Bayesian dynamic modeling of latent trait distributions

Studies of latent traits often collect data for multiple items measuring different aspects of the trait. For such data, it is common to consider models in which the different items are manifestations of a normal latent variable, which depends on covariates through a linear regression model. This article proposes a flexible Bayesian alternative in which the unknown latent variable density can change dynamically in location and shape across levels of a predictor. Scale mixtures of underlying normals are used in order to model flexibly the measurement errors and allow mixed categorical and continuous scales. A dynamic mixture of Dirichlet processes is used to characterize the latent response distributions. Posterior computation proceeds via a Markov chain Monte Carlo algorithm, with predictive densities used as a basis for inferences and evaluation of model fit. The methods are illustrated using data from a study of DNA damage in response to oxidative stress.     

Characterization of DNA degradation using direct current conductivity and dynamic dielectric relaxation techniques

The purpose of this study was to evaluate DNA degradation upon thermal heating using dielectric relaxation and direct current (DC) conductivity methods. Herring sperm DNA, human growth hormone (HgH) plasmid DNA, and secreted alkaline phosphatase (SEAP) plasmid DNA were used as the examples. DNA was heated at 80°C for 1 hour. The dielectric relaxation spectra as a function of the applied field frequency were measured for HgH DNA at 0.5 hours and at 1 hour. The frequency range covered was from 10 kHz to 100 kHz. The DC conductivity measurements were made for all 3 kinds of DNA at 4 time points: 0 hours, 0.5 hours, 0.75 hours, and 1 hour. At each time point the DC conductivity was measured for each sample as a function of concentration via water dilution. The results show that the dielectric relaxation method is less sensitive in characterizing heat-driven DNA degradation. Conversely, DC conductivity is very sensitive. The semiquantitative dependence of the conductivity upon heating suggests that DNA degradation involves more than plasmid DNA nicking. Double strand and single strand breaks may also occur. In addition, herring sperm DNA, HgH DNA, and SEAP DNA, though similar in their DC conductivity functional forms upon dilution, exhibit significant differences in their responses to sustained heating.     

A mover-stayer model for longitudinal marker data

Studies of chronic disease often focus on estimating prevalence and incidence in which the presence of active disease is based on dichotomizing a continuous marker variable measured with error. Examples include hypertension, asthma, and depression, where active disease is defined by setting a threshold on a continuous measure of blood pressure, respiratory function, and mood, respectively. This paper proposes a model for inference about prevalence and incidence when active disease is determined by dichotomizing a continuous marker variable in a population-based study. In this formulation, it is postulated that there are three groups of people, those that are not susceptible to the disease, those who are always in the disease state, and those who have the potential to transition between the disease and the disease-free states over time. The model is used to estimate the prevalence and incidence of the disease in the population while accounting for measurement error in the marker. An EM algorithm is used for parameter estimation and the methodology is illustrated on Framingham heart study hypertension data. A simulation study is conducted in order to demonstrate the importance of accounting for measurement error in estimating prevalence and incidence for this example.     

Design of validation studies for estimating the odds ratio of exposure-disease relationships when exposure is misclassified

We compared several validation study designs for estimating the odds ratio of disease with misclassified exposure. We assumed that the outcome and misclassified binary covariate are available and that the error-free binary covariate is measured in a subsample, the validation sample. We considered designs in which the total size of the validation sample is fixed and the probability of selection into the validation sample may depend on outcome and misclassified covariate values. Design comparisons were conducted for rare and common disease scenarios, where the optimal design is the one that minimizes the variance of the maximum likelihood estimator of the true log odds ratio relating the outcome to the exposure of interest. Misclassification rates were assumed to be independent of the outcome. We used a sensitivity analysis to assess the effect of misspecifying the misclassification rates. Under the scenarios considered, our results suggested that a balanced design, which allocates equal numbers of validation subjects into each of the four outcome/mismeasured covariate categories, is preferable for its simplicity and good performance. A user-friendly Fortran program is available from the second author, which calculates the optimal sampling fractions for all designs considered and the efficiencies of these designs relative to the optimal hybrid design for any scenario of interest.     

