1. INTRODUCTION TOINTRODUCTION TO
BIOSTATISTICS IN NURSINGBIOSTATISTICS IN NURSING
Mr.MIDHUN KURIANMr.MIDHUN KURIAN
Associate . ProfessorAssociate . Professor
Dept. of Pediatric NursingDept. of Pediatric Nursing
SGRRCOLLEGE OF NURSINGSGRRCOLLEGE OF NURSING
SGRR UNIVERSITYSGRR UNIVERSITY
2. This session covers:This session covers:
Introduction and development ofIntroduction and development of
BiostatisticsBiostatistics
Definition of Statistics and BiostatisticsDefinition of Statistics and Biostatistics
Reasons to know about BiostatisticsReasons to know about Biostatistics
Types of dataTypes of data
Graphical representation of a dataGraphical representation of a data
Frequency distribution of a dataFrequency distribution of a data
3. ““Statistics is the science whichStatistics is the science which
deals with collection,deals with collection,
classification and tabulation ofclassification and tabulation of
numerical facts as the basis fornumerical facts as the basis for
explanation, description andexplanation, description and
comparison of phenomenon”.comparison of phenomenon”.
------ Lovitt------ Lovitt
4. Origin and development of statistics inOrigin and development of statistics in
Medical ResearchMedical Research
In 1929 a huge paper on application ofIn 1929 a huge paper on application of
statistics was published in Physiologystatistics was published in Physiology
Journal by Dunn.Journal by Dunn.
In 1937, 15 articles on statistical methodsIn 1937, 15 articles on statistical methods
by Austin Bradford Hill, were published inby Austin Bradford Hill, were published in
book form.book form.
In 1948, a RCT of Streptomycin forIn 1948, a RCT of Streptomycin for
pulmonary tb., was published in whichpulmonary tb., was published in which
Bradford Hill has a key influence.Bradford Hill has a key influence.
Then the growth of Statistics in MedicineThen the growth of Statistics in Medicine
from 1952 was a 8-fold increase by 1982.from 1952 was a 8-fold increase by 1982.
6. ““BIOSTATISICSBIOSTATISICS””
(1) Statistics arising out of biological(1) Statistics arising out of biological
sciences, particularly from the fields ofsciences, particularly from the fields of
Medicine and public health.Medicine and public health.
(2) The methods used in dealing with(2) The methods used in dealing with
statistics in the fields of medicine, biologystatistics in the fields of medicine, biology
and public health for planning, conductingand public health for planning, conducting
and analyzing data which arise inand analyzing data which arise in
investigations of these branches.investigations of these branches.
7. Reasons to know about biostatistics:Reasons to know about biostatistics:
Medicine is becoming increasinglyMedicine is becoming increasingly
quantitative.quantitative.
The planning, conduct and interpretationThe planning, conduct and interpretation
of much of medical research are becomingof much of medical research are becoming
increasingly reliant on the statisticalincreasingly reliant on the statistical
methodology.methodology.
Statistics pervades the medical literature.Statistics pervades the medical literature.
8. WHAT DOES STAISTICSWHAT DOES STAISTICS
COVER ?COVER ?
PlanningPlanning
DesignDesign
Execution (Data collection)Execution (Data collection)
Data ProcessingData Processing
Data analysisData analysis
PresentationPresentation
InterpretationInterpretation
PublicationPublication
9. HOW A “BIOSTATISTICIAN” CANHOW A “BIOSTATISTICIAN” CAN
HELP ?HELP ?
