İkili Bozulmuş Veri Yapılarında Genel, Sağlam ve Kesin Lojistik Regresyon Yöntemlerinin Karşılaştırılması: Bir Simülasyon Çalışması

Klinik araştırmalarda, ölçüm hatasını yok etmek için dikkatli çalışma düzen tasarımları kullanılmasına rağmen aykırı değerler ile karşılaşılabilir. Veri setlerinde aykırı değerler bulunduğu durumda, koşullu olmayan asimptotik lojistik regresyon yöntemlerine alternatif olarak kesin koşullu ve sağlam lojistik regresyon yöntemleri kullanılmaktadır. Bu çalışmanın temel amacı, veri setinde aykırı değerler ile çarpık bir yapı olduğu durumda, kesin ve sağlam lojistik regresyon yöntemlerinin performanslarını Monte-Carlo simülasyon çalışmaları ile karşılaştırmaktır. Sağlam lojistik regreson yöntemi, % 1 kontaminasyon seviyesinde bile daha az yanlı parametre tahminleri yaptığı bulundu. Veri setlerinde aykırı değerler ile bozulma durumu söz konusu olduğunda sağlam lojistik regresyon yöntemi kullanılmasını öneriyoruz.

Anahtar Kelimeler:

Bozulmuş İkili Veri, Lojistik Regresyon, Sağlam Lojistik Regresyon, Kesin Lojistik Regresyon

Comparison of Unconditional Asymptotic, Exact Conditional and Robust Logistic Regression Methods for Binary Contaminated Data Sets: A Simulation Study

In clinical research outliers occurs in spite of careful study design, and implementation of error-prevention techniques. Exact conditional and Robust logistic regression techniques are alternatives to the unconditional asymptotic logistic regression analysis when the dataset is contaminated with outliers. Our specific objectives were to compare the performance of exact conditional and robust logistic regression methods by Monte-Carlo simulation study when the data is skewed with outliers. Robust logistic regression method had less biased parameter estimates even at the 1% contamination level. We proposed using robust logistic regression method rather than exact conditional method when the data set is contaminated.

Keywords:

Contaminated dataset, Logistic regression, Robust logistic regression, Exact logistic regression,

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