Biostatistics Workshop
(25 October 2006)

~ Abstracts ~

Peak detection of SELDI measurements for identifying protein biomarkers
Chuen Seng Tan, Centre for Molecular Epidemiology, Singapore

Aim:
Protein expression profiling data from the surface-enhanced laser desorption and ionization (SELDI) technology is used to discover biomarkers for clinical diagnosis, prognosis and therapy prediction. The pre-processing of the raw data however is still problematic. We aim to develop a peak detection method with much better specificity than the standard methods.

Methods:
Scientists inspect individual spectra visually and laboriously to verify that the peaks identified by the standard method are real. Motivated by this multi-spectral practice, we investigate an analytical approach that reduces the data to a single spectrum of F-statistics capturing significant variability between spectra. To account for multiple testing, we use a false discovery rate (FDR) criterion to identify potentially interesting proteins. To annotate the peaks, we extracted a peak template from all spectra via the principle component analysis (PCA). Finally, with the template, we estimate the amplitude and location of the peak in each spectrum with the least-squares method and refine the estimation of the amplitude via the mixture model.

Results:
We show that our approach has better operating characteristics than several existing methods and gives more accurate peak annotations than the standard method.

Conclusion:
We find that our approach alleviates the main problems in the preprocessing of SELDI-TOF spectra.

 

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Adaptive randomization in clinical trials
Feifang Hu, University of Virginia, USA

While clinical trials may provide information on new treatments that can impact countless lives in the future, the act of randomization means that volunteers in the clinical trial will receive the benefit of the new treatment only by chance. In most clinical trials, an attempt is made to balance the treatment assignments equally, thus the probability that a volunteer will receive the potentially better treatment is only 50%. Response-adaptive randomization uses accruing data to skew the allocation probabilities to favor the treatment performing better thus far in the trial, thereby mitigating the problem to some degree.

In this talk, I give a brief review of adaptive randomization. Then I propose some new response-adaptive randomization procedures that have some desirable properties. The resulting randomization procedures provide efficient methods to determine whether a new treatment is effective in a clinical trial, while simultaneously minimizing a clinical trial volunteer's chance of being assigned to the inferior treatment.
 

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Quantification of protein lysate arrays: a nonparametric approach
Xuming He, University of Illinois at Urbana-Champaign, USA

The reverse-phase protein lysate arrays is an emerging technology that allows us to quantify the relative expression levels of a protein in many different cellular samples. At this moment, the applications of protein lysate arrays are still exploratory with a lack of reliable analysis tools for quantifying the information from protein arrays. In this talk, we show that a nonparametric protein expression curve often provides better fit to the data from the dilution series, whereas rigid parametric models such as the commonly used logistic curves are prone to bias. The problem of quantifying protein expression levels demands serious statistical work, and this talk serves as an introduction. The talk will be based on some joint work with Dr. Jianhua Hu at the M.D Anderson Cancer Center at the University of Texas.

 

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On pedratric growth charts
Ying Wei, Columbia University, USA

Pediatric growth charts, consisting of a series of reference quantile curves of height or weight, have been widely used in clinics and medical centers to monitor an individual child’s growth status. Statistical methodologies have been developed recently to customize the growth charts per subject for more informative screenings. Specifically, we first studied a global semiparametric quantile regression model to take into account a subject‘s prior measurements, and possibly other covariates (such as parental information). We call such charts conditional growth charts. Moreover, as human growth involves several parameters, more comprehensive screening could be obtained by considering multiple growth measurements simultaneously. We propose to construct bivariate growth charts by a nested sequence of age-dependent and covariate-adjusted reference quantile contours on the joint distribution of height and weight. A two stage method based upon quantile regression was studied for estimation.
We will describe the proposed statistical methodologies, as well as a brief introduction to quantile regression, which is our main statistical tool. The talk aims at providing a general picture of the concepts between unconditional and conditional growth charts and between univariate and multivariate growth charts, we therefore will be more focused on illustrative examples of height and weight screening of young children in the Finland and the United States.


 

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Assessing seasonality with the von mises distribution
Fei Gao, National Cancer Centre, Singapore

We propose to use the von Mises distribution to summarise data arising from studies investigating the pattern of disease onset within a calendar year. Such data have been traditionally summarised into monthly counts summated over the complete years studied, and patterns have often been examined by use of Pearson chi-squared tests with 11 degrees of freedom. As an alternative, we suggest to first represent the date of onset for an individual as a point on a unit circle. In this way, the von Mises distribution with a single peak may provide a useful description of such data. Moreover, an extension to angular regression including covariates, analogous to that used routinely in other areas of clinical research, allows a potentially more systematic and detailed investigation of possible seasonal patterns in patient subgroups.


 

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Re-weighted inference about hepatitis C virus-infected communities when analyzing diagnosed patients referred to liver clinics
Bo Fu, Nanyang Technological University, Singapore

To project national hepatitis C virus (HCV) burden, unbiased estimation of HCV progression to liver cirrhosis is required for the whole community of HCV-infected individuals. However, widely varying estimates of progression rates to cirrhosis have been produced. This disparity is partly associated with statistical methods applied, but is mainly due to the different types of study cohort. We propose an inverse probability re-weighted method for estimation to recover the true parameters for the (Weibull regression) model that determines the incubation period from infection to cirrhosis for the community of HCV-infected individuals, when there is cirrhosis-related recruitment bias to the studied cohort. We apply our method to simulated data for a liver clinic which attracts patients from a community of 1,000 HCV-infected individuals under different event-biased referral patterns. We investigate how well the method performs in recovering the true community parameters, and then apply it to Edinburgh Royal Infirmary’s liver clinic series. The results obtained are compared to those from a Weibull survival analysis which ignores selection bias.
 

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