How to choose a time zero for patients in external control arms

When a sponsor carries out a single-arm trial of a novel oncology compound, it may wish to assess the efficacy of the compound via comparison of overall survival to an external control arm, constructed using patients included in some retrospective registry. If efficacy of the novel compound is compared to efficacy of physician's choice of chemotherapy, patients in the retrospective registry might qualify for inclusion in the external control arm at multiple different points in time, when they receive different chemotherapy treatments. For example, a patient might qualify at the start of their second, third and fourth lines of therapy. From the start of which line of therapy should this patient's survival be compared to survival of participants in the single-arm trial? Some sponsors have elected to include patients in the external control arm from the last available line of therapy in the retrospective database. Another possibility is to randomly select a line of therapy for each external control arm patient from among those available. In this paper, we show, via probabilistic arguments and also via simulation based on real data, that both of these methods give rise to a bias in favor of the single-arm trial. We further show that this bias can be avoided by instead including external control arm patients multiple times in the external control arm, once for each time they receive qualifying treatment.     

A statistical analysis of the handling characteristics of certain sporting arms: frontier regression,the moment of inertia,and the radius of gyration

This article applies composed error frontier regression techniques to estimate the minimal moments of inertia and radii of gyration for a unique and varied sample of shotguns. We find that minimum inertia depends on weight, center of gravity, length of pull, and barrel length, but not on gauge, action type, or number of barrels. Curiously, minimal radii of gyration does not depend on barrel length, suggesting that the constraints on these two related but non-identical measures of handling are significantly different despite their high correlation. We also provide evidence in support of G. T. Garwood's claim that a lower inertia, other things equal, is a market-validated characteristic associated with quality.     

An adaptive bayebsian solution to the control peoble1 for linear control systems

In this paper, a Bayesian procedure to solve the control problem in Linear Control Systems , when the precision Matrices of the obstsrvaiional and systems errors are unknown, is proposed. Prior information on those matrices is required, A numerical example illustrates the procedure applied to models Which contain bivariate observations and bivariate state vectors.     

Performance of the zone control chart

Average run lengths of the zone control chart are presented, The performance of this chart is compared with that of several Shewhart charts with and without runs rules, It is shown that the standard zone control chart has performance similar to some even simpler charts and a much higher false alarm rate than the Shewhart chart with all of the common runs rules. It is also shown that a slightly modified zone control chart outperforms the Shewhart chart with the common runs rules.     

Improving the performance of the zone control chart

A general model for the zone control chart is presented. Using this model, it is shown that there are score vectors for zone control charts which result in superior average run length performance in comparison to Shewhart charts with common runs rules.

A fast initial response (FIR) feature for the zone control chart is also proposed. Average run lengths of the zone control chart with this feature are calculated. It is shown that the FIR feature improves zone control chart performance by providing significantly earlier signals when the process is out of control.     

Loss-based optimal control statistics for control charts

This work proposes a means for interconnecting optimal sample statistics with parameters of the process output distribution irrespective of the specific way in which these parameters change during transition to the out-of-control state (jumps, trends, cycles, etc). The approach, based on minimization of the loss incurred by the two types of decision errors, leads to a unique sample statistic and, therefore, to a single control chart. The optimal sample statistics are obtained as a solution of the developed optional boundary equation. The paper demonstrates that, for particular conditions, this equation leads to the same statistics as are obtained through the Neyman-Pearson fundamental lemma. Application examples of the approach when the process output distribution is Gamma and Weibull are given. A special loss function representing out-of-control state detection as a pattern recognition problem is presented.     

Exponentially smoothed control charts for the inverse-gaussian process

Exponential smoothing technique is used to obtain centrality and dispersion control charts for an Inverse-Gaussian(IG)process. The centrality and dispersion control charts use linear combinations of IG and chi-square random variables, respectively. Simulation results are used to verify the approximate distribution of the linear combinations. A method for choosing the “best” value for the weighting constant is presented. An example illustrates the computations that are involved in finding the control limits.     

企业数据质量的评价与控制

如同质量是产品的生命一样 ,数据的准确性可以说是统计工作的生命。就政府统计而言 ,中国的统计数据内容广泛 ,有着其他国家无与伦比的丰富性 ;但是 ,我国统计数据的总体质量不高也是一个无法回避的问题。其中 ,数据审核体系的缺陷、统计方法的随意性和人为因素对数据质量的影响不容忽视。目前 ,企业数据直接上报的数据收集方式正在逐步推广 ,数据质量在报送环节上的控制因素在减少 ,而大范围的超级汇总常常掩盖分企业的数据错误。在这种情况下 ,进行数据的深层次开发和研究将面临一些意想不到的困难。在进行统计分析时 ,我们曾发现人均产值…     

Median rankit control chart by the quantile approach

It is desirable that the data for a statistical control chart be normally distributed. However, if the data are not normal, then a transformation can be used, e.g. Box-Cox transformations, to produce a suitable control chart. In this paper we will discuss a quantile approach to produce a control chart and to estimate median rankit for various non-normal distributions. We will also provide examples of logistic data to indicate how a quantile approach could be used to construct a control chart for a non-normal distribution using a median rankit.     

The performance of the truncated mixed control chart

Abstract

When the mixed chart proposed by Aslam et al. (2015 Aslam, M., M. Azam, N. Khan, and C.-H. Jun. 2015. A mixed control chart to monitor the process. International Journal of Production Research 53 (15):4684–93. doi:10.1080/00207543.2015.1031354.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) is in use, the sample items are classified as defective or not defective and, depending on the number of defectives, the quality characteristic X of the sample items are also measured. In this case, an Xbar chart decides the state of the process. The previous conforming/non-conforming classification truncates the X distribution and, because of that, the mathematical development to obtain the ARLs is complex. Aslam et al. (2015 Aslam, M., M. Azam, N. Khan, and C.-H. Jun. 2015. A mixed control chart to monitor the process. International Journal of Production Research 53 (15):4684–93. doi:10.1080/00207543.2015.1031354.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) didn’t pay attention to the fact that the X distribution is truncated and, due to that, they obtained incorrect ARLs.     

