A new dose-finding design for bivariate outcomes

For some drugs, toxicity events lead to early termination of treatment before a therapeutic response is observed. That is, there are three possible outcomes: toxicity (therapeutic response unknown), therapeutic response without toxicity, and no response with no toxicity. The optimal dose is the dose that maximizes the probability of the joint event, response, and no toxicity. The optimal safe dose is the dose, from among the doses with toxicity rate less than the maximum tolerable level, that maximizes the probability of response and no toxicity. We present a new sequential design to maximize the number of subjects assigned in the neighborhood of the optimal safe dose in a dose-finding trial with two outcomes.     

Shrinkage estimation for functional principal component scores with application to the population kinetics of plasma folate

We present the application of a nonparametric method to performing functional principal component analysis for functional curve data that consist of measurements of a random trajectory for a sample of subjects. This design typically consists of an irregular grid of time points on which repeated measurements are taken for a number of subjects. We introduce shrinkage estimates for the functional principal component scores that serve as the random effects in the model. Scatterplot smoothing methods are used to estimate the mean function and covariance surface of this model. We propose improved estimation in the neighborhood of and at the diagonal of the covariance surface, where the measurement errors are reflected. The presence of additive measurement errors motivates shrinkage estimates for the functional principal component scores. Shrinkage estimates are developed through best linear prediction and in a generalized version, aiming at minimizing one-curve-leave-out prediction error. The estimation of individual trajectories combines data obtained from that individual as well as all other individuals. We apply our methods to new data regarding the analysis of the level of 14C-folate in plasma as a function of time since dosing of healthy adults with a small tracer dose of 14C-folic acid. A time transformation was incorporated to handle design irregularity concerning the time points on which the measurements were taken. The proposed methodology, incorporating shrinkage and data-adaptive features, is seen to be well suited for describing population kinetics of 14C-folate-specific activity and random effects, and can also be applied to other functional data analysis problems.     

Testing the correlation for clustered categorical and censored discrete time-to-event data when covariates are measured without/with errors

In the analysis of clustered categorical data, it is of common interest to test for the correlation within clusters, and the heterogeneity across different clusters. We address this problem by proposing a class of score tests for the null hypothesis that the variance components are zero in random effects models, for clustered nominal and ordinal categorical responses. We extend the results to accommodate clustered censored discrete time-to-event data. We next consider such tests in the situation where covariates are measured with errors. We propose using the SIMEX method to construct the score tests for the null hypothesis that the variance components are zero. Key advantages of the proposed score tests are that they can be easily implemented by fitting standard polytomous regression models and discrete failure time models, and that they are robust in the sense that no assumptions need to be made regarding the distributions of the random effects and the unobserved covariates. The asymptotic properties of the proposed tests are studied. We illustrate these tests by analyzing two data sets and evaluate their performance with simulations.     

Multivariate approach for selecting sets of differentially expressed genes

An important problem addressed using cDNA microarray data is the detection of genes differentially expressed in two tissues of interest. Currently used approaches ignore the multidimensional structure of the data. However it is well known that correlation among covariates can enhance the ability to detect less pronounced differences. We use the Mahalanobis distance between vectors of gene expressions as a criterion for simultaneously comparing a set of genes and develop an algorithm for maximizing it. To overcome the problem of instability of covariance matrices we propose a new method of combining data from small-scale random search experiments. We show that by utilizing the correlation structure the multivariate method, in addition to the genes found by the one-dimensional criteria, finds genes whose differential expression is not detectable marginally.     

A unified parametric regression model for recapture studies with random removals in continuous time

Conditional likelihood based on counting processes are combined with a Horvitz-Thompson estimator to yield a population size estimator that is more efficient than the existing ones. Random removals are allowed in the recapturing process. Simulation studies are shown to assess the performance of the proposed estimators. Examples on a bird banding and a small mammal recapturing study are given.     