The multi-mode polarization modulation spectrometer: part 1: simultaneous detection of absorption, turbidity, and optical activity

Circular dichroism (CD) is an important spectroscopic technique for monitoring chirality and biological macromolecule conformation. However, during a CD measurement, absorbance, light scattering/turbidity, and fluorescence can also be detected. The simultaneous measurement of these different spectral features for a single sample is the basis of a multi-mode optical spectrometer. This allows time-efficient gathering of complementary information and provides a scheme to ensure that CD measurements are reliable. Aspects of circular polarization differential light scattering, pH, and temperature variation of a protein (antibody) solution are described. A procedure to help ensure that CD measurements are reliable is described.     

Bayesian analysis of serial dilution assays

In a serial dilution assay, the concentration of a compound is estimated by combining measurements of several different dilutions of an unknown sample. The relation between concentration and measurement is nonlinear and heteroscedastic, and so it is not appropriate to weight these measurements equally. In the standard existing approach for analysis of these data, a large proportion of the measurements are discarded as being above or below detection limits. We present a Bayesian method for jointly estimating the calibration curve and the unknown concentrations using all the data. Compared to the existing method, our estimates have much lower standard errors and give estimates even when all the measurements are outside the "detection limits." We evaluate our method empirically using laboratory data on cockroach allergens measured in house dust samples. Our estimates are much more accurate than those obtained using the usual approach. In addition, we develop a method for determining the "effective weight" attached to each measurement, based on a local linearization of the estimated model. The effective weight can give insight into the information conveyed by each data point and suggests potential improvements in design of serial dilution experiments.     

Accuracy and repeatability of a pressure measurement system in the patellofemoral joint

The objective of this study was to assess how accurately and repeatably the Iscan system measures force and pressure in the natural patellofemoral joint. These measurements must be made to test widely held assumptions about the relationships between mechanics, pain and cartilage degeneration. We assessed the system's accuracy by using test rigs in a materials testing machine to apply known forces and force distributions across the sensor. The root mean squared error in measuring resultant force (for five trials at each of seven load levels) was 6.5±4.4% (mean±standard deviation over all trials at all load levels), while the absolute error was −5.5±5.6%. For force distribution, the root mean squared error (for five trials at each of five force distributions) was 0.86±0.58%, while the absolute error was −0.22±1.03%. We assessed the repeatability of the system's measurements of patellofemoral contact force, pressure and force distribution in four cadaver specimens loaded in continuous and static flexion. Variability in measurement (standard deviation expressed as a percentage of the mean) was 9.1% for resultant force measurements and 3.0% for force distribution measurements for static loads, and 7.3% for resultant force and 2.2% for force distribution measurements for continuous flexion. Cementing the sensor to the cartilage lowered readings of resultant force by 31±32% (mean±standard deviation), area by 24±13% and mean pressure by 9±34% (relative to the uncemented sensor). Maximum pressure measurement, however, was 24±43% higher in the cemented sensor than in the uncemented sensor. The results suggest that the sensor measures force distribution more accurately and repeatably than absolute force. A limitation of our work, however, is that the sensor must be cemented to the patellar articular surface to make the force distribution measurements, and our results suggest that this process reduces the accuracy of force, pressure and area measurements. Our results suggest that the Iscan system's pressure measurement accuracy and repeatability are comparable to that of Fuji Prescale film, but its advantages are that it is thinner than most Fuji Prescale film, it measures contact area more accurately and that it makes continuous measurements of force, pressure and area.     

A simple Bayesian analysis of misclassified binary data with a validation substudy

A two-stage Bayesian method is presented for analyzing case-control studies in which a binary variable is sometimes measured with error but the correct values of the variable are known for a random subset of the study group. The first stage of the method is analytically tractable and MCMC methods are used for the second stage. The posterior distribution from the first stage becomes the prior distribution for the second stage, thus transferring all relevant information between the stages. The method makes few distributional assumptions and requires no asymptotic approximations. It is computationally fast and can be run using standard software. It is applied to two data sets that have been analyzed by other methods, and results are compared.     

Random effects logistic regression analysis with auxiliary covariates

We study a semiparametric estimation method for the random effects logistic regression when there is auxiliary covariate information about the main exposure variable. We extend the semiparametric estimator of Pepe and Fleming (1991, Journal of the American Statistical Association 86, 108-113) to the random effects model using the best linear unbiased prediction approach of Henderson (1975, Biometrics 31, 423-448). The method can be used to handle the missing covariate or mismeasured covariate data problems in a variety of real applications. Simulation study results show that the proposed method outperforms the existing methods. We analyzed a data set from the Collaborative Perinatal Project using the proposed method and found that the use of DDT increases the risk of preterm births among U.S. children.