Biomedical subjects

Anthony Y C Kuk

Publications and source records attributed to Anthony Y C Kuk.

3 recordsLinked to original sources

PubMed2005

The estimation of SARS incubation distribution from serial interval data using a convolution likelihood.

The incubation period of SARS is the time between infection of disease and onset of symptoms. Knowledge about the distribution of incubation times is crucial in determining the length of quarantine period and is an important parameter in modelling the spread and control of SARS. As the exact time of infection is unknown for most patients, the incubation time cannot be determined. What is observable is the serial interval which is the time from the onset of symptoms in an index case to the onset of symptoms in a subsequent case infected by the index case. By constructing a convolution likelihood based on the serial interval data, we are able to estimate the incubation distribution which is assumed to be Weibull, and justifications are given to support this choice over other distributions. The method is applied to data provided by the Ministry of Health of Singapore and the results justify the choice of a ten-day quarantine period. The indirect estimate obtained using the method of convolution likelihood is validated by means of comparison with a direct estimate obtained directly from a subset of patients for whom the incubation time can be ascertained. Despite its name, the proposed indirect estimate is actually more precise than the direct estimate because serial interval data are recorded for almost all patients, whereas exact incubation times can be determined for only a small subset. It is possible to obtain an even more efficient estimate by using the combined data but the improvement is not substantial.

Adult↗

PubMed2005

A shared response model for clustered binary data in developmental toxicity studies.

Existing distributions for modeling fetal response data in developmental toxicology such as the beta-binomial distribution have a tendency of inflating the probability of no malformed fetuses, and hence understating the risk of having at least one malformed fetus within a litter. As opposed to a shared probability extra-binomial model, we advocate a shared response model that allows a random number of fetuses within the same litter to share a common response. An explicit formula is given for the probability function and graphical plots suggest that it does not suffer from the problem of assigning too much probability to the event of no malformed fetuses. The EM algorithm can be used to estimate the model parameters. Results of a simulation study show that the EM estimates are nearly unbiased and the associated confidence intervals based on the usual standard error estimates have coverage close to the nominal level. Simulation results also suggest that the shared response model estimates of the marginal malformation probabilities are robust to misspecification of the distributional form, but not so for the estimates of intralitter correlation and the litter-level probability of having at least one malformed fetus. The proposed model is fitted to a set of data from the U.S. National Toxicology Program. For the same dose-response relationship, the fit based on the shared response distribution is superior to that based on the beta-binomial, and comparable to that based on the recently proposed q-power distribution (Kuk, 2004, Applied Statistics53, 369-386). An advantage of the shared response model over the q-power distribution is that it is more interpretable and can be extended more easily to the multivariate case. To illustrate this, a bivariate shared response model is fitted to fetal response data involving visceral and skeletal malformation.

2,4,5-Trichlorophenoxyacetic Acid↗

PubMed2002

Determination of the unknown age at first capture of western rock lobsters (Panulirus cygnus) by random effects model.

We propose a method for fitting growth curves to multiple recapture data of lobsters when the age at first capture is unknown. The von Bertalanffy growth curve is used to model the growth. To account for individual variability, the unknown age in logarithmic scale of a lobster at first capture, the individual asymptotic size, and the individual growth coefficient of its carapace length are modeled as random effects with a trivariate normal distribution. Unlike previously suggested models, the present model permits correlation between the growth coefficient and the age at first capture and can be fitted readily using existing software. The error structures between consecutive recaptures of a lobster are assumed to be a first-order autoregressive process with unequally spaced time points. A comparison between this model and the Fabens growth equation is given. The proposed method is a flexible method and can be applied to fit different growth equations when the age at first capture is unknown.

Age Factors↗