: 46.14 g/l – SD: 3.08 g/l
BMJ 1999 ; 318 : 1667.
51. Histogram – Positively skewed data
Baseline ulcer area from the leg ulcer trial (n: 233)
Peak at lower values & a long tail of higher values
BMJ 1998 ; 316 : 1487 – 91.
52. Histogram – Negatively skewed data
Baseline social functioning in leg ulcer trial (n: 233)
Long left tail of lower values & peak at higher values
BMJ 1998 ; 316 : 1487 – 91.
53. Number of categories in a histogram
No hard & fast rules about appropriate number
• Too few Much important information lost
• Too many Patterns obscured by too much detail
• Usually 5 – 15 categories will be enough
54. Number of categories in a histogram
Height for leg ulcer patients (n 233)
Too few
(6 categories)
Too many
(22 categories)
Good
(9 categories)
Freeman JV et all. How to display data. Blackwell Publishing, MA, USA, 2008.
55. Box-and-whiskers plots
Especially good to show differences between groups
Gonick L & Smith W. The cartoon guide to statistics.
HarperCollins Publishers, New York, USA, 1st edition, 1993
56. Anatomy of a box-whisker plot
Especially good to show differences between groups
Morgan GA et all. Understanding & evaluating research in applied & clinical settings.
Lawrence Erlbaum Associates, New Jersey, USA, 2006.
57. Box-Whisker Plot
Urinary lead concentration in urban & rural children
Swinscow TDV & Campbell MJ. Statistics at square one.
BMJ Books, London, 10th edition, 2002.
58. Box-whisker plot
As there are many variations, you have
to explain details of the plot
Freeman JV et all. How to display data. Blackwell Publishing, Massachusetts, 2008.
59. Box-and-whiskers plots
Liver stiffness for each Metavir stage in CHC
Vertical axis is in logarithmic scale (wide range of F4 values)
Gastroenterology 2005 ; 28 : 343 – 350.
60. Line graph
TB mortality in England &Wales
Farmer R Lawrenson R. Lecture Notes: Epidemiology & public health medicine.
Blackwell Publishing, Oxford, 5th edition, 2004
61. Line graph – Arithmetic scale
TB mortality in England &Wales
Mortality seems hardly affected by the events
They played little part in mortality decline
Farmer R Lawrenson R. Lecture Notes: Epidemiology & public health medicine.
Blackwell Publishing, Oxford, 5th edition, 2004
62. Line graph – Logarithmetic scale
TB mortality in England &Wales
Introduction of BCG vaccine &
chemotherapy was associated with
acceleration in established decline
in mortality
Farmer R, Lawrenson R. Lecture Notes: Epidemiology & public health medicine.
Blackwell Publishing, Oxford, 5th edition, 2004
63. It is frequently necessary to examine secular
trends both as changes in rates (arithmetic scale)
and as rates of change (logarithmic scale) if the
nature of a trend is to be fully appreciated
Farmer R Lawrenson R. Lecture Notes: Epidemiology & public health medicine.
Blackwell Publishing, Oxford, 5th edition, 2004
64. Line chart
Obesity among adults from 1990 – 2002 (US-CDC)
Boslaugh S & Watters PA. Statistics in a nutshell.
O’Reilly Media, California, USA, 1st edition, 2008.
65. Line chart
Obesity among adults from 1990 – 2002 (US-CDC)
Smaller range for y-axis increases visual impact of the trend
Boslaugh S & Watters PA. Statistics in a nutshell.
O’Reilly Media, California, USA, 1st edition, 2008.
66. Line chart
Obesity among adults from 1990 – 2002 (US-CDC)
Wider range for the y-axis decreases visual impact of the trend
Boslaugh S & Watters PA. Statistics in a nutshell.
O’Reilly Media, California, USA, 1st edition, 2008.
67. Which scale should be chosen?
• No perfect answer to this question
All present the same information
None strictly speaking are incorrect
• In this case, the scale would be the first
It shows true floor for data (0%, lowest possible value)
It includes reasonable range above highest data point
Boslaugh S & Watters PA. Statistics in a nutshell.
O’Reilly Media, California, USA, 1st edition, 2008.
68. Line chart
Obesity among adults from 1990 – 2002 (US-CDC)
Boslaugh S & Watters PA. Statistics in a nutshell.
O’Reilly Media, California, USA, 1st edition, 2008.
69. Line graph
Effect of tyramine solution on pupillary size
Gustavii B. How to write & illustrate scientific papers.
Cambridge University Press, Cambridge, UK, 2nd edition, 2008.
70. Line graph
Effect of tyramine solution on pupillary size
Two common defects:
1- Curves distinguished both by:
- Type of line
- Type of data-point symbol
2- Curves identified by separate key
Reader scan back & forth to the
key to see what they represent
Gustavii B. How to write & illustrate scientific papers.
Cambridge University Press, Cambridge, UK, 2nd edition, 2008.
71. Redrawn line graphs
Type of data-point symbol
Labeled directly
Type of line
Labeled directly
Gustavii B. How to write & illustrate scientific papers.
