Introduction to biostatistics new with table and graphs.pptx

Introduction to biostatistics new with table and graphs

1. INTRODUCTION TO BIOSTATISTICS Dr Prasanna Mohan MPT., PhD Professor ,KCPT

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

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 Fisher Karl Pearson C.R. Rao Gauss -

7. Basis of Biostatistics

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

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 infiltrates 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 medical uncertainties

17. Which of the following is a characteristic of statistics? a) Subjective decision-making b) Qualitative analysis only c) Deals with numerical data d) Always provides exact results

18. What is the role of biostatistics in clinical trials? a) To design the layout of the hospital b) To analyze and interpret treatment outcomes c) To administer drugs to patients d) To select patients for surgery

19. 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.

20. 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

21. Role of Biostatistics in Medical Research

22. Question?

23. What is biostatistics? A )The study of biological systems b) The application of statistical methods to biological and health-related fields c) The collection of biological samples d) The study of diseases in populations

24. 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 ? How does one interpret abnormal values ?

25. Importance of Biostatistics in Health Sciences, Including Physiotherapy Evidence-based Practice : Biostatistics forms the foundation for evidence-based practice by providing data that informs clinical decisions Clinical Trials : It is critical in designing, conducting, and analyzing clinical trials to evaluate new treatments, drugs, or interventions Public Health Policy : Biostatistics supports decision-making in public health by analyzing population data Quality Control : In healthcare settings, biostatistics helps ensure quality care by analyzing patient outcomes and service performance Risk Factor Analysis : It helps identify risk factors for diseases or injuries, such as determining whether excessive running contributes to knee injuries in athletes

26. Characteristics of Statistics 1. Quantitative Nature: Deals with numerical data. Example: Measuring post-surgery recovery time. 2. Aggregation: Groups data. Example: Recovery times of 100 patients. 3. Variability: Identifies data variations. Example: Variability in physiotherapy outcomes.

27. Objective Analysis : Statistical methods provide objective conclusions based on data. For example, a clinical trial comparing two treatments for back pain will use statistical analysis to objectively determine which treatment is more effective. Comparison : Statistics allows for comparisons between groups or variables. For instance, comparing the effectiveness of two types of physiotherapy in treating lower back pain involves statistical analysis to understand which method yields better results

28. Branches of Statistics 1. Descriptive Statistics: Summarizes data. Example: Average recovery time from ACL surgery. 2. Inferential Statistics: Generalizes results from a sample. Example: New lower back pain treatment trial.

29. Descriptive Statistics Descriptive Statistics are Used by Researchers to Report on Populations and Samples In Sociology: Summary descriptions of measurements (variables) taken about a group of people By Summarizing Information, Descriptive Statistics Speed Up and Simplify Comprehension of a Group’s Characteristics

30. Descriptive Statistics Class A--IQs of 13 Students 102 115 128 109 131 89 98 106 140 119 93 97 110 Class B--IQs of 13 Students 127 162 131 103 96 111 80 109 93 87 120 105 109 An Illustration: Which Group is Smarter? Each individual may be different. If you try to understand a group by remembering the qualities of each member, you become overwhelmed and fail to understand the group.

31. Descriptive Statistics Which group is smarter now? Class A--Average IQ Class B--Average IQ 110.54 110.23 They’re roughly the same! With a summary descriptive statistic, it is much easier to answer our question.

32. Inferential statistics Inferential statistics is a branch of statistics that allows researchers to make conclusions or predictions about a population based on data collected from a sample. It goes beyond mere description of the data (as in descriptive statistics) by applying techniques to infer properties of the larger population.

33. Key Features of Inferential Statistics:

34. Parameters and Estimates • Parameter: A measure obtained from a population. Example: Average height of all volleyball players. • Estimate: A measure obtained from a sample. Example: Average height of a sample of players.

35. Question?

36. Which branch of statistics deals with summarizing data? a) Inferential statistics b) Descriptive statistics c) Predictive statistics d) Analytical statistics

37. What is the key difference between a parameter and an estimate? a) A parameter is from a sample, and an estimate is from a population b) A parameter is from a population, and an estimate is from a sample c) They are the same thing d) A parameter is an assumption, and an estimate is an observation

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

39. 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

40. TYPES OF DATA QUALITATIVE DATA DISCRETE QUANTITATIVE CONTINOUS QUANTITATIVE

41. QUALITATIVE Nominal Example: Sex ( M, F) Exam result (P, F) Blood Group (A,B, O or AB) Color of Eyes (blue, green, brown, black)

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

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

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

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

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

47. 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)

48. 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.

49. 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.

50. 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