By
Dr Babatunde, OA
MBBS, PgCertDPMIS, MPH, FWACP
Department of Community Medicine,
FMC, Ido-Ekiti




Definition (C-O-S-A-I-P)
Collection
Organization
Summarizing
Analyzing
Interpreting
Presenting

Applications of bios...


A variable is any parameter that can be
observed or measured



Information collected on a variable is usually
unrefin...






Biostatistics = Medical statistics
Medical statistics is the scientific method of
collecting, organizing, summari...









Biostatistics is all about „curiosity‟3
Biostatistics is about asking medically
relevant questions and getti...






Name:
Matriculation Number:
Medical question of personal interest
Submit it at the end of the lecture
Also docu...






Research is the scientific investigation of
facts and relationships to establish
dependable solutions to problems...







It is a general phenomenon that many
students do not have interest in statistics
Many see it as too abstract to...








Biostatistics center around data
Hence what is data?
Data is information collected of an individual
or group ...




Example: How many students in this class use
condom during sexual intercourse:
5 data set:
1. Ever had sex
2. Age at...









Questionnaires
Observations (checklist)
Focus Group Discussion
Proforma
Records
Census
List other ways you...


4 Levels of measurement are involved in data
collection (N-O-I-R)
◦
◦
◦
◦

1.
2.
3.
4.

Nominal
Ordinal
Interval
Ratio
...









Lowest level
Mutually unordered category
No notion of numerical magnitude
Any number assigned has no numeri...







Ability to rank or order phenomenon
In addition to nominal propert
It is defined by related category
Examples:...







Measurements are expressed in numbers
The starting point is arbitrary depending
largely on the units of measure...






Measurement on this scale has 3 previously
mentioned properties but in addition has a
true zero point
The ratio o...
Level

Summary

Example

Nominal

Categories only. Data cannot be
arranged in an ordering scheme

Student’s car:
1 Ford, 2...
Theoretical interest is not the primary reason why
researchers and statisticians consider the level of
measurement of a va...





Raw data is usually not too useful
It has to be organized to make sense out of it
This brings us to types of stati...




Primary data
Data that is obtained directly from an
individual e.g. 2006 Census
Secondary data
Data that is obtained...


A Special type of Discrete Variable is the
Binary Variable which takes on exactly 2
possible values
◦ Gender (M/F)
◦ Pr...


Sometimes, discrete variables have a “natural
ordering” to them
◦ For example, names of consecutive days in a week
(M, ...






If in an experiment you measure a single
variable, it is called a Univariate experiment
If you measure 2 variable...







Concerned with summarizing series of
measurements or observations
A] Measures of Central tendency
B] Measures o...


Now that we have displayed our data, we want to
be able to characterize it quantitatively
◦ Measures of Central Tendenc...


Mean
◦ Arithmetic Average of a sample of data



Median
◦ If you order the data from smallest to highest,
the median i...






i. Arithmetic Mean: This is different from
other types of mean like geometric mean
and harmonic mean.
The arithme...






Median: Here the distribution is arrayed or
arranged in a particular pattern.
Then look at the value which cuts t...






Mode: This is the most frequently occurring
value in a distribution.
Some distributions are described as amodal
b...
 If

you stop learning you are
old, whether you are 20 or 80
years

Thank

you

Dr Babatunde OA MBBS,
PGCertDPMIS, MPH, ...
By
Dr Babatunde, OA
MBBS, PgCertDPMIS, MPH, FWACP
Department of Community Medicine,
FMC, Ido-Ekiti
 This is one of the simplest measures
of variability.
 This is simply the difference between
the highest and the lowest ...
 In the following distribution; 9, 4, 2, 5, 10
[which has a mean of 6], the total deviation
from the mean or the average ...
tables
 charts
 diagrams
 graphs
 pictures
 special curves


Dr Babatunde OA MBBS,
PGCertDPMIS, MPH, FWACP

2/10/201...
Numbering eg table 1, table 2, etc
 Title which must be brief and self explanatory
 Headings of columns and rows should ...


