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In clinical trials one traditionally models the effect of treatment on the mean response. The underlying assumption is that treatment affects the response distribution through a mean location shift on a suitable scale, with other aspects of the distribution (shape/dispersion/variance) remaining the same. This work is motivated by a trial in Parkinson's disease patients in which one of the endpoints is the number of falls during a 10-week period. Inspection of the data reveals that the Poisson-inverse Gaussian (PiG) distribution is appropriate, and that the experimental treatment reduces not only the mean, but also the variability, substantially. The conventional analysis assumes a treatment effect on the mean, either adjusted or unadjusted for covariates, and a constant dispersion parameter. On our data, this analysis yields a non-significant treatment effect. However, if we model a treatment effect on both mean and dispersion parameters, both effects are highly significant. A simulation study shows that if a treatment effect exists on the dispersion and is ignored in the modelling, estimation of the treatment effect on the mean can be severely biased. We show further that if we use an orthogonal parametrization of the PiG distribution, estimates of the mean model are robust to misspecification of the dispersion model. We also discuss inferential aspects that are more difficult than anticipated in this setting. These findings have implications in the planning of statistical analyses for count data in clinical trials., (© 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Speech intelligibility tests are conducted on hearing-impaired people for the purpose of evaluating the performance of a hearing device under varying listening conditions and device settings or algorithms. The speech reception threshold (SRT) is typically defined as the signal-to-noise ratio (SNR) at which a subject scores 50% correct on a speech intelligibility test. An SRT is conventionally measured with an adaptive procedure, in which the SNR of successive sentences is adjusted based on the subject's scores on previous sentences. The SRT can be estimated as the mean of a subset of the SNR levels, or by fitting a psychometric function. A set of SRT results is typically analyzed with a repeated measures analysis of variance. We propose an alternative approach for analysis, a zero-and-one inflated beta regression model, in which an observation is a single sentence score rather than an SRT. A parametrization of the model is defined that allows efficient maximum likelihood estimation of the parameters. Fitted values from this model, when plotted against SNR, are analogous to a mean psychometric function in the traditional approach. Confidence intervals for the fitted value curves are obtained by parametric bootstrap. The proposed approach was applied retrospectively to data from two studies that assessed the speech perception of cochlear implant recipients using different sound processing algorithms under different listening conditions. The proposed approach yielded mean SRTs for each condition that were consistent with the traditional approach, but were more informative. It provided the mean psychometric curve of each condition, revealing differences in slope, i.e. differential performance at different parts of the SNR spectrum. Another advantage of the new method of analysis is that results are stated in terms of differences in percent correct scores, which is more interpretable than results from the traditional analysis.

Quality of life (QOL) assessment is a key component of many clinical studies and frequently requires the use of single global summary measures that capture the overall balance of findings from a potentially wide-ranging assessment of QOL issues. We propose and evaluate an irregular multilevel latent variable model suitable for use as a global summary tool for health-related QOL assessments. The proposed model is a multiple indicator and multiple cause style of model with a two-level latent variable structure. We approach the modeling from a general multilevel modeling perspective, using a combination of random and nonrandom cluster types to accommodate the mixture of issues commonly evaluated in health-related QOL assessments--overall perceptions of QOL and health, along with specific psychological, physical, social, and functional issues. Using clinical trial data, we evaluate the merits and application of this approach in detail, both for mean global QOL and for change from baseline. We show that the proposed model generally performs well in comparing global patterns of treatment effect and provides more precise and reliable estimates than several common alternatives such as selecting from or averaging observed global item measures. A variety of computational methods could be used for estimation. We derived a closed-form expression for the marginal likelihood that can be used to obtain maximum likelihood parameter estimates when normality assumptions are reasonable. Our approach is useful for QOL evaluations aimed at pharmacoeconomic or individual clinical decision making and in obtaining summary QOL measures for use in quality-adjusted survival analyses., (Copyright © 2012 John Wiley & Sons, Ltd.)

Ordinal regression analysis is a convenient tool for analyzing ordinal response variables in the presence of covariates. In this paper we extend this methodology to the case of continuous self-rating scales such as the Visual Analog Scale (VAS) used in pain assessment, or the Linear Analog Self-Assessment (LASA) scales in quality of life studies. These scales measure subjects' perception of an intangible quantity, and cannot be handled as ratio variables because of their inherent nonlinearity. We express the likelihood in terms of a function connecting the scale with an underlying continuous latent variable and approximate this function either parametrically or non-parametrically. Then a general semi-parametric regression framework for continuous scales is developed. Two data sets have been analyzed to compare our method to the standard discrete ordinal regression model, and the parametric to the non-parametric versions of the model. The first data set uses VAS data from a study on the efficacy of low-level laser therapy in the treatment of chronic neck pain; the second comes from a study on chemotherapy treatments in advanced breast cancer and looks at the impact of different drugs on patients' quality of life. The continuous formulation of the ordinal regression model has the advantage of no loss of precision due to categorization of the scores and no arbitrary choice of the number and boundaries of categories. The semi-parametric form of the model makes it a flexible method for analysis of continuous ordinal scales.

Functional regression is used to model longitudinal data where the number of measurements on the functional covariate is much greater than the number of subjects in the study. Thus, functional regression can be thought of as singular longitudinal analysis. We have modified existing functional regression techniques to the case of a functional covariate with a repeated stimulus. We applied this modified functional regression to periodically stimulated foetal heart rates. The heart rate tracings were used as a predictor of the child's psychomotor development at approximately 18 months of age. In the past, this type of data has been analysed using the partially subjective concept of habituation. By using the entire heart rate tracings through functional regression, we have the advantage that habituation does not need to be defined and all available information is used to predict later child development., (Copyright 2002 John Wiley & Sons, Ltd.)