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BIOSTATISTICS Asst. Prof. NIRUWAN TURNBULL, PhD Faculty of Public Health Mahasarakham University 1

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Biostatistics “ Bio-Sadistics ” 2

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In God we trust. All others must bring data. 3

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“Statistical thinking will one day be as necessary for efficient citizenship as the ability to read and write.” H.G. Wells 4

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Statistical ideas can be intimidating and difficult Thus: Statistical results are often “skipped-over” when reading scientific literature Data is often mis-interpreted 6

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“On average, my class is doing well. Half of my students think that 2+2=3, the other half thinks that 2+2=5.” 7

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A Bar Chart is a map of the locations of the nearest taverns A p-value is the result of a urinalysis Martingale residuals are the droppings of a rare bird A t-test is a blinded taste test between black and green tea 8

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Spends Saturday nights in the library Favorite Movie: Revenge of the Nerds Doesn’t play golf A necessary evil SMART! 9

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“A statistician is a person that is good with numbers but that lacks the personality to become an accountant.” 10

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The science of collecting, monitoring, analyzing, summarizing, and interpreting data This includes study design 11

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Statistics applied to biological (life) problems, including: Public health Medicine Ecological and environmental Much more statistics than biology, however biostatisticians must learn the biology as well 12

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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. 13

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Combines rigors of mathematics with uncertainties of the real world. Can make contribution to advancement of science, statistics, medicine, and public health. Can study diseases/health problems in which you may have an interest (cancers, HIV, reproductive health, …). 14

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Much of life is composed of a systematic component (i.e., signal) and a random component (i.e., error or noise) Example: Smoking is associated with lung cancer. Yet not everyone that smokes, gets lung cancer, and not everyone that gets lung cancer, smokes Yet we know that there is an association (a systematic component) 15

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Our challenge Identify the systematic component (separate it from the random component), estimate it, and perhaps make inferences with it 16

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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 Often denoted with Greek letters ( μ, σ, ρ ) 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 17

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คือ ค่าต่างๆ ที่รวบรวมมาจากประชากรหรือ คำนวณได้จากประชากร ใช้อักษรกรีกเป็น สัญลักษณ์ ได้แก่ แทนค่า ค่าเฉลี่ยเลขคณิต แทนค่า ส่วนเบี่ยงเบนมาตรฐาน 2 แทนค่า ความแปรปรวน แทนค่า สัมประสิทธิ์สหสัมพันธ์

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คือค่าต่างๆ ที่รวบรวมมาจากกลุ่มตัวอย่างหรือ คำนวณได้จากกลุ่มตัวอย่าง ใช้ตัว ภาษาอังกฤษเป็นสัญลักษณ์ ได้แก่ แทนค่า ค่าเฉลี่ยเลขคณิต s แทนค่า ส่วนเบี่ยงเบนมาตรฐาน s 2 แทนค่า ความแปรปรวน r แทนค่า สัมประสิทธิ์สหสัมพันธ์

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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 20

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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) 21

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Populations and Samples 22 Sample / Statistics x, s, s 2 Population Parameters μ, σ, σ 2

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Two steps Descriptive Statistics Describe the sample Inferential Statistics Make inferences about the population using what is observed in the sample Primarily performed in two ways: Hypothesis testing Estimation 23

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Samples are random If we had chosen a different sample, then we would obtain different statistics (sampling variation or random variation) However, note that we are trying to estimate the same (constant) population parameters 24

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Describe the Sample Begin one variable at a time Describe important variables in your analyses (e.g., endpoints, demographics, confounders, etc.) 25

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Several types of data Nominal Ordinal Discrete Continuous Time-to-event with censoring The type of data influences the analysis methods to be employed 26

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ScaleCharacteristic Question Examples Nominal Is A different than B?Marital status Eye color Gender Religious affiliation Race Ordinal Is A bigger than B?Stage of disease Severity of pain Level of satisfaction Interval By how many units do A and B differ? Temperature SAT score Ratio How many times bigger than B is A? Distance Length Time until death Weight Height 27

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Mutually exclusive unordered categories Examples Sex (male, female) Race/ethnicity (white, black, latino, asian, native american, etc.) Site Can summarize in: Tables – using counts and percentages Bar chart/graph 28

