NECDET SÜT, MEVLÜT TÜRE, Mustafa ŞENOCAK

Sağlık alanında karar vermede döngüsel süreçlerin kullanımı: bir Markov model uygulaması

Amaç: Çalışmada Markov modelin kavramsal yapısına açıklık getirilerek, sağlık alanında karar verme ve kestirimde bulunmada kullanımının örnek bir model ile gösterilmesi amaçlandı. Çalışma Planı: Markov model kuramını irdelemek için hipotetik bir çalışmayla, St. Jude toplam terapi XIIIB protokolünün akut lenfoblastik lösemideki etkinliğine ilişkin örnek bir model kurgulandı. Başlangıç durumu remisyon olan 10000 denemelik bir kohort benzetim çözümlemesi düzenlenerek 10 döngü sürecinde olguların remisyonda kalma beklentisi hesaplandı. Bulgular: Olayların zamanlaması önemli olduğunda, zaman aşımı karar probleminde risk yaratıyorsa ve olaylar birden fazla kez tekrar edebiliyorsa (nüks gibi), Markov model söz konusu olayları tahmin etmede kullanılabilir bir modelleme türüdür. Kurguladığımız örnek modelin sonucu olarak, St. Jude toplam terapi XIIIB protokolü ile tedavi gören herhangi bir olgunun 2. döngüde relapssız remisyonda kalma olasılığının %43 olduğu ve bu döngüden sonra artan bir hızda düştüğü bulundu. Sonuç: Markov modeller yardımıyla klinik stratejilerin maliyet, etkinlik ve yaşam kalitesi ölçütleri sentez edilerek, bunların sonucunda yaşam beklentisi, kalite düzeltmeli yaşam beklentisi ve yaşam maliyeti hesaplanabilir.

Anahtar Kelimeler:

Öncül hücre lenfoblastik lösemi-lenfoma, Veri yorumu, istatistiksel, Testlerin kestirim değeri, Markov zinciri, Karar destek teknikleri

The use of cyclic processes in medical decision making: An application of the Markov model

Objectives: We aimed to explain the conceptual basis of the Markov model and to show the use of this model by an example application in medical decision making and medical predicting. Study Design: An example model regarding the effectiveness of St. Jude Total Therapy XIIIB protocol in Acute Lymphoblastic Leukemia (ALL) was hypothesised to evaluate the Markov model concept. The expected remission probabilities in 10 cycles were calculated in a cohort simulation with 10,000 trials, in a cohort in remission in the initial state. Results: Markov models are effective prediction models when the timing of events is important, when the decision problem involves risk over time and when events may happen more than once (as in recurrence). Markov models can be used in estimating such events. As a result of derived model, the remission probability without relaps of any case treatrd with St. Jude Total Therapy XIIIB protocol in ALL disease in the second cycle was found as 43% and it was sharply reduced after this cycle. Conclusion: Cost, effectiveness, and health-related quality of life criteria of clinical strategies can be synthesised by the help of Markov models and used in the calculation of life expectancy, quality adjusted life expectancy and lifetime cost.

Keywords:

Precursor Cell Lymphoblastic Leukemia-Lymphoma, Data Interpretation, Statistical, Predictive Value of Tests, Markov Chains, Decision Support Techniques,

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