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PubMed1999

Analysis of five Y-specific microsatellite loci in Asian and Pacific populations.

We have analyzed five Y-specific microsatellite loci (DYS388, DYS390, DYS391, DYS394, DYS395) in 17 Asian and Pacific populations representing a broad geographical area and different linguistic families, with an emphasis on populations from mainland and insular Southeast Asia. Analysis of gene diversity indicates that several of the studied populations have experienced substantial genetic isolation, and a reduction in male effective sizes (viz. the Northeast Indian populations Nishi, Adi and the Taiwanese aboriginals). The average values of the F(ST) and ((ST) statistics indicate a high degree of genetic differentiation among these populations at the five Y-specific markers (F(ST) =0.21 and ((ST) = 0.33, based on individual loci; F(ST) = 0.09 and ((ST) = 0.36, based on haplotypes), which conform to the expectation of a fourfold smaller effective size of the Y-linked loci compared with the autosomal loci. Dendrogram and principal coordinates analysis, with few exceptions, show a major separation between mainland and insular populations. Among the mainland populations, the Tibeto-Burman speakers from Northeast India cluster in a well-defined group, supported by high bootstrap values. The Southern Chinese, Northern Thai, So, and Cambodian also are integral to this cluster. The other major cluster is rather heterogeneous and includes, among others, the Austronesian-speaking populations. The Samoans of the Pacific, with a distinctive pattern of allelic distributions, stand as an outlier in the tree and PC representations. Although trends of genetic affinities among ethnically and geographically related populations are evident from the Y-specific microsatellite data, microsatellites are not optimal for deciphering complex migratory patterns of human populations, which could possibly be clarified by using additional and more stable genetic markers.

Asia, Southeastern↗

PubMed1999

Phylogenetic analysis of hepatitis E virus isolates from Egypt.

Hepatitis E virus (HEV) genome was detected by reverse transcriptase-polymerase chain reaction (RT-PCR) in fecal samples of two sporadic cases of hepatitis E in Cairo Egypt. Sequence of the complete putative structural region [open reading frame (ORF)-2] and complete region of unknown function (ORF-3) was determined for the two HEV isolates. Phylogenetic analysis of the nucleotide sequences was performed using neighbor joining or maximum parsimony methods of tree reconstruction. Direct correspondence between the HEV evolutionary trees and geographic origin of the HEV isolates was observed. Three genotypes of HEV were identified: genotype I (Asia-Africa), genotype II (US), and genotype III (Mexico). Genotype I was further divided into two subgenotypes (Asia and Africa). In the Asian subgenotype, three smaller genetic clusters were observed (China-like sequences, Burma-like sequences, and sequence from a fulminant case of HEV). The segregation of all these genetic clusters was supported by the high level of bootstrap probabilities. Four regions of the HEV genome were used for phylogenetic analysis. In all four regions, Egyptian HEV isolates were grouped in a separate African clade.

Adult↗

PubMed1999

Diversity of GB virus C/hepatitis G virus isolates in Singapore: predominance of group 2a and the Asian group 3 variant.

The occurrence of GBV-C/HGV infection was studied in 160 individuals from two high-risk groups in Singapore. RT-PCR of the 5'-UTR detected GBV-C/HGV RNA in 3/73 (4.1%) of patients undergoing maintenance hemodialysis for chronic renal failure, and in 17/87 (19.5%) of patients coinfected with HCV who tested positive for HCV RNA. Phylogenetic analysis of 5'-UTR sequences from these 20 samples showed that the Asian or group 3 variant was found in 45% of the samples sequenced, thus confirming the high frequency of this variant in the region. Group 2a variants accounted for 50% of the samples with a complete absence of group 2b. Our analysis also provided strong bootstrap support for the subdivision of group 2 into subgroups 2a and 2b. This study shows that isolates belonging to all three main groups of GBV-C/ HGV can be detected in Singapore, with the large majority belonging to groups 2a (50%) and 3 (45%). Only a single group 1-like sequence was detected within the 20 isolates. Of interest also is that all group 3 isolates were identified in Chinese patients while group 2a was found in both Chinese and Malay.

5' Untranslated Regions↗

PubMed1999

A method for identifying abrupt changes in U.S. cancer mortality trends.

