Statistics in medicine
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Statistics in medicine · Mar 2005
Comparative StudyThe use of quantile regression in health care research: a case study examining gender differences in the timeliness of thrombolytic therapy.
Investigators are frequently interested in determining patient and system characteristics associated with delays in the provision of essential medical treatment. Investigators have typically used either multiple linear regression or Cox proportional hazards models to assess the impact of patient and system characteristics on the timeliness of medical treatment. A drawback to the use of these two methods is that they allow, at best, a partial exploration of how a distribution of delays in treatment or of waiting times changes with patient characteristics. ⋯ Females were more likely to experience delays in treatment compared to males. Furthermore, gender had a greater impact upon those patients who had the greatest delays in treatment. Investigators who want to determine how a distribution of delays in treatment or of waiting times changes with patient or system characteristics should consider complementing their analyses with the use of quantile regression.
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Statistics in medicine · Oct 2004
Comparative StudyCombination of direct and indirect evidence in mixed treatment comparisons.
Mixed treatment comparison (MTC) meta-analysis is a generalization of standard pairwise meta-analysis for A vs B trials, to data structures that include, for example, A vs B, B vs C, and A vs C trials. There are two roles for MTC: one is to strengthen inference concerning the relative efficacy of two treatments, by including both 'direct' and 'indirect' comparisons. The other is to facilitate simultaneous inference regarding all treatments, in order for example to select the best treatment. ⋯ These models are applied to an illustrative data set and posterior parameter distributions are compared. We discuss model critique and model selection, illustrating the role of Bayesian deviance analysis, and node-based model criticism. The assumptions underlying the MTC models and their parameterization are also discussed.
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Statistics in medicine · Sep 2004
Comparative StudySensitivity of score tests for zero-inflation in count data.
In many biomedical applications, count data have a large proportion of zeros and the zero-inflated Poisson regression (ZIP) model may be appropriate. A popular score test for zero-inflation, comparing the ZIP model to a standard Poisson regression model, was given by van den Broek. ⋯ In this paper, diagnostic measures are derived to assess the influence of observations on the score statistics. Two examples that motivated the application of zero-inflated regression models are considered to illustrate the importance of sensitivity analysis of the zero-inflation tests.
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Meta-regression has become a commonly used tool for investigating whether study characteristics may explain heterogeneity of results among studies in a systematic review. However, such explorations of heterogeneity are prone to misleading false-positive results. It is unclear how many covariates can reliably be investigated, and how this might depend on the number of studies, the extent of the heterogeneity and the relative weights awarded to the different studies. ⋯ We demonstrate in particular that fixed effect meta-regression is likely to produce seriously misleading results in the presence of heterogeneity. The permutation test appropriately tempers the statistical significance of meta-regression findings. We recommend its use before a statistically significant relationship is claimed from a standard meta-regression analysis.
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Randomized Phase II or Phase III clinical trials that are powered based on clinical endpoints, such as survival time, may be prohibitively expensive, in terms of both the time required for their completion and the number of patients required. As such, surrogate endpoints, such as objective tumour response or markers including prostate specific antigen or CA-125, have gained widespread popularity in clinical trials. If an improvement in a surrogate endpoint does not itself confer patient benefit, then consideration must be given to the extent to which improvement in a surrogate endpoint implies improvement in the true clinical endpoint of interest. ⋯ One approach to the validation of surrogate endpoints involves ensuring that a valid between-group analysis of the surrogate endpoint constitutes also a valid analysis of the true clinical endpoint. The Prentice criterion is a set of conditions that essentially specify the conditional independence of the impact of treatment on the true endpoint, given the surrogate endpoint. It is shown that this criterion alone ensures that an observed effect of the treatment on the true endpoint implies a treatment effect also on the surrogate endpoint, but contrary to popular belief, it does not ensure the converse, specifically that the observation of a significant treatment effect on the surrogate endpoint can be used to infer a treatment effect on the true endpoint.