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Intro biostat1&2

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  • 1. By Dr Babatunde, OA MBBS, PgCertDPMIS, MPH, FWACP Department of Community Medicine, FMC, Ido-Ekiti
  • 2.   Definition (C-O-S-A-I-P) Collection Organization Summarizing Analyzing Interpreting Presenting Applications of biostatistics Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 2
  • 3.  A variable is any parameter that can be observed or measured  Information collected on a variable is usually unrefined and it is called data  The collection, analysis, interpretation and use of data is called statistics  The application of statistics to health-related fields is known as Biostatistics1 Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 3
  • 4.    Biostatistics = Medical statistics Medical statistics is the scientific method of collecting, organizing, summarizing, analyzing, interpreting, and presenting medical data1 Biostatistics is statistics applied to the biological sciences and to Medicine2 Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 4
  • 5.      Biostatistics is all about „curiosity‟3 Biostatistics is about asking medically relevant questions and getting answers using statistical methods Which age group dies most? Mortality rate What proportion of University students use condoms during sexual intercourse? Assignment 1: Each student should ask a medically related question of personal interest and submit it in the format below Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 5
  • 6.      Name: Matriculation Number: Medical question of personal interest Submit it at the end of the lecture Also document in your notebook because we will always make reference to this question throughout this class Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 6
  • 7.    Research is the scientific investigation of facts and relationships to establish dependable solutions to problems through systematic collection, analysis, and interpretation of data Research is described as systematic in that it involves an organized, formally structured methodology to obtain new knowledge Biostatistics is the basis for research Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 4 2/10/2014 7
  • 8.     It is a general phenomenon that many students do not have interest in statistics Many see it as too abstract to conceptualize However, it is the simplest form of all sciences being practiced by both literates and illiterates Grandmother statistics: A big stroke by a grandmother represents a birth while a small stroke represents a death (origin of tally sheet in immunization) Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 8
  • 9.      Biostatistics center around data Hence what is data? Data is information collected of an individual or group of individuals When entered into a computer, it is called dataset Assignment 2: List 5 examples of data you can collect to answer your question in assignment 1 Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 9
  • 10.   Example: How many students in this class use condom during sexual intercourse: 5 data set: 1. Ever had sex 2. Age at 1st sexual intercourse 3. Number of sexual intercourse in last 3 months 4. Number of times used condom 5. Number of sexual partners since sexual initiation Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 10
  • 11.        Questionnaires Observations (checklist) Focus Group Discussion Proforma Records Census List other ways you can collect data Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 11
  • 12.  4 Levels of measurement are involved in data collection (N-O-I-R) ◦ ◦ ◦ ◦ 1. 2. 3. 4. Nominal Ordinal Interval Ratio Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 12
  • 13.       Lowest level Mutually unordered category No notion of numerical magnitude Any number assigned has no numerical value other than to distinguish one category from another. Examples: Gender, Blood Group, Marital status Assignment 3: List 5 more examples of Nominal scale Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 13
  • 14.      Ability to rank or order phenomenon In addition to nominal propert It is defined by related category Examples: Patients pain coditions desribed as Mild, Moderate, Severe Assignment 4: List 5 more examples of Ordinal scale of measurement Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 14
  • 15.     Measurements are expressed in numbers The starting point is arbitrary depending largely on the units of measurement It is possible to attach physical meanings to differences of 2 measurements (intervals) but not to their ratios Examples: Temperature-Centigrade or Fahrenheit Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 15
  • 16.    Measurement on this scale has 3 previously mentioned properties but in addition has a true zero point The ratio of any 2 measurements on the scale is physically meaningful Examples: Height in cm, Weight in Kg, Age in years. Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 16