Cambridge University Press, Cambridge, UK, 2nd edition, 2008.
72. Line graph
HP seroprevalence in USA in function of age & race
Making trend lines thick for easy visibility
Maximum: 3 – 4 lines
Gastroenterology 1992 ; 103 : 813.
73. Characteristics of some graphs
Good for showing separate
unrelated pieces of data
Bar/column graph
Good for showing
Percentages
Pie graph
Good for showing how
data changes over time
Line graph
74. Spider or radar plot
Acupuncture vs usual care in persistent non-specific back pain
HRQol assessed over 12 months by SF-36
SF-36 dimensions scored on a 0 (poor) to 100 (good) health scale
BMJ 2006 ; 333 : 623 – 6.
75. Pictogram
Estimated annual incidence of TB in 2006
Global tuberculosis control: surveillance, planning, financing
WHO report 2008
76. Venn diagram
Any number of overlapping circles in theory
When > 3 – 4 circles, the diagram becomes rather cluttered
77. Example of a venn diagram
“the 3 components of EBM”
“EBM is the integration of best research evidence
with clinical expertise & patient values”
- David Sackett
EBM
Best research
evidence
Clinical
Expertise
Patient
Concerns
78. Place of graphs in your study
Types of data
Qualitative - Quantitative
Essentials you
need to get started
Null hypothesis
& alternative hypothesis
H0 & H1
What type of test? Choose the right type of test
Is it significant?
Compute the value of the statistic
& compare with the critical value
Software tools
Start with Excel (simple graphs)
& then move on to SPSS
& then STATA, SAS or R
Perera R et al. Statistics Toolkit. Blackwell Publishing, MA, USA, 1st edition, 2008
79. Useful questions to ask when considering
how to display your data
• What do you want to show?
Type of data – Normal or skewed distribution
• What methods are available for this?
Table – Graph – Type of graph
• Is the method chosen the best?
Would another have been better?
Freeman JV et al. How to display data. Blackwell Publishing, MA, USA, 1st edition, 2008.
In its day, the following table contained medical dynamite. It presented the results of a study that showed, for the first time in vivo, that certain anti-inflammatory substances, such as indomethacin or aspirin, inhibit the synthesis of prostaglandin.
Shown here is only the part of the table that includes the results from the three subjects receiving indomethacin.
I converted this part into a graph to show more clearly the dramatic effect of indomethacin.
Note that the curves have the same type of line because they do not need to be distinguished from each other; they are all intended
to show the same trend. The curves are bolder than the axes. Two zeros are used to label the point where the axes meet.
Axes are of equal length. The label of the vertical axis is parallel to the axis and reads from bottom to top.
Note also that the legend gives the message of the figure.
Start with Excel (simple graphs)
& then move on to SPSS
and then STATA, SAS or R
Clustered or grouped bar chart
Stacked or segmented bar charts
Clear title (with the sample size)
Labelled axes
No gridlines
Marital status categories are ordered by their frequency
Tufte’s principle
Clear title (with the sample size)
Labelled axes
No gridlines
Marital status categories are ordered by their frequency
Tufte’s principle
Clear title (with the sample size)
Labelled axes
No gridlines
Marital status categories are ordered by their frequency
The advantage of using the frequencies is that the numbers in each category on the horizontal (X) axis can be readily seen. Using the percentage scale the percentages in each category can be easily discerned. Use of the percentage scale facilitates the comparison of groups.
Tufte’s principle
Clear title (with the sample size)
Labelled axes
No gridlines
Marital status categories are ordered by their frequency
Consider for example the vaginal breech births category, there are only 16 individuals in this category compared to 2221 in the normal delivery category and so vaginal breech births comprise < 1% of births. However this is not the impression given in this three-dimensional chart.
More visually exciting than two-dimentional chart but they are less clearer and more ambiguous.
Easily displayed with computer technology
Seen more & more often in published reports
Data have only two dimensions & a third dimension falsely introduced in such cases.
Reference:
Gustavii B. How to write & illustrate scientific papers.
Cambridge University Press, Cambridge, UK, 2nd edition, 2008.
Space of a journal column (8 cm)
Column chart should include only few items
Chart on the verge of being overcrowded
Problem can be overcome with the use of a bar chart (computer term for horizontally arranged bars).
Clustered or grouped bar chart
Grouped bar charts to display two or more sets of proportions.
Clustered or grouped bar chart
Segmented bar charts to display three or more sets of proportions.
As the number of groups to be compared increases, a grouped bar chart can quickly become very busy and obscure patterns within the data. When the number of groups to be compared becomes greater than three or four, a better type of bar chart is the segmented bar chart, where the groups are arranged on the horizontal axis and the variable being compared between the groups is arranged on the vertical axis.
As the comparison of interest is between women of different ages, age should be on the horizontal axis and method of feeding on the vertical axis. From this segmented bar chart, it can easily be seen that there is a tendency for increasing breast-feeding as maternal age increases, with the exception of the oldest mothers. Note that the vertical axis has been scaled, from 0 to 100, to represent the percentage in each age group who use a particular feeding method.
