Statistics in medicine
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Statistics in medicine · Oct 2009
Comparative StudyA comparison of model choices for the Continual Reassessment Method in phase I cancer trials.
Determination of the maximum tolerated dose (MTD) is the main objective of phase I trials. Trials are typically carried out with restricted sample sizes. Model-based approaches proposed to identify the MTD (including the Continual Reassessment Method or CRM) suppose a simple model for the dose-toxicity relation. ⋯ We show that average performances of a one-parameter model are superior and that the power model has good operating characteristics. Some models can speed up dose escalation and lead to more aggressive designs. We derive some behavior related to the choice of model and insist on the use of simulations under several scenarios before the initiation of each new trial in order to determine the best model to be used.
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Statistics in medicine · Sep 2009
When are summary ROC curves appropriate for diagnostic meta-analyses?
Diagnostic tests are increasingly evaluated with systematic reviews and this has lead to the recent developments of statistical methods to analyse such data. The most commonly used method is the summary receiver operating characteristic (SROC) curve, which can be fitted with a non-linear bivariate random-effects model. This paper focuses on the practical problems of interpreting and presenting data from such analyses. ⋯ In these situations, a summary with two univariate meta-analyses of the true and false positive rates (TPRs and FPRs) may be more appropriate. We characterize the type of problems that can occur in fitting these models and present an algorithm to guide the analyst of such studies, with illustrations from analyses of published data. A set of R functions, freely available to perform these analyses, can be downloaded from (www.diagmeta.info).
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Statistics in medicine · Jul 2009
Do doctors need statistics? Doctors' use of and attitudes to probability and statistics.
There is little published evidence on what doctors do in their work that requires probability and statistics, yet the General Medical Council (GMC) requires new doctors to have these skills. This study investigated doctors' use of and attitudes to probability and statistics with a view to informing undergraduate teaching. An email questionnaire was sent to 473 clinicians with an affiliation to the University of East Anglia's Medical School. ⋯ Sixty-three per cent (78/124, 95 per cent CI 54 per cent, 71 per cent) said that there were activities that they could do better or start doing if they had an improved understanding of these areas and 74 of these participants elaborated on this. Themes highlighted by participants included: being better able to critically evaluate other people's research; becoming more research-active, having a better understanding of risk; and being better able to explain things to, or teach, other people. Our results can be used to inform how probability and statistics should be taught to medical undergraduates and should encourage today's medical students of the subjects' relevance to their future careers.
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Statistics in medicine · May 2009
Comparative StudyBayesian adjustment for covariate measurement errors: a flexible parametric approach.
In most epidemiological investigations, the study units are people, the outcome variable (or the response) is a health-related event, and the explanatory variables are usually environmental and/or socio-demographic factors. The fundamental task in such investigations is to quantify the association between the explanatory variables (covariates/exposures) and the outcome variable through a suitable regression model. The accuracy of such quantification depends on how precisely the relevant covariates are measured. ⋯ We investigate the performance of the proposed flexible parametric approach in comparison with a common flexible parametric approach through extensive simulation studies. We also compare the proposed method with the competing flexible parametric method on a real-life data set. Though emphasis is put on the logistic regression model, the proposed method is unified and is applicable to the other generalized linear models, and to other types of non-linear regression models as well.
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Statistics in medicine · Apr 2009
Modeling disease-state transition heterogeneity through Bayesian variable selection.
In many diseases, Markov transition models are useful in describing transitions between discrete disease states. Often the probability of transitioning from one state to another varies widely across subjects. This heterogeneity is driven, in part, by a possibly unknown number of previous disease states and by potentially complex relationships between clinical data and these states. ⋯ Our approach simultaneously estimates the order of the Markov process and the transition-specific covariate effects. The methods are assessed using simulation studies and applied to model disease-state transition on the expanded disability status scale (EDSS) in multiple sclerosis (MS) patients from the Partners MS Center in Boston, MA. The proposed methodology is shown to accurately identify complex covariate-transition relationships in simulations and identifies a clinically significant interaction between relapse history and EDSS history in MS patients.