Descriptive statistical analyses of serial dilution data

A hierarchical Bayesian latent class mixture model with censorship for detection of linear temporal changes in antibiotic resistance

Identifying and controlling the emergence of antimicrobial resistance (AMR) is a high priority for researchers and public health officials. One critical component of this control effort is timely detection of emerging or increasing resistance using surveillance programs. Currently, detection of temporal changes in AMR relies mainly on analysis of the proportion of resistant isolates based on the dichotomization of minimum inhibitory concentration (MIC) values. In our work, we developed a hierarchical Bayesian latent class mixture model that incorporates a linear trend for the mean log2MIC of the non-resistant population. By introducing latent variables, our model addressed the challenges associated with the AMR MIC values, compensating for the censored nature of the MIC observations as well as the mixed components indicated by the censored MIC distributions. Inclusion of linear regression with time as a covariate in the hierarchical structure allowed modelling of the linear creep of the mean log2MIC in the non-resistant population. The hierarchical Bayesian model was accurate and robust as assessed in simulation studies. The proposed approach was illustrated using Salmonella enterica I,4,[5],12:i:- treated with chloramphenicol and ceftiofur in human and veterinary samples, revealing some significant linearly increasing patterns from the applications. Implementation of our approach to the analysis of an AMR MIC dataset would provide surveillance programs with a more complete picture of the changes in AMR over years by exploring the patterns of the mean resistance level in the non-resistant population. Our model could therefore serve as a timely indicator of a need for antibiotic intervention before an outbreak of resistance, highlighting the relevance of this work for public health. Currently, however, due to extreme right censoring on the MIC data, this approach has limited utility for tracking changes in the resistant population.

A simple and powerful method for the estimation of intervention effects on serological endpoints using paired interval-censored data

Clinical trials often use a binary "fold increase" endpoint defined according to the ratio of interval-censored measurement at end-of-study to that at baseline. We propose a simple yet principled analytic approach based on the linear mixed-effects model for interval-censored data for the analysis of such paired measurements. Having estimated the model parameters, the risk ratio can be estimated by explicit composite estimand and the variance is estimated using the delta method. The estimation can be implemented using the existing procedures in popular statistical software. We use antibody data from the Chloroquine for Influenza Prevention Trial for illustration.

Antagonism of antifungal metabolites from Streptomyces griseus H7602 against Phytophthora capsici

In this study, evidences for antagonism were established by production of antifungal metabolites from Streptomyces griseus H7602, which were active to inhibit mycelial growth of Phytophthora capsici in the in vitro assays. Mycelial growth and zoosporangia formation of P. capsici was strongly inhibited in the medium containing the cell free culture filtrate of S. griseus H7602. Antifungal metabolites from the cell free culture filtrate of S. griseus H7602 showed substantial antagonistic effects on P. capsici. In addition, a purified antifungal compound was separated from the antifungal metabolites of S. griseus H7602 and identified to be 1H-pyrrole-2-carboxylic acid (PCA) by spectra analyses. PCA showed strong antifungal activity and was evaluated for the first time for its antagonism against P. capsici under in vitro conditions. Minimum inhibitory concentration (MIC) value of PCA was low (4 µg ml(-1)), and the mycelial growth of P. capsici was almost inhibited at concentration of 64 µg ml(-1). This study suggests that the PCA may be useful as biofungicides against P. capsici, and the prominent antagonism of antifungal metabolites from S. griseus H7602 highlights it as a candidate for biocontrol of P. capsici.

Handling missing data in vaccine clinical trials for immunogenicity and safety evaluation

In clinical trials, study subjects are usually followed for a period of time after treatment, and the missing data issue is almost inevitable due to various reasons, including early dropout or lost-to-follow-up. It is important to take the missing data into consideration at the study design stage to minimize its occurrence throughout the study and to prospectively account for it in the analyses. There are many methods available in the literature that are designed to handle the missing data issue under various settings. Vaccines are biological products that are primarily designed to prevent infectious diseases, and are different from pharmaceutical products, which traditionally have been chemical products designed to treat or cure diseases. While a lot of similarities exist between clinical trials for vaccines and those for pharmaceutical products, there are some unique issues in vaccine trials, including how to handle the missing data, which calls for special considerations. In this report we present a variety of statistical approaches for analyses of vaccine immunogenicity and safety trials in the presence of missing data. The methods are illustrated with numerical simulations and vaccine trial examples.

Correlation Coefficient Inference on Censored Bioassay Data

In vaccine clinical trials, immunologic responses sometimes can not be accurately measured by bioassays. For example, a serial dilution assay usually reports the range of the response instead of the exact value. In some other assays, the measurement is not available if the response is lower than the assay's detection limit. In both cases, the measurements are censored. We are interested in computing the confidence interval for the correlation coefficient of two assay measurements that are subject to censoring. We propose using the maximum likelihood method to estimate the correlation coefficient, and constructing its confidence interval based on the second-order Taylor's expansion of the Fisher Z transformation. The method can be viewed as an extension of the Fisher Z transformation to the case of censored data. Extensive simulations show that the proposed method provides satisfactory coverage probabilities under finite sample sizes. The proposed method performs well compared with existing methods, but it is computationally much simpler. In addition, the proposed method works with many types of censored data in a similar way. Furthermore, we proposed an Monte Carlo exact test to assess the goodness-of-fit of the model.

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.

Statistical inference for serial dilution assay data

Serial dilution assays are widely employed for estimating substance concentrations and minimum inhibitory concentrations. The Poisson-Bernoulli model for such assays is appropriate for count data but not for continuous measurements that are encountered in applications involving substance concentrations. This paper presents practical inference methods based on a log-normal model and illustrates these methods using a case application involving bacterial toxins.