BACKGROUND: Summaries of trends in cancer rates evaluated over a fixed, short period are informative, but methods that evaluate trends over a longer time period, identifying when changes occur as well as the magnitude of the changes, can provide additional information. METHODS: Cancer mortality trends from 1973-1995 were determined for more than 15 anatomic sites for white and black males and females using weighted piecewise linear regression analysis with a stepwise selection procedure. The dependent variable was the natural logarithm of the annual mortality rate, and the weight was the annual number of cancer deaths. The variability of estimated change points was examined by bootstrapping the residuals from the resulting models. RESULTS: For black males, cancer mortality rates declined in the 1990s due to decreases in lung, esophageal, oral cavity, and prostate cancer rates. However, there was no significant decline for cancer of the colon and rectum. For white males, cancer mortality rates declined in the 1990s due to declines in cancer of the lung and colon/rectum since the mid-1980s and declines in prostate cancer in the 1990s. For black females and white females, total cancer mortality rates declined, but not significantly. Cancer rates for all sites except the lung declined significantly in the 1990s for white, but not black, females due to declining trends for carcinoma of the colon and rectum since the mid-1980s and for breast cancer in the 1990s. CONCLUSIONS: A method for identifying major changes in cancer trends has been developed. Trends for cancer of the breast and colon/rectum indicate that gaps between rates for blacks and whites are widening.

Black People↗

PubMed1996

Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors.

Multivariable regression models are powerful tools that are used frequently in studies of clinical outcomes. These models can use a mixture of categorical and continuous variables and can handle partially observed (censored) responses. However, uncritical application of modelling techniques can result in models that poorly fit the dataset at hand, or, even more likely, inaccurately predict outcomes on new subjects. One must know how to measure qualities of a model's fit in order to avoid poorly fitted or overfitted models. Measurement of predictive accuracy can be difficult for survival time data in the presence of censoring. We discuss an easily interpretable index of predictive discrimination as well as methods for assessing calibration of predicted survival probabilities. Both types of predictive accuracy should be unbiasedly validated using bootstrapping or cross-validation, before using predictions in a new data series. We discuss some of the hazards of poorly fitted and overfitted regression models and present one modelling strategy that avoids many of the problems discussed. The methods described are applicable to all regression models, but are particularly needed for binary, ordinal, and time-to-event outcomes. Methods are illustrated with a survival analysis in prostate cancer using Cox regression.

Clinical Trials as Topic↗

PubMed1996

The incidence of cystic fibrosis.

We develop a statistical model for estimating cystic fibrosis (CF) incidence among infants born in the U.S.A. that accounts for under-diagnosis due to death prior to diagnosis and we apply it to the Cystic Fibrosis Foundation Patient Registry data for the years 1989 to 1991. The resulting estimate of incidence relative to live births among whites is 1:3419 while that among non-whites is 1:12,163. As a by-product of the modelling approach, estimates of the underlying average diagnosis age given survival to diagnosis are 4.09 years for whites and 4.55 years for non-whites, but this difference was not statistically significant and appears to demonstrate that diagnosis efforts may be approximately the same for whites and non-whites. Also, as another by-product of the modelling approach, CF mortality was estimated as more severe for females than males and marginally more severe for non-whites than whites. A variety of statistical methods underlie achievement of these results, including semi-parametric maximum likelihood, survival analysis, multiple imputation and bootstrap techniques.

Actuarial Analysis↗

PubMed1996

Mixture models for eye-tracking data: a case study.

Heterogeneity in biomedical data is often a source of great scientific interest and mixture models provide a general framework for modelling the various types that arise in practice. Finite mixture models model discrete subgroups within populations while continuous mixture models inflate the variance to account for over-dispersed data. A potential problem with the application of finite mixture models in practice is that these models may drastically overestimate the number of component densities when there is a lack of model fit. This can have severe consequences, leading the data analyst to attach substantive interpretations to spurious subgroups. For this reason, we propose using the continuous mixture model as an alternative when fitting finite mixture models with an arbitrary number of components. In the context of an example examining a specific oculomotor component of eye-tracking dysfunction in schizophrenia, we demonstrate why the continuous mixture model provides a viable alternative to the finite mixture model for small sample sizes. We present methods for fitting and comparing both models using the parametric bootstrap and EM algorithm, and show that the distinction between the models decreases as the number of component densities in the finite mixture model increases.

Algorithms↗

PubMed1996

Estimating confidence intervals for cost-effectiveness ratios: an example from a randomized trial.

Cost-effectiveness ratios usually appear as point estimates without confidence intervals, since the numerator and denominator are both stochastic and one cannot estimate the variance of the estimator exactly. The recent literature, however, stresses the importance of presenting confidence intervals for cost-effectiveness ratios in the analysis of health care programmes. This paper compares the use of several methods to obtain confidence intervals for the cost-effectiveness of a randomized intervention to increase the use of Medicaid's Early and Periodic Screening, Diagnosis and Treatment (EPSDT) programme. Comparisons of the intervals show that methods that account for skewness in the distribution of the ratio estimator may be substantially preferable in practice to methods that assume the cost-effectiveness ratio estimator is normally distributed. We show that non-parametric bootstrap methods that are mathematically less complex but computationally more rigorous result in confidence intervals that are similar to the intervals from a parametric method that adjusts for skewness in the distribution of the ratio. The analyses also show that the modest sample sizes needed to detect statistically significant effects in a randomized trial may result in confidence intervals for estimates of cost-effectiveness that are much wider than the boundaries obtained from deterministic sensitivity analyses.

