INTRODUCTION TO BIOSTATISTICS DR.S.Shaffi Ahamed Asst. Professor Dept. of Family and Comm. Medicine KKUH

This session covers:  Origin and development of Biostatistics  Definition of Statistics and Biostatistics  Reasons to know about Biostatistics  Types of data  Graphical representation of a data  Frequency distribution of a data

 “ Statistics is the science which deals with collection, classification and tabulation of numerical facts as the basis for explanation, description and comparison of phenomenon” Lovitt Lovitt

Origin and development of statistics in Medical Research  In 1929 a huge paper on application of statistics was published in Physiology Journal by Dunn.  In 1937, 15 articles on statistical methods by Austin Bradford Hill, were published in book form.  In 1948, a RCT of Streptomycin for pulmonary tb., was published in which Bradford Hill has a key influence.  Then the growth of Statistics in Medicine from 1952 was a 8-fold increase by 1982.

Douglas Altman Ronald FisherKarl Pearson C.R. Rao Gauss -

“BIOSTATISICS ”  (1) Statistics arising out of biological sciences, particularly from the fields of Medicine and public health.  (2) The methods used in dealing with statistics in the fields of medicine, biology and public health for planning, conducting and analyzing data which arise in investigations of these branches.

Reasons to know about biostatistics:  Medicine is becoming increasingly quantitative.  The planning, conduct and interpretation of much of medical research are becoming increasingly reliant on the statistical methodology.  Statistics pervades the medical literature.

Example: Evaluation of Penicillin (treatment A) vs Penicillin & Chloramphenicol (treatment B) for treating bacterial pneumonia in children< 2 yrs.  What is the sample size needed to demonstrate the significance of one group against other ?  Is treatment A is better than treatment B or vice versa ?  If so, how much better ?  What is the normal variation in clinical measurement ? (mild, moderate & severe) ?  How reliable and valid is the measurement ? (clinical & radiological) ?  What is the magnitude and effect of laboratory and technical error ? error ?  How does one interpret abnormal values ?

CLINICAL MEDICINE  Documentation of medical history of diseases.  Planning and conduct of clinical studies.  Evaluating the merits of different procedures.  In providing methods for definition of “normal” and “abnormal”.

PREVENTIVE MEDICINE  To provide the magnitude of any health problem in the community.  To find out the basic factors underlying the ill-health.  To evaluate the health programs which was introduced in the community (success/failure).  To introduce and promote health legislation.

WHAT DOES STAISTICS COVER ? Planning Planning Design Design Execution (Data collection) Execution (Data collection) Data Processing Data Processing Data analysis Data analysis Presentation Presentation Interpretation Interpretation Publication Publication

HOW A “BIOSTATISTICIAN” CAN HELP ?  Design of study  Sample size & power calculations  Selection of sample and controls  Designing a questionnaire  Data Management  Choice of descriptive statistics & graphs  Application of univariate and multivariate statistical analysis techniques statistical analysis techniques

INVESTIGATION

TYPES OF DATA  QUALITATIVE DATA  DISCRETE QUANTITATIVE  CONTINOUS QUANTITATIVE

QUALITATIVE Nominal 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)

ORDINAL ORDINAL Example: Example: Response to treatment Response to treatment (poor, fair, good) (poor, fair, good) Severity of disease Severity of disease (mild, moderate, severe) (mild, moderate, severe) Income status (low, middle, Income status (low, middle, high) high)

QUANTITATIVE (DISCRETE) Example: The no. of family members Example: The no. of family members The no. of heart beats The no. of heart beats The no. of admissions in a day The no. of admissions in a day QUANTITATIVE (CONTINOUS) Example: Height, Weight, Age, BP, Serum Example: Height, Weight, Age, BP, Serum Cholesterol and BMI Cholesterol and BMI

Discrete data -- Gaps between possible values Continuous data -- Theoretically, no gaps between possible values Number of Children Hb

CONTINUOUS DATA CONTINUOUS DATA DISCRETE DATA DISCRETE DATA wt. (in Kg.) : under wt, normal & over wt. wt. (in Kg.) : under wt, normal & over wt. Ht. (in cm.): short, medium & tall Ht. (in cm.): short, medium & tall

Table 1 Distribution of blunt injured patients according to hospital length of stay

Scale of measurement Qualitative variable: A categorical variable Nominal (classificatory) scale - gender, marital status, race Ordinal (ranking) scale - severity scale, good/better/best

Scale 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)

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.

Scales of Measure  Nominal – qualitative classification of equal value: gender, race, color, city  Ordinal - qualitative classification which can be rank ordered: socioeconomic status of families  Interval - Numerical or quantitative data: can be rank ordered and sizes compared : temperature  Ratio - Quantitative interval data along with ratio: time, age.

INVESTIGATION

Frequency Distributions Frequency Distributions  data distribution – pattern of variability.  the center of a distribution  the ranges  the shapes  simple frequency distributions  grouped frequency distributions  midpoint

Patien t No Hb(g/dl) Hb(g/dl) Hb(g/dl) Tabulate the hemoglobin values of 30 adult male patients listed below

Steps for making a table Step1 Find Minimum (9.1) & Maximum (15.7) Step2 Calculate difference 15.7 – 9.1 = 6.6 Step3 Decide the number and width of the classes (7 c.l) , ,---- the classes (7 c.l) , ,---- Step4 Prepare dummy table – Hb (g/dl), Tally mark, No. patients Hb (g/dl), Tally mark, No. patients

Hb (g/dl)Tall marksNo. patients 9.0 – – – – – – – 15.9 Total Hb (g/dl) Tall marksNo. patients 9.0 – – – – – – – 15.9 l lll llll lll ll Total-30 DUMMY TABLE Tall Marks TABLE

Hb (g/dl)No. of patients 9.0 – – – – – – – Total30 Table Frequency distribution of 30 adult male patients by Hb

Table Frequency distribution of adult patients by Hb and gender: Hb and gender: Hb (g/dl) GenderTotal MaleFemale < – – – – – – – Total30 60

Elements 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.,

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

DIAGRAMS/GRAPHS Discrete data --- Bar charts (one or two groups) --- Bar charts (one or two groups) Continuous 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

Example data

Histogram Figure 1 Histogram of ages of 60 subjects

Polygon

Example data

Stem and leaf plot Stem-and-leaf of Age N = 60 Leaf Unit = (11)

Box plot

Descriptive 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

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

Bar Graphs 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

HIV cases enrolment in USA by gender Bar chart

HIV cases Enrollment in USA by gender Stocked bar chart

Graphic Presentation of Data the histogram (quantitative data) the bar graph (qualitative data) the frequency polygon (quantitative data)

General rules for designing graphs  A graph should have a self-explanatory legend  A graph should help reader to understand data  Axis labeled, units of measurement indicated  Scales important. Start with zero (otherwise // break)  Avoid graphs with three-dimensional impression, it may be misleading (reader visualize less easily

Any Questions