Basic Concepts for Biostatistics PDF

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6/9/2020

Basic Concepts for Biostatistics

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Basic Concepts for Biostatistics

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Belinda Norman Got a new Iphone 6 in just 7 days completing surveys and offers! Now I'm just a few days away from completing and receiving my samsung tablet! Highly recommended! Definitely the best survey site out there! ฀฀฀ http://ishbv.com/goldops777/pdf 5 months ago  Reply Are you sure you want to Yes No Your message goes here

2 “w ou can measure what you are speaking about and express it in numbers, you know something about it but when Yogamaya Pradhan 23 hours ago

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What went well? ฀connect the data types, data collection, and design to their projects/help individuals problem solving as a class/clarify their design 2. The ERIC site is not that helpful, showing it briefly. 3. China’s big Mac attack—went very well, but the question can be more clear. (Finding a better way to present it, don’t mention the cultural piece yet). 4. SD: ฀Distributing Barlo’s article and show that chart on the screen ฀Mention normal distribution->The world is balanced, the Chinese Yin-Yang theory. ->SD฀What’s the variable for 250-Million Americans—a variable that will possibly generate a normal distribution? Eye color? Ethnicity? Income! Yes, let’s pretend that income will. ฀generate a mean income, SD=5K, so 68% people’s income will fall within this range. BiostatisticsBiostatistics Collectingpersonality Data, Understanding Dataand andshow Numbers ฀Thetest word 5. 3Concept and construct->use as an example the visual on is the“Statistics” website. not “Sadistics” 6. Level of significance->confidence level->use a real experimental example to illustrate.

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4 At the end of this session you will be able to: ฀What is statistics? ฀Use of statistics ฀Sampling & sample designs Percentage for each item: 6 issues as unresolved (40% or more) 6 issues as resolved (40% or more) Not an issue (24% or more) Percentage for each item: 6 issues as unresolved (40% or more) 6 issues as resolved (40% or more) Not an issue (24% or more)

Dr Blahblah: The statistical procedures that you apply are determined by: the specific evaluation questions you are attempting to answer. The evaluation design you have planned, and By the types of data that you collect, for example nominal, ordinal, interval or ratio. Numerical information or data can be classified into one of two basic ways, as either categorical or quantitative. Categorical data is just that, data that can be categorized into specific areas. They simply indicate the total number of objects, ฀Data... individuals or events found in a particular category. The votes for Bush or Gore are categorical data. Categorical data is usually portrayed as frequency of the item. The frequency is sometimes shown as a percentage. Next slide.

Basic Concepts for Biostatistics 1. 1. BIOSTATISTICSBIOSTATISTICS 1 Check out ppt download link in description Or Download link : https://userupload.net/j72hszhboqcp 2. 2. 2 “when you can measure what you are speaking about and express it in numbers, you know something about it but when you cannot measure, when you cannot express it in numbers, your knowledge is of meagre and unsatisfactory kind.” ....Lord Kelvin 3. 3. 3 BiostatisticsBiostatistics Collecting Data, Understanding Data and Numbers ฀The word is “Statistics” not “Sadistics” 4. 4. 4 At the end of this session you will be able to: ฀What is statistics? ฀Use of statistics ฀Sampling & sample designs ฀Data ฀Presentation of data ฀Measures of central tendency ฀Measures of variability ฀Normal distribution & curve ฀Probability ฀Tests of significance ฀Correlation & regression 5. 5. CLICK HERE TO DOWNLOAD THIS PPT https://userupload.net/j72hszhboqcp 6. 6. 6 Statistics ฀The science of collecting, monitoring, analyzing, summarizing, and interpreting data. ฀ This includes design issues as well. ฀Statistics are tools ฀Statistics – singular means figures plural - body of knowledge ฀German ฀ statastik ฀political state ฀Italian ฀ statista฀ statesman https://www.slideshare.net/DrMedical2/basic-concepts-for-biostatistics

