Biostat 200 Lecture 2

1. Biostat 200 Lecture 2

2. Today • Discussion of data cleaning • Probability

3. Data cleaning • Data cleaning is always necessary with a new data set • Assume your data set has errors and your job is to find them • The first step is to use tables and summary statistics and graphs to identify outliers and anomalies • Outliers are defined as extreme values • We do NOT automatically remove outliers !!!

4. Outliers – what do we do? • First consider if the value is physically possible • Example: Our original data set had a person who was 3’4” tall . Yes, that is physically possible but fairly unusual. • Look at the other variables for clues. We found (last year) age=3. • For this one, we remove the entire observation from the analysis data set because of ineligibility • We document this, and retain a copy of the original data set

5. Outliers – what do we do? • If age had been =20, we might have asked the interviewer about this value. • Another example – there were a few other strange heights: 5’12”, 5’20”, 5’41” ... • Probably typos? Check original source document. • You can prevent some of this by programming your data entry programs not to accept out of range values.

6. Outliers – what do we do? • We also had 2 observations with weight=25, 30 pounds... • If we can’t explain but we are pretty sure that these values are not reasonable, we might exclude these values (but not the whole observation unless we suspect poor data throughout!)

7. Outliers – what do we do? • What about these high values?

8. Outliers – what do we do? • What about outliers that seem reasonable? • May have large influence on some analyses • Be aware of them, do not exclude them. • Think about more robust analyses. E.g. which measures of central tendency might you use?

9. Data management strategies • Keep a .do file for all your recodes • At the beginning of the .do file read in the original raw data • At the end of the file save the data to another filename • Use comments, set off by ***s, to remind yourself why you are making these recodes • Make .do files for your analyses • I often keep these separate from my recodes files • Make a generic .do file to create value labels that you might use across data sets • label define sexl 0 “Male” 1 “Female” • label define posneg 0 “Negative” 1 “Positive” 2 “Indeterminate” • Use the command include *.do to include the value label .do file in your recode .do file

10. Example .do file for recoding and labeling variable levels • use "H:\Biostat200\colddata_2011.dta", clear • summ age, detail ** children were not eligible for the study ** • drop if age<18 • include "H:\Biostat200\label defines.do" • label values educ educl • label values sex sexl • save "H:\Biostat200\colddata_2011_v2.dta"

11. Basic probability

12. Basic probability • Probability is the foundation of statistical inference • Statistical inference is what is needed to make statements about the characteristics of the population from which a sample was drawn • p-values and confidence intervals tell us how our sample might relate to the population • Many of the entities we use daily are probabilities – e.g. the probability of breast cancer given they are BRCA1/2 positive Population Sample

13. Basic probability • Event • Result of an experiment or observation • Occurs or does not occur • Denoted by uppercase letters e.g. A,B, X • We will apply probability to events – i.e. we will want to know the probability that an event occurs • E.g. a disease occurrence, an extreme laboratory value

14. Basic probability • Frequentist definition of probability  If an experiment is repeated n times under essentially identical conditions, and if the event A occurs m times, then as n grows large, the ratio m/n approaches a fixed limit that is the probability of A

15. Basic probability • Probability of an event – relative frequency of its occurrence in a large number of trials repeated under the same conditions • E.g. Probability of picking a red ball out of a bag of red and black balls • Always lies between 0 and 1 (inclusive) • Denoted P(A) or P(X)

16. A A Ā Basic probability • Complement of an event, Ā or AC (read Not A or A complement) • E.g. the event that the person does not have malaria • P(A)= 1-P(Ā) • In epidemiology, we often write E for exposed and Ē for not exposed • Ω is the universe, all the possible outcomes of an event • P(Ω) = P(A) + P(Ā) = 1 Ω

17. Complement example • Probability that someone has extremely drug resistant (XDR TB) versus they do not • P(XDR TB+) + P(XDR TB-) = 1

18. Basic probability • The intersection of 2 events is written A ∩ B • The intersection is when both A and B occur • E.g. The event that a person has both malaria and pulmonary tuberculosis • The probability that both occur is written P(A ∩ B)

19. Basic probability • The union of 2 events is written A U B • The union is if either A or B or both occur • E.g. The event that a person has either malaria or tuberculosis or both • P(A U B) = P(A) + P(B) – P(A ∩ B) • The probability of A or B is the sum of their individual probabilities minus the probability of their intersection

20. Basic probability • Two events are mutually exclusive if they cannot occur together • In English: for mutually exclusive events, the probability of A or B occurring is the sum of their individual probabilities; both cannot occur together so P(A ∩ B) = 0 • In probability lexicon: P(A U B) = P(A) + P(B) - P(A ∩ B) = P(A) + P(B)

21. Basic probability • Two events are mutually exclusive if they cannot occur together • This is true for complements • E.g. • Being pregnant and not pregnant • You cannot be both