Design of studyDesign of study
Sample size & power calculationsSample size & power calculations
Selection of sample and controlsSelection of sample and controls
Designing a questionnaireDesigning a questionnaire
Data ManagementData Management
Choice of descriptive statistics & graphsChoice of descriptive statistics & graphs
Application of univariate and multivariateApplication of univariate and multivariate
statistical analysis techniquesstatistical analysis techniques
10. TYPES OF DATATYPES OF DATA
QUALITATIVE DATAQUALITATIVE DATA
DISCRETE QUANTITATIVEDISCRETE QUANTITATIVE
CONTINOUS QUANTITATIVECONTINOUS QUANTITATIVE
11. QUALITATIVEQUALITATIVE
NominalNominal
Example: Sex ( M, F)Example: Sex ( M, F)
Exam result (P, F)Exam result (P, F)
Blood Group (A,B, O or AB)Blood Group (A,B, O or AB)
Color of Eyes (blue, green,Color of Eyes (blue, green,
brown, black)brown, black)
12. ORDINALORDINAL
Example:Example:
Response to treatmentResponse to treatment
(poor, fair, good)(poor, fair, good)
Severity of diseaseSeverity of disease
(mild, moderate, severe)(mild, moderate, severe)
Income status (low, middle,Income status (low, middle,
high)high)
13. QUANTITATIVE (DISCRETE)QUANTITATIVE (DISCRETE)
Example: The no. of family membersExample: The no. of family members
The no. of heart beatsThe no. of heart beats
The no. of admissions in a dayThe no. of admissions in a day
QUANTITATIVE (CONTINOUS)QUANTITATIVE (CONTINOUS)
Example: Height, Weight, Age, BP,Example: Height, Weight, Age, BP,
SerumSerum
Cholesterol and BMICholesterol and BMI
14. Discrete data -- Gaps between possible values
Continuous data -- Theoretically,
no gaps between possible values
Number of Children
Hb
15. CONTINUOUS DATACONTINUOUS DATA
DISCRETE DATADISCRETE DATA
wt. (in Kg.) : under wt, normal & over wt.wt. (in Kg.) : under wt, normal & over wt.
Ht. (in cm.): short, medium & tallHt. (in cm.): short, medium & tall
16. hospital length of stay Number Percent
1 – 3 days 5891 43.3
4 – 7 days 3489 25.6
2 weeks 2449 18.0
3 weeks 813 6.0
1 month 417 3.1
More than 1 month 545 4.0
Total 14604 100.0
Mean = 7.85 SE = 0.10
Table 1 Distribution of blunt injured patients
according to hospital length of stay
17. Scale of measurementScale of measurement
Qualitative variable:
A categorical variable
Nominal (classificatory) scale
- gender, marital status, race
Ordinal (ranking) scale
- severity scale, good/better/best
18. Scale of measurementScale of measurement
Quantitative variable:
A numerical variable: discrete; continuous
Interval scale :
Data is placed in meaningful intervals and order. The unit of
measurement are arbitrary.
- Temperature (37º C -- 36º C; 38º C-- 37º C are equal) and
No implication of ratio (30º C is not twice as hot as 15º C)
19. Ratio scale:
Data is presented in frequency distribution in
logical order. A meaningful ratio exists.
- Age, weight, height, pulse rate
- pulse rate of 120 is twice as fast as 60
- person with weight of 80kg is twice as heavy
as the one with weight of 40 kg.
20. Scales of MeasureScales of Measure
NominalNominal – qualitative classification of– qualitative classification of
equal value: gender, race, color, cityequal value: gender, race, color, city
OrdinalOrdinal - qualitative classification- qualitative classification
which can be rank ordered:which can be rank ordered:
socioeconomic status of familiessocioeconomic status of families
IntervalInterval - Numerical or quantitative- Numerical or quantitative
data: can be rank ordered and sizesdata: can be rank ordered and sizes
compared : temperaturecompared : temperature
RatioRatio - Quantitative interval data along- Quantitative interval data along
with ratio: time, age.with ratio: time, age.
