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Understanding P- values and Confidence Intervals Thomas B. Newman, MD, MPH \Clinepi 2004\Understanding P- values and CI 10Nov04

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Overview Introduction and justification What P-values and Confidence Intervals don’t mean What they do mean: analogy between diagnostic tests and clinical research Useful confidence interval tips –CI for “negative” studies; absolute vs relative risk –Confidence intervals for small numerators

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Why cover this material here? P-values and confidence intervals are ubiquitous in clinical research Widely misunderstood and mistaught Pedagogical argument: –Is it important? –Can you handle it?

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Example: Douglas Altman Definition of 95% Confidence Intervals* "A strictly correct definition of a 95% CI is, somewhat opaquely, that 95% of such intervals will contain the true population value. Little is lost by the less pure interpretation of the CI as the range of values within which we can be 95% sure that the population value lies.“ Hard to understand Wrong!

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Understanding P-values and confidence intervals is important because It explains things which otherwise are paradoxical and do not make sense, e.g. need to state hypotheses in advance, correction for multiple hypothesis testing You will be using them all the time You are future leaders in clinical research

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You can handle it because We have already covered the important concepts at length earlier in this course –Prior probability –Posterior probability –What you thought before + new information = what you think now We will support you through the process

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Review of traditional statistical significance testing State null (Ho) and alternative (Ha) hypotheses Choose α Calculate value of test statistic from your study Calculate P- value from test statistic If P-value < α, reject Ho

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Problem: Traditional statistical significance testing has led to widespread misinterpretation of P-values

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What P-values don’t mean If the P-value is 0.05, that means that there is a 95% probability that… –The results did not occur by chance –The null hypothesis is false –There really is a difference between the groups

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Chalk board: Easy illustration of why non-Bayesian approach is wrong Analogy with diagnostic tests: 2x2 tables and “false positive confusion” Extending the analogy to understand a priori vs post hoc hypotheses, multiple hypotheses, etc. (This is covered step-by-step in the course book.)

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Bonferroni Inequality: If we do k different tests, each with significance level alpha, the probability that one or more will be significant is less than or equal to k*alpha Correction: If we test k different hypotheses and want our total Type 1 error rate to be no more than alpha, then we should reject H 0 only if P < alpha/k

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Confidence Intervals for negative studies: 5 levels of sophistication Example 1: Oral amoxicillin to treat possible occult bacteremia in febrile children* –Randomized, double-blind trial –3-36 month old children with T> 39 C (N= 955) –Treatment: Amox 125 mg/tid ( 10 kg) –Outcome: major infectious morbidity Jaffe et al., New Engl J Med 1987;317:

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Amoxicillin for possible occult bacteremia 2: Results Overall 27 children (~3%) bacteremic Of these 27, major infectious morbidity occurred in 3: 2 persistent bacteremia, 1 periorbital cellulitis: 2/19 (10.5%) with amoxicillin vs 1/8 (12.5%) with placebo. (P = 0.9) Conclusion: “Data do not support routine use of standard doses of amoxicillin…”

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5 levels of sophistication Level 1: P > 0.05 = treatment does not work Level 2: Look at power for study. (Authors reported power = 0.24 for OR=4. Therefore, study underpowered and negative study uniformative.)

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5 levels of sophistication, cont’d Level 3: Look at 95% CI for RR RR=.84; 95% CI (.09 to 8.0) (This was level of TBN and RHP letter to the editor, Note authors calculated OR= 1.2 and 95% CI 0.02 to 30.4)) Level 4: Make sure you do ITT analysis! (Not OK to restrict attention to bacteremic patients!) So it’s 2/507 vs 1/448; RR= 1.8 (amoxicillin worse); 95% CI (0.05 to 6.2)

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Level 5: the clinically relevant quantity is the Absolute Risk Reduction (ARR)! 2/507 (0.4%) with amoxicillin vs 1/448 (0.2%) with placebo ARR = -0.17% {amoxicillin worse} 95% CI (-0.9% {harm} to +.5% {benefit}) Therefore, LOWER limit of 95% CI for benefit (I.e., best case) is NNT= 1/0.5% = 200 So this study suggests need to treat >= 200 children to prevent Major Infectious Morbidity in one

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Stata output. csi | Exposed Unexposed | Total Cases | 2 1 | 3 Noncases | | Total | | 955 | | Risk | | | | | Point estimate | [95% Conf. Interval] | Risk difference | | Risk ratio | | Attr. frac. ex. | | Attr. frac. pop | | chi2(1) = 0.22 Pr>chi2 =

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Example 2: Pyelonephritis and new renal scarring in the International Reflux Study in Children* RCT of ureteral reimplantation vs prophylactic antibiotics for children with vesicoureteral reflux Overall result: surgery group fewer episodes of pyelonephritis (8% vs 22%; NNT = 7; P < 0.05) but more new scarring (31% vs 22%; P =.4) This raises questions about whether new scarring is caused by pyelonephritis Weiss et al. J Urol 1992; 148:

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Within groups no association between new pyelo and new scarring RR=0.34; 95% CI ( ) Weiss, J Urol 1992:148;1672 Trend goes in the OPPOSITE direction

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Stata output to get 95% CI:. csi | Exposed Unexposed | Total Cases | 2 28 | 30 Noncases | | Total | | 116 | | Risk | | | | | Point estimate | [95% Conf. Interval] | Risk difference | | Risk ratio | | Prev. frac. ex. | | Prev. frac. pop | | chi2(1) = 3.17 Pr>chi2 =

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Conclusions No evidence that new pyelonephritis causes scarring Some evidence that it does not P-values and confidence intervals are approximate, especially for small sample sizes (and subject to manipulation) Key concept: calculate 95% CI for ARR for negative studies

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Confidence intervals for small numerators

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P-values and Confidence Intervals Probably won’t cover this, but FYI: –Usually P < 0.05 means 95% CI excludes null value. –But both 95% CI and P-values are based on approximations, so this may not be the case –Illustrated by IRSC slide above –If you want 95% CI and P- values to agree, use “test-based” confidence intervals – see next slide

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Alternative Stata output: Test- based CI. csi , tb | Exposed Unexposed | Total Cases | 2 28 | 30 Noncases | | Total | | 116 | | Risk | | | | | Point estimate | [95% Conf. Interval] | Risk difference | | (tb) Risk ratio | | (tb) Prev. frac. ex. | | (tb) Prev. frac. pop | | chi2(1) = 3.17 Pr>chi2 =

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