A HEWMA-CUSUM control chart for the Weibull distribution

The Weibull distribution is one of the most popular distributions for lifetime modeling. However, there has not been much research on control charts for a Weibull distribution. Shewhart control is known to be inefficient to detect a small shift in the process, while exponentially weighted moving average (EWMA) and cumulative sum control chart (CUSUM) charts have the ability to detect small changes in the process. To enhance the performance of a control chart for a Weibull distribution, we introduce a new control chart based on hybrid EWMA and CUSUM statistic, called the HEWMA-CUSUM chart. The performance of the proposed chart is compared with the existing chart in terms of the average run length (ARL). The proposed chart is found to be more sensitive than the existing chart in ARL. A simulation study is provided for illustration purposes. A real data is also applied to the proposed chart for practical use.     

Birth control in the early stage of marriage

Results are presented from a 1985 survey containing questions on family planning conducted among 13,392 newly married couples in Poland during the period from two to six months following marriage. The opinions of husbands and wives concerning various methods of contraception are analyzed.     

On the α-risks for shewhart control charts

The performance of the usual Shewhart control charts for monitoring process means and variation can be greatly affected by nonnormal data or subgroups that are correlated. Define the α_k-risk for a Shewhart chart to be the probability that at least one “out-of-control” subgroup occurs in k subgroups when the control limits are calculated from the k subgroups. Simulation results show that the α_k-risks can be quite large even for a process with normally distributed, independent subgroups. When the data are nonnormal, it is shown that the α_k-risk increases dramatically. A method is also developed for simulating an “in-control” process with correlated subgroups from an autoregressive model. Simulations with this model indicate marked changes in the α_k-risks for the Shewhart charts utilizing this type of correlated process data. Therefore, in practice a process should be investigated thoroughly regarding whether or not it is generating normal, independent data before out-of-control points on the control charts are interpreted to be due to some real assignable cause.     

New V control chart for the Maxwell distribution

Control charts have been popularly used as a user-friendly yet technically sophisticated tool to monitor whether a process is in statistical control or not. These charts are basically constructed under the normality assumption. But in many practical situations in real life this normality assumption may be violated. One such non-normal situation is to monitor the process variability from a skewed parent distribution where we propose the use of a Maxwell control chart. We introduce a pivotal quantity for the scale parameter of the Maxwell distribution which follows a gamma distribution. Probability limits and L-sigma limits are studied along with performance measure based on average run length and power curve. To avoid the complexity of future calculations for practitioners, factors for constructing control chart for monitoring the Maxwell parameter are given for different sample sizes and for different false alarm rate. We also provide simulated data to illustrate the Maxwell control chart. Finally, a real life example has been given to show the importance of such a control chart.     

On the improvement of one-sided S control charts

The most common charting procedure used for monitoring the variance of the distribution of a quality characteristic is the S control chart. As a Shewhart-type control chart, it is relatively insensitive in the quick detection of small and moderate shifts in process variance. The performance of the S chart can be improved by supplementing it with runs rules or by varying the sample size and the sampling interval. In this work, we introduce and study one-sided adaptive S control charts, supplemented or not with one powerful runs rule, for detecting increases or decreases in process variation. The properties of the proposed control schemes are obtained by using a Markov chain approach. Furthermore, a practical guidance for the choice of the most suitable control scheme is also provided.     

Measurement error effect on the CUSUM control chart

The performance of the cumulative sum (CUSUM) control chart for the mean when measurement error exists is investigated. It is shown that the CUSUM chart is greatly affected by the measurement error. A similar result holds for the case of the CUSUM chart for the mean with linearly increasing variance. In this paper, we consider multiple measurements to reduce the effect of measurement error on the charts performance. Finally, a comparison of the CUSUM and EWMA charts is presented and certain recommendations are given.     

On the eve of birth control. II

The results of a 1981 sample survey on family planning in Poland show that the average birth ratio for couples married in 1975 is 2.1 (2.0 in the cities and 2.5 in rural areas). With a much lower mortality rate, this minimal reproduction ratio still provides the fairly high natural population growth which is characteristic of all industrialized nations. This birth ratio is maintained by almost universal use of various birth control methods after 6-7 years of marriage, i.e., an average family with 2 children is planned and created by means of contraceptives and abortions. The family planning situation in Poland is discussed for women who: 1) plan to have more children, 2) might change their decision not to have more children, and 3) made a final decision not to have more children. The reasons for these decisions are thoroughly analyzed. Particular attention is paid to the extent to which couples are able to reach their reproductive objectives. The analysis indicates that many couples would have had more children if the husband received a higher salary, housing conditions were better, and the mother could quit her job for at least some period of time. Birth control is used because most couples are satisfied with having 2 children and consider their reproductive objectives fulfilled.     

A modified economic-statistical design of the T2 control chart with variable sample sizes and control limits

Recent studies have shown that using variable sampling size and control limits (VSSC) schemes result in charts with more statistical power than variable sampling size (VSS) when detecting small to moderate shifts in the process mean vector. This paper presents an economic-statistical design (ESD) of the VSSC T² control chart using the general model of Lorenzen and Vance [22]. The genetic algorithm approach is then employed to search for the optimal values of the six test parameters of the chart. We then compare the expected cost per unit of time of the optimally designed VSSC chart with optimally designed VSS and FRS (fixed ratio sampling) T² charts as well as MEWMA charts.