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Chances Are

Chances Are

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Chances Are

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  1. Chances Are A look at probability and its application to beef production and diagnostic testing

  2. Everyday probabilities Some every day probabilities

  3. Probability concepts • Likelihood • Predictability • Certainty/Confidence (or the lack of)

  4. Basis of probability • Counting outcomes • How many cows do I have (100) • How many cows have calves at their side this year (82) • How many cows were exposed to the bull (97) • How many cows were diagnosed pregnant (91) • How many cows had bull calves (37) • How many cows required assistance at calving (7)

  5. Truth of Probability “Not everything that counts can be counted, and not everything that can be counted counts.”Einstein

  6. Likelihood • What is the likelihood that a cow selected from the herd described in the previous slide does not have a calf at her side? • Likelihood=Odds count the possibilities • The number of cows that did not have a calf at their side was • The number of cows that did have calves at their side was • So the likelihood/odds would be 18 82 18:82

  7. Predictability • If you select a cow at random from the herd what are the chances that you will select a cow without a calf. • Prediction=Probability= the potential of an event expressed as a relative frequency or mathematically as f/n where n is the total number of events and f is the number of events of interest. • The sum of all possible events is 1. If I flip a coin the probability it is a head is .5 a tail .5 the probability it is a head or a tail is 1 • In this example n=100 and f=18 the probability of any one cow selected meeting the criteria is 18/100 or .18 and if the selection was made 100 times 18% of the time you would expect to select a cow without a calf.

  8. Predictability (continued) • Sometimes we might be interested in how many times an event might occur, such as to evaluate a new test I test 5 cows from a herd for “Disease X” what is the probability that the new test will find 3 test positive and 2 test negative? • For simplicity assume. • We already know the status of the animals as being infected. The prevalence is 1.00 • After testing the animals are returned to the herd (in this scenario with known infection it doesn’t make any difference) • The Se of the test is .9 (the probability the test is negative is .1 and the probability the test is positive is .9) & the Sp is 1

  9. So how do we approach the problem? Let TP = test positive and TN = test negative TP=.9 TN=.1 If we perform the test 5 times the outcome would look like this: (TP+TN)5 TP5+5TP4TN1+10TP3TN2+10TP2TN3 +5TP1TN4 +TN5 The coefficient1 corresponds to the number of possible combinations and the exponent to the number of times that event might occur, there are 10 possible combinations of getting 3 test positives and 2 test negatives. The probability then is 10*.93*.12 or .0729. 1

  10. What would be the probability of at least 3 test positives. TP5+5TP4TN1+10TP3TN2 .590+.328+.073=.991 If you consider that you don’t know they are all infected but know the prevalence then values change by factoring in prevalence and the initial formula would change to look like this; (still Sp of 1) P5(TP+TN)5 + SP5(1-P)5 If you consider a test that is less than 100% specific then you ADD in the confusion of the false positive and a more complex formula. Expanding the problem

  11. Certainty • If I select 5 cows at random to check to see if the cows have been exposed to “X disease” using a newly validated diagnostic test, how certain/confident would I be that by testing 5 cows a statement regarding the presence of “X disease” could be made. THE PLOT THICKENS

  12. Sample size formulas n=required sample size α=confidence level D=number of diseased animals N=total population or herd size

  13. Short comings of the formula • Only applies to a perfect test • Diagnostic tests have limitations • Se • Sp • A positive test does not indicate disease nor a negative test an absence of disease • Trial and error to determine confidence (or a complex math formula)

  14. What information do you need to answer the questions about sample size? • Test parameters Se and Sp is the test perfect or imperfect? • Is the population in question large or small? • Estimated prevalence? • Sources for estimated prevalence • Literature • National reports, NAHMS • Diagnostic Labs (may be a biased answer) • Clinical experience/local surveys (a topic for later) • Others?

  15. Methods to estimate confidence Sample size spread sheet

  16. Formulas behind the spreadsheet

  17. Kennedy’s oversimplified definitions • P=the proportion diseased/infected/of interest (D) in a population (N) • (1-P)=the proportion NOT D • Se=the proportion that are D that a test detects (T+|D) • (1-Se)=the proportion D the test fails to detect (false negatives) • Sp=the proportion NOT D (ND) that a test correctly identifies negative (T-) • (1-Sp)=the proportion ND a test incorrectly identifies D (false positives)

  18. Kennedy’s oversimplified formulas N=Total Population D=Diseased ND=Not Diseased P=Prevalence Se=Sensitivity Sp=Specificity T+=Test positive T-=Test negative A. B. C. D. E. F.

  19. More of Kennedy’s oversimplified formulas PPV=Positive Predictive Value NPV=Negative Predictive Value EFF=Efficiency proportion of positives and negatives correctly classified AP=Apparent Prevalence A. B. C. D.

  20. Sources of diagnostic test error orthe lab never gets it right

  21. Where testing error happens • Pre-analytical error sources, wrong sample, mishandled sample, improper sample collection, etc. Starts from collection and goes until analysis begins. • Analytical error, analytic variation such as mechanical wear and tear or inherent error such as that seen with a set of spring type scales. • Biological variation, an average means some are higher and some are lower. • Post-analytical error, reporting errors misread values or misreported values, transposition of figures, etc.