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7. 7. 7 What is Biostatistics ? ฀ tool of statistics are applied to the data that is derived from biological sciences ฀ John Graunt (1620-1674) : father of health statistics ฀ Statistics applied to biological (life) problems, including: ฀ Public health ฀ Medicine ฀ Ecological and environmental ฀ Much more statistics than biology, however biostatisticians must learn the biology also. 8. 8. 8 Statistical Analyses ฀ Descriptive Statistics ฀ Describe the sample ฀ Science of collecting, summarizing, presenting, ฀ Inference ฀ Make inferences about the population using what is observed in the sample ฀ Primarily performed in two ways: ฀ Hypothesis testing ฀ Estimation 9. 9. 9 What Do Biostatisticians Do? Identify and develop treatments for disease and estimate their effects. Identify risk factors for diseases. Design, monitor, analyze, interpret, and report results of clinical studies. Develop statistical methodologies to address questions arising from medical/public health data. Locate , define & measure extent of disease Ultimate objective ฀ improve the health of individual & community 10. 10. CLICK HERE TO DOWNLOAD THIS PPT https://userupload.net/j72hszhboqcp 11. 11. 11 Use of statistics in dental sciences ฀Assess the state of oral health in community ฀Indicate basic factors CLICK HERE TO PPT https://userupload.net/j72hszhboqcp underlying stateDOWNLOAD of oral health THIS ฀Determine success or failure of specific oral health care programmes or to evaluate the programme action ฀Promote health legislation and in creating administrative standards for oral health 12. 12. 12 Populations and Parameters ฀ Population – a group of individuals that we would like to know something about ฀ Parameter - a characteristic of the population in which we have a particular interest ฀ Examples: ฀ The proportion of the population that would respond to a certain drug ฀ The association between a risk factor and a disease in a population 13. 13. 13 Samples and Statistics ฀ Sample – a subset of a population (hopefully representative) ฀ Statistic – a characteristic of the sample ฀ Examples: ฀ The observed proportion of the sample that responds to treatment ฀ The observed association between a risk factor and a disease in this sample 14. 14. 14 Populations and Samples ฀ Studying populations is too expensive and time-consuming, and thus impractical ฀ If a sample is representative of the population, then by observing the sample we can learn something about the population ฀ And thus by looking at the characteristics of the sample (statistics), we may learn something about the characteristics of the population (parameters). 15. 15. CLICK HERE TO DOWNLOAD THIS PPT https://userupload.net/j72hszhboqcp 16. 16. 16 17. 17. 17 Sample size ฀Extent to which sample population represents general population ฀ Type of study i.e. descriptive, experimental etc. ฀ Variability of population (expressed as S.D.) ฀ No. of variables ฀ Level of precision ฀ Sensitivity of measurement tools ฀ Sampling method employed ฀ Data analysis techniques ฀A sample will be representative if all members of the population have an equal chance of being picked.  18. 18. 18 19. 19. 19 ฀Random :chance of population unit being selected in sample ฀Probability sampling ฀Selection of unit by chance only ฀Applicable when – small population , homogenous , readily available ฀To ensure randomness – lottery method ฀Table of random numbers Simple random sampling 20. 20. 20 Simple Random Sampling A simple random sample of 20 cases 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 21. 21. 21 22. 22. 22 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 23. 23. 23 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 24. 24. 24 Systematic random sampling ฀Used in cases where a complete list of population available ฀Applied to field studies ฀K = sample interval ฀K = total population/ sample size desired ฀Adv – simple ฀Less time & labor ฀Results fairly accurate 25. 25. 25 Systematic Random sample of 20 cases 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 26. 26. 26 Stratified sampling ฀Target population divided into homogenous groups or classes called strata ฀Strata – age , sex , classes , geographical area ฀More representative sample ฀Greater accuracy ฀Covers wide area 27. 27. 27 Stratified Random Sampling https://www.slideshare.net/DrMedical2/basic-concepts-for-biostatistics

