eISSN: 1896-9151
ISSN: 1734-1922
Archives of Medical Science
Current issue Archive Manuscripts accepted About the journal Special issues Editorial board Abstracting and indexing Subscription Contact Instructions for authors
SCImago Journal & Country Rank
3/2018
vol. 14
 
Share:
Share:
more
 
 
abstract:

The number of strata in propensity score stratification for a binary outcome

Markus Neuhäuser, Matthias Thielmann, Graeme D. Ruxton

Arch Med Sci 2018; 14, 3: 695–700
Online publish date: 2016/08/16
View full text
Get citation
ENW
EndNote
BIB
JabRef, Mendeley
RIS
Papers, Reference Manager, RefWorks, Zotero
AMA
APA
Chicago
Harvard
MLA
Vancouver
 
Introduction
Non-interventional and other observational studies have become important in medical research. In such observational, non-randomized studies, groups usually differ in some baseline covariates. Propensity scores are increasingly being used in the statistical analysis of these studies. Stratification, also called subclassification, based on propensity scores is one of the possible methods. There is the quasi-standard of using five strata. In this paper we focus on a binary outcome and evaluate the above-mentioned standard of using five strata.

Material and methods
Bias and power for different numbers of strata are investigated with a simulation study. The methods are illustrated using data from a study where patients with diabetes mellitus and triple vessel disease undergoing coronary artery bypass surgery with and without previous percutaneous coronary intervention were compared.

Results
We show that more than five strata can be more powerful and give less biased results. However, using more than ten strata hardly gives any further benefit.

Conclusions
When applying a stratification, more than five strata may be preferable, especially because of increased power. Our simulation study does not show a clear winner; hence a useful strategy could be to work with five as well as with ten strata.

keywords:

logistic regression, propensity score, stratification

references:
Hartmann M, Schaffner P. Legal requirements, definitions, and standards for non-interventional drug studies: a global picture of variability – results and conclusions from a single-institution survey. Therap Innov Regulat Sci 2013; 47: 684-91.
Piffaretti G, Mariscalco G, Riva F, et al. Abdominal aortic aneurysm repair: long-term follow-up of endovascular versus open repair. Arch Med Sci 2014; 10: 273-82.
Rosenbaum PR, Rubin DB. Reducing bias in observational studies using subclassification on the propensity score. J Am Stat Assoc 1984; 79: 516-24.
D’Agostino RB. Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group. Stat Med 1998; 17: 2265-81.
Guo S, Fraser MW. Propensity score analysis. Sage Publications, Thousand Oaks, CA, 2nd edition. 2014.
Austin PC. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behav Res 2011; 46: 399-424.
Austin PC. The relative ability of different propensity score methods to balance measured covariates between treated and untreated subjects in observational studies. Medical Decision Making 2009; 29: 661-77.
Stürmer T, Rothman KJ, Avorn J, et al. Treatment effects in the presence of unmeasured confounding: dealing with observations in the tails of the propensity score distribution – a simulation study. Am J Epidemiol 2010; 172: 843-54.
Lunceford JK, Davidian M. Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study. Stat Med 2004; 23: 2937-60.
Cochran WG. The effectiveness of adjustment by subclassification in removing bias in observational studies. Biometrics 1968; 24: 295-313.
Neuhäuser M, Becher H. Improved odds ratio estimation by post hoc stratification of case-control data. Stat Med 1997; 16: 993-1004.
Austin PC. The performance of different propensity score methods for estimating marginal hazard ratios. Stat Med 2013; 32; 2837-49.
Thielmann M, Neuhäuser M, Knipp S, et al. Prognostic impact of previous percutaneous coronary intervention in patients with diabetes mellitus and triple-vessel disease undergoing coronary artery bypass surgery. J Thorac Cardiovasc Surg 2007; 134: 470-6.
Massoudy P, Thielmann M, Lehmann N, et al. Impact of prior percutaneous coronary intervention on the outcome of coronary artery bypass surgery: a multi-center analysis. J Thorac Cardiovasc Surg 2009; 137: 840-5.
Rubin DB. On principles for modeling propensity scores in medical research. Pharmacoepidemiol Drug Saf 2004; 13: 855-7.
Heinze G, Jüni P. An overview of the objectives of and the approaches to propensity score analyses. Eur Heart J 2011; 32: 1704-8.
Brookmeyer R, Liang KY, Linet M. Matched case-control designs and overmatched analyses. Am J Epidemiol 1986; 124: 693-701.
FEATURED PRODUCTS
Quick links
© 2018 Termedia Sp. z o.o. All rights reserved.
Developed by Bentus.
PayU - płatności internetowe