Turkiye Klinikleri Journal of Biostatistics

.: ORIGINAL RESEARCH
Comparison of GAM and DLNM Methods for Disease Modeling in Environmental Epidemiology
Çevre Epidemiyolojisinde Hastalıkların Modellenmesi için Kullanılan GAM ve DLNM Metotlarının Karşılaştırılması
Mehmet KARADAĞa, Seval KULb, Saim YOLOĞLUc, Mustafa BOĞANd, Behçet ALe
aDepartment of Biostatistics and Medical Information, Hatay Mustafa Kemal University Faculty of Medicine, Hatay, TURKEY
bDepartment of Biostatistics, Gaziantep University Faculty of Medicine, Gaziantep, TURKEY
cDepartment of Biostatistics and Medical Information, İnönü University Faculty of Medicine, Malatya, TURKEY
dDepartment of Emergency Medicine, Düzce University Faculty of Medicine, Düzce, TURKEY
eDepartment of Emergency Medicine, Gaziantep University Faculty of Medicine, Gaziantep, TURKEY
Turkiye Klinikleri J Biostat. 2021;13(1):57-69
doi: 10.5336/biostatic.2020-77137
Article Language: EN
Full Text
ABSTRACT
 Objective: In this study, it was aimed to compare the performance results of the methods modeled by using generalized additive models (GAM) and distributed lag non-linear models (DLNM) methods from real data of three different outcome variables of three separate diseases related to air pollution. Material and Methods: The data were retrospectively obtained from three hospitals under the General Secretariat of Gaziantep province public hospitals for a total of 1,916 days between 01 January 2009 and 31 March 2014. Response variables were number of the emergency unit admission, hospitalization and mortality due to asthma, chronic obstructive pulmonary disease (COPD) and pneumonia. The response variables were estimated by GAM and DLNM methods by building four different models and the performances of the models were compared. Results: When the estimation performances of GAM and DLNM methods are compared for each of the dependent variables in the prediction of hospitalizations due to asthma, GAM model IV [Akaike Information Criteria (AIC) (4,280.63)] values were found to perform the best. It was observed that DLNM method performed better than GAM in models established for the prediction of almost all other dependent variables. For when compare the odds ratio (OR) plot estimated on particulate matter (PM10); it was seen that GAM method made predictions with lower standard error compared to DLNM methods. Conclusion: When the models created with each dependent variable were compared; it was generally observed that superior performance was obtained from the DLNM method. However, the lowest standard error in the OR charts were observed in the models using the GAM method.

Keywords: Generalized additive models; distributed lag non-linear models; environmental epidemiology; epidemiologic modeling
ÖZET
 Amaç: Bu çalışmada, hava kirliliğiyle ilişkili olduğu düşünülen, 3 farklı hastalığın 3 farklı sonuç değişkeni, gerçek veriler üzerinden genelleştirilmiş eklemeli modeller (GAM) ve dağıtılmış gecikmeli doğrusal olmayan modeller (DLNM) yöntemleri kullanılarak, modellenmesi ve model performanslarının karşılaştırılması amaçlanmıştır. Gereç ve Yöntemler: Gaziantep ili Kamu Hastaneleri Genel Sekreterliğine bağlı 3 hastaneden 1 Ocak 2009-31 Mart 2014 tarihleri arasında toplam 1.916 gün boyunca geriye dönük olarak izlenmesiyle elde edilen veriler kullanılarak oluşturuldu. Cevap değişkenleri; astım, kronik obstrüktif akciğer hastalığı (KOAH) ve pnömoni nedeniyle 'acil servislerine başvurular', 'hastanede yatış' ve 'mortalite' sayısı şeklindedir. Tahminlerde GAM ve DLNM yöntemleri kullanılmış, aynı yöntemle kurulan 4 farklı modelden en iyi performansa sahip model, ilgili yöntem için karşılaştırma modeli olarak kullanılmıştır. Bulgular: Bağımlı değişkenlerin her biri için GAM ve DLNM metotlarının performansları karşılaştırıldığında, astım tanısı ile hastanede yatan sayısının tahmin edildiği IV. modelde GAM [Akaike Information Criteria (AIC) (4.280,63)] yöntemi en iyi performansı göstermiştir. Geriye kalan bağımlı değişkenlerin neredeyse tümünün tahmini için oluşturulan modellerde ise DLNM yönteminin GAM'den daha iyi performans gösterdiği gözlenmiştir. Partikül maddesi (PM10) üzerinden tahmin edilen göreceli olasılıklar oranı (OR) değerleri için GAM ve DLNM yöntemlerine göre daha düşük standart hataya sahip olduğu görülmüştür. Sonuç: İncelenen bağımlı değişkenlerle oluşturulan modeller kıyaslandığında, genelde DLNM yönteminin, GAM yönteminden daha iyi performans gösterdiği gözlenmiştir. Fakat PM10 üzerinden tahmin edilen OR grafiklerinde GAM yönteminin, DLNM yöntemlerinde daha düşük standart hata ve dolayısıyla daha dar güven aralığına sahip tahminler yaptığı görülmüştür.

Anahtar Kelimeler: Genelleştirilmiş eklemeli modeller; dağıtılmış gecikmeli doğrusal olmayan modeller; çevre epidemiyolojisi; epidemiyolojik modelleme
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