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Analisis multivariat konvensioanal tidak selalu merupakan metode ideal untuk memprediksi efek pajanan pada studi-studi observasional. Ketika distribusi kovariat antara kelompok pajanan berbeda besar, penyesuaan dengan teknik multivariat konvensioanl tidak cukup menyeimbangkan kelompok tersebut. Bias yang tersisa dapat menghambat penarikan kesimpulan yang valid. Tujuan penelitian ini adalah membandingkan hasil analisis multivariat konvensional dengan analisis metoda propensity score matching pada studi kasus data sekunder imunisasi bayi ASUH KAP2 2003. Penelitian ini menemukan nilai OR metoda regresi logistik (0,99) berbeda dengan metoda propensity score matching (0,96). Metoda propensity score matching berhasil menjodohkan 574 subjek (68,27%). Untuk evaluasi pengaruh faktor risiko disarankan menggunakan model PSM karena mengurangi bias seleksi, tetapi untuk analisis faktor determinan yang banyak variabel independent, gunakan matching kerena variabel tersebut mempunyai posisi yang sama. Kata kunci : Regresi logistik, propensity score matching. Abstract Conventional multivariable analyses may not always be the ideal method for estimating exposure effects in observational studies. Where there are large differences in the distribution of covariates between expose groups, adjusting with conventional multivariable techniques may not adequately balance the groups, and the remaining bias may limit valid causal inference. The objective of this research is to compare the result of convensional multiariate analysis versus propensity score matching analysis in case study of infant immunization using secondary data of ASUH KAP2 2003. Model will be compared without interaction variable. The results show that the OR from logistic regression (0,99) differs to propensity score matching (0,96). Propensity score matching is successful in matching 574 subjects (68,27%). It is recommended to evaluate risk factor effect using PSM model, but to use logistic regression analysis for determinat factor analysis with many independent variables because the variables have the same position. Keywords: Logistic regression, propensity score matching.

Perbandingan Analisis Regresi Logistik dengan Analisis Propensity Score Matching pada Studi Kasus Imunisasi Bayi