PubMed · 15284312
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Norman Vetter. 2004. Some immaculate misconceptions.. https://doi.org/10.1093/pubmed%2Ffdh137
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We consider a joint model for exploring association between several correlated longitudinal markers and a clinical event. A nonlinear growth mixture model exhibits the different latent classes of evolution of the latent quantity underlying the correlated longitudinal markers and a logistic regression models the probability of occurence of the clinical event according to the latent classes. By introducing a flexible nonlinear transformation including parameters to be estimated between each marker and the latent process, the model also deals with non-Gaussian continuous markers. Through an application on cognitive ageing, the two advantages of the model are underlined: (1) the latent profiles of evolution associated with the clinical event are described including covariate effects in the longitudinal model but also in the probability of class membership and in the probability of occurence of the event, and (2) a diagnostic and a prognostic tools are derived from the model for early detection of the clinical event using any available information about the longitudinal markers.
In this paper we demonstrate the adverse effect of serially observed data sequences containing transient events on the calculation of Cohen's kappa as an index of inter-rater agreement in the detection of these events. We develop and use a Monte-Carlo-based permutation technique to produce an empiric distribution of kappa in the presence of serial dependence. We find that the empiric confidence intervals for kappa tend to be wider than parametrically derived intervals and in the case of longer event lengths, are markedly so. We evaluate the effect of number and length of events, and further, describe and evaluate three permutation methods which match specific rating situations. Finally, we apply these techniques to the measurement of inter-rater agreement for sleep disordered breathing events, a transient event identified during nocturnal polysomnography, for which traditionally computed confidence intervals for kappa are incorrect.
Adaptions of weighted rank regression to the accelerated failure time model for censored survival data have been successful in yielding asymptotically normal estimates and flexible weighting schemes to increase statistical efficiencies. However, for only one simple weighting scheme, Gehan or Wilcoxon weights, are estimating equations guaranteed to be monotone in parameter components, and even in this case are step functions, requiring the equivalent of linear programming for computation. The lack of smoothness makes standard error or covariance matrix estimation even more difficult. An induced smoothing technique overcame these difficulties in various problems involving monotone but pure jump estimating equations, including conventional rank regression. The present paper applies induced smoothing to the Gehan-Wilcoxon weighted rank regression for the accelerated failure time model, for the more difficult case of survival time data subject to censoring, where the inapplicability of permutation arguments necessitates a new method of estimating null variance of estimating functions. Smooth monotone parameter estimation and rapid, reliable standard error or covariance matrix estimation is obtained.