Use color with caution
Difficult to read and interpret
The area displayed should be proportional to the relative frequencies for each group.
However, when the charts are displayed as three dimensional this relationship is lost as what is displayed becomes a volume.
Only the front face is proportional to the numbers in the categories and so only these should be displayed.
In particular, categories with only a few individuals are given undue weight in three dimensional charts as the top face is much more prominent.
Consider for example the vaginal breech births category, there are only 16 individuals in this category compared to 2221 in the normal delivery category and so vaginal breech births comprise < 1% of births. However this is not the impression given in the three-dimensional chart.
One of the simplest ways of displaying all the data.
Each point represents a value for a single individual.
Always display continuous data as dotplots if the sample size per group is low (100 subjects).
One of the simplest ways of displaying all the data.
Each point represents a value for a single individual.
Always display continuous data as dotplots if the sample size per group is low (100 subjects).
Stem: ساق
Leaf:ورقة
For example, for a height of 1.58 m, the leaf would be 8 and the stem would be 1.5
Number of data points in each stem can also be displayed on the left.
Simple matter to work out the median in the stem & leaf plot.
In this case there are 77 observations and thus the median is the 39th value (when the data are ordered), as 38 observations lie below this point and 38 lie above. Looking at the plot, it can be seen that the 39th value occurs in stem 1.75 and the leaf value corresponding to the 39th value is 8. Thus the median for these data is a height of 1.78 m.
The stem and leaf plot resembles a histogram turned over onto its side.
The advantage of a stem and leaf plot over a histogram is that not only does it show the frequency in each stem but that it
retains the individual values of the data.
The bars may be labelled by using the midpoint of the corresponding interval, or by having a label at the start (or end) of the interval. For histograms, we recommend that you label the horizontal axis, at the start (or end) of each interval, since with this method it is easier to work out the width of the interval.
However bar charts are used for discontinuous data, where the categories are entirely separate while histograms are used for continuous data. Thus bar charts have gaps between the categories on the horizontal axis in order to emphasise that the categories are completely
separate, whereas there are no spaces in between the bins for a histogram, as the width of these bins can be set by the investigator.
A useful feature of a histogram is that it is possible to assess the distribution form of the data; in particular whether the data are approximately normally distributed, or are skewed.
The bars may be labelled by using the midpoint of the corresponding interval, or by having a label at the start (or end) of the interval. For histograms, we recommend that you label the horizontal axis, at the start (or end) of each interval, since with this method it is easier to work out the width of the interval.
However bar charts are used for discontinuous data, where the categories are entirely separate while histograms are used for continuous data. Thus bar charts have gaps between the categories on the horizontal axis in order to emphasise that the categories are completely
separate, whereas there are no spaces in between the bins for a histogram, as the width of these bins can be set by the investigator.
A useful feature of a histogram is that it is possible to assess the distribution form of the data; in particular whether the data are approximately normally distributed, or are skewed.
The use of health-related quality of life (HRQoL) measures is becoming more frequent in clinical trials and health services research, both as primary and secondary outcomes. It is typically assessed by a self-completed questionnaire which asks a series of standardised questions about various aspects or facets of a person’s HRQoL. The Medical Outcomes Study 36-Item Short Form (SF-36) is the most commonly used HRQoL measure in the world today. It contains 36 questions measuring health across eight dimensions: physical functioning (PF); role limitation because of physical health (RP); social functioning (SF); vitality (VT); bodily pain (BP); mental health (MH); role limitation because of emotional problems (RE) and general health (GH). These eight dimensions are usually regarded as a continuous outcome and are scored on a 0–100 scale, where 100 indicates ‘good health’.
Keep the intervals in histogram (bin width) equal
Secular: قرني – حادث مرة كل قرن
So which scale should be chosen?
There is no perfect answer to this question.
All present the same information, and none strictly speaking are incorrect.
In this case, if I were presenting this chart without reference to any other graphics, the scale would be the first because it shows the true floor for the data (0%, which is the lowest possible value) and includes a reasonable range above the highest data point.
In the right-hand graph you will probably not miss the data points, as you can easily discern the change of line direction where
the points have been omitted.
This graph may be the more attractive of the two.
Data points are probably overused in scientific papers.
The use of health-related quality of life (HRQoL) measures is becoming more frequent in clinical trials and health services research, both as primary and secondary outcomes. It is typically assessed by a self-completed questionnaire which asks a series of standardised questions about various aspects or facets of a person’s HRQoL. The Medical Outcomes Study 36-Item Short Form (SF-36) is the most commonly used HRQoL measure in the world today. It contains 36 questions measuring health across eight dimensions: physical functioning (PF); role limitation because of physical health (RP); social functioning (SF); vitality (VT); bodily pain (BP); mental health (MH); role limitation because of emotional problems (RE) and general health (GH). These eight dimensions are usually regarded as a continuous outcome and are scored on a 0–100 scale, where 100 indicates ‘good health’.
In North America, an increase in the number of cases with TB has been observed since the mid-1980s mainly attributable to immigration, human immunodeficiency virus and the development of multidrug-resistant strains of TB.