  • 17. Level Summary Example Nominal Categories only. Data cannot be arranged in an ordering scheme Student’s car: 1 Ford, 2 Toyota, 3 BMW Ordinal Categories are ordered, but differences cannot be determined or they are meaningless Student’s car: 1 Compact, 2 Mid-size, 3 Full size Interval Differences between values can be found, but there may be no inherent starting point. Ratios are not meaningful Temperature: 45 , 80 , 90 Ratio Like interval scale, but with an inherent starting point. Ratios are meaningful Weights of football players: 200 lbs, 300 lbs, 400 lbs Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 17
  • 18. Theoretical interest is not the primary reason why researchers and statisticians consider the level of measurement of a variable. Level of measurement is important because the kinds of statistical procedures that can be appropriately used depend on the level of measurement of the variable studied. Calculating mean telephone number of a group of people’s telephone number would be possible but ridiculous, since telephone number is a nominal scale level variable. Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 18
  • 19.    Raw data is usually not too useful It has to be organized to make sense out of it This brings us to types of statistics: ◦ Descriptive: Frequency tables, Diagrams ◦ Inferential: Use of statistical tests Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 19
  • 20.   Primary data Data that is obtained directly from an individual e.g. 2006 Census Secondary data Data that is obtained from outside source e.g. studying of hospital records 5 Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 20
  • 21.  A Special type of Discrete Variable is the Binary Variable which takes on exactly 2 possible values ◦ Gender (M/F) ◦ Pregnant? (Y/N) ◦ Hypertensive? (Y/N) Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 21
  • 22.  Sometimes, discrete variables have a “natural ordering” to them ◦ For example, names of consecutive days in a week (M, Tu, Wed, Thurs, Fri, Sat, Sun)  Other types of discrete variables do not have a natural order and are called Nominal Variables ◦ Race (African American, Caucasian, Asian, Hispanic etc.) Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 22
  • 23.    If in an experiment you measure a single variable, it is called a Univariate experiment If you measure 2 variables, it is called a Bivariate experiment And if you measure multiple variables, it is called a Multivariate experiment Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 23
  • 24.     Concerned with summarizing series of measurements or observations A] Measures of Central tendency B] Measures of Variability/Dispersion C] Measures of Relative standing Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 24
  • 25.  Now that we have displayed our data, we want to be able to characterize it quantitatively ◦ Measures of Central Tendency  Mean, Median, Mode ◦ Measures of Variability  Range, Variance, Standard Deviation ◦ Measures of Relative Standing  Z-Scores, Percentiles, Quartiles Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 25
  • 26.  Mean ◦ Arithmetic Average of a sample of data  Median ◦ If you order the data from smallest to highest, the median is the middle value, assuming an odd number of data elements ◦ If you have an even number of elements, it is the average of the 2 middle numbers.  Mode ◦ The most common value in a set of values Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 26
  • 27.    i. Arithmetic Mean: This is different from other types of mean like geometric mean and harmonic mean. The arithmetic mean is simply the average, denoted by the symbols shown: [μ,-x, ie miu or x-bar]. These symbols are used to represent arithmetic mean of population [N] and sample [n] respectively. Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 27
  • 28.    Median: Here the distribution is arrayed or arranged in a particular pattern. Then look at the value which cuts this distribution into two equal parts. That value in array which divides it into two equal parts is called the median. Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 28
  • 29.    Mode: This is the most frequently occurring value in a distribution. Some distributions are described as amodal because they have no mode. A distribution with one mode is uni-modal and that with two modes is called bimodal distribution. Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 29
  • 30.  If you stop learning you are old, whether you are 20 or 80 years Thank you Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 30
  • 31. By Dr Babatunde, OA MBBS, PgCertDPMIS, MPH, FWACP Department of Community Medicine, FMC, Ido-Ekiti
  • 32.  This is one of the simplest measures of variability.  This is simply the difference between the highest and the lowest values; R=XH-XL.  The range has a problem of looking at two extremes alone and ignores other values. Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 32