Survival analysis using auxiliary variables via multiple imputation, with application to AIDS clinical trial data

We develop an approach, based on multiple imputation, to using auxiliary variables to recover information from censored observations in survival analysis. We apply the approach to data from an AIDS clinical trial comparing ZDV and placebo, in which CD4 count is the time-dependent auxiliary variable. To facilitate imputation, a joint model is developed for the data, which includes a hierarchical change-point model for CD4 counts and a time-dependent proportional hazards model for the time to AIDS. Markov chain Monte Carlo methods are used to multiply impute event times for censored cases. The augmented data are then analyzed and the results combined using standard multiple-imputation techniques. A comparison of our multiple-imputation approach to simply analyzing the observed data indicates that multiple imputation leads to a small change in the estimated effect of ZDV and smaller estimated standard errors. A sensitivity analysis suggests that the qualitative findings are reproducible under a variety of imputation models. A simulation study indicates that improved efficiency over standard analyses and partial corrections for dependent censoring can result. An issue that arises with our approach, however, is whether the analysis of primary interest and the imputation model are compatible.     

Characteristics of trajectory in the migration of Amoeba proteus

Summary. We investigated the behavior of migration of Amoeba proteus in an isotropic environment. We found that the trajectory in the migration of A. proteus is smooth in the observation time of 500-1000 s, but its migration every second (the cell velocity) on the trajectory randomly changes. Stochastic analysis of the cell velocity and the turn angle of the trajectory has shown that the histograms of the both variables well fit to Gaussian curves. Supposing a simple model equation for the cell motion, we have estimated the motive force of the migrating cell, which is of the order of piconewton. Furthermore, we have found that the cell velocity and the turn angle have a negative cross-correlation coefficient, which suggests that the amoeba explores better environment by changing frequently its migrating direction at a low speed and it moves rectilinearly to the best environment at a high speed. On the other hand, the model equation has simulated the negative correlation between the cell velocity and the turn angle. This indicates that the apparently rational behavior comes from intrinsic characteristics in the dynamical system where the motive force is not torquelike.     

Evaluation and comparison of random amplification of polymorphic DNA, pulsed-field gel electrophoresis and ADSRRS-fingerprinting for typing Serratia marcescens outbreaks

Amplification of DNA fragments surrounding rare restriction sites (ADSRRS-fingerprinting) is a novel assay based on suppression of polymerase chain reaction (PCR). This phenomenon allows the amplification of only a limited subset of DNA fragments, since only those with two different oligonucleotides ligated at the ends of complementary DNA strands are amplified in the PCR. The DNA fragments can be easily analyzed on polyacrylamide gels, stained with ethidium bromide. We have implemented this method using a set of clinical Serratia marcescens isolates from three outbreaks ongoing in the Public Hospital in Gdańsk (Poland). Clustering of ADSRRS-fingerprinting data matched epidemiological, microbiological, random amplification of polymorphic DNA (RAPD) and pulsed-field gel electrophoresis (PFGE) data. Based on this study, we found that there is at least a similar power of discrimination between the present 'gold-standard' PFGE and the novel method, ADSRRS-fingerprinting. Although the ADSRRS-fingerprinting method may appear to be more complex than the RAPD technique, we found it fast and reproducible.     

Studies on somaclonal variation in Phalaenopsis

The morphological and genetic variations in somaclones of Phalaenopsis True Lady “B79-19” derived from tissue culture were evaluated. In 1360 flowering somaclones, no apparent difference was found in the shape of the leaves, whereas flowers in some somaclones were deformed. We have demonstrated that 38 selected random primers can be used to generate amplified segments of genomic DNA and to differentiate polymorphisms of somaclonal variations in Phalaenopsis. The random amplified polymorphic DNA (RAPD) data indicated that normal and variant somaclones are not genetically identical. We also studied the banding patterns of aspartate aminotransferase (AAT) and phosphoglucomutase (PGM) in young leaves of variant and normal somaclones of Phalaenopsis. With respect to AAT, three distinct banding patterns were found in normal somaclones and only two-banded phenotypes were detected in variant somaclones. In a comparison of the banding patterns of PGM isozymes, three to four bands were detected in normal somaclones and two to three bands in variant ones. Received: 15 August 1997 / Revision received: 16 February 1998 / Accepted: 1 May 1998