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Biomedical subjects

R V Lenth

Publications and source records attributed to R V Lenth.

6 recordsLinked to original sources

Monte Carlo validation of a multireader method for receiver operating characteristic discrete rating data: factorial experimental design.

RATIONALE AND OBJECTIVES: The authors conducted a series of null-case Monte Carlo simulations to evaluate the Dorfman-Berbaum-Metz (DBM) method for comparing modalities with multireader receiver operating characteristic (ROC) discrete rating data. MATERIALS AND METHODS: Monte Carlo simulations were performed by using discrete ratings on fully crossed factorial designs with two modalities and three, five, and 10 hypothetical readers. The null hypothesis was true for all simulations. The population ROC areas, latent variable structures, case sample sizes, and normal/abnormal case sample ratios used in another study were used in these simulations. RESULTS: For equal allocation ratios and small (Az = 0.702) and moderate (Az = 0.855) ROC areas, the empirical type I error rate closely matched the nominal alpha level. For very large ROC areas (Az = 0.961), however, the empirical type I error rate was somewhat smaller than the nominal alpha level. This conservatism increased with decreasing case sample size and asymmetric normal/abnormal case allocation ratio. The empirical type I error rate was sometimes slightly larger than the nominal alpha level with many cases and few readers, where there was large residual, relatively small treatment-by-case interaction and relatively large treatment-by-reader interaction. CONCLUSION: The results suggest that the DBM method provides trustworthy alpha levels with discrete ratings when the ROC area is not too large and case and reader sample sizes are not too small. In other situations, the test tends to be somewhat conservative or slightly liberal.

Diagnostic Imaging↗

Proper receiver operating characteristic analysis: the bigamma model.

RATIONALE AND OBJECTIVES: The standard binormal model is the most commonly used model for fitting receiver operating characteristic rating data; however, it sometimes produces inappropriate fits that cross the chance line with degenerate data sets. The authors proposed and evaluated a proper constant-shape bigamma model to handle binormal degeneracy. METHODS: Monte Carlo samples were generated from both a standard binormal population model and a proper constant-shape bigamma model in a series of Monte Carlo studies. RESULTS: The results confirm that the standard binormal model is robust in large samples with no degenerate data sets and that the standard binormal model is not robust in small samples because of degenerate data sets. CONCLUSION: A proper constant-shape bigamma model seems to solve the problem of degeneracy without inappropriate chance line crossings. The bigamma fitting model outperformed the standard binormal fitting model in small samples and gave similar results in large samples.

Decision Making↗

Multireader, multicase receiver operating characteristic methodology: a bootstrap analysis.

RATIONALE AND OBJECTIVES: We evaluated by bootstrapping the conclusions obtained by the Dorfman-Berbaum-Metz (DBM) receiver operating characteristic (ROC) method and by the Toledano-Gatsonis (TG) method on a well-known data set. METHODS: We bootstrapped in two ways, resampled cases while holding readers fixed and resampled both cases and readers. RESULTS: When an analysis of variance of pseudovalues implies that reader variance and all random interactions with treatment are essentially zero, then case-resampling bootstrap and the DBM and TG methods should give the same results. Case-resampling bootstrap and the DBM and TG methods did give highly similar results for both individual readers and the averages over all readers. Both the case-resampling bootstrap and the reader-case resampling bootstrap gave smaller standard errors for group than for individual reader means, thereby providing evidence for a trade-off of readers and cases with regard to precision and power in this data set. CONCLUSION: Case-resampling bootstrap provides some justification for the DBM and TG methods.

Analysis of Variance↗

Effect of signal bandwidth upon threshold of the acoustic reflex and upon loudness.

The effect of activating-signal bandwidth upon the threshold of the acoustic reflex (TAR) was measured. Subsequently, loudness measurements were made for the same signals at the same intensity levels that were required to elicit an acoustic-reflex response. When loudness and TAR are compared at comparable levels, similar trends emerged. Results from this experiment provide evidence for both qualitative and quantitative similarities between acoustic reflex and the perception of loudness. This, in turn, suggest that signals at TAR may be equally loud for listeners with normal hearing.

Acoustic Stimulation↗

Pressure-induced modifications of the acoustic nerve. Part I: The acoustic reflex.

It is commonly thought that as an acoustic neuroma grows it exerts pressure on the acoustic nerve resulting in alterations of the acoustic reflex and brain stem audiometry. This hypothesis has not been confirmed. In this study in an animal model, acute pressure was applied to the nerves of the internal auditory canal, and changes in the acoustic reflex were measured. Our results support the theory that pressure on the acoustic nerve causes an increase in the rate of adaptation and a decrease in the amplitude of the acoustic reflex. The contralateral reflex appears to be the most sensitive indicator of these effects. We feel that this animal model can be useful to investigate the effects of pressure on the acoustic nerve.

Acoustic Impedance Tests↗