1 / 175

From bench to the bedside Statistics Issues in RCT

From bench to the bedside Statistics Issues in RCT. Ferran Torres Biostatistics and Data Management Platform IDIBAPS - Hospital Clinic Barcelona Universitat Autònoma Barcelona. EMA: Scientific Advice Working Party (SAWP) Biostatistics Working Party (BSWP). Disclaimer.

olive
Download Presentation

From bench to the bedside Statistics Issues in RCT

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Frombenchtothebedside StatisticsIssues in RCT Ferran Torres Biostatistics and Data Management Platform IDIBAPS - Hospital Clinic Barcelona UniversitatAutònoma Barcelona. EMA: Scientific Advice Working Party (SAWP) BiostatisticsWorkingParty (BSWP).

  2. Disclaimer • The opinions expressed today are personal views and should not be understood or quoted as being made on behalf of any organization. • Regulatory • Spanish Medicines Agency (AEMPS) • European Medicines Agency (EMA) • Scientific Advice Working Party (SAWP) • Biostatistics Working Party (BSWP) • Hospital - Academic - Independent Research • IDIBAPS. Hospital Clinic Barcelona • Autonomous University of Barcelona (UAB) • SCREN. SpanishClinicalTrialsPlatform

  3. Documentation

  4. Documentation http://ferran.torres.name/edu/stats_rct • Power Point presentation • Selected References • Direct links to guidelines Password: stats_rct

  5. Globalisation

  6. LACK OF HARMONISATION INTERNATIONAL CONFERENCES HARMONISATION www.ich.org USA JAPAN Similar Basic Technical Requirements Data to register in all regions EU

  7. Regulatory Agencies

  8. Regulatory Guidances • CPMP/EWP/908/99 CPMP Points to Consider on Multiplicityissues in Clinical Trials • CPMP/EWP/2863/99 Points to Consider on Adjustment for BaselineCovariates • CPMP/2330/99 Points to Consider on Application with 1.) Meta-analysesand 2.) One Pivotal study • Choice of a Non-Inferiority Margin CPMP/EWP/482/99 Points to Consider on Switching between Superiority and Non-inferiority • CPMP/EWP/1776/99 Points to Consider on Missing Data • CHMP/EWP/83561/05 Guideline on Clinical Trials in Small Populations • CHMP/EWP/2459/02 Reflection Paper on Methodological Issues in Confirmatory Clinical Trials with Flexible Design and Analysis Plan

  9. “Scientific Recomendations” • Consort Statement:  Summary, // General, // non-inferiority • Lancet: Series de Methodological & Stats Series • BMJ: Statistics Notes (Bland & Altman) or in BMJ 10 10

  10. http://www.equator-network.org

  11. Today’s talk is on statistics

  12. Basic statistics Why Statistics? Samples and populations P-Value Statistical errors Sample size Confidence Intervals Interpretation of CI: superiority, non-inferiority, equivalence

  13. The role of statistics “Thus statistical methods are no substitute for common sense and objectivity. They should never aim to confuse the reader, but instead should be a major contributor to the clarity of a scientific argument.” The role of statistics. Pocock SJ Br J Psychiat 1980; 137:188-190

  14. Why Statistics? Variation!!!!

  15. SAMPLE AND POPULATIONS P-VALUE AND CONFIDENCE INTERVALS BACKGROUNG

  16. p

  17. Population and Samples Sample Population of the Study Target Population

  18. Extrapolation Study Results Sample Inferential analysis Statistical Tests Confidence Intervals Population “Conclusions”

  19. P-value The p-value is a “tool” to answer the question: Could the observed results have occurred by chance*? Remember: Decision given the observed results in a SAMPLE Extrapolating results to POPULATION *: accounts exclusively for the random error, not bias p < .05 “statistically significant”

  20. P-value: an intuitive definition • The p-value is the probability of having observed our data when the null hypothesis is true (no differences exist) • Steps: • Calculate the treatment differences in the sample (A-B) • Assume that both treatments are equal (A=B) and then… • …calculate the probability of obtaining a magnitude of at least the observed differences, given the assumption 2 • We conclude according the probability: • p<0.05: the differences are unlikely to be explained by random, • we assume that the treatment explains the differences • p>0.05: the differences could be explained by random, • we assume that random explains the differences

  21. Factors influencing statistical significance • Difference • Variance (SD) • Quantity of data • Signal • Noise (background) • Quantity

  22. P-value. Some reflexions • Tell us NOTHING about clinical or scientific importance. Only, that the results were not due to chance. • A “very low” p-value do NOT imply: • Clinical relevance (NO!!!) • Magnitude of the treatment effect (NO!!) With n or variability  p • Please never compare p-values!! (NO!!!)