  22. Test expectations • Repeatability • Consider flipping a fair coin 5 times the chances of all being heads is.5x.5x.5x.5.x5 or 3.125%. • Consider flipping a weighted coin 5 times that is expected to be heads 90% of the time the chances that 5 heads will be returned is .9x.9x.9x.9x.9 or 59%, meaning 41% of the time one or more tails would occur. • The same principle applies to a diagnostic test. • So how do you interpret when two labs disagree? Did the test Miss

  23. Rates • How many miles would you expect to drive before you get a nail in your tire? • In 2004 how many aviation fatalities occurred per 100,000 hours flown? • General aviation 2.15 • Commercial aviation .08 • In every 1000 diagnostic tests performed how many times does the test fail beyond incorrect results due to Se and Sp?

  24. Repetition/Rates? • Does repeating a task increase the probability something will go wrong? i.e. • You and your neighbor purchase new tires for your cars. Both of you drive to the same place to work each day on the same road, but you come home for lunch while he doesn’t, who is more likely to get a nail in one of their tires? • Aviation gas gets cheap so I fly twice as much will I be more likely to become a fatality? Maybe • If I run 1000 individual diagnostic tests am I more likely to misclassify an animal than if I ran 10 test each containing samples of 100 animals? Controversial Each mile you drive, each hour you fly, or each test you run are independent events. Over a given period the number of times that an event occurred is a rate (rates have units probabilities do not) Risk is the probability of a negative event. Think about life insurance. On the other hand dependent events have changing probabilities.

  25. Why pooled testing? • Pooled testing offers advantages over individual testing • Allows the diagnostician to take advantage of highly sensitive and specific tests while minimizing cost • Diminishes cumulative testing error over individual tests

  26. Why not pooled testing? • Potential impact of dilution diminishing Se • Logistical requirements for pooling samples (pooling of individual samples can be labor intensive) • Loss of samples for follow-up testing on positive pools

  27. Assumptions associated with pooled testing • Pooled test Se must be approximately the same as individual test Se • Samples must be easily obtainable • Pools must represent a homogenous mixture of samples • The outcome is binomially distributed, i.e. a discrete probability distribution of the number of successes in a sequence of independent yes/no events each yielding success with a probability p

  28. Our human counterparts institute pooled testing strategies • For generations the military has attempted to screen its applicants/inductees to insure they were healthy and would not become a liability on the battlefield. • Early screening involved a physical exam to insure all parts were present and properly located. • Later blood tests for infectious disease became available and were included in the screening process

  29. Pooled testing during WWII • Syphilis had plagued the military since the first soldier marched off to war. • They could mandate controls after recruitment to help slow its spread but that was not enough • To minimize the risk they looked to tests that would detect carriers before they were inducted.

  30. Military Test for Syphilis • The test used during WWII to insure inductees were free from infection was a Wasserman type blood test. • A sample of blood is drawn from each inductee. • Then each sample is tested. • The procedure was expensive, time consuming and amplified testing error.

  31. Time and cost of test encouraged a change in the process • The military implemented a procedure where a small quantity of blood from multiple inductees was pooled and a single test was run on the pool • Sufficient blood remained that the positive individual among the pool could be identified. • The study of using pooled testing as a screen lead to two conclusions/considerations on pooling. • Prevalence must be low enough to make pooling more economical • It must be easier to obtain an observation on a group than on the individuals within the group (minimize the number of tests). reference Robert Dorffman The Detection of Defective Members of Large Populations, Annals of Mathematical Statistics, Vol. 14, Dec 1943

  32. More Recent Use of Pooled Testing Strategy in Human Medicine • ELISA and Western Blot tests were used to screen for human immunodeficiency virus (HIV). • An ELISA test was used initially and then a Western Blot was the confirmatory test. • The ELISA alone was prone to falsely classifying samples positive and therefore may result in an overestimation of prevalence, • Western blots were done to confirm HIV presence, but are expensive. Reference; Tu, Litvak, Pagano. Studies of AIDS and HIV surveilance, screening tests: can we get more by doing less?, Xin M. Tu Eugene Litvak, Marcello Pagano, Statistics in Medicine, Vol 13, 1905-1919 (1994).

  33. Pooling to Screen for HIV • Blood Mobile—time and money made individual testing at the human “herd” level unappealing plus creating issues of false classifications. • So what about pooling samples, • Up to 15 samples were pooled without a loss of Se • Pooling diminished false positives • Less cost • Fewer tests were needed

  34. A step further on the pooling • A JAMA article Jul 2002 described the following protocol. • Pool samples of blood in groups of 10 to determine the absence of HIV antibodies • From the negative pools, form pools of 90 individuals and run RT-PCR to detect the presence of the HIV virus • Used to find the presence of the virus prior to the time antibodies are formed allowing earlier treatment and preventing spread

  35. Trial results • 8000 people visiting publicly funded HIV clinics in the Southern USA were subjects of the test • Antibody tests found 39 long term infected individuals (those that had formed Ab to HIV) • RT-PCR testing of pools of 90 serologically negative samples found 4 additional positive individuals. • The cost to find the 4 additional individuals was $4109.00 per individual, if individual PCR’s had been done the cost would have been ~$360,000.00 per positive individual.

  36. Pooled testing/Screening Human Applications • Screening tests have been used to identify infected individuals in large populations, such as the military or blood donors. • Screening tests are used to estimate prevalence.

  37. Veterinarians and screening tests • Limited applications of screening test strategies • Salmonella contamination of eggs • Johne’s fecal pools • BVD • T. foetus

  38. Point • Human medicine has implemented the concept of pooling when human life is at risk, should veterinarians be open to the concept to address herd health issues in livestock? • Possible veterinary applications • Screening to evaluate treatment success • Determine prevalence prior to instituting control programs • Screening to evaluate vaccine success

  39. Estimation of prevalence using pooled PCR =