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INTRODUCTION TO BIOSTATISTICS

INTRODUCTION TO BIOSTATISTICS. DR.S.Shaffi Ahamed Asst. Professor Dept. of Family and Comm. Medicine KKUH. This session covers:. Background and need to know Biostatistics Definition of Statistics and Biostatistics Types of data Graphical representation of a data

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INTRODUCTION TO BIOSTATISTICS

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  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 • Definition of Statistics and Biostatistics • Types of data • Graphical representation of a data • Frequency distribution of a data

  3. Basis

  4. Dynamic nature of theU n i v e r s ethe very continuous change in Nature brings - uncertainty and - variabilityin each and every sphere of the Universe

  5. We by no mean cancontrol or over-powerthe factor of uncertainty but capable of measuring it in terms of Probability

  6. Sources of Medical Uncertainties • Natural variation 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

  7. Biostatistics is the science which helps in managing health care uncertainties

  8. “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

  9. “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.

  10. 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.

  11. 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”.

  12. 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.

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

  14. TYPES OF DATA • QUALITATIVE DATA • DISCRETE QUANTITATIVE • CONTINOUS QUANTITATIVE

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

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

  17. 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

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

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

  20. Scale of measurement Quantitative variable: A numerical variable: discrete; continuous Intervalscale : 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)

  21. 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.

  22. 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.

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

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

  25. 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

  26. INVESTIGATION

  27. Frequency Distributions “A Picture is Worth a Thousand Words”

  28. Frequency Distributions • What is a frequency distribution? A frequency distribution is an organization of raw data in tabular form, using classes (or intervals) and frequencies. • What is a frequency count? The frequency or the frequency count for a data value is the number of times the value occurs in the data set.

  29. Frequency Distributions • data distribution – pattern of variability. • the center of a distribution • the ranges • the shapes • simple frequency distributions • grouped & ungrouped frequency distributions

  30. Categorical or Qualitative Frequency Distributions • What is a categorical frequency distribution? A categorical frequency distribution represents data that can be placed in specific categories, such as gender, blood group, & hair color, etc.

  31. Categorical or Qualitative Frequency Distributions -- Example • Example: The blood types of 25 blood donors are given below. Summarize the data using a frequency distribution. AB B A O B O B O A O B O B B B A O AB AB O A B AB O A

  32. Categorical Frequency Distribution for the Blood Types -- Example Continued Note: The classes for the distribution are the blood types.

  33. Quantitative Frequency Distributions -- Ungrouped • What is an ungrouped frequency distribution? An ungrouped frequency distribution simply lists the data values with the corresponding frequency counts with which each value occurs.

  34. Quantitative Frequency Distributions – Ungrouped -- Example • Example: The at-rest pulse rate for 16 athletes at a meet were 57, 57, 56, 57, 58, 56, 54, 64, 53, 54, 54, 55, 57, 55, 60, and 58. Summarize the information with an ungrouped frequency distribution.

  35. Quantitative Frequency Distributions – Ungrouped -- Example Continued Note: The (ungrouped) classes are the observed values themselves.

  36. Example of a simple frequency distribution (ungrouped) • 5 7 8 1 5 9 3 4 2 2 3 4 9 7 1 4 5 6 8 9 4 3 5 2 1 f • 9 3 • 8 2 • 7 2 • 6 1 • 5 4 • 4 4 • 3 3 • 2 3 • 1 3 f = 25

  37. Relative Frequency Distribution • Proportion of the total N • Divide the frequency of each score by N • Rel. f = f/N • Sum of relative frequencies should equal 1.0 • Gives us a frame of reference

  38. Relative Frequency Example:The relative frequency for the ungrouped class of 57 will be 4/16 = 0.25.

  39. Relative Frequency Distribution Note:The relative frequency for a class is obtained by computing f/n.

  40. Example of a simple frequency distribution • 5 7 8 1 5 9 3 4 2 2 3 4 9 7 1 4 5 6 8 9 4 3 5 2 1 f rel f • 9 3 .12 • 8 2 .08 • 7 2 .08 • 6 1 .04 • 5 4 .16 • 4 4 .16 • 3 3 .12 • 2 3 .12 • 1 3 .12 • f = 25  rel f = 1.0

  41. Cumulative Frequency and Cumulative Relative Frequency • NOTE: Sometimes frequency distributions are displayed with cumulative frequencies and cumulative relative frequencies as well.

  42. Cumulative Frequency and Cumulative Relative Frequency • What is a cumulative frequency for a class?The cumulative frequency for a specific class in a frequency table is the sum of the frequencies for all values at or below the given class.

  43. Cumulative Frequency and Cumulative Relative Frequency • What is a cumulative relative frequency for a class?The cumulative relative frequency for a specific class in a frequency table is the sum of the relative frequencies for all values at or below the given class.

  44. Cumulative Frequency and Cumulative Relative Frequency Note:Table with relative and cumulative relative frequencies.

  45. Example of a simple frequency distribution (ungrouped) • 5 7 8 1 5 9 3 4 2 2 3 4 9 7 1 4 5 6 8 9 4 3 5 2 1 f cf rel f rel. cf • 9 3 3 .12 .12 • 8 2 5 .08 .20 • 7 2 7 .08 .28 • 6 1 8 .04 .32 • 5 4 12 .16 .48 • 4 4 16 .16 .64 • 3 3 19 .12 .76 • 2 3 22 .12 .88 • 1 3 25 .12 1.0 • f = 25  rel f = 1.0

  46. Quantitative Frequency Distributions -- Grouped • What is a grouped frequency distribution? A grouped frequency distribution is obtained by constructing classes (or intervals) for the data, and then listing the corresponding number of values (frequency counts) in each interval.

  47. Tabulate the hemoglobin values of 30 adult male patients listed below

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