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Ordered Categories Examples Adverse events Mild, moderate, severe, life-threatening, death Income Low, medium, high 29

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Often only integer numbers are possible If there are many different discrete values, then discrete data is often treated as continuous Examples: CD4 count, HIV viral load If there are very few discrete values, then discrete data is often treated as ordinal 30

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Any value on the continuum is possible (even fractions or decimals) Examples: Height Weight Many “discrete” variables are often treated as continuous Examples: CD4 count, viral load 31

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Time to an event (continuous variable) The event does not have to be survival Concept of “Censoring” If we follow a person until the event, then the survival time is clear If we follow someone for a length of time but the event does not occur, the the time is censored (but we still have partial information; namely that the event did not occur during the follow up period) Examples: time to progression (cancer), time to response, time to relapse, time to death 32

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Think of data as a rectangular matrix of rows and columns Simplest structure Rows represent the “experimental unit” (e.g., person) Each row is an independent observation Columns represent “variables” measured on the experimental unit More complex structures Multiple rows per person (e.g., multiple timepoints) 33

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It is ALWAYS a good idea to summarize your data (at least for important variables) You become familiar with the data and the characteristics of the sample that you are studying You can also identify problems with data collection or errors in the data (data management issues) Range checks for illogical values 35

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Some visual ways to summarize data (one variable at a time): Tables Graphs Bar charts Histograms Box plots 36

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Summarizes a variable with counts and percentages The variable is categorical (e.g., nominal or ordinal) 37

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Cause of Death FrequencyPercent Motor Vehicle48 Drowning14 House Fire12 Homicide77 Other19 Total100 38

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Note that you can take a continuous variable and create categories with it How do you create categories for a continuous variable? Choose cutoffs that are biologically meaningful Natural breaks in the data Precedent from prior research 39

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How to choose the categories? Talk to physician about risk categories May look at National Cholesterol Education Program (NCEP) guidelines and categories 40

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Bar Graphs Nominal data No order to horizontal axis Histograms Continuous or ordinal data on horizontal axis Box Plots Continuous data 42

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The width of the plot has no meaning 25% of the data <13 25% of the data 13-16 25% of the data 16-18 25% of the data >18 43

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1. http://www.medpagetoday.com/lib/content/Medpage- Guide-to-Biostatistics.pdf http://www.medpagetoday.com/lib/content/Medpage- Guide-to-Biostatistics.pdf 2. http://faculty.franklin.uga.edu/dhall/sites/faculty.frank lin.uga.edu.dhall/files/lec1.pdf http://faculty.franklin.uga.edu/dhall/sites/faculty.frank lin.uga.edu.dhall/files/lec1.pdf 3. http://www.cartercenter.org/resources/pdfs/health/ep hti/library/lecture_notes/env_health_science_student s/ln_biostat_hss_final.pdf http://www.cartercenter.org/resources/pdfs/health/ep hti/library/lecture_notes/env_health_science_student s/ln_biostat_hss_final.pdf 4. http://www.meduniwien.ac.at/msi/biometrie/Lehre/p hdpropedeutics/Skripten/Medical%20Biostatistics%20 I%20version%202009-10.pdf http://www.meduniwien.ac.at/msi/biometrie/Lehre/p hdpropedeutics/Skripten/Medical%20Biostatistics%20 I%20version%202009-10.pdf 5. http://www.hstathome.com/tjziyuan/biostatistical%20 method.pdf http://www.hstathome.com/tjziyuan/biostatistical%20 method.pdf 44

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- Read through Biostatistics book from http://www.hstathome.com/tjziyuan/biostatistical%20method.pdf http://www.hstathome.com/tjziyuan/biostatistical%20method.pdf, then - We need four groups to solve out these problems, then present it, what are they talking about? 1. Statistical Methods for Assessing Biomarkers (P.81-109) 2. Power and Sample Size Considerations in Molecular Biology (P.111-130) 3. Multiple Tests for Genetic Effects in Association Studies (P. 143-168) 4. Statistical Considerations in Assessing Molecular Markers for Cancer Prognosis and Treatment Efficacy (P. 169-189) 45

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