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Biomedical subjects

Aiyi Liu

Publications and source records attributed to Aiyi Liu.

At least 19 recordsLinked to original sources
PubMed2006

Pooling biospecimens and limits of detection: effects on ROC curve analysis.

Frequently, epidemiological studies deal with two restrictions in the evaluation of biomarkers: cost and instrument sensitivity. Costs can hamper the evaluation of the effectiveness of new biomarkers. In addition, many assays are affected by a limit of detection (LOD), depending on the instrument sensitivity. Two common strategies used to cut costs include taking a random sample of the available samples and pooling biospecimens. We compare the two sampling strategies when an LOD effect exists. These strategies are compared by examining the efficiency of receiver operating characteristic (ROC) curve analysis, specifically the estimation of the area under the ROC curve (AUC) for normally distributed markers. We propose and examine a method to estimate AUC when dealing with data from pooled and unpooled samples where an LOD is in effect. In conclusion, pooling is the most efficient cost-cutting strategy when the LOD affects less than 50% of the data. However, when much more than 50% of the data are affected, utilization of the pooling design is not recommended.

Biomarkers↗
PubMed2006

The limitations due to exposure detection limits for regression models.

Biomarker use in exposure assessment is increasingly common, and consideration of related issues is of growing importance. Exposure quantification may be compromised when measurement is subject to a lower threshold. Statistical modeling of such data requires a decision regarding the handling of such readings. Various authors have considered this problem. In the context of linear regression analysis, Richardson and Ciampi (Am J Epidemiol 2003;157:355-63) proposed replacement of data below a threshold by a constant equal to the expectation for such data to yield unbiased estimates. Use of such an imputation has some limitations; distributional assumptions are required, and bias reduction in estimation of regression parameters is asymptotic, thereby presenting concerns about small studies. In this paper, the authors propose distribution-free methods for managing values below detection limits and evaluate the biases that may result when exposure measurement is constrained by a lower threshold. The authors utilize an analytical approach and a simulation study to assess the effects of the proposed replacement method on estimates. These results may inform decisions regarding analytical plans for future studies and provide a possible explanation for some amount of the discordance seen in extant literature.

Bias↗
PubMed2006

Efficient design and analysis of biospecimens with measurements subject to detection limit.

Pooling biospecimens is a well accepted sampling strategy in biomedical research to reduce study cost of measuring biomarkers, and has been shown in the case of normally distributed data to yield more efficient estimation. In this paper we examine the efficiency of pooling, in the context of information matrix related to estimators of unknown parameters, when the biospecimens being pooled yield incomplete observations due to the instruments' limit of detection. Our investigation of three sampling strategies shows that, for a range of values of the detection limit, pooling is the most efficient sampling procedure. For certain other values of the detection limit, pooling can perform poorly.

Analytic Sample Preparation Methods↗
PubMed2006

Multistage evaluation of measurement error in a reliability study.

We introduce sequential testing procedures for the planning and analysis of reliability studies to assess an exposure's measurement error. The designs allow repeated evaluation of reliability of the measurements and stop testing if early evidence shows the measurement error is within the level of tolerance. Methods are developed and critical values tabulated for a number of two-stage designs. The methods are exemplified using an example evaluating the reliability of biomarkers associated with oxidative stress.

Biomarkers↗
PubMed2006

Expression patterns among interferon regulatory factor-1, human X-box binding protein-1, nuclear factor kappa B, nucleophosmin, estrogen receptor-alpha and progesterone receptor proteins in breast cancer tissue microarrays.

Interferon regulatory factor-1 (IRF-1), human X-box binding protein-1 (hXBP-1), nuclear factor kappa B p65 (NFkappaB p65) and nucleophosmin (NPM) have been implicated in a signaling network of endocrine responsiveness. Expression of these proteins was measured by immunohistochemistry in tissue microarrays of 54 breast tumors. Correlations between each protein and established prognostic markers were assessed by Spearman's rank order correlation coefficient and partial correlation coefficient analyses. Moderate/strong staining is seen for hXBP-1 (79% of tumors) and NFkappaB p65 (57%). NPM exhibits nuclear staining (95%); IRF-1 exhibits both cytosolic (IRF-1c; 90%) and nuclear staining (IRF-1n; 51%). IRF-1c is associated with age (p=0.034); IRF-1n and PgR expression are correlated (p=0.014). NFkappaB p65 shows a borderline association with S phase (p=0.062). Coexpression of IRF-1c and hXBP1 (p=0.001), IRF-1c and NFkappaB (p=0.002), and hXBP-1 and NFkappaB (p=0.018) is observed. An inverse correlation exists between IRF-1n and NFkappaB (p=0.034). All four proteins are detected in breast tumors and their expression patterns support their role(s) in a key signaling network.

