Ningsih, Setia (2016) Propensity Score Dengan Pemodelan Structural Equation Model-Partial Least Square (SEM-PLS) Pada Kasus HIV/AIDS. Masters thesis, Institut Technology Sepuluh Nopember.
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Abstract
Propensity score merupakan salah satu metode statistika yang dapat digunakan
untuk menganalisis data observasional dimana Randomized Controlled Trial (RCT)
tidak dapat untuk dilakukan. Metode ini digunakan untuk mengurangi bias dalam
estimasi dampak dari perlakuan pada data yang bersifat observasi karena adanya
faktor confounding. Dalam penelitian observasional, kovariat biasanya tidak
seimbang antara kelompok perlakuan dan kelompok kontrol. Metode yang
digunakan adalah propensity score marginal mean weighting through stratification
untuk menggetahui effek confounding. Jika variabel yang digunakan adalah
variabel laten, maka pendekatannya menggunakan structural equation model
(SEM). Namun penerapan SEM berbasis covarian memiliki asumsi-asumsi yang
harus terpenuhi, seperti data harus berdistribusi multivariate normal dan
memerlukan jumlah sampel yang relatif cukup besar. Untuk mengatasi
permasalahan dalam pemenuhan asumsi tersebut, sebagai alternatif maka
dikembangkan SEM berbasis varian atau Partial least square (SEM-PLS). Pada
penelitian ini, data yang digunakan yaitu data sekunder hasil rekam medis penderita
HIV/AIDS. Kepatuhan terapi ARV sebagai confounding, sedangkan variabel
respon yaitu infeksi oportunistik. Estimasi propensity score menggunakan
pemodelan SEM-PLS. Selanjutnya, memeriksa keseimbangan kovariat antara
kelompok yang patuh terapi ARV dan kelompok yang tidak patuh terapi ARV. Jika
kelompok yang patuh dan tidak patuh seimbang (balance), maka dilakukan
pengujian effect treatment. Hasil propensity score marginal mean weighting
through stratification pada kasus HIV/AIDS diperoleh bahwa pada 3 strata effect
treatment sebesar 0.0275.
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Propensity score is a statistical method that can be used to analyze the
observational data where Randomized Controlled Trial (RCT) is not able to do.
This method is used to reduce bias in the estimation as the impact of treatment on
observational data because of their confounding factors. In the observational study,
covariates usually not balanced between the treatment group and the control group.
The method used is propensity score marginal mean weighting through
stratification to recognize the confounding effects. If the variable used is the latent
variables, the approach will be using structural equation modeling (SEM). But the
application of covariance-based SEM has the assumptions that must be met, such
data must be normally multivariate distributed and the number of samples requiring
relatively large. To solve the problems in the fulfillment of these assumptions, as
an alternative solution, it is developed SEM-based variant or Partial Least Square
(PLS-SEM). In this study, the data used is secondary data from medical records of
patients with HIV / AIDS. ARV therapy adherence as confounding, while the
response variable is an opportunistic infection. Propensity score estimation used is
SEM-PLS approach. Furthermore, examine the balance between the adherent
covariates ARV therapy and those who do not comply ARV therapy. If the group
of adherent and non-adherent balance, then tested with treatment effect will be
conducted. The results of propensity score with marginal mean weighting through
stratification in the case of HIV / AIDS found that in three strata there is a different
effect treatment between those obey adherent antiretroviral therapy and who do not
obey ARV therapy at 0.0275.
| Item Type: | Thesis (Masters) |
|---|---|
| Additional Information: | RTSt 519.53 Nin p |
| Uncontrolled Keywords: | SEM-PLS, Propensity Score, MMW-S, HIV/AIDS |
| Subjects: | Q Science > QA Mathematics > QA278.3 Structural equation modeling. |
| Divisions: | Faculty of Mathematics and Science > Statistics > 49101-(S2) Master Thesis |
| Depositing User: | Mr. Tondo Indra Nyata |
| Date Deposited: | 20 Jan 2020 03:12 |
| Last Modified: | 29 Apr 2024 01:45 |
| URI: | http://repository.its.ac.id/id/eprint/72752 |
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