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Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.1 Introduction to Biostatistics.

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Presentation on theme: "Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.1 Introduction to Biostatistics."— Presentation transcript:

1 Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.1 Introduction to Biostatistics

2 Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.2 Syllabus  Review  Questions?

3 Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.3 Questions  Website or Email list – Lynne  STATA – Katie  Homeworks and Graduate Projects  Specific TA for each assignment  Check the website  Distance issues – Lynne  Course issues - Scott

4 Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.4 Homework  Register (if not already)  Get textbook  Visit the course website and get notes and other vital information  Regularly check email for announcements  Get access to statistical software  STATA for most students

5 Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.5 Homework  Get class notes for next class  Read “NARC_007_final.pdf” to motivate next class discussion

6 Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.6 Statistician’s Image  Dull, dry, humorless  Speaks in technical jargon that no one understands  Wears thick glasses and carries a calculator in the pocket  Inflexible (… always says “You can’t do that!”)

7 Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.7 Statistician’s Image  Spends Saturday nights in the library  Favorite Movie: Revenge of the Nerds  Doesn’t play golf  A necessary evil  SMART!

8 Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.8 “A statistician is a person that is good with numbers but that lacks the personality to become an accountant.”

9 Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.9 The opposite sex ignores us because we are boring.

10 Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.10 It was God that made me so beautiful. If I weren’t, then I’d be a teacher. Supermodel Linda Evangelista

11 Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.11 “Bio-Sadistics”

12 Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.12 In God we trust. All others must bring data.

13 Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.13

14 Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.14 Challenges  Statistical ideas can be difficult and intimidating  Thus:  statistical results are often “skipped- over” when reading scientific literature  Data is often mis-interpreted

15 Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.15 Mis-Interpretation of Data “On average, my class is doing well. Half of my students think that 2+2=3, the other half thinks that 2+2=5.”

16 Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.16 You may think that:  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

17 Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.17 Data  Pieces of information  Scales of Measurement  Nominal – unordered categories  Ordinal – ordered categories  Discrete – only whole numbers are possible, order and magnitude matters  Continuous – any value is conceivable

18 Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.18 Data  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.

19 Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.19 Statistics  The science of collecting, monitoring, analyzing, summarizing, and interpreting data.  This includes design issues as well.

20 Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.20 Biostatistics  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.

21 Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.21 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.

22 Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.22 Why Can it be Interesting? 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, …).

23 Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.23 A Challenge  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)

24 Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.24 A Challenge  Our challenge is to identify the systematic component (separate it from the random component), estimate it, and perhaps make inferences with it.

25 Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.25 The Big Picture Populations and Samples Sample / Statistics x, s, s 2 Population Parameters μ, σ, σ 2

26 Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.26 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  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

27 Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.27 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

28 Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.28 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).

29 Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.29 Statistical Analyses  Descriptive Statistics  Describe the sample  Inference  Make inferences about the population using what is observed in the sample  Primarily performed in two ways:  Hypothesis testing  Estimation

30 Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.30 Issues  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 (unchanged) population parameters.

31 Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.31 Step I – Descriptive Statistics Describe the Sample Begin one variable at a time.

32 Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.32 Nominal Data  Mutually exclusive unordered categories  Examples  Sex (male, female)  Race/ethnicity (white, black, latino, asian, native american, etc.)  Can summarize in:  Tables – using counts and percentages  Bar Chart

33 Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.33 Nominal Data  Example Table  Insert accrual_site_treatment  Bar Graphs

34 Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.34 Ordinal Data  Ordered Categories  Examples  Injury – mild, moderate, severe  Income – low, medium, high

35 Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.35 Discrete Data  If there are many different discrete values, then discrete data is often treated as continuous.  If there are very few discrete values, then discrete data is often treated as ordinal

36 Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.36 Continuous Data  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

37 Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.37 Survival Data  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 response, time to relapse, time to death

38 Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.38 Dataset Structure  Think of data as a rectangular matrix of rows and columns.  Rows represent the “experimental unit” (e.g., person)  Columns represent “variables” measured on the experimental unit  Insert dataset_example

39 Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.39 Data Summaries  It is ALWAYS a good idea to summarize your data  You become familiar with the data and the characteristics of the people that you are studying  You can also identify problems with data collection or errors in the data (data management issues).

40 Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.40 Visual Data Summaries  Some visual ways to summarize data (one variable at a time):  Tables  Graphs  Bar charts  Histograms  Box plots

41 Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.41 Frequency Tables  Summarizes a variable with counts and percentages  The variable is categorical  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

42 Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.42 Example: Serum Cholesterol Levels  Insert Cholesterol_Example  How to choose the categories?  Talk to physician about risk categories  May look at National Cholesterol Education Program (NCEP) guidelines and categories

43 Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.43 Graphical Summaries  Histograms  Continuous or ordinal data on horizontal axis  Bar Graphs  Nominal data  No order to horizontal axis  Box Plots  Continuous data

44 Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.44 More Examples  Insert accrual_by_month  Insert More_Examples


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