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정맥내 통증자가조절법을 이용한 Nalbuphine-Ketorolac과 Butorphanol-Ketorolac의 술후 진통효과 비교
윤석화,이원형,손수창,신용섭,김윤희,양신영 충남대학교 의과대학 의학연구소 2003 충남의대잡지 Vol.30 No.1
This study aimed to compare analgegic efficacy, satisfaction score and side effect of nalbuphine-ketorolac and butorphanol-ketorolac by using intravenous patient controlled analgesia(IV-PCA) for postoperative anlgesia following a gastrectomy for stomach cancer. Ninety patients who had undergone gastrectomy for stomach cancer under general anesthesia were randomly divided into two groups. Each group recieived nalbuphine 50mg with ketorolac 150mg(Group 1, n=45) and butorphanol 5mg with ketorolac 150mg(Group 2, n=45) by using IV-PCA during postoperative 48hrs. Assessments for pain with numerical rating scale(NRS), and side effects were evaluated at 2hr, 6hr, 12hr, 24hr, 36hr and 48hr after the operation. There were no significant difference in NRS for pain during rest, but Group 2 requested significantly greater amount of supplementary diclofenac during first 24 hours. Side effects were higher Group 1 in pruritus, nausea and vomiting and Group 2 in sedation, nausea and vomiting. This study suggests that adding ketorolac with intravenous nalbuphine or butrophanol in using an intravenous PCA can decrease analgesics requirement and improve analgesic property without the major morbidity like respiratory depression, but needs for the careful observation and treatment on the side effects like nausea. vomiting, pruritis and sedation...
Comparison of Parameter Estimation Methods for Time Series Models in the Presence of Outliers
Sin Sup Cho(조신섭),Jae June Lee(이재준),Soo Hwa Kim(김수화) 한국통계학회 1992 응용통계연구 Vol.5 No.2
본 논문에서는 이상점이 포함된 시계열 자료의 모수추정법으로 반복보간추정법을 제안하였다. 제안된 방법은 이상점이 더 이상 탐지되지 않을 때까지 모수추정의 단계와 이상점의 탐지 단계를 반복하는 접근 방법이다. 이상점의 탐지를 위해서는 비정상적인 자료를 보간추정법으로 대치하는 보간 검진기법을 적용하였다. 또한 추정과정에서 비정상적인 자료의 비중을 적게하는 대신에 비정상적인 자료를 시계열모형의 구조를 이용한 1-시점후의 예측값으로 대치하는 수정된 GM-추정법을 제안하였다. 모의실험에 의해 제안된 추정법들과 기존의 로버스트추정법들의 성질을 비교하였다. 모의실험의 결과 반복보간추정법이 다른 추정법보다 우월한 성질을 가짐을 알 수 있었으며, 특히 AO가 하나만 있는 경우와 모수의 절대값이 큰 경우에 가장 우수함을 확인할 수 있었다. We propose an iterated interpolation approach for the estimation of time series parameters in the presence of outliers. The proposed approach iterates the parameter estimation stage and the outlier detection stage until no further outliers are detected. For the detection of outliers, interpolation diagnostic is applied, where the atypical observations are interpolated. The modified GM-estimate which replace atypical observations by the one-step-ahead predictor instead of downweighting is also proposed. The performance of the proposed estimation methods is compared with other robust estimation methods by simulation study. It is observed that the iterated interpolation approach performs reasonably well in general, especially for single AO case and large φ in absolute values.
Outlier Detection Diagnostic based on Interpolation Method in Autoregressive Models
Cho, Sin-Sup,Ryu, Gui-Yeol,Park, Byeong-Uk,Lee, Jae-June The Korean Statistical Society 1993 Journal of the Korean Statistical Society Vol.22 No.2
An outlier detection diagnostic for the detection of k-consecutive atypical observations is considered. The proposed diagnostic is based on the innovational variance estimate utilizing both the interpolated and the predicted residuals. We adopt the interpolation method to construct the proposed diagnostic by replacing atypical observations. The perfomance of the proposed diagnositc is investigated by simulation. A real example is presented.
조신섭(Sin Sup Cho),류귀열(Gui Yeol Ryu) 한국통계학회 1987 응용통계연구 Vol.1 No.1
일반적으로 시계열 자료의 분석은 시간에 대한 모수들의 정상성가정(stationary assumption)하에서 이루어지고 있다. 본 논문에서는 예측하기 힘든 시점에서 분산들이 변화할 수 있는 AR(1) 모형에서 분산의 변화점(variance ahange point)을 추정하는 방법을 제안했으며 모의자료 및 실제자료를 이용하여 다른 방법들과 비교하여 보았다. In time series analysis, we usually require the assumption that time series are stationary. But we may often encounter time series whose parameter values subject to change. Inthis paper w propose a method which can detect the variance change point in anAR(1) model which is subjct to changesat non-predictable time points. Proposed method is compared with other methods using the simulated and real data.
Generalized Durbin-Watson Statistics in the Nonstationary Seasonal Time Series Model
Cho, Sin-Sup,Kim, Byung-Soo,Park, Young J. The Korean Statistical Society 1997 Journal of the Korean Statistical Society Vol.26 No.3
In this paper we study the behaviors of the generalized Durbin-Watson (DW) statistics when the nonstationary seasonal time series regression model is misspecified. It is observed that when the series is seasonally integrated the generalized DW statistic for the seasonal period order autocorrelation converges in probability to zero while teh generalized DW statistic for the first order autocorrelation has nondegenerate asymptotic distribution. When the series is regularly and seasonally integrated the generalized DW for the first order autocorrelation still converges in probability to zero.