Dealing with selective dropout in clinical trials

Clinical trials in severely diseased populations often suffer from a high dropout rate that is related to the investigated target morbidity. These dropouts can bias estimates and treatment comparisons, particularly in the event of an imbalance. Methods to describe such selective dropout are presented that use the time in study distribution to generate so‐called population evolution charts. These charts show the development of a distribution of a covariate or the target morbidity measure as it changes as a result of the dropout process during the follow‐up time. The selectiveness of the dropout process with respect to a variable can be inferred from the change in its distribution. Different types of selective dropout are described with real data from several studies in metastatic bone disease, where marked effects can be seen. A general strategy to cope with selective dropout seems to be the inclusion of dropout events into the endpoint. Within a time‐to‐event analysis framework this simple approach can lead to valid conclusions and still retains conservative elements. Morbidity measures that are based on (recurrent) event counts react differently in the presence of selective dropout. They differ mainly in the way dropout is included. One simple measure achieves good performance under selective dropout by introducing a non‐specific penalty for premature study termination. The use of a prespecified scoring system to assign a weight for each works well. This simple and transparent approach performs well even in the presence of unbalanced selective dropout. Copyright © 2002 John Wiley & Sons, Ltd.     

A Robust Control Chart for Monitoring the Mean of an Autocorrelated Process

Residual control charts are frequently used for monitoring autocorrelated processes. In the design of a residual control chart, values of the true process parameters are often estimated from a reference sample of in-control observations by using least squares (LS) estimators. We propose a robust control chart for autocorrelated data by using Modified Maximum Likelihood (MML) estimators in constructing a residual control chart. Average run length (ARL) is simulated for the proposed chart when the underlying process is AR(1). The results show the superiority of the new chart under several situations. Moreover, the chart is robust to plausible deviations from assumed distribution of errors.     

Variable Sampling Interval Cumulative Count of Conforming Chart with Runs Rules

The cumulative count of conforming (CCC) chart is effective in detecting very low fraction of nonconforming items for high yield manufacturing processes. In this study, a combination of runs rules and variable sampling interval feature is proposed to a lower sided CCC chart by inspecting the items one by one. The performance measures of the control chart are derived by using the Markov chain approach. The numerical comparisons show that the performance of the CCC chart can be improved by adding the runs rules and varying the sampling interval.     

A Semi‐empirical Bayesian Chart to Monitor Weibull Percentiles

This paper develops a Bayesian control chart for the percentiles of the Weibull distribution, when both its in‐control and out‐of‐control parameters are unknown. The Bayesian approach enhances parameter estimates for small sample sizes that occur when monitoring rare events such as in high‐reliability applications. The chart monitors the parameters of the Weibull distribution directly, instead of transforming the data as most Weibull‐based charts do in order to meet normality assumption. The chart uses accumulated knowledge resulting from the likelihood of the current sample combined with the information given by both the initial prior knowledge and all the past samples. The chart is adapting because its control limits change (e.g. narrow) during Phase I. An example is presented and good average run length properties are demonstrated.     

Nonparametric control chart based on change-point model

A change-point control chart for detecting shifts in the mean of a process is developed for the case where the nominal value of the mean is unknown but some historical samples are available. This control chart is a nonparametric chart based on the Mann–Whitney statistic for a change in mean and adapted for repeated sequential use. We do not require any knowledge of the underlying distribution such as the normal assumption. Particularly, this distribution robustness could be a significant advantage in start-up or short-run situations where we usually do not have knowledge of the underlying distribution. The simulated results show that our approach has a good performance across the range of possible shifts and it can be used during the start-up stages of the process.      

Weighted Renyi's entropy for lifetime distributions

Recently, Di Crescenzo and Longobardi (2006 Di Crescenzo, A., Longobardi, M. (2006). On weighted residual and past entropies. Sci. Math. Jpn. 64:255–266. [Google Scholar]) have studied “length-biased” shift-dependent information measure and its dynamic versions. On the other hand, Renyi's entropy plays a vital role in the literature of information theory that is a generalization of Shannon's entropy. In this article, the concepts of weighted Renyi's entropy, weighted residual Renyi's entropy, and weighted past Renyi's entropy are introduced and their properties are discussed.     

The mean general residual lifetime of (n − k + 1)-out-of-n systems with exchangeable components

The study of the reliability properties of (n ? k + 1)-out-of-n systems has gained a great deal of attention, from both theoretical and practical perspectives. In this article, we consider (n ? k + 1)-out-of-n systems with exchangeable components and study the stochastic properties of two forms of residual lifetimes of such systems under the following conditions: n ? r + 1 (r ? k) components of the system are operating at time t > 0, and/or the rth (r < k) component has failed, but the system is working at time t. In addition, some results relating to the functions of the mean general residual lifetimes (MGRL) are derived for these systems. Finally, in accordance with the generalized Farlie–Gumbel–Morgenstern model, we present the reliability properties of the general residual lifetime of (n ? k + 1)-out-of-n systems and investigate the asymptotic behavior of the proposed MGRL functions with exponential marginals.     

A joint-adaptive np control chart with multiple dependent state sampling scheme

Although the classical Shewhart np control chart has been widely used to detect an out-of-control status of manufacturing process, it is static and there is lack of responsiveness to slight process changes. In this paper, an adaptive np control chart with a joint sampling strategy combining double sampling (DS) and variable sampling interval (VSI) is developed. The multiple dependent state sampling scheme is adopted to further improve the performance of the control chart. An economical design model to minimize the general cost of using the proposed chart is established and solved by a genetic algorithm. The numerical results show that comparing to traditional static np control chart, the proposed np chart yields better performance in terms of shorter time to signal an out-of-control process and less expected cost per unit of time. Comparisons are made to show the capability of the proposed chart in yielding average reductions of 5.01% and 8.89%, in the cost of the proposed model compared to situations in which either the DSVSI np chart or the traditional np chart is used.