Charts and diagrams;
These methods of presentation have powerful
impact on the imagination of people. So they are a
pop...


simple bar chart; bars may be vertical or horizontal are
usually separated by appropriate spaces with an eye on
neatnes...


b. Histogram; this is a pictorial diagram of
frequency distribution



It consists of a series of block



The class ...






i. it is like the simple bar chart except that
the bars of histogram touch each other
ii. The height of each box ...




c.
Frequency
polygon;
a
frequency
distribution may also be represented
diagrammatically by the frequency polygon
It‟...


d. Pie charts; Instead of comparing the length
of a bar
the areas of segments of a circle are
compared.
The Area of eac...


e. Graphs / scatter diagrams; this comes in when
there
are two different factors involved eg age
/height. If
after plot...
47
46.5
46
45.5
45
44.5
44
43.5
43
42.5
42

East
West
North

1st Qtr

2/10/2014

2nd Qtr

3rd Qtr

4th Qtr

Dr Babatunde O...
90
80
70
60
50
40
30

East
West
North

20
10
0
1st Qtr 2nd Qtr 3rd Qtr 4th Qtr

2/10/2014

Dr Babatunde OA MBBS, PGCertDPM...
100%
90%
80%
70%
60%
Series2

50%

Series1

40%
30%
20%
10%
0%
1

2

3

4

5

Dr Babatunde OA MBBS,
PGCertDPMIS, MPH, FWAC...
1
2
3
4

Dr Babatunde OA MBBS,
PGCertDPMIS, MPH, FWACP

2/10/2014

46
60
50
40
30

Series1

20
10
0
0

5

10

15

Dr Babatunde OA MBBS,
PGCertDPMIS, MPH, FWACP

2/10/2014

47
50
45
40
35
30

Series1

25

Series2

20
15
10
5
0
1

2

3

4

5

Dr Babatunde OA MBBS,
PGCertDPMIS, MPH, FWACP

2/10/2014...




This refers to the applications of statistical
tests to study results with a view to ascertain
presence of statistic...
• 1. The observed difference of 10% might be a TRUE
DIFFERENCE, which also exist in the total pop from
which the sample wa...
• Statistical tests estimate the likelihood that such a
result occur by chance

• If the likelihood or probability is less...






Once the alpha level has been set and the
statistical test applied to results the P-value
is obtained
If the P-va...




If the Null hypothesis is rejected when it is
true ie no true difference exist ( P value >
than alpha value) then a ...
•

•

Clinicians often have to evaluate and use new
information through out their practice lives.
The most important reaso...
3. Use of vital statistics-effective diagnosis and
treatment of patients requires an understanding of
how to make sense ou...
5. Assessing information on drugs and equipmentcompanies present information on their products in
charts, graph and clinic...








Public health (Epidemiology, Nutrition etc)
Clinical trials
Population genetics
Genomics analysis
Ecology/E...
1.

2.

3.
4.

5.

Bamgboye EA. A companion of Medical statistics.
Ibipress & Publishing Company, Ibada Nigeria 1st
Editio...
6. Dawnson B, Trapp R. Introduction to Medical
Research in Basic and Clinical Biostatistics. Fourth
Edition. McGraw-Hill C...
 What

doesn‟t kill us makes us
stronger
 So
see
challenges
as
opportunities for
personal
growth

Thank

you

Dr Babatu...
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Intro biostat1&2

