Statistics in medicine
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Statistics in medicine · May 2005
Review Comparative StudyIdentification and impact of outcome selection bias in meta-analysis.
The systematic review community has become increasingly aware of the importance of addressing the issues of heterogeneity and publication bias in meta-analyses. A potentially bigger threat to the validity of a meta-analysis appears relatively unnoticed. The within-study selective reporting of outcomes, defined as the selection of a subset of the original variables recorded for inclusion in publication of trials, can theoretically have a substantial impact on the results. ⋯ In cases where the level of suspicion was high, sensitivity analysis was undertaken to assess the robustness of the conclusion to this bias. Although within-study selection was evident or suspected in several trials, the impact on the conclusions of the meta-analyses was minimal. This paper deals with the identification of, sensitivity analysis for, and impact of within-study selective reporting in meta-analysis.
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Statistics in medicine · May 2005
Adjusting for observable selection bias in block randomized trials.
In this paper, we propose a model-based approach to detect and adjust for observable selection bias in a randomized clinical trial with two treatments and binary outcomes. The proposed method was evaluated using simulations of a randomized block design in which the investigator favoured the experimental treatment by attempting to enroll stronger patients (with greater probability of treatment success) if the probability of the next treatment being experimental was high, and enroll weak patients (with less probability of treatment success) if the probability of the next treatment being experimental was low. The method allows not only testing for the presence of observable selection bias, but also testing for a difference in treatment effects, adjusting for possible selection bias.
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Statistics in medicine · May 2005
Assessing intra, inter and total agreement with replicated readings.
In clinical studies, assessing agreement of multiple readings on the same subject plays an important role in the evaluation of continuous measurement scale. The multiple readings within a subject may be replicated readings by using the same method or/and readings by using several methods (e.g. different technologies or several raters). The traditional agreement data for a given subject often consist of either replicated readings from only one method or multiple readings from several methods where only one reading is taken from each of these methods. ⋯ The relationship of the total-CCC with the inter-CCC and the ICCs is investigated. We propose a generalized estimating equations approach for estimation and inference. Simulation studies are conducted to assess the performance of the proposed approach and data from a carotid stenosis screening study is used for illustration.
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Statistics in medicine · Apr 2005
Comparative StudyFunnel plots for comparing institutional performance.
'Funnel plots' are recommended as a graphical aid for institutional comparisons, in which an estimate of an underlying quantity is plotted against an interpretable measure of its precision. 'Control limits' form a funnel around the target outcome, in a close analogy to standard Shewhart control charts. Examples are given for comparing proportions and changes in rates, assessing association between outcome and volume of cases, and dealing with over-dispersion due to unmeasured risk factors. We conclude that funnel plots are flexible, attractively simple, and avoid spurious ranking of institutions into 'league tables'.
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Statistics in medicine · Apr 2005
Adjusting for partially missing baseline measurements in randomized trials.
Adjustment for baseline variables in a randomized trial can increase power to detect a treatment effect. However, when baseline data are partly missing, analysis of complete cases is inefficient. We consider various possible improvements in the case of normally distributed baseline and outcome variables. ⋯ Secondly, imputation should be carried out in a deterministic way, using other baseline variables if possible, but not using randomized arm or outcome. Thirdly, if baselines are not missing completely at random, then a dummy variable for missingness should be included as a covariate (the missing indicator method). The methods are illustrated in a randomized trial in community psychiatry.