PubMed · 14514950
Two patients are presented with primary low grade pleural B cell lymphomas with no history of a pyothorax.
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H Ahmad, J Pawade, S Falk, J A Morgan, L Balacumaraswami. 2003. Primary pleural lymphomas.. https://doi.org/10.1136/thorax.58.10.908
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For comparing the validity of rating methods, the adjusted kappa (S coefficient) and Yule's Y index are better than Cohen's kappa which is affected by marginal probabilities. We consider a validity study in which a subject is assessed as exposed or not-exposed by two competing rating methods and the gold standard. We are interested in one of the methods, which is closer in agreement with the gold standard. We present statistical methods taking correlations into account for comparing the validity of the rating methods using S coefficient and Y index. We show how the S coefficient and Yule's Y index are related to sensitivity and specificity. In comparing the two rating methods, the preference is clear when the inference is the same for both S and Y. If the inference using S differs from that using Y, then it is not obvious how to decide a preference. This may occur when one rating method is better than the other in sensitivity but not in specificity. Numerical examples for comparing asbestos-exposure assessment methods are illustrated.
Two-phase designs, in which a subsample with validation or complete information data is sampled stratified both on outcome and covariate from a first-phase study with incomplete data, have been proposed over 10 years ago and have been proven to result in efficient estimates with respect to standard designs. The efficiency depends, however, on the sampling fractions within each stratum. Our aim is to present a strategy for obtaining an optimized design, i.e. sampling fractions, that makes use of available phase-one data of an existing case-control study considered as the first-phase sample when the focus is on estimating a parameter vector. No global optimal design exists and local optimal designs depend on scenarios comprising the true disease model and the association between the phase-one and phase-two information. We develop an admissibility test that rejects scenarios inconsistent with the phase-one data and, for the selected scenarios, determine a minmax D- or A-optimal design that protects against worst-case scenarios. This work is applied on two examples.
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