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Comment optimiser la recherche en réanimation Comment jinterprète les résultats statistiques? Jean-François TIMSIT MD PhD Medical ICU Outcome of cancers.

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Présentation au sujet: "Comment optimiser la recherche en réanimation Comment jinterprète les résultats statistiques? Jean-François TIMSIT MD PhD Medical ICU Outcome of cancers."— Transcription de la présentation:

1 Comment optimiser la recherche en réanimation Comment jinterprète les résultats statistiques? Jean-François TIMSIT MD PhD Medical ICU Outcome of cancers and critical illnesses University hospital A Michallon INSERM U 823 Grenoble FRANCE

2 Biais de toutes les études Biais de sélection : échantillon trop différent de la population cible, ou si la manière de sélectionner les patients à inclure ne permet pas despérer obtenir un population cliniquement représentative Biais dinformation : les facteurs de risques et les critères de jugement ne sont pas recueillis correctement (pas dHC =pas de septicémie..) Biais de confusion: variable (évènement) qui contribue à la fois au critère de jugement et aux facteurs de risque.

3 Regardez bien vos (les) données+++ 90% de lénergie nécessaire pour tirer des conclusions… –Distribution des variables –Outliers –Reproductibilité –Valeurs manquantes –Correlation entre les variables data reduction

4 Stroke 1999;30: à 77.7 Data structure

5 N Engl J Med 2002;549:556 Analyze the data structure Lancet 2001;357:9-14

6 External validity

7 Demonstrate that the patients you enroled are the ones of interest?? Mortality of the control group

8 Prowess 1690 pts/ 11 countries/ 164 sites!!!! A very few % of the severe sepsis admitted The overal treatment are not standardized… External validity..? –More pragmatic studies enrolling all the patients with severe sepsis…. –But…there was a learning curve!!

9 « CONCLUSIONS: A learning curve appeared to be present within the PROWESS trial … efficacy improved with increasing site experience... Investigational sites may need to require a minimum level of protocol-specific experience to appropriately implement a given trial. …This experience should be an important consideration in designing trials and analysis plans. … » Macias et al – Crit care med 2004;32:2385

10 The control group… « is an exagerate real life » Finney – JAMA 2003 Control group in the Vandenberghe study (2006)

11 Why should we wary of single-center trials? Bellomo et al – Crit Care Med 2009; 37: Bcp ont été contredites par des études multicentriques –Prone position (Drakulovic 1999) vsVan Neiwenhoven 2006 –Van den Berghe vs Nice-Sugar… –EGDT Importance de leffet –EGDT DC 46.9% 30.5% (RRR=35%!!!) Validité externe –Population particulière –Critère de jugement « maison » –Mode de prise en charge globale Stable ( variabilité) mais mal décrit ou non standart? Nécessitent une charge de travail particulière (dévouement à létude)

12 Registries for rubust evidence+++ Dreyer et al – JAMA 2009; 302:790-1 Permettent de valider les résultats des RCT dans la « vraie vie » Permettent de générer des hypothèses pour des études complémentaires

13 Le critère de jugement Précis Reproductible Reflet de ce que vous voulez mesurer++..attention à ce choix+++

14 « Surrogate end-points » Closely linked to clinical end-point? Surrogate clinical end-point Good calibration of the surrogate end point and more sensitive to change –Caution!!!. Bucher HG – JAMA 1999; 282:771

15 Surrogate end-points…example of failure Blood pressure DC LNMA BP Lopez A et al – Crit Care Med 2004;32:21-30

16 Estimated rate of nosocomial pneumonia? The real rate of NP is 20% The rate of misclassification vary according to the accuracy of the diagnosis True VAP True non VAP total Diagnosed VAP acx Diagnosed Non-VAP bdy Totala+bc+dTotal Se=a/(a+b) Sp=d/(c+d) a+c=x b+d=y

17 True rate vs estimated rate of an event No VAP VAP T- T Rate of VAP: 26% No VAP VAP T-800 T Se=p[T+]/[D+]= 1 Sp=p[T-]/[D-]= 1 Rate of VAP: 20% No VAP VAP T-722 T = 0.9 X 20 = 0.9 X 80 Se=p[T+]/[D+]= 90% Sp=p[T-]/[D-]= 90%

18 Estimated effect of a new treatment PlaceboTreatment No CRI CRI Sp=p[T+]/[D+]= 100% Se=p[T-]/[D-]= 100% True rate of CRI: 5% RR=2 « True » 0R=2.05, p= Whats happen if the diagnostic test is not perfectly accurate?

19 Estimated effect of a new treatment PlaceboTreatment No CRI?? CRI?? 1000 Sp=p[T+]/[D+]= 90% Se=p[T-]/[D-]= 100% True rate of CRI: 5% RR=2 =True CRI * Se + True no CRI*(1-Sp) =50* *0.1=145!!!! 145 « True » 0R=2.05, p=

20 Estimated effect of a new treatment PlaceboTreatment No CRI CRI Sp=p[T+]/[D+]= 90% Se=p[T-]/[D-]= 100% True rate of CRI: 5% RR=2 « True » 0R=0.49, p= Estimated 0R=0.82, P value= 0.051

21 Estimated effect of a new treatment PlaceboTreatment No CRI CRI Sp=p[T+]/[D+]= 100% Se=p[T-]/[D-]= 70% True rate of CRI: 5% RR=2 Estimated 0R=1.98, P value= 0,0006 =True CRI * Se + True no CRI*(1-Sp) =50* *0=35 « True » 0R=2.05, p=

22 Measurement errors If the prevalence of the event is low, you need a very specific test to avoid measurement error of the treatment effect If the prevalence is high, you need a very sensitive one….

