Statistics in medicine
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Statistics in medicine · Sep 1997
Design for sample size re-estimation with interim data for double-blind clinical trials with binary outcomes.
Estimation of sample size in clinical trials requires knowledge of parameters that involve the treatment effect and variability, which are usually uncertain to medical researchers. The recent release within the European Union of a Note for Guidance from the Commission for Proprietary Medical Products (CPMP) highlights the importance of this issue. Most previous papers considered the case of continuous response variables that assume a normal distribution; some regarded the portion up to the interim stage as an 'internal pilot study' and required unblinding. ⋯ We offer a design with a simple stratification strategy that enables us to verify and update the assumption of the response rates given initially in the protocol. The design provides a method to re-estimate the sample size based on interim data while preserving the trial's blinding. An illustrative numerical example and simulation results show slight effect on the type I error rate and the decision making characteristics on sample size adjustment.
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Statistics in medicine · May 1997
Improved odds ratio estimation by post hoc stratification of case-control data.
We propose a logistic regression analysis of unmatched or frequency matched case-control studies with conditional maximum likelihood estimation through post hoc stratification. In this model fewer parameters have to be estimated. ⋯ A more refined post hoc stratification reduces computing time, but to the cost of a larger bias and a loss in efficiency. The model was also applied to data of unmatched case-control studies on laryngeal cancer, oesophageal cancer and lung cancer.
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Multi-centre databases are making an increasing contribution to medical understanding. While the statistical handling of randomized experimental studies is well documented in the medical literature, the analysis of observational studies requires the addressing of additional important issues relating to the timing of entry to the study and the effect of potential explanatory variables not introduced until after that time. A series of analyses is illustrated on a small data set. ⋯ The aim of each analysis, the choice of data used, the essentials of the methodology, the interpretation of the results and the limitations and underlying assumptions are discussed. It is emphasized that, in contrast to randomized studies, the basis for selection and timing of interventions in observational studies is not precisely specified so that attribution of a survival effect to an intervention must be tentative. A glossary of terms is provided.
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Statistics in medicine · Mar 1997
Comparative StudyAn empirical comparison of statistical tests for assessing the proportional hazards assumption of Cox's model.
In the analysis of survival data using the Cox proportional hazard (PH) model, it is important to verify that the explanatory variables analysed satisfy the proportional hazard assumption of the model. This paper presents results of a simulation study that compares five test statistics to check the proportional hazard assumption of Cox's model. ⋯ The simulation results demonstrate that the time-dependent covariate test, the weighted residuals score test and the linear correlation test have equally good power for detection of non-proportionality in the varieties of non-proportional hazards studied. Using illustrative data from the literature, these test statistics performed similarly.
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The analysis of prognostic factor studies by Cox or logistic regression models is often impeded by missing covariate values. In 1990 Schemper and Smith recommended a conditional probability imputation technique (PIT) for the analysis of treatment studies which can be easily applied using standard software and which has been demonstrated to outperform the complete case and omission of covariates strategies. Recent research, however, showed that PIT cannot universally be recommended and it was concluded that model-based methods should be preferred. ⋯ Furthermore, comparisons of PIT with multiple imputation in the same context did not indicate an advantage of the latter more involved technique. By means of an analysis of a prostate cancer data set various aspects of application of PIT are discussed, in particular that PIT permits direct comparability of marginal and partial effects analyses. We conclude that PIT continues to be an appropriate and attractive choice for analyses of prognostic factor studies.