  23. Interval Estimation Intuitive interpretation: “A probability that the population parameter falls somewhere within the interval” Sample statistic (point estimate) Confidence interval Confidence limit (lower) Confidence limit (upper)

  24. 95%CI • Better than p-values… • …use the data collected in the trial to give an estimate of the treatment effect size, together with a measure of how certain we are of our estimate • CI is a range of values within which the “true” treatment effect is believed to be found, with a given level of confidence. • 95% CI is a range of values within which the ‘true’ treatment effect will lie 95% of the time • Generally, 95% CI is calculated as • Sample Estimate ± 1.96 x Standard Error

  25. Superiority study Control better Test better IC95% d < 0 - effect d = 0 No differences d > 0 + effect

  26. STATISTICAL ERRORS SAMPLE SIZE MINIMUM IMPORTANT CLINICALY IMPORTANT DIFFERENCE (MICD) DESIGN

  27. Type I & II Error & Power

  28. Type I & II Error & Power • Type I Error (a) • False positive • Rejecting the null hypothesis when in fact it is true • Standard: a=0.05 • In words, chance of finding statistical significance when in fact there truly was no effect • Type II Error (b) • False negative • Accepting the null hypothesis when in fact alternative is true • Standard: b=0.20 or 0.10 • In words, chance of not finding statistical significance when in fact there was an effect

  29. CxVariance n = (MICD)2 C: function of  and  MICD:Minimum Important Clinically Difference Sample size and MICD

  30. Minimum Important Clinically Difference (MICD or MID) • “Smallest difference that is considered clinically important, this can be a specified difference (the Minimum Important Clinically Difference (MICD)” • One can observe a difference between two groups or within one group over time that is statistically significance but small. • With a large enough sample size, even a tiny difference could be statistically significant. • The MID is the smallest difference that we care about.

  31. ABBSOLUTE AND RELATIVE DIFERENCES Effect scales

  32. Absolute and Relative Scales • Incidence events / population at risk • Absolute Risk Reduction (ARR) Incidence in Test – Incidence in control • Relative Risk Reduction (RRR) (Incidence in Test – Incidence in control) / Incidence in control • Number Needed to Treat (NNT) 1/ ARR • Relative Risk (RR) Incidence in Test / Incidence in control

  33. Absolute and Relative effects Risks …

  34. RR & OR • RR or OR > 1 • RR or OR =1 • RR or OR < 1 Risk Factor Absence of effect Protection Factor

  35. RR=2 • Rate in Exposed • 2/4 => 0.50 • Rate in non-Exposed • 1/4 => 0.25 Non-Exposed Exposed • Odds in Exposed: 2/2=> 1 • Odds in non-Exposed 1/3 OR=3 RR & OR Ills

  36. Example • Treatment A: relative risk of 0.81 • Treatment B: reduction of 19% in risk • Treatment C: absolute rate reduction of 3% • Treatment D: survival increase from 84% to 87% • Treatment E: relative mortality reduction of 19% • Treatment F: avoids 1 death per 33 treated patients

  37. Example • Treatment A: relative risk of 0.81 RR = 13% / 16% => 0.81 • Treatment B: reduction of 19% in risk RRR = 1-0.81 => 19% • Treatment C: absolute rate reduction of 3% ARR = 16% - 13% => 3% • Treatment D: survival increase from 84% to 87% ARR = 87%-84% = 16% - 13% = 3% • Treatment E: relative mortality reduction of 19% RRR = (16%-13%) / 16% = 19% o bé 100*(1-RR) => 19% • Treatment F: avoids 1 death per 33 treated patients NNT = 33; ARR = 1/33 = 0,3 = 3%

  38. SUPERIORITY, NON-INFERIORITY AND EQUIVALENCE DESIGNS CLINICAL RELEVANCE-INTERPRETATION

  39. Superiority study Control better Test better IC95% d < 0 - effect d = 0 No differences d > 0 + effect

  40. Superiority 1 2 3 4 5 0 <- Treatment less effective Treatment more effective -> Treatment-Control

  41. Equivalence Upper equivalence boundary Lower equivalence boundary 1 2 3 4 5 0 <- Treatment less effective Treatment more effective -> Treatment-Control

  42. Non-Inferiority Lower equivalence boundary 1 2 3 4 5 0 <- Treatment less effective Treatment more effective -> Treatment-Control

  43. 1/2 ? 1/3 ? B A d 30% P

  44. JAMA 2002; 287: 1807-1814 51

  45. d 30%

More Related