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L J Wei

Publications and source records attributed to L J Wei.

At least 19 recordsLinked to original sources
PubMed2005

One- and two-sample nonparametric inference procedures in the presence of a mixture of independent and dependent censoring.

In survival analysis, the event time T is often subject to dependent censorship. Without assuming a parametric model between the failure and censoring times, the parameter Theta of interest, for example, the survival function of T, is generally not identifiable. On the other hand, the collection Omega of all attainable values for Theta may be well defined. In this article, we present nonparametric inference procedures for Omega in the presence of a mixture of dependent and independent censoring variables. By varying the criteria of classifying censoring to the dependent or independent category, our proposals can be quite useful for the so-called sensitivity analysis of censored failure times. The case that the failure time is subject to possibly dependent interval censorship is also discussed in this article. The new proposals are illustrated with data from two clinical studies on HIV-related diseases.

Anti-HIV Agents↗
PubMed2005

Frailty approach for the analysis of clustered failure time observations in dental research.

Because dental implant failure patterns tend to cluster within subjects, we hypothesized that the risk of implant failure varies among subjects. To address this hypothesis in the setting of clustered, correlated observations, we considered a retrospective cohort study where we identified a cohort having at least one implant placed. The cohort was composed of 677 patients who had 2349 implants placed. To test the hypothesis, we applied an innovative analytic method, i.e., the Cox proportional hazards model with frailty, to account for correlation within subjects and the heterogeneity of risk, i.e., frailty, among subjects for implant failure. Consistent with our hypothesis, risk for implant failure among subjects varied to a statistically significantly degree (p=0.041). In addition, the risk for implant failure is significantly associated with several factors, including tobacco use, implant length, immediate implant placement, staging, well size, and proximity of adjacent implants or teeth.

Analysis of Variance↗
PubMed2003

Combining dependent tests for linkage or association across multiple phenotypic traits.

A robust statistical method to detect linkage or association between a genetic marker and a set of distinct phenotypic traits is to combine univariate trait-specific test statistics for a more powerful overall test. This procedure does not need complex modeling assumptions, can easily handle the problem with partially missing trait values, and is applicable to the case with a mixture of qualitative and quantitative traits. In this note, we propose a simple test procedure along this line, and show its advantages over the standard combination tests for linkage or association in the literature through a data set from Genetic Analysis Workshop 12 (GAW12) and an extensive simulation study.

Computer Simulation↗
PubMed2003

Estimating predictors for long- or short-term survivors.

Suppose that the response variable in a well-executed clinical or observational study to evaluate a treatment is the time to a certain event, and a set of baseline covariates or predictors was collected for each study patient. Furthermore, suppose that a significant number of study patients had nontrivial, long-term adverse effects from the treatment. A commonly posed question is how to use these covariates from the study to identify future patients who would (or would not) benefit from the treatment. In this article, we present "point" and "interval" estimates for the set of covariate or predictor vectors associated with a specific patient survival status, e.g., long- (or short-) term survival, in the presence of censoring. These estimates can be easily displayed on a two-dimensional plane, even for the case with high-dimensional covariate vectors. These simple numerical and graphical procedures provide useful information for patient management and/or the design of future studies, which are key issues in pharmacogenomics with genetic markers. The new proposal is illustrated with a data set from a cancer study for treating multiple myeloma.

Biometry↗
PubMed2003

A simple nonparametric test for linkage with sib-pair censored event time observations.

Consider the case that individual phenotype and genotype observations were collected from a large or moderate number of pedigrees. Some of the pedigrees have multi-generation nuclear families. For each nuclear family, the phenotype trait value of each sibling is the time to onset for a specific event (e.g., disease). Often, this event time may be right censored, that is, an individual is event-free at the study examination time point. In this article, we propose a purely nonparametric test for testing if the distribution of a Haseman-Elston distance measure between two siblings' event times is independent of their mean genetic sharing identical by descent at a genetic marker based on such incomplete observations from all the nuclear families. The new test can be implemented easily and is illustrated with a data set from the Genetic Analysis Workshop 12. The validity of the new test is examined via a simulation study.

Genetic Linkage↗
PubMed2002

Model-checking techniques based on cumulative residuals.

Residuals have long been used for graphical and numerical examinations of the adequacy of regression models. Conventional residual analysis based on the plots of raw residuals or their smoothed curves is highly subjective, whereas most numerical goodness-of-fit tests provide little information about the nature of model misspecification. In this paper, we develop objective and informative model-checking techniques by taking the cumulative sums of residuals over certain coordinates (e.g., covariates or fitted values) or by considering some related aggregates of residuals, such as moving sums and moving averages. For a variety of statistical models and data structures, including generalized linear models with independent or dependent observations, the distributions of these stochastic processes tinder the assumed model can be approximated by the distributions of certain zero-mean Gaussian processes whose realizations can be easily generated by computer simulation. Each observed process can then be compared, both graphically and numerically, with a number of realizations from the Gaussian process. Such comparisons enable one to assess objectively whether a trend seen in a residual plot reflects model misspecification or natural variation. The proposed techniques are particularly useful in checking the functional form of a covariate and the link function. Illustrations with several medical studies are provided.

