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

2. This session covers:  Background and need to know Biostatistics  Origin and development of Biostatistics  Definition of Statistics and Biostatistics  Types of data  Graphical representation of a data  Frequency distribution of a data

3.  “ 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

4. “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.

5. 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.

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

7. Basis

8. Sources of Medical Uncertainties 1.Intrinsic due to biological, environmental and sampling factors 2.Natural variation among methods, observers, instruments etc. 3.Errors in measurement or assessment or errors in knowledge 4.Incomplete knowledge

9. Intrinsic variation as a source of medical uncertainties  Biological due to age, gender, heredity, parity, height, weight, etc. Also due to variation in anatomical, physiological and biochemical parameters  Environmental due to nutrition, smoking, pollution, facilities of water and sanitation, road traffic, legislation, stress and strains etc.,  Sampling fluctuations because the entire world cannot be studied and at least future cases can never be included  Chance variation due to unknown or complex to comprehend factors

10. Natural variation despite best care as a source of uncertainties  In assessment of any medical parameter  Due to partial compliance by the patients  Due to incomplete information in conditions such as the patient in coma

11. Medical Errors that cause Uncertainties  Carelessness of the providers such as physicians, surgeons, nursing staff, radiographers and pharmacists.  Errors in methods such as in using incorrect quantity or quality of chemicals and reagents, misinterpretation of ECG, using inappropriate diagnostic tools, misrecording of information etc.  Instrument error due to use of non-standardized or faulty instrument and improper use of a right instrument.  Not collecting full information  Inconsistent response by the patients or other subjects under evaluation

12. Incomplete knowledge as a source of Uncertainties  Diagnostic, therapeutic and prognostic uncertainties due to lack of knowledge  Predictive uncertainties such as in survival duration of a patient of cancer  Other uncertainties such as how to measure positive health

13. Biostatistics is the science that helps in managing medical uncertainties Biostatistics is the science that helps in managing medical uncertainties

14. 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.

15. 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”.

16. Role of Biostatistics in patient care  In increasing awareness regarding diagnostic, therapeutic and prognostic uncertainties and providing rules of probability to delineate those uncertainties  In providing methods to integrate chances with value judgments that could be most beneficial to patient  In providing methods such as sensitivity-specificity and predictivities that help choose valid tests for patient assessment  In providing tools such as scoring system and expert system that can help reduce epistemic uncertainties

17. 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.

18. Role of Biostatics in Health Planning and Evaluation  In carrying out a valid and reliable health situation analysis, including in proper summarization and interpretation of data.  In proper evaluation of the achievements and failures of a health programme

19. Role of Biostatistics in Medical Research  In developing a research design that can minimize the impact of uncertainties  In assessing reliability and validity of tools and instruments to collect the infromation  In proper analysis of data

20. 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 ?

21. 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

22. BASIC CONCEPTS Data : Set of values of one or more variables recorded on one or more observational units Categories of data 1. Primary data: observation, questionnaire, record form, interviews, survey, 2. Secondary data: census, medical record,registry Sources of data 1. Routinely kept records 2. Surveys (census) 3. Experiments 4. External source

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

24. 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)

25. 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)

26. 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

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

28. CONTINUOUS DATA CONTINUOUS DATA QUALITATIVE DATA QUALITATIVE 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

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

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

31. 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)

32. 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.

33. 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.

34. CLINIMETRICS A science called clinimetrics in which qualities are converted to meaningful quantities by using the scoring system. Examples: (1) Apgar score based on appearance, pulse, grimace, activity and respiration is used for neonatal prognosis. (2) Smoking Index: no. of cigarettes, duration, filter or not, whether pipe, cigar etc., (3) APACHE( Acute Physiology and Chronic Health Evaluation) score: to quantify the severity of condition of a patient

38. INVESTIGATION Data Colllection Data Presentation Tabulation Diagrams Graphs Descriptive Statistics Measures of Location Measures of Dispersion Measures of Skewness & Kurtosis Inferential Statistiscs Estimation Hypothesis Testing Ponit estimate Inteval estimate Univariate analysis Multivariate analysis

39. 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

40. Patien t No Hb(g/dl) Hb(g/dl) Hb(g/dl) 112.01111.22114.9 211.91213.62212.2 311.51310.82312.2 414.21412.32411.4 512.31512.32510.7 613.01615.72612.5 710.51712.62711.8 812.8189.12815.1 913.21912.92913.4 1011.22014.63013.1 Tabulate the hemoglobin values of 30 adult male patients listed below

41. 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) 9.0 -9.9, 10.0-10.9,---- the classes (7 c.l) 9.0 -9.9, 10.0-10.9,---- Step4 Prepare dummy table – Hb (g/dl), Tally mark, No. patients Hb (g/dl), Tally mark, No. patients

42. Hb (g/dl)Tall marksNo. 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 Total Hb (g/dl) Tall marksNo. 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 l lll llll lll ll 1 3 6 10 5 3 2 Total-30 DUMMY TABLE Tall Marks TABLE

43. 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 Total30 Table Frequency distribution of 30 adult male patients by Hb

44. Table Frequency distribution of adult patients by Hb and gender: Hb and gender: Hb (g/dl) GenderTotal MaleFemale <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 2358642023586420 4 8 14 16 9 5 2 Total30 60

45. 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.,

46. 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

47. 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

48. Example data 6863422730362832 7927222824254465 4325745136422831 2825451257511232 4938422731503821 1624644723224327 4928231911524631 30434912

49. Histogram Figure 1 Histogram of ages of 60 subjects

50. Polygon

51. Example data 6863422730362832 7927222824254465 4325745136422831 2825451257511232 4938422731503821 1624644723224327 4928231911524631 30434912

52. Stem and leaf plot Stem-and-leaf of Age N = 60 Leaf Unit = 1.0 6 1 122269 19 2 1223344555777788888 (11) 3 00111226688 13 4 2223334567999 5 5 01127 4 6 3458 2 7 49

53. Box plot

54. 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

55. 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

56. 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

57. HIV cases enrolment in USA by gender Bar chart

58. HIV cases Enrollment in USA by gender Stocked bar chart

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

61. 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

62. Any Questions

63. 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.