25. Frequency DistributionsFrequency Distributions
data distribution – pattern ofdata distribution – pattern of
variability.variability.
the center of a distributionthe center of a distribution
the rangesthe ranges
the shapesthe shapes
simple frequency distributionssimple frequency distributions
grouped frequency distributionsgrouped frequency distributions
midpointmidpoint
26. PatienPatien
t Not No
HbHb
(g/dl)(g/dl)
PatienPatien
t Not No
HbHb
(g/dl)(g/dl)
PatienPatien
t Not No
HbHb
(g/dl)(g/dl)
11 12.012.0 1111 11.211.2 2121 14.914.9
22 11.911.9 1212 13.613.6 2222 12.212.2
33 11.511.5 1313 10.810.8 2323 12.212.2
44 14.214.2 1414 12.312.3 2424 11.411.4
55 12.312.3 1515 12.312.3 2525 10.710.7
66 13.013.0 1616 15.715.7 2626 12.512.5
77 10.510.5 1717 12.612.6 2727 11.811.8
88 12.812.8 1818 9.19.1 2828 15.115.1
99 13.213.2 1919 12.912.9 2929 13.413.4
1010 11.211.2 2020 14.614.6 3030 13.113.1
Tabulate the hemoglobin values of 30 adultTabulate the hemoglobin values of 30 adult
male patients listed belowmale patients listed below
27. Steps for making a tableSteps for making a table
Step1 Find Minimum (9.1) & MaximumStep1 Find Minimum (9.1) & Maximum
(15.7)(15.7)
Step2 Calculate difference 15.7 – 9.1 = 6.6Step2 Calculate difference 15.7 – 9.1 = 6.6
Step3 Decide the number and width ofStep3 Decide the number and width of
the classes (7 c.l) 9.0 -9.9, 10.0-the classes (7 c.l) 9.0 -9.9, 10.0-
10.9,----10.9,----
Step4 Prepare dummy table –Step4 Prepare dummy table –
Hb (g/dl), Tally mark, No. patientsHb (g/dl), Tally mark, No. patients
29. Hb (g/dl) No. of
patients
9.0 – 9.9
10.0 – 10.9
11.0 – 11.9
12.0 – 12.9
13.0 – 13.9
14.0 – 14.9
15.0 – 15.9
1
3
6
10
5
3
2
Total 30
Table Frequency distribution of 30 adult maleTable Frequency distribution of 30 adult male
patients by Hbpatients by Hb
30. Table Frequency distribution of adult patients byTable Frequency distribution of adult patients by
Hb and gender:Hb and gender:
Hb
(g/dl)
Gender Total
Male Female
<9.0
9.0 – 9.9
10.0 – 10.9
11.0 – 11.9
12.0 – 12.9
13.0 – 13.9
14.0 – 14.9
15.0 – 15.9
0
1
3
6
10
5
3
2
2
3
5
8
6
4
2
0
2
4
8
14
16
9
5
2
Total 30 30 60
31. Elements of a TableElements of a Table
Ideal table should have Number
Title
Column headings
Foot-notes
Number – Table number for identification in a report
Title,place - Describe the body of the table, variables,
Time period (What, how classified, where and when)
Column - Variable name, No. , Percentages (%), etc.,
Heading
Foot-note(s) - to describe some column/row headings,
special cells, source, etc.,
32. Death rate (/1000 per annum)No. of divisions
7.0-7.9 4 (3.3)
8.0 - 8.9 13 (10.8)
9.0 - 9.9 20 (16.7)
10.0 - 10.9 27 (22.5)
11.0 - 11.9 18 (15.0)
12.0 - 12.9 11 (0.2)
13.0 - 13.9 11 (9.2)
14.0 - 14.9 6 (5.0)
15.0 - 15.9 2 (1.7)
16.0 - 16.9 4 (3.3)
17.0 - 18.9 3 (2.5)
19.0 + 1 (0.8)
Total 120 (100.0)
Table II. Distribution of 120 (Madras) Corporation divisions
according to annual death rate based on registered deaths in
1975 and 1976
Figures in parentheses indicate percentages
33. DIAGRAMS/GRAPHSDIAGRAMS/GRAPHS
Discrete dataDiscrete data
--- Bar charts (one or two groups)--- Bar charts (one or two groups)
Continuous dataContinuous data
--- Histogram--- Histogram
--- Frequency polygon (curve)--- Frequency polygon (curve)
--- Stem-and –leaf plot--- Stem-and –leaf plot
--- Box-and-whisker plot--- Box-and-whisker plot
40. Descriptive statistics report: BoxplotDescriptive statistics report: Boxplot
- minimum score
- maximum score
- lower quartile
- upper quartile
- median
- mean
- the skew of the distribution:
positive skew: mean > median & high-score whisker is longer
negative skew: mean < median & low-score whisker is longer
41. 10%
20%
70%
Mild
Moderate
Severe
The prevalence of different degree of
Hypertension
in the population
Pie Chart
•Circular diagram – total -100%
•Divided into segments each
representing a category
•Decide adjacent category
•The amount for each category is
proportional to slice of the pie
42. Bar GraphsBar Graphs
9
12
20
16
12
8
20
0
5
10
15
20
25