  • 33.  In the following distribution; 9, 4, 2, 5, 10 [which has a mean of 6], the total deviation from the mean or the average is always zero.  Since the total or average mean deviation is useless, something is done to get around the problem.  Thus we square the deviations and sum them up and we get 46.  Now the average of the squared deviations is got by dividing by number of observations.  This is called variance [S2, σ2], sample and population variance respectively. Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 33
  • 34. tables  charts  diagrams  graphs  pictures  special curves  Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 34
  • 35. Numbering eg table 1, table 2, etc  Title which must be brief and self explanatory  Headings of columns and rows should be clear and concise  Data must be presented according to size or importance, chronologically, alphabetically or geographically  If percentages or averages are to be compared, they must be placed as close as possible  No table may be too large  Footnotes may be given where necessary  Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 35
  • 36.  Charts and diagrams; These methods of presentation have powerful impact on the imagination of people. So they are a popular media of exposing statistical data a. Bar charts; these are a way of presenting a set of numbers by the length of a bar- length of bar being proportional to the magnitude to be represented Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 36
  • 37.  simple bar chart; bars may be vertical or horizontal are usually separated by appropriate spaces with an eye on neatness and clear presentation   Multiple bar charts; Here two or more bars are grouped together. Component bar chart; Here the bar may be divided into two or more parts. Each part represents a certain item and proportional to the magnitude of that particular item. Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 37
  • 38.  b. Histogram; this is a pictorial diagram of frequency distribution  It consists of a series of block  The class intervals are given along the horizontal axis and frequency on the vertical axis  The area of each block or rectangle is proportional to the frequency  The histogram is apt for representing continuous variables. Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 38
  • 39.    i. it is like the simple bar chart except that the bars of histogram touch each other ii. The height of each box is equal to the frequency {ie for equal intervals} of class it represents iii. The interval with the highest box is called the modal interval ie interval that contains the mode. Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 39
  • 40.   c. Frequency polygon; a frequency distribution may also be represented diagrammatically by the frequency polygon It‟s obtained by joining the midpoints of the histogram blocks. Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 40
  • 41.  d. Pie charts; Instead of comparing the length of a bar the areas of segments of a circle are compared. The Area of each segment depends upon the angle. A circle of any considerable large size is divided into the number of components that make up the total such that the area of each sector is proportional to the component it represents. Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 41
  • 42.  e. Graphs / scatter diagrams; this comes in when there are two different factors involved eg age /height. If after plotting the points, and they are such that the points cannot be joined by any line, then graphs will not apply and so we have scatter diagram. Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 42
  • 43. 47 46.5 46 45.5 45 44.5 44 43.5 43 42.5 42 East West North 1st Qtr 2/10/2014 2nd Qtr 3rd Qtr 4th Qtr Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 43
  • 44. 90 80 70 60 50 40 30 East West North 20 10 0 1st Qtr 2nd Qtr 3rd Qtr 4th Qtr 2/10/2014 Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 44
  • 45. 100% 90% 80% 70% 60% Series2 50% Series1 40% 30% 20% 10% 0% 1 2 3 4 5 Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 45
  • 46. 1 2 3 4 Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 46
  • 47. 60 50 40 30 Series1 20 10 0 0 5 10 15 Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 47
  • 48. 50 45 40 35 30 Series1 25 Series2 20 15 10 5 0 1 2 3 4 5 Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 48
  • 49.   This refers to the applications of statistical tests to study results with a view to ascertain presence of statistical significance Suppose we find in a study on level of physical activity, 40% of men included in the sample are physically active whereas only 30% of women qualified as active. How should one interpret this result? Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 49
