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This paper reviews some of the main approaches to the analysis of multivariate censored survival data. Such data typically have correlated failure times. The correlation can be a consequence of the observational design, for example with clustered sampling and matching, or it can be a focus of interest as in genetic studies, longitudinal studies of recurrent events and other studies involving multiple measurements. We assume that the correlation between the failure or survival times can be accounted for by fixed or random frailty effects. We then compare the performance of conditional and mixture likelihood approaches to estimating models with these frailty effects in censored bivariate survival data. We find that the mixture methods are surprisingly robust to misspecification of the frailty distribution. The paper also contains an illustrative example on the times to onset of chest pain brought on by three endurance exercise tests during a drug treatment trial of heart patients.

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This paper investigates the effect of introducing quasi‐market forces into secondary education on the allocation of pupils between schools and on the exam performance of pupils. A unique database is used which covers all publicly‐funded secondary schools in England over the period 1992–98. We find several effects consistent with the operation of a quasi‐market. Firstly, new admissions are found to be positively related to a school’s own exam performance and negatively related to the exam performance of competing schools. Secondly, a school’s growth in pupil numbers is positively related to its exam performance compared to its immediate competitors. Thirdly, there is strong evidence that schools experiencing an excess demand for places have responded by increasing their physical capacity. Fourthly, there is some evidence of an increase in the concentration of pupils from poor family backgrounds in those schools with the poorest exam performance of schools during 1992–98 can be attributed to the introduction of quasi‐market forces.

The generalized estimating equation (GEE) approach to the analysis of longitudinal data has many attractive robustness properties and can provide a`population average' characterization of interest, for example, to clinicians who have to treat patients on the basis of their observed characteristics. However, these methods have limitations which restrict their usefulness in both the social and the medical sciences. This conclusion is based on the premise that the main motivations for longitudinal analysis are insight into microlevel dynamics and improved control for omitted or unmeasured variables. We claim that to address these issues a properly formulated random-effects model is required. In addition to a theoretical assessment of some of the issues, we illustrate this by reanalysing data on polyp counts. In this example, the covariates include a base-line outcome, and the effectiveness of the treatment seems to vary by base-line. We compare the random-effects approach with the GEE approach and conclude that the GEE approach is inappropriate for assessing the treatment effects for these data.