Adult↗
PubMed2005

Sequential testing for efficacy in clinical trials with non-transient effects.

This paper describes a new type of sequential testing for clinical trials. The sequential nature of the data is not from additional patients, but rather from longer follow-up times. At each analysis, the null hypothesis that all treatments are equivalent in effect on the outcome after that amount of time is tested. The trial might still have staggered entry or not, but the key feature is that a different statistical hypothesis is tested at each analysis. It is assumed that any effect of treatment is non-transient, allowing a conclusion to be drawn in favour of one treatment or the other based on a difference at a single follow-up time. It is shown that a general method based on the Bonferroni inequality can be used to obtain critical cutpoints for sequential testing, that controls the chance of a type I error for the clinical decision. This method is applicable regardless of the test used at each analysis. In the case of a two-armed trial with a Gaussian outcome variable, it is shown how simulation can be used to obtain critical cutpoints that maintain the chance of a type I error for the clinical decision. The methods are compared by Monte-Carlo simulation, and it is seen that in most practical cases the Bonferroni method is not very conservative. The Bonferroni procedure is illustrated on the results of a real clinical trial of Pirfenidone on pulmonary fibrosis in Hermansky-Pudlak Syndrome.

Anti-Inflammatory Agents, Non-Steroidal↗
PubMed2005

Estrogen receptor alpha-negative breast cancer tissues express significant levels of estrogen-independent transcription factors, ERbeta1 and ERbeta5: potential molecular targets for chemoprevention.

We have investigated the expression of two estrogen receptor beta (ERbeta) isoforms, ERbeta1 and ERbeta5, which activate gene transcription independent of estrogen or growth factors, in ERalpha-negative breast cancer tissues. We report here, for the first time, that ERalpha-negative tissues express significant levels of ERbeta1 and ERbeta5, and their expression levels are not different from levels in ERalpha positive tumors. However, significant differences exist between the two racial groups, African American and Caucasian, in that the patients from the former group express higher levels of ERbeta1 and ERbeta5 but not ERalpha. These two transcription factors could be potential molecular targets for designing chemopreventive drugs to treat ERalpha-negative breast cancers.

Black or African American↗
PubMed2005

Supplementary analysis of probabilities at the termination of a group sequential phase II trial.

We consider estimation of various probabilities after termination of a group sequential phase II trial. A motivating example is that the stopping rule of a phase II oncologic trial is determined solely based on response to a drug treatment, and at the end of the trial estimating the rate of toxicity and response is desirable. The conventional maximum likelihood estimator (sample proportion) of a probability is shown to be biased, and two alternative estimators are proposed to correct for bias, a bias-reduced estimator obtained by using Whitehead's bias-adjusted approach, and an unbiased estimator from the Rao-Blackwell method of conditioning. All three estimation procedures are shown to have certain invariance property in bias. Moreover, estimators of a probability and their bias and precision can be evaluated through the observed response rate and the stage at which the trial stops, thus avoiding extensive computation.

Bias↗
PubMed2005

On linear combinations of biomarkers to improve diagnostic accuracy.

We consider combining multiple biomarkers to improve diagnostic accuracy. Su and Liu derived the linear combinations that maximize the area under the receiver operating characteristic (ROC) curves. These linear combinations, however, may have unsatisfactory low sensitivity over a certain range of desired specificity. In this paper, we consider maximizing sensitivity over a range of specificity. We first present a simpler proof for Su and Liu's main theorem and further investigate some other optimal properties of their linear combinations. We then derive alternative linear combinations that have higher sensitivity over a range of high (or low) specificity. The methods are illustrated using data from a study evaluating biomarkers for coronary heart disease.

Adult↗
PubMed2005

A simple and efficient bias-reduced estimator of response probability following a group sequential phase II trial.