  • 50. • 1. The observed difference of 10% might be a TRUE DIFFERENCE, which also exist in the total pop from which the sample was drawn   2. This difference might also be DUE to CHANCE; ie in reality there is no difference b/w men and women but that the sample of men just happened to differ from the sample of women –probably due to sample variation 3. The observed difference of 10% is due to defect in the study design (bias)-ie with an appropriate study design no such difference would have occurred Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 50
  • 51. • Statistical tests estimate the likelihood that such a result occur by chance • If the likelihood or probability is less than 5% it implies that a true difference exist and the notion of chance occurrence is rejected • This level of 5% is known as the alpha level while the actual likelihood or probability calculated is know as the P-value • In statistical terms the assumption that in the total population no real difference exists between the groups is called the NULL HYPOTHESIS Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 51
  • 52.    Once the alpha level has been set and the statistical test applied to results the P-value is obtained If the P-value is lower than the alpha value it implies that a true difference exists and the Null Hypothesis is rejected while the result is said to be statistically significant If the P-value is higher than the alpha value the Null hypothesis is accepted and the result is taken as having occurred by chance and considered not significant Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 52
  • 53.   If the Null hypothesis is rejected when it is true ie no true difference exist ( P value > than alpha value) then a type I error is committed If the Null hypothesis is accepted when a true difference exist (P-value < than alpha value) then a type II error is committed Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 53
  • 54. • • Clinicians often have to evaluate and use new information through out their practice lives. The most important reasons for learning biostatistics include the following: 1. Assessing medical literature-evidence based information is often made available in journals and clinicians must understanding biostatistics to be able to make sense of such information 2. Patient care- results of research work are often meant for patient care and clinicians want to know best diagnostic procedure, optimal care and how treatment regimens should be designed and implemented Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 54
  • 55. 3. Use of vital statistics-effective diagnosis and treatment of patients requires an understanding of how to make sense out of vital statistics which often results from the recording of vital events such as births and deaths 4. Deploying diagnostic procedures-knowing the appropriate diagnostic procedure to use in a given patient is essential for effective care. Clinicians should be conversant with the sensitivity, specificity, positive and negative predictive values of a procedure Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 55
  • 56. 5. Assessing information on drugs and equipmentcompanies present information on their products in charts, graph and clinical studies and clinicians need to good knowledge of biostatistics to make sense out of such presentation and information 6. Understanding epidemiologic problems-disease prevalence, variation by seasons and by location, and relationship to risk factors constitute epidemiological parameters of utmost importance to the clinician in practice. Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 56
  • 57.        Public health (Epidemiology, Nutrition etc) Clinical trials Population genetics Genomics analysis Ecology/Ecological forecasting Biological Sequence Analysis Systems biology for gene network inference Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 57
  • 58. 1. 2. 3. 4. 5. Bamgboye EA. A companion of Medical statistics. Ibipress & Publishing Company, Ibada Nigeria 1st Edition 2006: 1-16. Dunn OJ. Basic statistics: A primer for the Biomedical Sciences. Johm Wiley and Sons Publishers 2nd Edition: 1-11. Kolawole EB. Statistical methods. Bolabay Publications Lagos, Nigeria 1st Edition 2006: 1-12. Taofeek I. Research methodology and dissertation writing for allied professionals. Cress Global Link Limited, Abuja 1st Edition 2006: 1-24 Park K. Park‟s textbook of Preventive Medicine and Social Medicine. M/s Banarsidas Bhanot Publishers 2004 18th Edition: 608-615 Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 58
  • 59. 6. Dawnson B, Trapp R. Introduction to Medical Research in Basic and Clinical Biostatistics. Fourth Edition. McGraw-Hill Companies Inc: USA, 2004;p1-6 7. Prabhakara GN. Basics of Statistics in Biostatistics. JAYPEE:New Delhi; 2006; p11-16. 8. Dawnson B, Trapp R. Summarising Data and Presenting data in Tables and Graphs in Basic and Clinical Biostatistics. Fourth Edition. McGraw-Hill Companies Inc:USA, 2004;p23-60 Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 59
  • 60.  What doesn‟t kill us makes us stronger  So see challenges as opportunities for personal growth Thank you Dr Babatunde OA MBBS, PGCertDPMIS, MPH, FWACP 2/10/2014 60