Clinical Epidemiology II: Deciding on Appropriate Therapy

Transcription

1 Clinical Epidemiology II: Deciding on Appropriate Therapy 1

2 Clinical Scenario [UG2B] 65 yo man controlled HTN 6-mo Hx cardioversion-resistant afib Benefit vs risk of long-term anticoagulation:? prevent embolic stroke? induce hemorrhage 2

3 Reader's Guide: Articles about Therapy Recap: Are the results valid? Primary Guides Secondary Guides What were the results? Describing effects of treatment How will the results help my patients? 3

4 Are the results valid? 4

5 Primary Guides for validity Was assignment of patients to treatments randomized? Were all patients who entered the trial properly accounted for and attributed at its conclusion? Was follow-up complete? Were patients analyzed in groups to which randomized (ITT)? 5

6 Secondary Guides for validity Were patients, health workers, study personnel blind to treatment? Were groups similar at the start of the trial? Aside from experimental intervention, were groups treated equally? 6

7 Secondary Guides for validity Were patients, health workers, study personnel blind to treatment? Were groups similar at the start of the trial? Aside from experimental intervention, were groups treated equally? 7

8 Blinding Good practice: factors that can affect the evaluation of outcome should not be permitted to influence the evaluation process Double-blind design Neither patient nor outcome evaluator knows Rx to which patient was assigned Single-blind Patient or evaluator is blinded as to Rx, but not both Triple-blind Patient, Physician, and Data analyst are blinded as to Rx identity 8

9 Are unblinded trials invalid? NO!! Blinding may not be possible Medical vs surgical treatment for CHF Outcome assessment may not be subject to bias Lab results Assessor of outcome may not be able to influence outcome Length of stay 9

10 Secondary Guides for validity Were patients, health workers, study personnel blind to treatment? Were groups similar at the start of the trial? Aside from experimental intervention, were groups treated equally? 10

11 Similarity of comparison groups Randomization tends to produce this Imbalance can often be adjusted for in analysis Major differences between groups on prognostic factors can influence interpretation 11

12 Secondary Guides for validity Were patients, health workers, study personnel blind to treatment? Were groups similar at the start of the trial? Aside from experimental intervention, were groups treated equally? 12

13 Example: Differential Followup Outcome: Time to metastatic disease in prostate cancer Treatments: Watchful waiting Radical prostatectomy RP pts evaluated every two months; WW pts PRN RP pts relapse faster BUT RP patients were more likely to have their relapse discovered early 13

14 What were the results? 14

15 Clinical Scenario 65 yo man, post MI no GI history? ASA to prevent second MI? 15

16 Study A RCT, Double-blind n=1000 on low-dose ASA, n=1000 on placebo ASA: 150 MIs over five years of follow-up (15%) Placebo: 200 MIs over same period (20%) 16

17 What were the results? How large was the treatment effect? How precise was the estimate of the treatment effect? 17

18 Measuring Effects of Therapy Baseline Risk (X) Risk without therapy Example: 200/1000 = 0.20 or 20% Risk with Therapy (Y) Example: 150/1000 = 0.15 or 15% Absolute risk reduction (X-Y) Risk difference Example: =

19 Measuring Effects of Therapy Relative risk (Y/X) Example: 0.15/0.20 = 0.75 Relative risk reduction [1 - (Y/X)] x 100% Example: [1-0.75] x 100% = 25% 95% Confidence interval for RRR Example: 9% to 40% 19

20 Other ways of measuring effects Purpose: characterize the impact of a treatment Some characterizations of size: Mean Median Proportion incurring a particular outcome (risk) Odds of a particular outcome 20

21 Other ways of measuring effects Some characterizations of variability: Standard deviation Quartiles Interquartile range Boxplots 21

22 Other ways of measuring effects Some characterizations of limiting behavior: Extremes (min, max) Range Note dependence on sample sizes setting records 22

23 Example: Boxplots Lower Quartile Median Upper Quartile High end of data Outlier Button Post Screw Breaking strength of anterior cruciate ligament reconstructions by three methods of fixation [G Fedor]

24 What were the results (continued)? How large was the treatment effect? How precise was the estimate of the treatment effect? Standard errors Give indication of precision associated with an estimated effect Confidence Intervals Give a range of estimates for an effect consistent with the observed data CIs and SEs reflect imprecision due to sample size CIs and SEs reflect imprecision due to variability in population under study 24

25 Three Studies A: 150/1000 vs 200/1000 RRR = 25% CI = 9% to 38% B: 15/100 vs 20/100 RRR = 25% CI = - 38% to 59% C: 3/20 vs 4/20 RRR = 25% CI = - 193% to 80% 25