A phase II clinical trial evaluating the rate of response of a new therapeutic treatment is often designed to have one or two interim analyses, allowing possible termination of the trial at an early stage if lack of treatment efficacy is evident. Due to the sequential nature of such a trial, the sample proportion yields biased estimation for the rate of response. In this paper we propose an estimator of response rate by subtracting the estimated bias directly from the sample proportion. The proposed estimator is simple, intuitive, and easy to compute. When the bias of the sample proportion is of concern to the investigators, the proposed estimator is an efficient alternative to other estimators in the literature in terms of bias and mean squared error.

Bias↗
PubMed2005

Optimal cut-point and its corresponding Youden Index to discriminate individuals using pooled blood samples.

Costs can hamper the evaluation of the effectiveness of new biomarkers. Analysis of smaller numbers of pooled specimens has been shown to be a useful cost-cutting technique. The Youden index (J), a function of sensitivity (q) and specificity (p), is a commonly used measure of overall diagnostic effectiveness. More importantly, J is the maximum vertical distance or difference between the ROC curve and the diagonal or chance line; it occurs at the cut-point that optimizes the biomarker's differentiating ability when equal weight is given to sensitivity and specificity. Using the additive property of the gamma and normal distributions, we present a method to estimate the Youden index and the optimal cut-point, and extend its applications to pooled samples. We study the effect of pooling when only a fixed number of individuals are available for testing, and pooling is carried out to save on the number of assays. We measure loss of information by the change in root mean squared error of the estimates of the optimal cut-point and the Youden index, and we study the extent of this loss via a simulation study. In conclusion, pooling can result in a substantial cost reduction while preserving the effectiveness of estimators, especially when the pool size is not very large.

Algorithms↗
PubMed2003

Group sequential design for comparative diagnostic accuracy studies.

In the field of diagnostic medicine, comparative clinical trials are necessary for assessing the utility of one diagnostic test over another. The area under the receiver operating characteristic (ROC) curve, commonly referred to as AUC, is a general measure of a test's inherent ability to distinguish between patients with and without a condition. Standardized AUC difference is the most frequently used statistic for comparing two diagnostic tests. In therapeutic comparative clinical trials with sequential patient entry, fixed sample design (FSD) is unjustified on ethical and economical grounds and group sequential design (GSD) is frequently used. In this paper, we argue that the same reasoning exists for the comparative clinical trials in diagnostic medicine and hence GSD should be utilized in this field for designing trials. Since computation of the stopping boundaries of GSD and data analysis after a group sequential test rely heavily on Brownian motion approximation, we derive the asymptotic distribution of the standardized AUC difference statistic and point out its resemblance to the Brownian motion. Boundary determination and sample size calculation are then illustrated through an example from a cancer clinical trial.

Area Under Curve↗
PubMed2002

Block principal component analysis with application to gene microarray data classification.

We propose a block principal component analysis method for extracting information from a database with a large number of variables and a relatively small number of subjects, such as a microarray gene expression database. This new procedure has the advantage of computational simplicity, and theory and numerical results demonstrate it to be as efficient as the ordinary principal component analysis when used for dimension reduction, variable selection and data visualization and classification. The method is illustrated with the well-known National Cancer Institute database of 60 human cancer cell lines data (NCI60) of gene microarray expressions, in the context of classification of cancer cell lines.

DNA, Neoplasm↗
PubMed2002

Mediating mechanisms for the impact of bupropion in smoking cessation treatment.

Several studies have documented the effectiveness of bupropion for smoking cessation, yet little is known about the mechanisms by which it facilitates abstinence. In this placebo-controlled randomized trial. We examined whether bupropion's effects on cessation were mediated by changes in withdrawal and/or negative or positive affect (PA). Two hundred and fifty-one smokers received 10-week treatment with bupropion or placebo, plus behavioral counseling. Changes in affect and withdrawal symptoms from pre-quit to 1 week post quit were examined as mediating variables in structural equation models. Cotinine-verified 7-day point prevalence cessation rates at the end of treatment (8-weeks post quit date) were 48% for bupropion and 29% for placebo (P=0.001). There were significant treatment effects on withdrawal and negative affect (NA); however, only change in NA predicted cessation. In a path model, change in NA was a significant mediator of bupropion's effects on cessation. However, the proportion of variance accounted for by this mediator was small, suggesting that other unmeasured factors play an important role. Laboratory-based paradigms may be useful to identify other mediators of bupropion's effects, thereby pointing to mechanisms of effect that can be bolstered in future treatment studies.

Adult↗
PubMed2002

Sample size and power determination for clustered repeated measurements.