Adolescent↗
PubMed2002

Simultaneous inferences on the contrast of two hazard functions with censored observations.

In survival analysis, often times the pattern of instantaneous risk over time is more interesting than that of the cumulative risk. For this case, a nonparametric hazard function estimate is more appropriate for summarizing the risk experience of a group of patients than the corresponding Kaplan-Meier estimate. In comparing a new treatment with a standard therapy, it is important to know if the treatment loses it potency during the follow-up period, and if it does, one would like to know when it becomes ineffective. Unfortunately, with a plot of the differences of two Kaplan-Meier curves, it is rather difficult to capture such temporal trends. In this article, we propose simple procedures for constructing confidence bands for the contrast of two hazard functions with censored data. The simultaneous interval estimates are quite useful for identifying possible values of the contrast over time with a certain degree of confidence. The new proposals are illustrated with an example and a small simulation study.

Adult↗
PubMed2002

Risk factors for dental implant failure: a strategy for the analysis of clustered failure-time observations.

This study's objective was to identify, in a statistically valid and efficient manner, the risk factors associated with dental implant failure. We hypothesize that factors exist which can be modified by clinicians to enhance outcome. A retrospective cohort study design was used. Cohort members had >or= one implant placed. Risk factors were classified as demographic, health status, implant-, anatomic-, or prosthetic-specific, and reconstructive variables. The outcome variable was implant failure. The cohort was composed of 677 patients who had 2349 implants placed. Based on the adjusted multivariate model, factors associated with implant failure were tobacco use, implant length, staging, well size, and immediate implants (p <or= 0.05). In the setting of correlated survival observations, we recommend adjusting for the correlation of the observations to provide statistically valid and efficient results. Three of the identified factors--tobacco use, immediate implants, and implant staging--potentially may be modified to enhance implant survival.

Age Factors↗
PubMed2002

Predicting dental implant survival by use of the marginal approach of the semi-parametric survival methods for clustered observations.

The analyses of clustered survival observations within the same subject are challenging. This study's purpose was to compare and contrast predicted dental implant survival estimates assuming the independence or dependence of clustered observations. Using a retrospective cohort composed of 677 patients (2,349 implants), we applied an innovative analytic marginal approach to produce point and variance estimates of survival predictions given the covariates smoking status, implant staging, and timing of placement adjusted for clustered observations (dependence method). We developed a second model assuming independence of the clustered observations (naïve method). The 95% confidence intervals for survival prediction point estimates given the naive method were 5.9% to 14.3% more narrow than the dependence method estimates, resulting in an increased risk for type I error and erroneous rejection of the null hypothesis. To obtain statistically valid confidence intervals for survival prediction of the Aalen-Breslow estimates, we recommend adjusting for dependence among clustered survival observations.

Cluster Analysis↗
PubMed2001

Analysis of human immunodeficiency virus type 1 drug resistance in children receiving nucleoside analogue reverse-transcriptase inhibitors plus nevirapine, nelfinavir, or ritonavir (Pediatric AIDS Clinical Trials Group 377).

In Pediatric AIDS Clinical Trials Group 377, antiretroviral therapy-experienced children were randomized to 4 treatment arms that included different combinations of stavudine, lamivudine (3TC), nevirapine (Nvp), nelfinavir (Nfv), and ritonavir (Rtv). Previous treatment with zidovudine (Zdv), didanosine (ddI), or zalcitabine (ddC) was acceptable. Drug resistance mutations were assessed before study treatment (baseline) and at virologic failure. Zdv, ddI, and ddC mutations were detected frequently at baseline but were not associated with virologic failure. Children with drug resistance mutations at baseline had greater reductions in virus load over time than did children who did not. Nvp and 3TC mutations were detected frequently at virologic failure, and Nvp mutations were more common among children receiving 3-drug versus 4-drug Nvp-containing regimens. Children who were maintained on their study regimen after virologic failure accumulated additional Nvp and 3TC mutations plus Rtv and Nfv mutations. However, Rtv and Nfv mutations were detected at unexpectedly low rates.

Adolescent↗
PubMed2001

Combining multiple phenotypic traits optimally for detecting linkage with sib-pair observations.

A number of investigators have proposed regression methods for testing linkage between a phenotypic trait and a genetic marker with sib-pair observations. Xu et al. [Am J Hum Genet 67:1025-8, 2000] studied a unified method for testing linkage, which tends to be more powerful than existing procedures. Often there are multiple traits, which are linked to a common set of genetic markers. In this paper, we present a simple generalization of the unified test to combine information from multiple traits optimally. We use the simulated Genetic Analysis Workshop 12 data to illustrate this methodology and show the advantage of using the combined tests over the single-trait tests. For the four quantitative traits (Q1,...,Q4) studied, our linkage results suggest that major loci affecting Q1 and Q2 localize at or near markers D02G172, D19G032, and D09G122, while loci affecting Q3 and Q4 localize at or near markers D09G122 and D17G051.

Adult↗
PubMed2001

Global expression changes of constitutive and hormonally regulated genes during endometrial neoplastic transformation.