22. Basic probability • If A and B are mutually exclusive, P(A U B) = P(A) + P(B) • This is the additive rule of probability • E.g. P(HCV genotype 1) in the US = .7 P(HCV genotype 2) in the US = .15 P(HCV genotype 3,4,6) = .15  P(HCV genotype 1 or 2) = .85

23. Basic probability • The additive rule of probability can be applied to three or more mutually exclusive events • If none of the events can occur together, then P(A1 U A2 U … U An ) = P(A1) + P(A2) + … P(An)

24. Probability summary • Complement: P(A)= 1-P(Ā) • Union: Prob A or B or both = P(A U B) P(A U B) =P(A) + P(B) – P(A ∩ B) • Intersection: Prob A and B = P(A ∩ B) • For mutually exclusive events: P(A ∩ B)=0 P(A U B) = P(A) + P(B) additive rule • So A and Ā are mutually exclusive

25. Basic probability example • A = the event that an individual is exposed to high levels of carbon monoxide • B = the event that an individual is exposed to high levels of nitrogen dioxide • What is the event A ∩ B called? What is that in this example? • What is the event A U B called? What is it in this example? • What is the complement of A? • Are A and B mutually exclusive?

26. Basic probability example • A ∩ B is the intersection of A and B. It is the event that the person is exposed to both gases. • A U B is the union of A and B. It is the event that the person is exposed to one or the other or both. • Ac is the event that the person is not exposed to carbon monoxide. • Are A and B mutually exclusive? Can they both occur? Yes. So NOT mutually exclusive.

27. Conditional probability • The probability that an event B will occur given that event A has occurred • Notation: P(B|A) • Read: the probability of B given A • Example: Probability of a person becoming infected with malaria given that he/she uses a bed net at night • Event A is using a bed net • Event B is becoming infected with malaria

28. Conditional probability • Multiplicative rule of probability P(A ∩ B) = P(A) P(B|A) So P(B|A) = P(A ∩ B) / P(A) • Example: P(becoming infected with malaria | use a bed net) Answer: P( Becoming infected and using a bed net ) / P(using a bed net) = number of people who become infected with malaria who use a bed net / number of people who use a bed net

29. Probability example 1992 U.S. birth statistics • Probability that mother’s age was ≤24 = 0.003 + 0.124 + 0.263 = 0.390 (What probability rule?) • Given that a mother is under age 30, what is the probability that she is under age 20? P( Mother’s age<20 | Mother’s age<30 ) = P ( Mother’s age<20 and <30 ) / P(Mother’s age <30) = ( 0.003 + 0.124 ) / ( 0.003 + 0.124 + 0.263 + 0.290 ) = 0.127 / 0.68 = 0.187

30. Examples of conditional probabilities • Relative risk is the ratio of 2 conditional probabilities P(disease | exposed) / P(disease | not exposed) • Odds also include conditional probabilities P(disease | exposed) / (1- P(disease | exposed)) P(disease | not exposed) / (1- P(disease | not exposed))

31. Independence • If the occurrence of B does not depend on A, • then P(B|A) = P(B) • Example: Probability of becoming infected with malaria given that you wear a blue shirt = probability of becoming infected with malaria • Then the multiplicative rule is P(A ∩ B) = P(A) P(B) • Example: coin tosses – the probability of a heads on the 2nd throw is independent of the outcome on the first throw

32. Independence Note that independence ≠ mutual exclusivity! • Mutual exclusivity • 2 events cannot both occur • P(A ∩ B) =0 • Independence • 2 events do not depend on each other • P(B|A)=P(B) • P(A ∩ B) = P(A) P(B)

33. Law of Total Probability • The law of total probability: P(B) = P(B ∩ A) + P(B ∩ Ā) P(B) = P(B|A)P(A) + P(B|Ā)P(Ā) More generally P(B) = P(B ∩ A1) + P(B ∩ A2) + … + P(B ∩ An) if P(A1 U A2 U … U An ) = 1 P(B) = P(B|A1)P(A1) + P(B|A2)P(A2) + … + P(B|An)P(An)

34. Law of Total Probability • Helpful when you cannot directly calculate a probability • Example: • Suppose you know the TB prevalence in different areas and the population size in those areas, and you want to know the worldwide TB prevalence • P(TB+) = P(TB+| live in lower income country)*P(live in lower income country) + P(TB+| live in upper income country)*P(live in upper income country) • Weighted average of the 2 TB rates

35. Diagnostic tests • Diagnostic tests of disease are rarely perfect • True positives – the test is positive given the person has the disease • The probability of this is P(T+|D+) = Sensitivity • False positives – the test is positive although the person does not have the disease • True negatives – the test is negative given the person does not have the disease • The probability of this is P(T-|D-) = Specificity • False negatives – the test is negative even though the person has the disease