  1. 1. By Dr Babatunde, OA MBBS, PgCertDPMIS, MPH, FWACP Department of Community Medicine, FMC, Ido-Ekiti
  2. 2.   Definition (C-O-S-A-I-P) Collection Organization Summarizing Analyzing Interpreting Presenting Applications of biostatistics Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 2
  3. 3.  A variable is any parameter that can be observed or measured  Information collected on a variable is usually unrefined and it is called data  The collection, analysis, interpretation and use of data is called statistics  The application of statistics to health-related fields is known as Biostatistics1 Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 3
  4. 4.    Biostatistics = Medical statistics Medical statistics is the scientific method of collecting, organizing, summarizing, analyzing, interpreting, and presenting medical data1 Biostatistics is statistics applied to the biological sciences and to Medicine2 Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 4
  5. 5.      Biostatistics is all about „curiosity‟3 Biostatistics is about asking medically relevant questions and getting answers using statistical methods Which age group dies most? Mortality rate What proportion of University students use condoms during sexual intercourse? Assignment 1: Each student should ask a medically related question of personal interest and submit it in the format below Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 5
  6. 6.      Name: Matriculation Number: Medical question of personal interest Submit it at the end of the lecture Also document in your notebook because we will always make reference to this question throughout this class Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 6
  7. 7.    Research is the scientific investigation of facts and relationships to establish dependable solutions to problems through systematic collection, analysis, and interpretation of data Research is described as systematic in that it involves an organized, formally structured methodology to obtain new knowledge Biostatistics is the basis for research Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 4 2/10/2014 7
  8. 8.     It is a general phenomenon that many students do not have interest in statistics Many see it as too abstract to conceptualize However, it is the simplest form of all sciences being practiced by both literates and illiterates Grandmother statistics: A big stroke by a grandmother represents a birth while a small stroke represents a death (origin of tally sheet in immunization) Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 8
  9. 9.      Biostatistics center around data Hence what is data? Data is information collected of an individual or group of individuals When entered into a computer, it is called dataset Assignment 2: List 5 examples of data you can collect to answer your question in assignment 1 Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 9
  10. 10.   Example: How many students in this class use condom during sexual intercourse: 5 data set: 1. Ever had sex 2. Age at 1st sexual intercourse 3. Number of sexual intercourse in last 3 months 4. Number of times used condom 5. Number of sexual partners since sexual initiation Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 10
  11. 11.        Questionnaires Observations (checklist) Focus Group Discussion Proforma Records Census List other ways you can collect data Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 11
  12. 12.  4 Levels of measurement are involved in data collection (N-O-I-R) ◦ ◦ ◦ ◦ 1. 2. 3. 4. Nominal Ordinal Interval Ratio Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 12
  13. 13.       Lowest level Mutually unordered category No notion of numerical magnitude Any number assigned has no numerical value other than to distinguish one category from another. Examples: Gender, Blood Group, Marital status Assignment 3: List 5 more examples of Nominal scale Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 13
  14. 14.      Ability to rank or order phenomenon In addition to nominal propert It is defined by related category Examples: Patients pain coditions desribed as Mild, Moderate, Severe Assignment 4: List 5 more examples of Ordinal scale of measurement Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 14
  15. 15.     Measurements are expressed in numbers The starting point is arbitrary depending largely on the units of measurement It is possible to attach physical meanings to differences of 2 measurements (intervals) but not to their ratios Examples: Temperature-Centigrade or Fahrenheit Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 15
  16. 16.    Measurement on this scale has 3 previously mentioned properties but in addition has a true zero point The ratio of any 2 measurements on the scale is physically meaningful Examples: Height in cm, Weight in Kg, Age in years. Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 16
  17. 17. Level Summary Example Nominal Categories only. Data cannot be arranged in an ordering scheme Student’s car: 1 Ford, 2 Toyota, 3 BMW Ordinal Categories are ordered, but differences cannot be determined or they are meaningless Student’s car: 1 Compact, 2 Mid-size, 3 Full size Interval Differences between values can be found, but there may be no inherent starting point. Ratios are not meaningful Temperature: 45 , 80 , 90 Ratio Like interval scale, but with an inherent starting point. Ratios are meaningful Weights of football players: 200 lbs, 300 lbs, 400 lbs Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 17
  18. 18. Theoretical interest is not the primary reason why researchers and statisticians consider the level of measurement of a variable. Level of measurement is important because the kinds of statistical procedures that can be appropriately used depend on the level of measurement of the variable studied. Calculating mean telephone number of a group of people’s telephone number would be possible but ridiculous, since telephone number is a nominal scale level variable. Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 18
  19. 19.    Raw data is usually not too useful It has to be organized to make sense out of it This brings us to types of statistics: ◦ Descriptive: Frequency tables, Diagrams ◦ Inferential: Use of statistical tests Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 19
  20. 20.   Primary data Data that is obtained directly from an individual e.g. 2006 Census Secondary data Data that is obtained from outside source e.g. studying of hospital records 5 Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 20
