Browsing by Author "Sonesson, Christian"

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Detection of intra-uterine growth restriction
(University of Gothenburg, 2003-03-01) Petzold, Max; Sonesson, Christian; Bergman, Eva; Kieler, Helle
A new methodology for on-line detection of intrauterine growth restriction (IUGR) is proposed where traditional methods for statistical surveillance are applied. Here, deficient growth rate is used to detect IUGR instead of the common surrogate measure "small for gestational age" (SGA). Foetal growth is estimated by repeated measurements of symphysis-fundus (SF) height. At each time point the new method, based on the Shiryaev-Roberts method, is used to evaluate the growth in SF height. We use Swedish data to model a normal growth pattern, which is used to evaluate the capability of the new method to detect IUGR in comparison with a method used in practice today. Results from simulations indicate that the new method performs considerably better than the method used today. We also illustrate the effect of some important factors, which influence the detection ability and illuminate the tendency of the method used today to misclassify SGA cases as IUGR.
Evaluations of some exponentially weighted moving average methods
(University of Gothenburg, 2001-06-01) Sonesson, Christian
Several versions of the EWMA (Exponentially Weighted Moving Average) method for monitoring a process with the aim of detecting a shift in the mean are studied both for the onesided and the two-sided case. The effects of using barriers for the one-sided alarm statistic are also studied. One important issue is the effect of different types of alarm limits. Different measures of evaluation are considered such as the expected delay, the ARLI, the probability of successful detection and the predictive value of an alarm to give a broad picture of the features of the methods. Results are presented both for a fixed ARLO and a fixed probability of a false alarm. The differences highlight the essential problem of how to define comparability between surveillance methods. The results are from a large-scale simulation study. Special attention is given to the effect on the confidence in the final results by the stochastic variation in the calibration of the methods. It appears that important differences from an inferential point of view exist between the one- and two-sided versions of the methods. It is demonstrated that the method, usually considered as a convenient approximation, is to be preferred over the exact version in many respects.
On Statistical Surveillance. Issues of Optimality and Medical Applications
(2003) Sonesson, Christian
Optimal surveillance Based on exponentially weighted moving averages
(University of Gothenburg, 2002-01-01) Frisén, Marianne; Sonesson, Christian
Statistical surveillance is used to detect an important change in a process as soon as possible after it has occurred, with control of false alarms. The EWMA, exponentially weighted moving average, method for surveillance is used in different areas, such as industry, economy and medicine. Three optimality criteria of surveillance are studied and the implications are described for the EWMA method and for suggested modifications. The first criterion concerns the average run length to alarm, ARL. This is the most commonly used criterion. Results on ARL optimality for EWMA are demonstrated. Equal weight for old and recent observations give good ARL-properties but bad properties otherwise. Thus, uncritical use of this criterion should be avoided. The second criterion is the ED criterion based on the minimal expected delay from change to detection. The full likelihood ratio method is optimal according to this criterion. Various approximations of this method tum out to be modifications of the EWMA method. Two of these modifications keep the EWMA statistic unchanged and just alter the alarm limits slightly. The approximations lead to a formula for the value of the weight parameter of the EWMA statistic. The usefulness of this formula is demonstrated. The conventional EWMA and the modifications are compared to the optimal full likelihood ratio method. No modification of EWMA is necessary for detection of large changes (where also the Shewhart 2 method is useful) but all the modifications give considerable improvement for small changes. The third criterion is based on the minimax of the expected delay after a change with respect to the time of the change. It is demonstrated that the value of the smoothing parameter, which is optimal according to this criterion, agrees well with that of the ED criterion but not with that of the ARL criterion. A restriction on the false alarm property is necessary. For the ARL criterion it is the ARL without any change. For the other two criteria we here use the false alarm probability. It is demonstrated that these two restrictions favor different methods.
Optimal Surveillance Based on Exponentially Weighted Moving Averages
(Taylor & Francis, 2006) Frisén, Marianne; Sonesson, Christian
Statistical surveillance is used to detect an important change in a process as soon as possible after it has occurred. The EWMA method is used in industry, economics and medicine. Three optimality criteria of surveillance are studied. The ARL criterion violates commonly accepted inference principles and the drawbacks are demonstrated. The ED criterion is based on the minimal expected delay from change to detection. The full likelihood ratio method is optimal according to this criterion. Approximations of this method turn out to be modifications of the EWMA method. The approximations lead to a formula for the optimal value of the smoothing parameter of the EWMA statistic. The usefulness of this formula is shown. It is demonstrated that, for EWMA, the minimax criterion agrees well with that of the ED criterion but not with that of the ARL criterion.
Statistical issues in public health monitoring - A review and discussion
(University of Gothenburg, 2001-02-01) Sonesson, Christian; Bock, David
A review of methods, suggested in the literature, for sequential detection of changes in public health surveillance data is presented. Many authors have noticed the need for prospective methods and there has been an increased interest in both the statistical as well as epidemiological literature on this type of problem in the recent years. However, most of the vast literature in public health monitoring deals with retrospective methods. This is especially apparent dealing with spatial methods. Evaluations with respect to the statistical properties of special interest for on-line surveillance are rare. The special aspects of prospective statistical surveillance as well as different ways of evaluating such methods are described. Attention is given to methods including only the time domain as well as methods for detection where observations have a spatial structure. In the case of surveillance of a change in a Poisson process the likelihood ratio method and the ShiryaevRoberts method are derived.
Statistical surveillance - Exponentially weighted moving average methods and public health monitoring
(University of Gothenburg, 2001-07-01) Sonesson, Christian
The need for statistical surveillance has been noticed in many different areas and examples of applications include the detection of an increased incidence of a disease, the detection of an increased radiation level and the detection of a turning point in a leading index for a business cycle. In all cases, preventive actions are possible if the alarm is made early. If the change is detected too late, this can have severe consequences both at a personal level for affected individuals and to society as a whole. In these important situations we must evaluate the evidence value of the information we have about the process in order to guide us in the choice of making an alarm or not. The aim is to detect the change as quickly as possible and at the same time control the rate of false alarms. To do this efficiently we construct alarm systems using the available observations of the process, which are taken sequentially in time. (Note the difference from a test of one hypothesis.) The theory of statistical surveillance deals with the construction of alarm systems and the evaluation of such systems. This licentiate thesis consists of two papers with this common subject. The first paper (1) deals with the properties of a special type of surveillance methods called EWMA methods. One attractive feature of EWMA methods is the easily interpretable alarms statistic, which is an exponentially weighted moving average of all available observations of the process. Several ways of constructing alarm limits to this statistic have previously been suggested in the literature. In this paper new types of evaluations of the performance of suggested variants are made and the results cast new light on both the merits of the variants and the optimality criteria commonly used. Methodological issues of general interest in the area of statistical surveillance are also treated, such as the definition of comparability between methods. The second paper (2) deals with statistical surveillance in the area of public health. A critical review with emphasis on the inferential issues is made. The merits of different approaches are discussed and a new method is derived. Especially noticeable from the review is the lack of methods of surveillance of a spatial pattern, an area that includes many important applications, not only in public health.