23 What is the optimal clinical end-point? Underlying illnesses Acute disease time Day 14Day 28Day 901y

24 What is the best??? Day 14 more related to the disease itself…low noise (death due to other cause) Day 28 compromise? Day 90 competing events?, probably more important at the patients point of view 1 year competing events, more important for patient and at the societal point of view All of the end-points YES!!BUT Multiple comparisons ( NNT, power) « Survival analyses? »

25 (Type I error (%)) 1- (Power (%)) Number of tests

26 Genetic profiles > 1000 signals for bacterias > signals for humans Decrease of power and increase in the type I error Signal 1 Signal 2.. Pat 1 Pat 2 Pat 3 Pat 4 Pat 5 Pat 6 Pat 7 Pat 8 Pat 9 Pat 10 Pat 11 Pat 12 …….. Signal 1 Signal 2 Signal 3 Signal 4 signal 5 Signal 6 Signal 7… Pat 1 Pat 2 Pat 3 Pat 4 Pat 5 Mondial consortium, external validation

27 Time pitfalls Time to measurement of exposure Competing events

28 NIV failure has not been measured at the beginning of the follow up (time dependent event) JAMA 2000 NIV success NIV failure Invasive ventilation 1,0 0,2 0,4 0,6 0, ,0 0,2 0,4 0,6 0, Cumulative proportion Of patients without penumonia days

29 Risque compétitif= censure informative temps de survenue du décès (analyse de survie) tous les modèles pour données censurées considèrent que la censure nest pas informative « un individu i qui est censuré au temps t estexposé au même risque de décès au temps t+1 quun autre patient encore exposé au risque » Cette hypothèse forte est fréquemment fausse, surtout en réanimation ou le délai de survenue de la sortie vivant et le délai de survenue du décès sont complètement liés La sortie de réanimation est un risque compétitif mortalité à date fixe plutôt que mortalité ICU++++

30 Randomization…what for? Well done multivariate analysis is able to adjust on known confonders Random allocation is the only way to equilibrate groups on confounding factors..known AND UNKNOWN +++ Treatment A Treatment B DC 5%DC 40% SAPSII 32SAPS II 40 Genetic Fact X 90% Genetic Fact X 10%

31 RCT: le dogme Principes de base 1 avez vous atteint vos objectifs concernant la puissance statistique de votre étude? 2 Avez vous analysé tous les patients inclus? 3 Avez vous limité lanalyse au seul critère de jugement principal? Dans une étude randomisée contrôlée, si tous les objectifs sont atteints un test statistique suffit et aucune comparaison entre les populations nest nécessaire

32 But… In practice not really applicable –Intermediate analysis should lead to early and more ethical studies (LnMMA, HCG) –It should be more appropriate to analyze data about patients that were effectively treated or with a confirmation of the disease there have been hypothesized at inclusion Ex: Severe sepsis definition needs the occurrence of an infection proven or suspected… Gram negative septicemia need to be immediately treated before the results of the BC –At least 2 judgment criteria: efficacy and side effects… But inflation of type I and II errors (acceptable if a priori designed)

33 In practice Exclusion is possible if exclusion criteria has been obtained before randomization (even the results are not available) at random if planned in the original protocol Exclusion criteria should not depend of the attending physician expertise One primary end-point and previously designed secondary end-points As final groups are not fully decided at random, group comparability is needed.

34 A CONFOUNDER… A confounder is associated with the risk factor and causally related to the outcome Carrying matches Lung cancer Smoking

35 In ICU Many intercurrent events Many interactions between events DNR orders++

36 Crit Care Med patients included, 1415 (39.2%) experienced one or more AEs 821 (22.7%) had two or more AEs Mean number of AEs per patient was 2.8 (range, 1–26). Six AEs were associated with death: primary or catheter-related BSI OR 2.9;95% CI, 1.6 –5.32 BSI from other sources OR, 5.7; 95% CI 2.66 –12.05 nonbacteremic pneumoniaOR, 1.7; 95% CI 1.17–2.44 deep and organ/space SSI without BSI OR, 3.0; 95% CI, 1.3– 6.8 pneumothorax OR, 3.1; 95% CI, 1.5– 6.3 gastrointestinal bleeding OR, 2.6; 95% CI, 1.4–4.9

37 Adjustement using a magic « multivariate model » x y z Truth universe in your sample

38 Adjustement using a magic « multivariate model » x y z

39 x y z

40 x y z

41 x y z

42 x y z Model using interactions and polynomes…

43 Validation using external samples x y z Other representative sample of the truth universe

44 Messages As many possible models as individuals (even more!!) Parcimony decreases model discrimination but improves external validity the statistical analyses should be precisely designed a priori Primary and secondary analyses should be precisely planned

45 Rules for multivariate models Select the model according to the end point Check for its hypotheses The explanatory variables should be –Precisely defined –Not related one to another –Sufficiently frequent in both groups (problem with perfect or quasi perfect discrimination) Ex: Multiple logistic regression in CCM ( ) (Poster 0524 – P Lambrecht and D Benoit – Ghent, Belgium) –Median 6 shortcomings by multiple logistic regression –(significantly decreased when a statistician is a co-author)

46 How I interpret the result? Discussion with a statistician if you are not familiar with statistics What is the title of the paper you want to do? Subgroup analyses lead to a important increase in the type I error and also in a decrease of the power of your study -exploratory analyses that should be confirmed

47 Interpréter les résultats avec une certaine distance…


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