  21. 21.  A Special type of Discrete Variable is the Binary Variable which takes on exactly 2 possible values ◦ Gender (M/F) ◦ Pregnant? (Y/N) ◦ Hypertensive? (Y/N) Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 21
  22. 22.  Sometimes, discrete variables have a “natural ordering” to them ◦ For example, names of consecutive days in a week (M, Tu, Wed, Thurs, Fri, Sat, Sun)  Other types of discrete variables do not have a natural order and are called Nominal Variables ◦ Race (African American, Caucasian, Asian, Hispanic etc.) Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 22
  23. 23.    If in an experiment you measure a single variable, it is called a Univariate experiment If you measure 2 variables, it is called a Bivariate experiment And if you measure multiple variables, it is called a Multivariate experiment Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 23
  24. 24.     Concerned with summarizing series of measurements or observations A] Measures of Central tendency B] Measures of Variability/Dispersion C] Measures of Relative standing Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 24
  25. 25.  Now that we have displayed our data, we want to be able to characterize it quantitatively ◦ Measures of Central Tendency  Mean, Median, Mode ◦ Measures of Variability  Range, Variance, Standard Deviation ◦ Measures of Relative Standing  Z-Scores, Percentiles, Quartiles Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 25
  26. 26.  Mean ◦ Arithmetic Average of a sample of data  Median ◦ If you order the data from smallest to highest, the median is the middle value, assuming an odd number of data elements ◦ If you have an even number of elements, it is the average of the 2 middle numbers.  Mode ◦ The most common value in a set of values Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 26
  27. 27.    i. Arithmetic Mean: This is different from other types of mean like geometric mean and harmonic mean. The arithmetic mean is simply the average, denoted by the symbols shown: [μ,-x, ie miu or x-bar]. These symbols are used to represent arithmetic mean of population [N] and sample [n] respectively. Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 27
  28. 28.    Median: Here the distribution is arrayed or arranged in a particular pattern. Then look at the value which cuts this distribution into two equal parts. That value in array which divides it into two equal parts is called the median. Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 28
  29. 29.    Mode: This is the most frequently occurring value in a distribution. Some distributions are described as amodal because they have no mode. A distribution with one mode is uni-modal and that with two modes is called bimodal distribution. Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 29
  30. 30.  If you stop learning you are old, whether you are 20 or 80 years Thank you Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 30
  31. 31. By Dr Babatunde, OA MBBS, PgCertDPMIS, MPH, FWACP Department of Community Medicine, FMC, Ido-Ekiti
  32. 32.  This is one of the simplest measures of variability.  This is simply the difference between the highest and the lowest values; R=XH-XL.  The range has a problem of looking at two extremes alone and ignores other values. Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 32
  33. 33.  In the following distribution; 9, 4, 2, 5, 10 [which has a mean of 6], the total deviation from the mean or the average is always zero.  Since the total or average mean deviation is useless, something is done to get around the problem.  Thus we square the deviations and sum them up and we get 46.  Now the average of the squared deviations is got by dividing by number of observations.  This is called variance [S2, σ2], sample and population variance respectively. Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 33
  34. 34. tables  charts  diagrams  graphs  pictures  special curves  Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 34
  35. 35. Numbering eg table 1, table 2, etc  Title which must be brief and self explanatory  Headings of columns and rows should be clear and concise  Data must be presented according to size or importance, chronologically, alphabetically or geographically  If percentages or averages are to be compared, they must be placed as close as possible  No table may be too large  Footnotes may be given where necessary  Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 35
  36. 36.  Charts and diagrams; These methods of presentation have powerful impact on the imagination of people. So they are a popular media of exposing statistical data a. Bar charts; these are a way of presenting a set of numbers by the length of a bar- length of bar being proportional to the magnitude to be represented Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 36
  37. 37.  simple bar chart; bars may be vertical or horizontal are usually separated by appropriate spaces with an eye on neatness and clear presentation   Multiple bar charts; Here two or more bars are grouped together. Component bar chart; Here the bar may be divided into two or more parts. Each part represents a certain item and proportional to the magnitude of that particular item. Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 37
  38. 38.  b. Histogram; this is a pictorial diagram of frequency distribution  It consists of a series of block  The class intervals are given along the horizontal axis and frequency on the vertical axis  The area of each block or rectangle is proportional to the frequency  The histogram is apt for representing continuous variables. Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 38
  39. 39.    i. it is like the simple bar chart except that the bars of histogram touch each other ii. The height of each box is equal to the frequency {ie for equal intervals} of class it represents iii. The interval with the highest box is called the modal interval ie interval that contains the mode. Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 39
  40. 40.   c. Frequency polygon; a frequency distribution may also be represented diagrammatically by the frequency polygon It‟s obtained by joining the midpoints of the histogram blocks. Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 40
  41. 41.  d. Pie charts; Instead of comparing the length of a bar the areas of segments of a circle are compared. The Area of each segment depends upon the angle. A circle of any considerable large size is divided into the number of components that make up the total such that the area of each sector is proportional to the component it represents. Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 41
