Aetiology versus Prediction - correct for Confounding? Friedo Dekker ERA-EDTA Registry / LUMC

Transcription

1 Aetiology versus Prediction - correct for Confounding? Friedo Dekker ERA-EDTA Registry / LUMC

2 Aetiology Study effect of a risk factor on an outcome Consider potential confounding: other risk factor for the outcome, also related to risk factor of interest, (and not being in the causal pathway) may interfere with relationship to be studied Adjust for confounding (stratification or multivariate analysis) real effect

3 Example Crude effect of grey hair on mortality: RR=2 People with grey hair are older Old age is risk factor for mortality in itself Age is not in the casual pathway Age is potential confounder adjust for age RR adj = Age indeed was a confounder here Age? Grey

4 Confounding Previous knowledge necessary to decide whether: Something is an independent risk factor Something could be related to risk factor of interest Something is NOT in the causal pathway Can the data themselves tell you whether something is a confounder? NO!!! Can your statistician tell you? NO!!!!!

5 Please note The risk factor you study today, will be only a confounder tomorrow... A risk factor is not per se a causal factor itself, it could be a risk marker or something reflecting an underlying causal factor

6 Examples of confounding? Study effect of ace-inhibitor on survival. Is subsequent blood pressure (on ace-inhibitors) a confounder? Study effect of blood pressure on mortality. Is use of subsequent medication a confounder? Study effect of blood pressure on mortality. Is color of your eyes a confounder? And color of your socks?

7 Examples of confounding? Association between HD/PD and survival. Is ACE-polymorphism a confounder? Association between ACE-polymorphism and survival. Is HD/PD a confounder? Anti-hypertensive drugs? Any other confounders possible?

8 Examples from the literature (1) Effect of BMI on risk to develop ESRD What confounders to adjust for? age? sex? systolic blood pressure? proteinuria?

9 Kidney International 2004 Adjusted for age, sex, SBP, proteinuria

10 Examples from the literature (2) Effect of haematologic malignancy on mortality in patients with acute renal failure at ICU Age Sex Apach e Haematologic malignancies +/-?

11 NDT 2005 Crude HR haem. Malignancy = 1.52 (p=0.018) HR adj for Apache-score= 1.28 (p=0.16) HR adj for Apache, hospital stay, and vasopressor use: p=0.78

12 NDT 2005 Crude HR haem. Malignancy = 1.52 (p=0.018) HR adj for Apache-score = 1.28 (p=0.16) HR adj for Apache, hospital stay, and vasopressor use: p=0.78

13 Examples from the literature (3) Effect of S ALB on mortality. Adjust for CRP? CR P S AL B? NOT because of confounding Perhaps to demonstrate that S ALB is a marker of inflammation (if so, effect of S ALB will disappear after adjustment for CRP)

14 Kidney International 1999 However, in the multivariate Cox regression analysis, S alb lost its significance as an independent risk factor for cardiovascular death, and the remaining predictive value for all-cause mortality was much lower than in the univariate analysis. However, if CRP was not taken into account in the multivariate analysis, S alb remained, after age, the strongest predictor for both overall and cardiovascular mortality.

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16 Prognosis (1) Try to predict outcome based on one or more risk factors What risk factors can be used as predictors? anything! (e.g., blood pressure, use of medication, color of your hair, even color of your socks)

17 Prognosis (2) Put all possible risk factors in a model Let computer decide what is the strongest combination Confounding is not an issue here Cave interpretation: Of two strongly related real risk factors, in general only one will be in the model

18 Examples (4): AJKD 2000 Aim: to determine which factors (albumin, CRP, ) are most closely related to CV death Both CRP and albumin are univariate predictors in HD patients (p<0.005) Cox model: high CRP is predictive of CV death (p=0.0001) while albumin is not (p=0.5) Conclude that albumin is not prognostic? Conclude that albumin does not play a role?

19 Cave! In prediction models, all significant predictors are in the model Intermediate variables or risk markers can be selected in stead of true causes e.g., grey hair can be selected in stead of age Important confounders are sometimes not selected Consequently, variables in prediction model are not per se important in the aetiology of the outcome Before model building: Define your specific aim: either aetiology, or prediction

20 DO and DON T in aetiology DO consider confounding in aetiology: independent risk factor for outcome associated with risk factor of primary interest DON T adjust for a variable in the causal pathway

21 DO and DON T in prognosis DO consider stepwise models in prognosis DON T interpret variables in a prognostic model as aetiology