OBJECTIVE: Endometrioid endometrial carcinoma is caused by a combination of mutational events and hormonal factors. We used large-scale messenger RNA expression analysis to discover genes that distinguish neoplastic transformation and examine the patterns of tumor expression of those genes which are normally regulated during the menstrual cycle. METHODS: Expression of approximately 6000 unique genes was quantified in 4 normal (2 proliferative, 2 secretory) and 10 malignant endometria using Affymetrix Hu6800 GeneChip probe arrays. Expression differences between normal and malignant tissue groups were measured by a test of statistical significance comparing the individual t statistic for each gene to the distribution of maximum t statistics among all genes following 1001 permutations of the tissue group assignments (Permax test). Hormonally responsive genes, selected by comparison of proliferative and secretory subsets of normal endometria using a combination of filters applied to the group means and t test rankings, were then examined in the tumors. RESULTS: Fifty genes with a Permax <0.50 provided excellent discrimination between normal and malignant groups and were predominantly characterized by diminished expression levels in the cancers. We found that 100 genes which are hormonally regulated in normal tissues are expressed in a disordered and heterogeneous fashion in cancers, with tumors resembling proliferative more than secretory endometrium. CONCLUSION: Neoplastic transformation is accompanied by predominant loss of activity of many genes constitutively expressed in normal source tissues and absence of expression profiles which characterize the antitumorigenic progestin response.

Adult↗
PubMed2001

Kaplan-Meier analysis of dental implant survival: a strategy for estimating survival with clustered observations.

The study's purposes were to estimate dental implant survival in a statistically valid manner and to compare three models for estimating survival. We estimated survival using three different statistical models: (1) randomly selecting one implant per patient; (2) utilizing all implants, assuming independence among implants from the same subject; and (3) utilizing all implants, assuming dependence among implants from the same subject. The cohort was composed of 660 patients who had 2286 implants placed. Due to the high success rates of implants, the five-year survival point and standard error estimates varied little among the three models. Patients at high risk for implant failure (smokers) manifested greater variation in the standard error estimates among the three models, 8.2%, 4.0%, and 5.6%, respectively. To obtain statistically valid survival confidence intervals when performing Kaplan-Meier survival analyses, we recommend adjusting for dependence when there are multiple observations within the same subject.

Dental Implantation, Endosseous↗
PubMed2000

A unified Haseman-Elston method for testing linkage with quantitative traits.

The Haseman and Elston (H-E) method uses a simple linear regression to model the squared trait difference of sib pairs with the shared allele identical by descent (IBD) at marker locus for linkage testing. Under this setting, the squared mean-corrected trait sum is also linearly related to the IBD sharing. However, the resulting slope estimate for either model is not efficient. In this report, we propose a simple linkage test that optimally uses information from the estimates of both models. We also demonstrate that the new test is more powerful than both the traditional one and the recently revisited H-E methods.

Alleles↗
PubMed1999

Issues in genomic screening: critical values, sample sizes, and the ability to detect linkage.

The aims of this study were to empirically investigate the ability of affected sib pairs (ASPs) to localize a gene through screening and to explore estimation of lod score critical values through resampling. To do so, we repartitioned 25 replicates of 100 simulated nuclear families into six data sets of sizes 100, 200, 300, 400, 500, and 1,000 and chose at most one mildly ASP per family. Using all marker data, we calculated maximum lod scores across the six-chromosome genome for each set. Then, we determined the cutoff value corresponding to a 5% genome-wide false positive rate using both the method of Lander and Kruglyak [1995] and a simple resampling algorithm that allows greater scan-specific flexibility. For chromosome 1, the ability of the ASPs to detect the region between markers 9 and 10 clearly increases with the sample size, and genome-wide significance is achieved for samples of size 400 or greater. Also, as expected, the critical values based on the less conservative resampling approach are generally slightly smaller than those from theoretical calculations based on the Ornstein-Uhlenbeck diffusion process of Lander and Kruglyak.

Genetic Linkage↗
PubMed1998

Prediction of cumulative incidence function under the proportional hazards model.

In the presence of dependent competing risks in survival analysis, the Cox model can be utilized to examine the covariate effects on the cause-specific hazard function for the failure type of interest. For this situation, the cumulative incidence function provides an intuitively appealing summary curve for marginal probabilities of this particular event. In this paper, we show how to construct confidence intervals and bands for such a function under the Cox model for future patients with certain covariates. Our proposals are illustrated with data from a prostate cancer trial.

Aged↗
PubMed1997

An overview of statistical methods for multiple failure time data in clinical trials.

In a long term clinical trial to evaluate a new treatment, quite often each study subject may experience a number of 'failures' that correspond to repeated occurrences of the same type of event or events of entirely different natures during his/her follow-up period. To obtain efficient inference procedures for the therapeutic effect over time, it is desirable to utilize those multiple event times in the analysis. In this article, we review some useful procedures for analysing different kinds of multivariate failure time data. Specifically, we discuss the two-sample problems and the general regression problems with various survival models. We also give some recommendations of appropriate procedures for each type of multiple event data structure for practical usage.

Humans↗