Statistics in medicine
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Statistics in medicine · Dec 2001
Intention-to-treat: methods for dealing with missing values in clinical trials of progressively deteriorating diseases.
Since it came up in the 1960s, the principle of intention-to-treat (ITT) has become widely accepted for the analysis of controlled clinical trials. In this context the question of how to perform such an analysis in the presence of missing information about the main endpoint is of major importance. Uncritical use of several ad hoc strategies for dealing with missing values is common in the practice of clinical trials. ⋯ Because of the drastic consequences of increasing drop-out rates, it has to be a primary goal in clinical trials to keep missing values to a minimum. Unobserved information cannot be reliably regained by any methodological resources. As there are no strategies for universal use, reasons for the choice of a certain method have to be provided when designing and analysing clinical trials.
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Statistics in medicine · Nov 2001
Comparative StudyApplication of robust estimating equations to the analysis of quantitative longitudinal data.
A model fit by general estimating equations (GEE) has been used extensively for the analysis of longitudinal data in medical studies. To some extent, GEE tries to minimize a quadratic form of the residuals, and therefore is not robust in the sense that it, like least squares estimates, is sensitive to heavy-tailed distributions, contaminated distributions and extreme values. This paper describes the family of truncated robust estimating equations and its properties for the analysis of quantitative longitudinal data. ⋯ For this application, GEE seems to be sensitive to the working correlation specification in that different working correlation structures may lead to different conclusions about the effect of intensive diabetes treatment. On the other hand, the robust estimating equations consistently conclude that the treatment effect is highly significant no matter which working correlation structure is used. The DCCT Research Group also demonstrated a significant effect using a mixed-effects longitudinal model.
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Statistics in medicine · Oct 2001
Comparative StudyThe continual reassessment method: comparison of Bayesian stopping rules for dose-ranging studies.
The continual reassessment method (CRM) provides a Bayesian estimation of the maximum tolerated dose (MTD) in phase I clinical trials and is also used to estimate the minimal efficacy dose (MED) in phase II clinical trials. In this paper we propose Bayesian stopping rules for the CRM, based on either posterior or predictive probability distributions that can be applied sequentially during the trial. These rules aim at early detection of either the mis-choice of dose range or a prefixed gain in the point estimate or accuracy of estimated probability of response associated with the MTD (or MED). ⋯ The stopping rules were then applied to a data set from a dose-ranging phase II clinical trial aiming at estimating the MED dose of midazolam in the sedation of infants during cardiac catheterization. All these findings suggest the early use of the two first rules to detect a mis-choice of dose range, while they confirm the requirement of including at least 20 patients at the same dose to reach an accurate estimate of MTD (MED). A two-stage design is under study.
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Statistics in medicine · Sep 2001
Exact inference for categorical data: recent advances and continuing controversies.
Methods for exact small-sample analyses with categorical data have been increasingly well developed in recent years. A variety of exact methods exist, primarily using the approach that eliminates unknown parameters by conditioning on their sufficient statistics. In addition, a variety of algorithms now exist for implementing the methods. ⋯ Controversy continues about the appropriateness of some exact methods, primarily relating to their conservative nature because of discreteness. This issue is examined for two simple problems in which discreteness can be severe--interval estimation of a proportion and the odds ratio. In general, adjusted exact methods based on the mid-P-value seem a reasonable way of reducing the severity of this problem.
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Statistics in medicine · Aug 2001
Comparative StudyMeta-analysis of continuous outcome data from individual patients.
Meta-analyses using individual patient data are becoming increasingly common and have several advantages over meta-analyses of summary statistics. We explore the use of multilevel or hierarchical models for the meta-analysis of continuous individual patient outcome data from clinical trials. A general framework is developed which encompasses traditional meta-analysis, as well as meta-regression and the inclusion of patient-level covariates for investigation of heterogeneity. ⋯ We focus on models with fixed trial effects, although an extension to a random effect for trial is described. The methods are illustrated on an example in Alzheimer's disease in a classical framework using SAS PROC MIXED and MLwiN, and in a Bayesian framework using BUGS. Relative merits of the three software packages for such meta-analyses are discussed, as are the assessment of model assumptions and extensions to incorporate more than two treatments.