It is common in epidemiological and clinical studies that each subject has repeated measurements on a single common variable, while the subjects are also 'clustered'. To compute sample size or power of a test, we have to consider two types of correlation: correlation among repeated measurements within the same subject, and correlation among subjects in the same cluster. We develop, based on generalized estimating equations, procedures for computing sample size and power with clustered repeated measurements. Explicit formulae are derived for comparing two means, two slopes and two proportions, under several simple correlation structures.

Alveolar Bone Loss↗
PubMed2002

Association of interferon regulatory factor-1, nucleophosmin, nuclear factor-kappaB, and cyclic AMP response element binding with acquired resistance to Faslodex (ICI 182,780).

To identify genes associated with survival from antiestrogens, both serial analysis of geneexpression and gene expression microarrays were used to explore the transcriptomes of antiestrogen-responsive (MCF7/LCC1) and -resistant variants(MCF7/LCC9) of the MCF-7 human breast cancer cell line. Structure of the gene microarray expression data was visualized at the top level using a novel algorithm that derives the first three principal components,fitted to the antiestrogen-resistant and -responsive gene expression data, from Fisher's information matrix. The differential regulation of several candidate genes was confirmed. Functional studies of the basal expression and endocrine regulation of transcriptional activation of implicated transcription factors were studied using promoter-reporter assays. The putative tumor suppressor interferon regulatory factor-1 is down-regulated in resistant cells, whereas its nucleolar phosphoprotein inhibitor nucleophosmin is up-regulated. Resistant cells also up-regulate the transcriptional activation of cyclic AMP response element (CRE) binding and nuclear factor kappaB (NFkappaB) while down-regulating epidermal growth factor receptor protein expression. Inhibition of NFkappaB activity by ICI 182,780 is lost in resistant cells, but CRE activity is not regulated by ICI 182,780 in either responsive or resistant cells. Parthenolide, a potent and specific inhibitor of NFkappaB, inhibits the anchorage-dependent proliferation of antiestrogen-resistant but not antiestrogen-responsive cells. This observation implies a greater reliance on their increased NFkappaB signaling for proliferation in cells that have survived prolonged exposure to ICI 182,780. These data from serial analysis of gene expression and gene microarray studies implicate changes in a novel signaling pathway, involving interferon regulatory factor-1, nucleophosmin, NFkappaB, and CRE binding in cell survival after antiestrogen exposure. Cells can up-regulate some estrogen-responsive genes while concurrently losing the ability of antiestrogens to regulate their expression. Signaling pathways that are not regulated by estrogens also can be up-regulated. Thus, some breast cancer cells may survive antiestrogen treatment by bypassing specific growth inhibitory signals induced by antagonist-occupied estrogen receptors.

Animals↗
PubMed2002

Estrogen receptor beta splice variant mRNAs are differentially altered during breast carcinogenesis.

We previously identified 10 exon deletion ERbeta variant mRNAs in various human tissues [FEBS Lett. 516 (2002) 133]. In the current study, we have investigated the expression of these variant mRNAs in normal breast tissues and their alterations in cancer tissues. A total of 43 cancer tissues in comparison with their matched normal tissues were analyzed by RT PCR using the newly developed 'Splice Targeted Primer Approach'. The data presented here show that normal breast tissues express 9 of the 10 identified variant mRNAs. Of the nine variants, the mRNAs with exons 5-6 deletions were significantly decreased ( approximately 80%) in a large majority of cancer tissues (two-sided paired t-test, n=43 patients, P<0.00001). The expression of ERbeta exon 5Delta, that could potentially have transactivating property in the absence of hormone, was changed differently among different grade tumors (analysis of variance F-test, n=43 patients, P=0.0452; Kruskal-Wallis test, n=43 patients, P=0.0356). When change in expression of ERbeta exon 5Delta mRNA levels was used as a categorical variable, a significant association was found between the change status (increase, no change, decrease) of this variant and grade of the tumor (Fischer's exact test, n=43 patients, P=0.0129). In particular, it was significantly increased in grade III tumors and decreased in grade II tumors. This variant was also changed differently in pre- and post-menopausal women. Its expression levels were increased in the tumors of post-menopausal women (mean change=3.6685), while they were decreased in pre-menopausal women (mean change=-24.3662). Thus a significant association was observed between the expression of this variant and menopausal status (a two-sided paired t-test, n=43 patients, P=0.03). Other variants were either expressed at very low frequency or not significantly altered.

Adult↗