Blinded continuous monitoring in clinical trials with recurrent event endpoints

In studies with recurrent event endpoints, misspecified assumptions of event rates or dispersion can lead to underpowered trials or overexposure of patients. Specification of overdispersion is often a particular problem as it is usually not reported in clinical trial publications. Changing event rates over the years have been described for some diseases, adding to the uncertainty in planning. To mitigate the risks of inadequate sample sizes, internal pilot study designs have been proposed with a preference for blinded sample size reestimation procedures, as they generally do not affect the type I error rate and maintain trial integrity. Blinded sample size reestimation procedures are available for trials with recurrent events as endpoints. However, the variance in the reestimated sample size can be considerable in particular with early sample size reviews. Motivated by a randomized controlled trial in paediatric multiple sclerosis, a rare neurological condition in children, we apply the concept of blinded continuous monitoring of information, which is known to reduce the variance in the resulting sample size. Assuming negative binomial distributions for the counts of recurrent relapses, we derive information criteria and propose blinded continuous monitoring procedures. The operating characteristics of these are assessed in Monte Carlo trial simulations demonstrating favourable properties with regard to type I error rate, power, and stopping time, ie, sample size.     

Sample sizes for estimation in clinical research

A sample size justification should be given for all clinical investigations. However, sometimes the objective of a trial is to estimate an effect with a view to planning a later definitive study. This paper describes the calculations for designing studies where one wishes to adopt an estimation approach through using confidence intervals around the overall response. Calculations are given for data anticipated to take a Normal form. Copyright © 2004 John Wiley & Sons, Ltd.     

Estimating household structure in ancient China by using historical data: a latent class analysis of partially missing patterns

Summary.  Social data often contain missing information. The problem is inevitably severe when analysing historical data. Conventionally, researchers analyse complete records only. Listwise deletion not only reduces the effective sample size but also may result in biased estimation, depending on the missingness mechanism. We analyse household types by using population registers from ancient China (618–907 AD) by comparing a simple classification, a latent class model of the complete data and a latent class model of the complete and partially missing data assuming four types of ignorable and non-ignorable missingness mechanisms. The findings show that either a frequency classification or a latent class analysis using the complete records only yielded biased estimates and incorrect conclusions in the presence of partially missing data of a non-ignorable mechanism. Although simply assuming ignorable or non-ignorable missing data produced consistently similarly higher estimates of the proportion of complex households, a specification of the relationship between the latent variable and the degree of missingness by a row effect uniform association model helped to capture the missingness mechanism better and improved the model fit.     

Quasi-maximum likelihood estimation and inference in dynamic models with time-varying covariances

We study the properties of the quasi-maximum likelihood estimator (QMLE) and related test statistics in dynamic models that jointly parameterize conditional means and conditional covariances, when a normal log-likelihood os maximized but the assumption of normality is violated. Because the score of the normal log-likelihood has the martingale difference property when the forst two conditional moments are correctly specified, the QMLE is generally Consistent and has a limiting normal destribution. We provide easily computable formulas for asymptotic standard errors that are valid under nonnormality. Further, we show how robust LM tests for the adequacy of the jointly parameterized mean and variance can be computed from simple auxiliary regressions. An appealing feature of these robyst inference procedures is that only first derivatives of the conditional mean and variance functions are needed. A monte Carlo study indicates that the asymptotic results carry over to finite samples. Estimation of several AR and AR-GARCH time series models reveals that in most sotuations the robust test statistics compare favorably to the two standard (nonrobust) formulations of the Wald and IM tests. Also, for the GARCH models and the sample sizes analyzed here, the bias in the QMLE appears to be relatively small. An empirical application to stock return volatility illustrates the potential imprtance of computing robust statistics in practice.     

Rural/Urban Differences in Child Growth and Survival in Bolivia

Abstract Using data from a national survey in Bolivia and from a regional sample of poor communities in two regions of Bolivia, we model the effects of community conditions, household characteristics, and social relations on child health as measured by child mortality and stunted growth. Based on national data, children in rural areas are twice as likely to die before the age of two than are children in large cities, and about one‐third of the children in rural areas are stunted. Including measures of community, household, and maternal characteristics accounts for at least two‐thirds of the urban/rural difference in child health. We conclude that expansion of health services, improvements in household and community sanitation, and socioeco‐nomic development are all needed to overcome the rural disadvantage in child health.     

Pen Dudley''s name finally disappears from the public relations marquee

When the public relations firm D-A-Y was dissolved into Ogilvy & Mather Public Relations November 1, 1988, the longest lived public relations agency disappeared into the sands of time that enveloped the first four agencies started in the first decade of the waning century. The D in D-A-Y stood for Pendleton Dudley, a venerable pioneer in this vocation, who had opened his agency in Wall Street in 1909 at the urging of his good friend, Ivy Lee. Dudley, a rugged, independent product of frontier America, headed his firm for fifty-seven years—a longevity record surpassed only by that of Edward L. Bernays, who started his firm in 1919 in the Post World War I public relations boom.Pendleton Dudley—known to his close friends as Pen and to his associates in his firm as PD, was born September 8, 1876, in a small frontier town of Troy, Missouri, when America was an agricultural nation. He came to pioneer as a counselor to the corporate giants, AT&T among them, in a complex, interdependent corporate industrial America. Pen Dudley did much to infuse this field with respectability in a time when it was viewed with suspicion or disdain in its early years. He was a strong advocate of research as the only sound basis for planning and executing programs to influence public behavior. He was also active in the Public Relations Society of America after it was formed in 1948 and was in the forefront of those creating the now extinct Foundation for Public Relations Research and Education. In 1965, he received the Distinguished Service Award from the New York PRSA Chapter. He died at the age of 90 in 1966.The author is Dean Emeritus of Journalism at the University of Georgia, and co-author Effective Public Relations, 6th Ed.     

SOCIOECONOMIC STATUS, JOB CONDITIONS, AND WELL-BEING: Self-Concept Explanations for Gender-Contingent Effects

Using data from a representative sample of adults age eighteen to fifty‐five who reside in Toronto, Canada, and are employed in the paid labor force, this study asks: Are the health benefits of education, income, job autonomy, and nonroutinized work different for women and men? If so, do mastery and self‐esteem contribute to those differences? Results show that women and men derive different personal benefits from socioeconomic status and job qualities: (1) education, job autonomy, and nonroutinized work are associated more positively with the sense of mastery among women, (2) job autonomy is associated more positively with self‐esteem among women, (3) education, job autonomy, and nonroutinized work are associated more negatively with depressive symptoms among women, and (4) job autonomy and nonroutinized work are associated more positively with global health among women. Moreover, the patterns in (1) and (2) explain the gender‐contingent effects in (3) and (4). In addition, unexpected suppression effects reveal that, among men, education is associated negatively with mastery and self‐esteem—but only after adjustment for job conditions. I interpret the findings in the context of the disadvantaged status thesis and speculate about status‐related social comparison processes.