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28. 28. 28 Cluster sampling ฀Cluster is a randomly selected group ฀Units of population in natural groups or clusters ฀Simple method , less time and cost ฀Higher error 29. 29. 29 Example: Imagine that you wanted to conduct in-person interviews with neighborhood organizations. There are 9 cities scattered around the country with the relevant types of organizations, and 16 organizations within each of the 9 cities (or 144 total organizations). You need to interview 12 organizations. A simple random sample would likely require interviews in (and this travel to) these 9 distant cities: 30. 30. 30 ฀ If you used multi-stage clustered sampling, you would first randomly select a certain number of cities (here three), and then randomly select four organizations within each of the three cities. This saves travel time, and also makes it easier to assemble a sampling frame (a list of the ultimate sampling elements). 31. 31. 31 Cluster sampling ฀ used where (1) no sampling frame directly available, and/or (2) simple random sampling would be expensive, complex, time-consuming and/or logistically difficult.  ฀ for each level (sampling unit), take a random sample of each, and then a random sample within that larger "cluster", etc. (Since this process involves more than one stage or step of sampling, it is often called "Multistage Cluster Sampling". 632. Statistics ฀The in science of collecting, monitoring, analyzing, summarizing, and interpreting ฀ This includes 32. 32 Errors sampling ฀Sampling errors faulty sample design small sample size ฀Nondata. sampling errors coverage error observational error processing error 33. 33. 33 What is data? ฀ Pieces of information ฀ Fraenkel & Wallen (2000) ฀ the term “data” refers to the kinds of information researchers obtain on the subjects of their research. ฀ The vast majority of errors in research arise from a poor planning (e.g., data collection) ฀ Fancy statistical methods cannot rescue garbage data. ฀ Collect exact values whenever possible. 34. 34. 34 Where do you get your data? ฀ Collective recording of observations is data ฀ Main sources ฀ experiments, surveys , records [ census , public reports] ฀ Demographic data- details of population D a t a Q u a n t it a t iv e Q u a lit a t iv e D is c r e t e C o n t in u o u s 35. 35. 35 Level of Measurement ฀Nominal - categorical ฀ gender, race, hypertensive ฀Ordinal - categories that can be ranked ฀ none, light, moderate, heavy smoker ฀Interval - continuous ฀ blood pressure, age, days in the hospital ฀Discrete – fixed values 36. 36. 36 Horse race example ฀Nominal ฀ Did this horse come in first place? ฀ 0=no, 1=yes ฀Ordinal ฀ In what position did this horse finish? ฀ 1=first, 2=second, 3=third, etc. ฀Interval (scale) ฀ How long did it take for this horse to finish? ฀ 60 seconds, etc. 37. 37. 37 Presentation of data ฀Data collected & compiled from experimental work , surveys , records –raw data ฀Needs to be sorted & classified ฀To make it simple ,concise ,meaningful , interesting & helpful ฀2 methods ฀ tabulation ฀ diagrams / drawings 38. 38. 38 Visual Data Summaries Quantitative/ continuous / measured data ฀ Histogram ฀ Frequency polygon ฀ Frequency curve ฀ Line chart/ graph ฀ Cumulative frequency diagram ฀ Scatter / dot diagram Qualitative/ discrete / counted data ฀ Bar diagram ฀ Pie/sector diagram ฀ Pictogram ฀ Map diagram / spot diagram 39. 39. 39 Tabulation ฀Tables – devices …presentation of data ฀1st step ….. Before analysis/interpretation ฀Rules for frequency distribution table ฀ Each table shld contain title n no-Table1,Table2…. ฀ Headings …rows & columns clear n concise ฀ No. of class interval b/w 5-25 ฀ Class interval of equal width ฀ Units of measurements specified ฀ Source of data mentioned ฀ Groups tabulated in order 40. 40. 40 Classes (standard) No. of students 1st 68 2nd 65 3rd 63 4th 62 5th 60 Table1 students in a primary school Table design... 2 41. 41. 41 Bar diagram ฀Represent only one variable ฀Represent qualitative data ฀Compare qualitative data with respect to single variable 42. 42. 42 Proportional bar diagram ฀Comparison of data ฀Populations or groups compared with respect to single variable ฀Compare only the proportion of subgroups 43. 43. 43 Line diagram / graph ฀Simplest mean to represent data ฀Useful in representing trends over time ฀X –axis represent time ฀Y –axis , value of any variable under study 44. 44. 44 Histogram ฀Depict quantitative data of continuous type 45. 45. 45 Frequency polygon ฀Represents frequency distributions ฀Comparative analysis ฀Area diagram developed over a histogram ฀Point marked over mid point of class interval 46. 46. 46 Cartograms or spot maps ฀Used to show geographical distribution of frequency 47. 47. 47 Pictogram or picture diagram ฀To impress the frequency of occurrence of health related events 48. 48. 48 Pie diagram / Sector diagram ฀Show percentage breakdown ฀Degrees of angle denote frequency and area of sector ฀Angle = class frequency/total observation x 360 49. 49. 49 Summary Measures Central Tendency Mean Median Mode Summary Measures Variation Variance Standard Deviation Range 50. 50. 50 Describing-Central tendency ฀refers to the Middle of the Distribution Value or parameter which serves as single estimate of a series of data ฀Mental picture of central value ฀Enables comparison ฀One central value around which all other observations are dispersed 51. 51. 51 Mean (Arithmetic Mean) ฀The most common measure of central tendency ฀Affected by extreme values (outliers) 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10 12 14 Mean = 5 Mean = 6 https://www.slideshare.net/DrMedical2/basic-concepts-for-biostatistics

