1. To Add Make it clearer that excluding variables from a model because it is not “predictive” removes all meaning from a CI since this is infinite repetitions Talk about stats being based on sampling variability – assumes sample is a random sample of some super- population (even if narrowly defined), but they are not a random sample, they self-select, so we couldn’t have infinite samples

2. Random Error I: p-values, confidence intervals, hypothesis testing, etc. Matthew Fox Advanced Epidemiology

3. Do you like/use p-values?

4. What is a relative risk? What is a pvalue?

7. Table 1 of a randomized trial of asbestos and LC FactorAsbestosNo Asbestos pvalue Female10%25%0.032 Smoking60%40%0.351 >60 yrs5%7%0.832 HBP25%24%0.765 Alcohol use37%45%0.152

8. Which result is more precise? RR 2.0 (95% CI: 1.0 – 4.0) RR 5.0 (95% CI: 2.5 – 10.0)

9. RR 2.0 (95% CI: 1.0 – 4.0) What are the chances the true results is between 1.0 and 4.0?

10. If yes, what does it mean to be “by chance?” What is it that is caused by chance? In a randomized trial, could the finding be by change?

11. This Morning Randomization – Why do we do it? P-values – What are they? – How do we calculate them? – What do they mean? Confidence Intervals

12. Last Session Selection bias – Results from selection into our out of study related to both exposure and outcome – Structural: conditioning on common effects – Adjustment for selection proportions – Weighting for LTFU Matching – In a case control study, creates selection bias by design, must be controlled in analysis

13. “There’s a certain feeling of ease and pleasure for me as a scientist that any way you slice the data, it’s statistically significant,” said Dr. Anthony S. Fauci, a top AIDS expert in the United States government, which paid most of the trial’s costs.Anthony S. Fauci

14. Randomization Randomization lends meaning to likelihoods, p-values and confidence intervals It can reduce the probability of severe confounding to an acceptable level But randomization does not prevent confounding

15. Greenland: Randomization, statistics and causal inference Objective: – Clarify the meaning and limitations of inferential statistics in the absence of randomization Example — lidocaine therapy after acute MI – Patient 1: doomed – Patient 2: immune – lidocaine therapy assigned at random – two results are equally likely

16. Greenland: Randomization, statistics and causal inference True RD = 0, so both possible results are confounded Expectation = 0 = (1 + -1)/2 – Statistically unbiased (expectation equals truth) Conclusions – Randomization does not prevent confounding – Randomization does provide a known probability distribution for the possible results under a specified hypothesis about the effect – Statistical unbiasedness of randomized exposure corresponds to an average confounding of zero over the distribution of results

17. Probability Theory With an assigned probability distribution, can calculate expectation The expectation does not have to be in the set of possible outcomes – Here, the expectation equals zero

18. Probability If we randomize and assume null is true (as we do when calculating p-values) – We expect half of the subjects to be exposed and half the events to be among the exposed If truly no effect of exposure, all data combinations, permutations are possible – Everyone was either type 1 or 4 – All the events (deaths) would occur regardless of whether assigned the exposure or not

19. Probability Theory The probability of each possible data result in a 2x2 table is: – A function of the number of combinations (permutations implies order matters) – Probability of each event is number of ways to assign X subjects to exposure out of Y and A events out of a total of B total events – Assumes the margins are fixed

20. E+E-Total D+??100 D-??900 Total500 1000 Fixed margins, how many parameters (cells) do I need to estimate to fill in the entire table?

21. Greenland: Randomization, statistics and causal inference What comfort does this provide scientists trying to interpret a single result? Can make probability of severe confounding small by increasing the sample size E+E-Total D+3070100 D-470430900 Total500 1000 Risk0.060.14 RR0.43RD-0.08

22. Greenland: Randomization, statistics and causal inference Given there were 100 cases and an even distribution of exposed and unexposed, how many cases would we expect to be exposed? E+E-Total D+3070100 D-470430900 Total500 1000 Risk0.060.14 RR0.43RD-0.08

23. Greenland: Randomization, statistics and causal inference What comfort does this provide scientists trying to interpret a single result? Can make probability of severe confounding small by increasing the sample size Probability under the null that randomization would yield a result with at least as much downward confounding as the observed result

24. Back to the counterfactual If association we measure differs from the truth, even if by chance, what explains it? – Unexposed can’t stand in for what would have happened to exposed had they been unexposed This is confounding – But on average, zero confounding – This gives us a probability distribution to calculate the probability of confounding explaining the results This is a p-value

25. Randomized trial of E on D in 4 patients We find: E+E- D+20 D-02 Total22

26. Randomized trial of E on D in 4 patients If the null is true, what CST types must they be? E+E- D+20 D-02 Total22

27. Hypergeometric distribution The hypergeometric distribution: Where X = random variable, x = exposed cases, n = exposed population, M is = total cases, and N = total population. M!/ x!(M-x)!

