Propensity scores in the presence of effect modification: A case study using the comparison of mortality on hemodialysis versus peritoneal dialysis.
OBJECTIVE
To control for confounding bias from non-random treatment assignment in observational data, both traditional multivariable models and more recently propensity score approaches have been applied. Our aim was to compare a propensity score-stratified model with a traditional multivariable-adjusted model, specifically in estimating survival of hemodialysis (HD) versus peritoneal dialysis (PD) patients.


METHODS
Using the Dutch End-Stage Renal Disease Registry, we constructed a propensity score, predicting PD assignment from age, gender, primary renal disease, center of dialysis, and year of first renal replacement therapy. We developed two Cox proportional hazards regression models to estimate survival on PD relative to HD, a propensity score-stratified model stratifying on the propensity score and a multivariable-adjusted model, and tested several interaction terms in both models.


RESULTS
The propensity score performed well: it showed a reasonable fit, had a good c-statistic, calibrated well and balanced the covariates. The main-effects multivariable-adjusted model and the propensity score-stratified univariable Cox model resulted in similar relative mortality risk estimates of PD compared with HD (0.99 and 0.97, respectively) with fewer significant covariates in the propensity model. After introducing the missing interaction variables for effect modification in both models, the mortality risk estimates for both main effects and interactions remained comparable, but the propensity score model had nearly as many covariates because of the additional interaction variables.


CONCLUSIONS
Although the propensity score performed well, it did not alter the treatment effect in the outcome model and lost its advantage of parsimony in the presence of effect modification.
Liem
Ylian S
YS
Program for the Assessment of Radiological Technology (ART Program), Department of Epidemiology & Biostatistics and the Department of Radiology, Erasmus University Medical Center Rotterdam, Dr, Molewaterplein 50, 3015 GE Rotterdam, the Netherlands. mail@ylian.nl.
Wong
John B
JB
Hunink
Mg Myriam
MM
de Charro
Frank Th
FT
Winkelmayer
Wolfgang C
WC
Am J Epidemiol. 1976 Dec;104(6):609-20
998608
Am J Epidemiol. 2006 Feb 1;163(3):262-70
16371515
J Clin Epidemiol. 2005 Jun;58(6):550-9
15878468
Stat Med. 2007 Feb 20;26(4):734-53
16708349
J Am Soc Nephrol. 2002 Sep;13(9):2353-62
12191980
Semin Dial. 2002 Mar-Apr;15(2):98-102
11952935
Nephrol Dial Transplant. 2002 Sep;17(9):1655-60
12198219
Pharmacoepidemiol Drug Saf. 2006 Oct;15(10):698-709
16528796
Pharmacoepidemiol Drug Saf. 2005 Apr;14(4):227-38
15386700
Kidney Int. 2003 Dec;64(6):2222-8
14633146
JAMA. 2007 Jan 17;297(3):314-6
17227985
Ann Intern Med. 1997 Oct 15;127(8 Pt 2):757-63
9382394
Kidney Int. 2007 Jan;71(2):153-8
17136031
J Clin Epidemiol. 2006 May;59(5):437-47
16632131
Pharmacoepidemiol Drug Saf. 2004 Dec;13(12):841-53
15386709
Stat Med. 2005 May 30;24(10):1563-78
15706581
Am J Epidemiol. 2003 Aug 1;158(3):280-7
12882951
Kidney Int. 1994 Apr;45(4):1163-9
8007587
Kidney Int. 2004 Dec;66(6):2389-401
15569331