Smo Alc Chol DM HTN No
Exer
F-H
Riskfactor
Number
The distribution of risk factor among cases with
Cardio vascular Diseases
Heights of the bar indicates
frequency
Frequency in the Y axis
and categories of variable
in the X axis
The bars should be of equal
width and no touching the
other bars
43. HIV cases enrolment in USA byHIV cases enrolment in USA by
gendergender
0
2
4
6
8
10
12
1986 1987 1988 1989 1990 1991 1992
Year
Enrollment(hundred)
Men
Women
Bar chart
44. HIV cases EnrollmentHIV cases Enrollment
in USA by genderin USA by gender
0
2
4
6
8
10
12
14
16
18
1986 1987 1988 1989 1990 1991 1992
Year
Enrollment(Thousands)
Women
Men
Stocked bar chart
45. Graphic Presentation of DataGraphic Presentation of Data
the histogram
(quantitative data)
the bar graph
(qualitative data)
the frequency polygon
(quantitative data)
46.
47. General rules for designing graphsGeneral rules for designing graphs
A graph should have a self-explanatoryA graph should have a self-explanatory
legendlegend
A graph should help reader toA graph should help reader to
understand dataunderstand data
Axis labeled, units of measurementAxis labeled, units of measurement
indicatedindicated
Scales important. Start with zeroScales important. Start with zero
(otherwise // break)(otherwise // break)
Avoid graphs with three-dimensionalAvoid graphs with three-dimensional
impression, it may be misleading (readerimpression, it may be misleading (reader
visualize less easilyvisualize less easily
Nominal variables allow for only qualitative classification. That is, they can be measured only in terms of whether the individual items belong to some distinctively different categories, but we cannot quantify or even rank order those categories. For example, all we can say is that 2 individuals are different in terms of variable A (e.g., they are of different race), but we cannot say which one &quot;has more&quot; of the quality represented by the variable. Typical examples of nominal variables are gender, race, color, city, etc.
Ordinal variables allow us to rank order the items we measure in terms of which has less and which has more of the quality represented by the variable, but still they do not allow us to say &quot;how much more.&quot; A typical example of an ordinal variable is the socioeconomic status of families. For example, we know that upper-middle is higher than middle but we cannot say that it is, for example, 18% higher. Also this very distinction between nominal, ordinal, and interval scales itself represents a good example of an ordinal variable. For example, we can say that nominal measurement provides less information than ordinal measurement, but we cannot say &quot;how much less&quot; or how this difference compares to the difference between ordinal and interval scales.
Interval variables allow us not only to rank order the items that are measured, but also to quantify and compare the sizes of differences between them. For example, temperature, as measured in degrees Fahrenheit or Celsius, constitutes an interval scale. We can say that a temperature of 40 degrees is higher than a temperature of 30 degrees, and that an increase from 20 to 40 degrees is twice as much as an increase from 30 to 40 degrees.
Ratio variables are very similar to interval variables; in addition to all the properties of interval variables, they feature an identifiable absolute zero point, thus they allow for statements such as x is two times more than y. Typical examples of ratio scales are measures of time or space. For example, as the Kelvin temperature scale is a ratio scale, not only can we say that a temperature of 200 degrees is higher than one of 100 degrees, we can correctly state that it is twice as high. Interval scales do not have the ratio property. Most statistical data analysis procedures do not distinguish between the interval and ratio properties of the measurement scales.
This shows relative trends in admissions by gender.
This emphasizes the constancy of the overall admissions and shows the trends subtly.