28. Spreadsheet

29. Greenland: Randomization, statistics and causal inference When treatment is assigned by the physician, Expectation depends on physician behavior – Expectation does not necessarily equal truth For observational data we DON’T have probability distribution for confounding – When E isn’t randomized, statistics don’t provide valid probability statements about exposure effects because – p-values, CIs, & likelihoods calculated with assumption all data interchanges are equally likely

30. Greenland: Randomization, statistics and causal inference A lternatives – Limit statistics to data description (e.g., visual summaries, tables of risks or rates, etc.) – Influence analysis: explore degree to which effect estimates would change under small perturbations of the data, such as interchanging a few subjects – Employ more elaborate statistical models – Sensitivity analysis – At the very least, interpret conventional statistics as minimum estimates of the error

31. (1) The p-value is: Probability under the test hypothesis (usually the null) that a test statistic would be ≥ to its observed value, assuming no bias in data collection or analysis – Why the null? Our job is to measure – 1-sided upper p-value is test stat ≥ observed value – 1-sided lower p-value is test stat ≤ observed value – Mid-p assigns only half probability of the observation to the 1-sided upper p-value – 2-sided p-value is twice the smaller of the 1-sideds

32. (2) The p-value is not: Probability that a test hypothesis (null hypothesis) is true – Calculated assuming that test hypothesis is true. – Cannot calculate probability of an event that is assumed in the calculation Probability of observing the result under the test hypothesis (null) [likelihood] – Also includes probability of results more extreme

33. (3) The p-value is not: An  -level (the Type 1 error rate) – More on that later A significance level – Used to refer to both p-values and Type 1 error rates – Should be avoided to prevent confusion

34. (4) The 2-sided p-value is not: Because 2-sided p-value is twice smaller of lower and upper 1-sided p- values, which may not be same and may be > 1, it is not the: – Probability that the data would show as strong an association as observed or stronger if the null hypothesis were true; – Probability that a point estimate would be as far or further from the test value as observed

35. Significance testing: Compares p-value to an arbitrary or conventional Type 1 error rate –  =0.05 Emphasizes decision making, not measurement – Derives from agricultural and industrial applications of statistics – Reflects the roots of epidemiology as the union of statistics and medicine

36. JNCI announces materials to “help journalists get it right”

38. Response They acknowledge the definitions were incorrect, however: “We were not convinced that working journalists would find these definitions user- friendly, so we sacrificed precision for utility. We will add references to standard textbooks for journalists who want to learn more.”

39. Frequentist Statistics

40. Alternatives to pvalues Two studies which is more precise? – RR 10.0, p = 0.039 – RR 1.3, p = 0.062 The pvalue conflates the size of the effect and its precision – RR 10.0, p = 0.039, 95% CI: 1.5-66.7 – RR 1.3, p = 0.062, 95% CI: 0.99-1.7

41. Frequentist intervals (1) Definition: – If the statistical model is correct and no bias, a confidence interval derived from a valid test will, over unlimited repetitions of the study, contain the true parameter with a frequency no less than its confidence level (e.g. 95%). But the statistical model is only correct under randomization CAN’T say that the probability the interval includes the truth equals the interval’s coverage probability (e.g., 95%).

42. Confidence Interval Simulation

43. Frequentist intervals (2) Advantages – Provides more information than significance tests or p-values: direction, magnitude, and variability – Economical compared with p-value function Disadvantages – Less information than the p-value function – Underlying assumptions (valid statistical model, no bias, repeated experiments)

44. Approximations: Test-based

45. Approximations: Wald

46. Standard Errors (basic over i strata):

47. How do we measure precision? Width of the confidence interval Measured how? – If I tell you the 95% CI for an RR is 2 to 8, can you tell me the point estimate? – Sqrt(U*L) – Difference measures, just subtract Remember relative measures are on the log scale, so width of a CI is measured by the RATIO of the upper to the lower CI

49. Frequentist Intervals (4): Interpretation

50. Conclusion about confidence intervals A CI used for hypothesis testing is an abuse of the CI – The goal is precision, not significance The goal of epi is precision, not significance – A precise null estimate is just as important as a precise significant estimate – An imprecise, statistically significant estimate is as useless as a non-statistically significant, imprecise estimate

51. What results are published or highlighted in publications? Find a publication with multiple results Rank them in order of precision Then see what is highlighted in the abstract

52. CIPRA Trial Trial of nurse vs. Doctor managed HIV care For primary results, co-investigators wanted pvalues and confidence intervals Didn’t want hypothesis testing even though was aware people would do it anyway I fought initially, and lost the debate Put in both

53. CIPRA Trial Reviewer comment: Table 3: Column with p-values can be dropped given that 95% confidence intervals are presented; perhaps mark significance as * (e.g. for p<0.025) and ** (e.g. for p<0.005) after the 95% CI's.

54. Summary Randomization gives meaning to statistics – Gives a probability distribution for confounding When randomization doesn’t hold, we have no probability distribution Pvalues aren’t probability of chance, null, etc. CIs allows us to assess precision – But are based on infinite repetitions – Do not contain the true value with 95% probability