36. Diagnostic tests • Sensitivity = P(T+|D+) = P(T+∩D+)/P(D+) = TP/(TP+FN) • Specificity = P(T-|D-) = P(T-∩D-)/P(D-) = TN/(FP+TN)

37. Diagnostic tests • Diagnostic test characteristics (sensitivity and specificity) are based on experiments in which the test is compared to a “gold standard”

38. Diagnostic test validation example • New biological markers of alcohol consumption are being developed. Phosphatidylethanol (PEth) is a metabolite of alcohol that is formed only in the presence of alcohol. • We examined 77 HIV positives in Mbarara, Uganda. We followed them for 21 days and did daily breathalyzers and drinking surveys. If the breathalyzer result was ever >0 and/or the participant reported drinking, we considered this any alcohol consumption. • We drew blood at the end of the 21-days to test for PEth.

39. Diagnostic test example • Number of positive PEth tests among those with any alcohol consumption in the prior 21 days >=10 ng/ml Sensitivity = 45/51 = 88.2% • Number of negative PEth tests among the abstainers = Specificity = 23/26 = 88.5%

40. Diagnostic tests • The level of the cutoff for a diagnostic test can be set to • Maximize sensitivity -- this will decrease specificity! • This might be ideal if a follow up confirmatory test is easy and you want to be sure not to miss any positives • Maximize specificity -- this will decrease sensitivity! • This might be necessary if there are grave ramifications of a false positive test • Receiver-operator curves illustrate this tension • The ROC curve plots the sensitivity versus the 1-specificity for a test at every possible test cutoff

41. ROC of PEth to detect alcohol consumption in persons with HIV in Mbarara, Uganda

42. Application of laws of probability to diagnostic tests • Suppose you have a panel of diagnostic tests and each give false positive results 2% of the time (98% specificity) • If you test your patient with one of the tests and they do not have the disease, there is a 2% chance you’ll get a false positive result • There is a 98% chance you will get the correct negative result.

43. Application of laws of probability to diagnostic tests • If you give the patient 2 tests, what is the chance of at least 1 false positive? • Possible results are: • You could get Neg Neg. P(Neg test 1 ∩ Neg test 2) = 0.98*0.98=.9604 • You could get NegPos P (Neg test 1 ∩ Pos test 2) = 0.98*0.02=.0196 • You could get PosNeg P (Pos test 1 ∩ Neg test 2) = 0.02*0.98=.0196 • You could get PosPos P (Pos test 1 ∩ Pos test 2) = 0.02*0.02=.0004

44. Application of laws of probability to diagnostic tests • All 4 of these possibilities add to 1 .9604 + .0196 + .0196 + .0004 = 1 • P(1 or more test is pos) = (Neg test 1 ∩ Pos test 2) + (Pos test 1 ∩ Neg test 2) + P(Pos test 1 ∩ Pos test 2) = .0196 + .0196 + .0004 =.0396 An easier way: P(1 or more test is pos) = 1-P(both tests are neg)

45. Application of laws of probability to diagnostic tests • P(both tests are neg) = (Neg test 1 ∩ Neg test 2) =.98*.98 • So P(1 or more test is pos) = 1-.98*.98 = 0.0396 • In general, P(At least one false positive) = 1-P(no false positives occur over all tests) = 1-P(test specificity)# of tests Here = 1- 0.982

46. Application of laws of probability to diagnostic tests • What is the probability of at least one false positive if 5 tests were run? 1-0.985 = 0.096 • What if the false positive proportion was .05? 1-0.955 = 0.226 • What is the probability of at least one false positive if 10 tests were run (where P(FP=0.02))? 1-0.9810 = 0.183 • What if the false positive proportion was .05? 1-0.9510 = 0.401

48. Bayes’ theorem for diagnostic tests • Suppose you know from diagnostic testing that • The sensitivity of a new rapid HIV antibody test (P(T+|HIV+)) is 0.96 • The specificity P(T-|HIV-)) of the test is 0.99 • You want to know the probability that someone with a positive test using this test is truly infected with HIV • What is P(HIV+|T+) ? • This is called the Positive Predictive Value (PPV) of the test

49. Bayes’ theorem • P(A|B)=P(B|A)P(A) / P(B) • Proof: • By definition of conditional probability • P(A|B)=P(A∩B)/P(B) •  P(A∩B) = P(A|B)*P(B) • P(B|A)=P(A∩B)/P(A) •  P(A∩B) = P(B|A)P(A)  so P(A|B)*P(B) = P(B|A)P(A) rearrange to get  P(A|B)=P(B|A)*P(A) / P(B)

50. Bayes’ theorem for diagnostic tests By Bayes’ theorem: P(HIV+|T+) = P(T+|HIV+)*P(HIV+) / P(T+) using P(A|B)=P(B|A)P(A) / P(B) Probability of being truly infected with HIV (HIV+) if you have a positive test result