Choice of the primary analysis in longitudinal clinical trials

Missing data, and the bias they can cause, are an almost ever‐present concern in clinical trials. The last observation carried forward (LOCF) approach has been frequently utilized to handle missing data in clinical trials, and is often specified in conjunction with analysis of variance (LOCF ANOVA) for the primary analysis. Considerable advances in statistical methodology, and in our ability to implement these methods, have been made in recent years. Likelihood‐based, mixed‐effects model approaches implemented under the missing at random (MAR) framework are now easy to implement, and are commonly used to analyse clinical trial data. Furthermore, such approaches are more robust to the biases from missing data, and provide better control of Type I and Type II errors than LOCF ANOVA. Empirical research and analytic proof have demonstrated that the behaviour of LOCF is uncertain, and in many situations it has not been conservative. Using LOCF as a composite measure of safety, tolerability and efficacy can lead to erroneous conclusions regarding the effectiveness of a drug. This approach also violates the fundamental basis of statistics as it involves testing an outcome that is not a physical parameter of the population, but rather a quantity that can be influenced by investigator behaviour, trial design, etc. Practice should shift away from using LOCF ANOVA as the primary analysis and focus on likelihood‐based, mixed‐effects model approaches developed under the MAR framework, with missing not at random methods used to assess robustness of the primary analysis. Copyright © 2004 John Wiley & Sons, Ltd.     

Markov chain Monte Carlo methods for high dimensional inversion in remote sensing

Summary.  We discuss the inversion of the gas profiles (ozone, NO₃, NO₂, aerosols and neutral density) in the upper atmosphere from the spectral occultation measurements. The data are produced by the 'Global ozone monitoring of occultation of stars' instrument on board the Envisat satellite that was launched in March 2002. The instrument measures the attenuation of light spectra at various horizontal paths from about 100 km down to 10–20 km. The new feature is that these data allow the inversion of the gas concentration height profiles. A short introduction is given to the present operational data management procedure with examples of the first real data inversion. Several solution options for a more comprehensive statistical inversion are presented. A direct inversion leads to a non-linear model with hundreds of parameters to be estimated. The problem is solved with an adaptive single-step Markov chain Monte Carlo algorithm. Another approach is to divide the problem into several non-linear smaller dimensional problems, to run parallel adaptive Markov chain Monte Carlo chains for them and to solve the gas profiles in repetitive linear steps. The effect of grid size is discussed, and we present how the prior regularization takes the grid size into account in a way that effectively leads to a grid-independent inversion.     

State of the unions: Assessment by elite American labor leaders

One hundred and sixty-four elite union leaders in the United States completed a survey to assess the condition of the American labor movement and the factors affecting it. The respondents included high-level international union and state federation officers, central labor presidents, and 58 shop stewards. This study compares the responses of these labor officials and finds that they have similar, negative assessments of the condition of the American labor movement. The study also finds that these leaders agree that the most important factors affecting the labor movement are collective bargaining rights, union leadership, union member solidarity, and the NLRB.     

Assessment of influence in non-linear measurement error models

We consider the assessment of outliers and influential observation in non-linear measurement error models. Residuals, leverage measures and case-deletiondiagonostics are examined. The method of local influence is also applied to the models. In particular, the perturbation of measurement error variances has been found useful in assessing the adequancy of the model assumptions. A numerical example is given to illustrate the application of the diagonostics.     

A puzzlement: health-related organisations in the Nonprofit Almanac

Conclusion Data systems created with one purpose inevitably have shortcomings when used for other purposes, so it is not surprising that a database compiled from informational tax returns has serious flaws when used to compile a statistical picture of the non-profit sector. The two-digit NTEE level of aggregation that is published in theAlmanac conceals many problems that appear when the IRS-based data are broken down to the three-digit level.Identification of the problems in the NTEE and in the use of the IRS data is a necessary step toward their correction. At this point, it is clear that the IRS-based numbers in theAlmanac should be regarded, at best, as rough approximations and as a starting point for additional work. My assessment of theAlmanac suggests that, at least as far as health care is concerned, the NTEE needs revision. Correcting the problems with the IRS data will be much, much more difficult.I am grateful for assistance from Steve Noga, Chris Toppe and Virginia Hodgkinson at Independent Sector.     

Public Perceptions about Food Safety in the United States and Japan1

Abstract Consumer concerns over pesticide residues and food additives have been growing in industrialized societies, including the United States and Japan. However, little comparative research has been carried out to determine which household characteristics may be significantly associated with these heightened concerns, and whether or not the same factors are useful for understanding variation in these worries in more than one society. This paper examines food safety attitudes in Seattle, Washington, and Kobe, Japan, and discovers that while the absolute level of expressed concern is higher in Kobe, the predictive power of household characteristics in explaining attitudinal differences within countries is similar. Implications for rural areas and future research on family structures in capitalist societies are discussed.