An overview of risk-adjusted charts

Summary.  The paper provides an overview of risk-adjusted charts, with examples based on two data sets: the first consisting of outcomes following cardiac surgery and patient factors contributing to the Parsonnet score; the second being age–sex-adjusted death-rates per year under a single general practitioner. Charts presented include the cumulative sum (CUSUM), resetting sequential probability ratio test, the sets method and Shewhart chart. Comparisons between the charts are made. Estimation of the process parameter and two-sided charts are also discussed. The CUSUM is found to be the least efficient, under the average run length (ARL) criterion, of the resetting sequential probability ratio test class of charts, but the ARL criterion is thought not to be sensible for comparisons within that class. An empirical comparison of the sets method and CUSUM, for binary data, shows that the sets method is more efficient when the in-control ARL is small and more efficient for a slightly larger range of in-control ARLs when the change in parameter being tested for is larger. The Shewart p -chart is found to be less efficient than the CUSUM even when the change in parameter being tested for is large.     

Statistical consequences of an adaptive treatment allocation in a clinical trial

An examination of the likelihood arising from data in experiments comparing two therapies for neonatal respiratory failure illustrates a difficulty in the use of a design with adaptive treatment allocation as opposed to the use of the classical fixed-sample-size design. The requirement of obtaining objective scientific evidence as to which treatment is better for use on future patients conflicts with attempts to give the treatment thought to be the best to the patient at hand.     

Likelihood estimation for a longitudinal negative binomial regression model with missing outcomes

Joint damage in psoriatic arthritis can be measured by clinical and radiological methods, the former being done more frequently during longitudinal follow-up of patients. Motivated by the need to compare findings based on the different methods with different observation patterns, we consider longitudinal data where the outcome variable is a cumulative total of counts that can be unobserved when other, informative, explanatory variables are recorded. We demonstrate how to calculate the likelihood for such data when it is assumed that the increment in the cumulative total follows a discrete distribution with a location parameter that depends on a linear function of explanatory variables. An approach to the incorporation of informative observation is suggested. We present analyses based on an observational database from a psoriatic arthritis clinic. Although the use of the new statistical methodology has relatively little effect in this example, simulation studies indicate that the method can provide substantial improvements in bias and coverage in some situations where there is an important time varying explanatory variable.     

Tracing studies and analysis of the effect of loss to follow-up on mortality estimation from patient registry data

Summary. Before patient registries are used for studies of the long-term mortality that is associated with chronic medical conditions, the potential bias resulting from patients who become lost to follow-up must be investigated. A study design, used for a systemic lupus erythematosus patient registry, is described. The design involves tracing patients who are defined as 'lost to follow-up' according to specific criteria. This provides supplementary information on the mortality experience of patients who are lost to (regular) follow-up. Some methods of analysis are described, based on comparing the mortality experience of patients when under regular follow-up with the experience of patients after they are deemed to be lost to follow-up. The effect of loss to follow-up, death reporting and visits to the clinic on estimation procedures is illustrated and recommendations are made for patient registries which are to be used in mortality studies.     

Randomization in experimental science

The purpose of deliberate randomization in controlled scientific experiments is discussed.     

Goodness-of-link testing in ordinal regression models

McCullagh (1980) presented a comprehensive review of regression models for ordinal response variables. In these models, the functional relationship between the covariates and the response categories is dependent on the link function. This paper shows that discrimination between links is feasible when the response variable is ordinal. Using the log-gamma distribution of Prentice (1974), a generalized link function is constructed which allows discrimination between the probit, log-log, and complementary log-log links. Sample-size considerations are noted, and examples are presented.     

A test for diagnostic utility

A criterion of usefulness for a diagnostic test is suggested. Based on sensitivity and specificity data, Bayesian and likelihood-ratio procedures to examine whether the criterion is satisfied are presented.     

The use of variance components for the assessment of outcome measures in rheumatology

There is current interest in the development of new or improved outcome measures for rheumatological diseases. In the early stages of development, attention is usually directed to how well the measure distinguishes between patients and whether different observers attach similar values of the measure to the same patient. An approach, based on variance components, to the assessment of outcome measures is presented. The need to assess different aspects of variation associated with a measure is stressed. The terms ‘observer reliability’ and ‘agreement’ are frequently used in the evaluation of measurement instruments, and are often used interchangeably. In this paper, we use the terms to refer to different concepts assessing different aspects of variation. They are likely to correspond well in heterogeneous populations, but not in homogeneous populations where reliability will generally be low but agreement may well be high. Results from a real patient exercise, designed to study a set of tools for assessing myositis outcomes, are used to illustrate the approach that examines both reliability and agreement, and the need to evaluate both is demonstrated. A new measure of agreement, based on the ratio of standard deviations, is presented and inference procedures are discussed. To facilitate the interpretation of the combination of measures of reliability and agreement, a classification system is proposed that provides a summary of the performance of the tools. The approach is demonstrated for discrete ordinal and continuous outcomes.     

The sensitivity of estimates of the change in population behaviour to realistic changes in bias in repeated surveys

Summary.  The first British National Survey of Sexual Attitudes and Lifestyles (NATSAL) was conducted in 1990–1991 and the second in 1999–2001. When surveys are repeated, the changes in population parameters are of interest and are generally estimated from a comparison of the data between surveys. However, since all surveys may be subject to bias, such comparisons may partly reflect a change in bias. Typically limited external data are available to estimate the change in bias directly. However, one approach, which is often possible, is to define in each survey a sample of participants who are eligible for both surveys, and then to compare the reporting of selected events that occurred before the earlier survey time point. A difference in reporting suggests a change in overall survey bias between time points, although other explanations are possible. In NATSAL, changes in bias are likely to be similar for groups of sexual experiences. The grouping of experiences allows the information that is derived from the selected events to be incorporated into inference concerning population changes in other sexual experiences. We use generalized estimating equations, which incorporate weighting for differential probabilities of sampling and non-response in a relatively straightforward manner. The results, combined with estimates of the change in reporting, are used to derive minimum established population changes, based on NATSAL data. For some key population parameters, the change in reporting is seen to be consistent with a change in bias alone. Recommendations are made for the design of future surveys.     

Hilda Mary Woods MBE, DSc, LRAM, FSS (1892-1971): reflections on a Fellow of the Royal Statistical Society

We have previously described the content of a text by Woods and Russell, An Introduction to Medical Statistics, compared it with Principles of Medical Statistics by Hill and set both volumes against the background of vital statistics up until 1937. The two books mark a watershed in the history of medical statistics. Very little has been recorded about the life and career of the first author of the earlier textbook, who was a Fellow of the Royal Statistical Society for at least 25 years, an omission which we can now rectify with this paper. We describe her education, entry into medical statistics, relationship with Major Greenwood and her subsequent career and life in Ceylon, Kenya, Australia, England and South Africa.