Comparison of greedy matching methods on cigarette usage of individuals in Turkey

Yıl 2020, Cilt: 41 Sayı: 2, 369 - 376, 25.06.2020

Ezgi Nazman Hülya Olmuş

https://doi.org/10.17776/csj.677510

Öz

There has been a growing interest in using observational studies to estimate treatment effects on outcomes where treatment selection is often influenced by covariates. Recently, propensity score matching (PSM) method has increasingly being used to reduce bias in estimated treatment effect for observational studies. Greedy Matching (GM), one of the PSM methods, is widely preferred in many studies because of the calculation simplicity of the method. However, GM is still open to be evaluated in terms of bias reduction and classification performances. For this purpose, data including cigarette usage of 17242 individuals in Turkey were used for the comparison of nearest neighbor, caliper, stratification, Mahalanobis metric, and combined propensity score and Mahalanobis metric matching methods in terms of average standardized bias, bias reduction, and accuracy rate. The stratification-matching method should be preferred for not only low standardized bias and high bias reduction, but also high accuracy rate.

Anahtar Kelimeler

bias reduction, greedy matching methods, standardized bias, cigarette usage

Kaynakça

• [1] Rosenbaum P.R., Rubin, D.B. Assessing Sensitivity to an Unobserved Binary Covariate in an Observational Study with Binary Outcome. J. R. Stat. Soc. Series B, 45 (1983) 212–218.
• [2] Morgan C.J. Reducing Bias Using Propensity Score Matching. J. Nucl. Cardiol., 25 (2018) 404-406.
• [3] Rubin D.B. Estimating Causal Effects From Large Data Sets Using Propensity Scores. Ann. Int. Med., 15 (1997) 757-763.
• [4] Larsen M.D. An Analysis of Survey Data on Smoking Using Propensity Scores. Sankhyā: Ind. J. Stat., Series B, (1960-2002), (1999) 61.
• [5] Austin P.C. A Tutorial and Case Study in Propensity Score Analysis: An Application to Estimating the Effect of In-hospital Smoking Cessation Counselling on Mortality. Multivar. Behav. Res., 46 (2011) 119-151.
• [6] Taylor G., Girling A., McNeill A., and Aveyard P. Does Smoking Cessation Result in Improved Mental Health? A Comparison of Regression Modelling and Propensity Score Matching. BMJ Open, (2015) 5:e008774. doi: 10.1136/bmjopen-2015-008774.
• [7] Bidwell L.C., Palmer R.H.C., Brick L., Madden P.A.F., Heath A.C., and Knopik V.S. A Propensity Scoring Approach to Characterizing the Effects of Maternal Smoking During Pregnancy on Offspring's Initial Responses to Cigarettes and Alcohol. Behav. Gen., 46 (2016) 416–430.
• [8] Lin K.F., Wu H.F., Huang W.C., Tang P.L., Wu M.T., and Wu F.Z. Propensity Score Analysis of Lung Cancer Risk in a Population with High Prevalence of Non-Smoking Related Lung Cancer. BMC Pulm. Med., 17 (2017) 120.
• [9] Sahota S., Lovecchio F., Harold R.E., Beal M.D., and Manning D.W. The Effect of Smoking on Thirty-Day Postoperative Complications after Total Joint Arthroplasty: A Propensity Score-Matched Analysis. J. Arthroplasty, 33 (2017) 30-35.
• [10] Song Y., Lee D., Suh D.C., Kim J.G., Kim J.K., Han M., Liu H., Zhao L., Kim E.H., Jung S.C., Lee D.G., Koo H.J., Kim M.J., Baek S., Hwang S.M., Kim B.J., Kim Y.J., Cho H.J., Kim S.J., Jeon S.B., and Kim J.S. Cigarette Smoking Preferentially Affects Intracranial Vessels in Young Males: A Propensity-Score Matching Analysis. Neurointervention, 14 (2019) 43-52.
• [11] Rubin D.B., Thomas N. Matching Using Estimated Propensity Scores: Relating Theory to Practice. Biometrics, 52 (1996) 249-264.
• [12] Caliendo M., Kopeinig S. Some Practical Guidance for the Implementation of Propensity Score Matching. J. Econ. Surv., 22 (2005) 31-72.
• [13] Ho D.E., Imai K., King G., and Stuart E.A. MatchIt: Nonparametric Preprocessing for Parametric Causal Inference. J. Stat. Soft., 42 (2011) 1–28.
• [14] Stuart E.A., Rubin D.B. Matching with Multiple Control Groups and Adjusting for Group Differences. J. Ed. Behav. Stat., 33 (2008) 279-306.
• [15] Cochran W.G. The Effectiveness of Adjustment by Subclassification in Removing Bias in Observational Studies. Biometrics, 24, (1965) 295–313.
• [16] Guo X.S., Fraser M.W. Propensity score analysis: statistical methods and applications. 2nd ed. London, Thousand Oaks: Sage Publications 2015; pp. 100-150.
• [17] Olmos A., Govindasamy P. Propensity Scores: A Practical Introduction Using R. J. MultiDiscip. Eva., 11 (2015) 1-88.
• [18] Normand S.L., Landrum M.B., Guadagnoli E., Ayanian J.Z., Ryan T.J., Cleary P.D., and McNeil B.J. Validating Recommendations for Coronary Angiography Following an Acute Myocardial Infarction in the Elderly: A Matched Analysis Using Propensity Scores. J. Clin. Epide., 54 (2001) 387-398.
• [19] Cochran W.G., Rubin, D.B. Controlling Bias in Observational Studies: A Review. Ind. J. Stat., 35 (1973) 417–446.
• [20] Turkish Statistical Institute. Available at: http://www.tuik.gov.tr. Retrieved Oct 23, 2019.