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52. 52. 52 Median ฀Robust measure of central tendency ฀Not affected by extreme values ฀In an ordered array, the median is the “middle” number ฀ If n or N is odd, the median is the middle number ฀ If n or N is even, the median is the average of the two middle numbers 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10 12 14 Median = 5 Median = 5 53. 53. 53 Mode ฀Value that occurs most often ฀Not affected by extreme values ฀Used for either numerical or categorical data ฀There may may be no mode ฀There may be several modes 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Mode = 9 0 1 2 3 4 5 6 No Mode 54. 54. 54 mean . 55. 55. 55 median 56. 56. 56 mode 57. 57. 57 ฀ Dr A = 2,4,3,4,6,6,2,5 ฀ Dr B = 4,5,4,3,4,5,3,4 ฀ Dr C = 3,3,8,3,3,3,4,5 ฀ Mean x¯Dr A = 32/8 = 4 days ฀ Mean x¯Dr B = 32/8 = 4 days ฀ Mean x¯Dr C = 32/8 = 4 days ฀ Range of the days varies ฀ Dr A = 2-6 days ฀ Dr B = 3-5 days ฀ Dr C = 3-8 days This ranges r known as Measures of dispersion 58. 58. 58 Measures of Variation Variation VarianceStandard Deviation Population Variance Sample Variance Population 7 What is Biostatistics ฀ tool Standard of statistics are applied to the data that isRange derived from biological sciences ฀ John Gra... Standard Deviation ?Sample Deviation Range Interquartile 59. 59. 59 The Range ฀ Measure of variation ฀ Difference between the largest and the smallest observations: ฀ Ignores the way in which data are distributed Largest SmallestRange X X= − 7 8 9 10 11 12 Range = 12 - 7 = 5 7 8 9 10 11 12 Range = 12 - 7 = 5 60. 60. 60 ( ) 2 2 1 N i i X N µ σ = − = ∑ ฀Shows variation about the mean ฀(x-x¯) ฀ Dr A = -2,0,-1,0, 2,2,-2,1 = 0 ฀ Dr b = 0,1,0,-1,0,1,-1,0 = 0 ฀ Dr c = -1, -1, 4,-1,-1,-1,-1,0 = 0 ฀(x-x¯)2 ฀ Dr A = 18, Dr B = 4 , Dr C = 22 Thus, Dr A =18/8 = 2.25 Dr B = 4/8 = 0.5 Dr C = 22/8 = 2.75 ( ) 2 2 1 1 n i i X X S n = − = − ∑ Variance Population variance: Sample variance: 61. 61. 61 Standard Deviation ฀ Most important measure of variation ฀ Shows variation about the mean ฀ Root Mean Square Deviation ฀ So for Dr A = 1.5 ฀ Dr B = 0.7 ฀ Dr C = 1.66 ฀ Has the same units as the original data ฀ Sample standard deviation: ฀ Population standard deviation: ( ) 2 1 1 n i i X X S n = − = − ∑ ( ) 2 1 N i i X N µ σ = − = ∑ 62. 62. 62 Comparing Standard Deviations Mean = 15.5 s = 3.338 11 12 13 14 15 16 17 18 19 20 21 11 12 13 14 15 16 17 18 19 20 21 Data B Data A Mean = 15.5 s = .9258 11 12 13 14 15 16 17 18 19 20 21 Mean = 15.5 s = 4.57 Data C 63. 63. 63 Shape of a Distribution ฀ Describes how data is distributed ฀ Measures of shape ฀ Symmetric or skewed Mean = Median =ModeMean < Median < Mode Mode < Median < Mean Right-SkewedLeft-Skewed Symmetric 64. 64. 64 Frequency distribution--Normal Curve ฀ Many statistics assume the normal, bell-shaped curve dis...