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Öğe Assessment of relative risk measurement comparing with odds ratio, attributable risk and number needed to treat(Ekin Tibbi Yayincilik Ltd Sti-Ekin Medical Publ, 2007) Suet, Necdet; Senocak, MustafaObjectives: It was aimed to clarify the theoretical aspects of relative risk (RR) and other risk measurements (odds ratio OR, attributable risk AR and number needed to treat NNT), to examine the associations between RR and others, to refer the limitations of RR in medical decision making and to put forward some solution suggestions. Study Design: All possible RR, OR, AR, NNT values and their confidence intervals were calculated in a sample of 200 subjects. In addition, an example practice was performed based on real clinical data. Results: The value of RR was the same in different clinical conditions. The change in absolute risk measurements such as AR and NNT was parallel with clinical change rate. Conclusion: Interpretation of RR causes problem since it can take the same value in different clinical conditions. Therefore, in addition to RR, considering the AR measurement which is parallel with clinical change rate, may be useful while interpreting the RR values.Öğe Assessment of the performances of multilayer perceptron neural networks in comparison with recurrent neural networks and two statistical methods for diagnosing coronary artery disease(Wiley, 2007) Suet, Necdet; Senocak, MustafaWe aimed to examine the diagnostic performances of multilayer perceptron neural networks (MLPNNs) for predicting coronary artery disease and to compare them with different types of artificial neural network methods, namely recurrent neural networks (RNNs) and two statistical methods (quadratic discriminant analysis (QDA) and logistic regression (LR)). MLPNNs were trained with backpropagation, quick propagation, delta-bar-delta and extended delta-bar-delta algorithms as classifiers; the RNN was trained with the Levenberg-Marquardt algorithm; LR and QDA were used for predicting coronary artery disease. Coronary artery disease was classified with accuracy rates varying from 79.9% to 83.9% by MLPNNs. Even though MLPNNs achieved higher accuracy rates than the statistical methods, LR (73.2%) and QDA (58.4%), their performances were lower compared to the RNN (84.7%). Among the four different types of training algorithms that trained MLPNNs, quick propagation achieved the highest accuracy rate; however, it was lower than the RNN trained with the Levenberg-Marquardt algorithm. RNNs, which demonstrated 84.7% accuracy and 86.5% positive predictive rates, may be a helpful tool in medical decision making for diagnosis of coronary artery disease.Öğe CAN THE EQ-5D BE USED AS A HEALTH RELATED QUALITY OF LIFE INSTRUMENT IN PATIENTS WITH BEHCET'S SYNDROME?(Clinical & Exper Rheumatology, 2008) Sut, Necdet; Fresko, Izzet; Celik, Selda; Senocak, Mustafa[Abstract Not Available]Öğe Evaluating superiority, 'equivalence and non-inferiority in clinical trials(K Faisal Spec Hosp Res Centre, 2007) Turan, Fatma Nesrin; Senocak, MustafaClinical studies are usually performed with the aim of justifying that a new treatment approach is superior to the common standard approach (active control) with respect to benefits. In a general sense, this justification is carried out on the basis of the null hypothesis significance test with the P value based on this test used for justification. Today, new drugs differ so little from existing ones that factors such as cost and side effects affect the choice of therapy, when the bioavailability of treatment methods are found equivalent. Therefore, the aim of comparative clinical trials has extended beyond showing that a treatment is superior and now attempts to show that new treatments are equal and non-inferior to existing treatments. New approaches have become necessary since the classical null hypothesis approach is insufficient to justify the use of new agents, especially in cases of equivalence and non-inferiority. This new approach to justification makes use of the clinical equivalence interval, which determines the limits of the differences between specific endpoints that can be regarded as clinically equal to the value that was pre-specified based on studies of established therapies. It also makes use of the quantitative-based confidence intervals as the criteria for statistical justification. Many analyses can be done confidently when these tools are applied and the data are interpreted correctly.Öğe The use of cyclic processes in medical decision making(Aves Yayincilik, Ibrahim Kara, 2007) Sut, Necdet; Ture, Mevlut; Senocak, MustafaObjectives: 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.