  42. 42.  e. Graphs / scatter diagrams; this comes in when there are two different factors involved eg age /height. If after plotting the points, and they are such that the points cannot be joined by any line, then graphs will not apply and so we have scatter diagram. Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 42
  43. 43. 47 46.5 46 45.5 45 44.5 44 43.5 43 42.5 42 East West North 1st Qtr 2/10/2014 2nd Qtr 3rd Qtr 4th Qtr Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 43
  44. 44. 90 80 70 60 50 40 30 East West North 20 10 0 1st Qtr 2nd Qtr 3rd Qtr 4th Qtr 2/10/2014 Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 44
  45. 45. 100% 90% 80% 70% 60% Series2 50% Series1 40% 30% 20% 10% 0% 1 2 3 4 5 Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 45
  46. 46. 1 2 3 4 Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 46
  47. 47. 60 50 40 30 Series1 20 10 0 0 5 10 15 Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 47
  48. 48. 50 45 40 35 30 Series1 25 Series2 20 15 10 5 0 1 2 3 4 5 Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 48
  49. 49.   This refers to the applications of statistical tests to study results with a view to ascertain presence of statistical significance Suppose we find in a study on level of physical activity, 40% of men included in the sample are physically active whereas only 30% of women qualified as active. How should one interpret this result? Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 49
  50. 50. • 1. The observed difference of 10% might be a TRUE DIFFERENCE, which also exist in the total pop from which the sample was drawn   2. This difference might also be DUE to CHANCE; ie in reality there is no difference b/w men and women but that the sample of men just happened to differ from the sample of women –probably due to sample variation 3. The observed difference of 10% is due to defect in the study design (bias)-ie with an appropriate study design no such difference would have occurred Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 50
  51. 51. • Statistical tests estimate the likelihood that such a result occur by chance • If the likelihood or probability is less than 5% it implies that a true difference exist and the notion of chance occurrence is rejected • This level of 5% is known as the alpha level while the actual likelihood or probability calculated is know as the P-value • In statistical terms the assumption that in the total population no real difference exists between the groups is called the NULL HYPOTHESIS Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 51
  52. 52.    Once the alpha level has been set and the statistical test applied to results the P-value is obtained If the P-value is lower than the alpha value it implies that a true difference exists and the Null Hypothesis is rejected while the result is said to be statistically significant If the P-value is higher than the alpha value the Null hypothesis is accepted and the result is taken as having occurred by chance and considered not significant Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 52
  53. 53.   If the Null hypothesis is rejected when it is true ie no true difference exist ( P value > than alpha value) then a type I error is committed If the Null hypothesis is accepted when a true difference exist (P-value < than alpha value) then a type II error is committed Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 53
  54. 54. • • Clinicians often have to evaluate and use new information through out their practice lives. The most important reasons for learning biostatistics include the following: 1. Assessing medical literature-evidence based information is often made available in journals and clinicians must understanding biostatistics to be able to make sense of such information 2. Patient care- results of research work are often meant for patient care and clinicians want to know best diagnostic procedure, optimal care and how treatment regimens should be designed and implemented Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 54
  55. 55. 3. Use of vital statistics-effective diagnosis and treatment of patients requires an understanding of how to make sense out of vital statistics which often results from the recording of vital events such as births and deaths 4. Deploying diagnostic procedures-knowing the appropriate diagnostic procedure to use in a given patient is essential for effective care. Clinicians should be conversant with the sensitivity, specificity, positive and negative predictive values of a procedure Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 55
  56. 56. 5. Assessing information on drugs and equipmentcompanies present information on their products in charts, graph and clinical studies and clinicians need to good knowledge of biostatistics to make sense out of such presentation and information 6. Understanding epidemiologic problems-disease prevalence, variation by seasons and by location, and relationship to risk factors constitute epidemiological parameters of utmost importance to the clinician in practice. Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 56
  57. 57.        Public health (Epidemiology, Nutrition etc) Clinical trials Population genetics Genomics analysis Ecology/Ecological forecasting Biological Sequence Analysis Systems biology for gene network inference Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 57
  58. 58. 1. 2. 3. 4. 5. Bamgboye EA. A companion of Medical statistics. Ibipress & Publishing Company, Ibada Nigeria 1st Edition 2006: 1-16. Dunn OJ. Basic statistics: A primer for the Biomedical Sciences. Johm Wiley and Sons Publishers 2nd Edition: 1-11. Kolawole EB. Statistical methods. Bolabay Publications Lagos, Nigeria 1st Edition 2006: 1-12. Taofeek I. Research methodology and dissertation writing for allied professionals. Cress Global Link Limited, Abuja 1st Edition 2006: 1-24 Park K. Park‟s textbook of Preventive Medicine and Social Medicine. M/s Banarsidas Bhanot Publishers 2004 18th Edition: 608-615 Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 58
  59. 59. 6. Dawnson B, Trapp R. Introduction to Medical Research in Basic and Clinical Biostatistics. Fourth Edition. McGraw-Hill Companies Inc: USA, 2004;p1-6 7. Prabhakara GN. Basics of Statistics in Biostatistics. JAYPEE:New Delhi; 2006; p11-16. 8. Dawnson B, Trapp R. Summarising Data and Presenting data in Tables and Graphs in Basic and Clinical Biostatistics. Fourth Edition. McGraw-Hill Companies Inc:USA, 2004;p23-60 Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 59
  60. 60.  What doesn‟t kill us makes us stronger  So see challenges as opportunities for personal growth Thank you Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 60
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