Analysis of Variance↗

PubMed1996

Predictive diagnostics for logistic models.

Novel methodology is implemented to assess the predictive power of covariate information associated with sequential binary events. Logistic models are first fitted on the basis of a subset of the observations and then evaluated sequentially on the rest. The probabilistic forecasts are compared to the outcomes via a scoring function, but as most validation samples are small, the usual reference distribution for the test statistics is inadequate. However, bootstrap-based distributions can easily be constructed. The first example pertains to the evaluation of screening tests for major depression. It illustrates that goodness-of-fit and predictive assessments lead to the selection of very different models. The second example deals with the prediction of a major event in the natural history of HIV-induced disease. It shows that this type of analysis can reveal features missed by other approaches.

Calibration↗

PubMedDate not supplied

Using multiple decrement models to estimate risk and morbidity from specific AIDS illnesses. Multicenter AIDS Cohort Study (MACS).

A simple non-parametric approach is developed to simultaneously estimate net incidence and morbidity time from specific AIDS illnesses in populations at high risk for death from these illnesses and other causes. The disease-death process has four-stages that can be recast as two sandwiching three-state multiple decrement processes. Non-parametric estimation of net incidence and morbidity time with error bounds are achieved from these sandwiching models through modification of methods from Aalen and Greenwood, and bootstrapping. An application to immunosuppressed HIV-1 infected homosexual men reveals that cytomegalovirus disease, Kaposi's sarcoma and Pneumocystis pneumonia are likely to occur and cause significant morbidity time.

AIDS-Related Opportunistic Infections↗

PubMedDate not supplied

An approach to estimating exposure-specific rates of breast cancer from a two-stage case-control study within a cohort.

The Breast Cancer Detection and Demonstration Project (BCDDP) included a large cohort of women followed for incidence of breast cancer and from whom an initial case-control sample was drawn and standard risk factors obtained. In order to study the effect of mammographic features on breast cancer risk, a nested subsample of cases and controls was drawn. Therefore, these data can be viewed as two-stage case-control data within a cohort, or as cohort data with two nested levels of missingness, since basic characteristics like age were measured on all members of the cohort, standard risk factors were elicited only in the initial case-control sample, and mammographic features were assessed only in the nested subsample of cases and controls. We present a Poisson pseudo-likelihood approach to estimating age- and exposure-specific breast cancer incidence rates based on the three types of variables. This approach takes into account the nested missingness as well as two other type of missingness namely, that for basic variables and standard risk factors, some levels (i) were omitted by design in the nested subsample of case and controls or (ii) were empty because of the sparsity of the data in that subsample. Estimates of standard errors are obtained from a parametric bootstrap. The approach seems to be efficient when applied to the BCDDP data and is flexible for modelling breast cancer rates and taking the special missingness features of these data into account.

Adult↗

PubMed1997

Confidence intervals for directly standardized rates: a method based on the gamma distribution.

We offer an approximation to central confidence intervals for directly standardized rates, where we assume that the rates are distributed as a weighted sum of independent Poisson random variables. Like a recent method proposed by Dobson, Kuulasmaa, Eberle and Scherer, our method gives exact intervals whenever the standard population is proportional to the study population. In cases where the two populations differ non-proportionally, we show through simulation that our method is conservative while other methods (the Dobson et al. method and the approximate bootstrap confidence method) can be liberal.

Chi-Square Distribution↗

PubMed1997

Estimating the mean hazard ratio parameters for clustered survival data with random clusters.

We consider a latent variable hazard model for clustered survival data where clusters are a random sample from an underlying population. We allow interactions between the random cluster effect and covariates. We use a maximum pseudo-likelihood estimator to estimate the mean hazard ratio parameters. We propose a bootstrap sampling scheme to obtain an estimate of the variance of the proposed estimator. Application of this method in large multi-centre clinical trials allows one to assess the mean treatment effect, where we consider participating centres as a random sample from an underlying population. We evaluate properties of the proposed estimators via extensive simulation studies. A real data example from the Studies of Left Ventricular Dysfunction (SOLVD) Prevention Trial illustrates the method.

Adult↗

PubMed1997

A Markov model for HIV disease progression including the effect of HIV diagnosis and treatment: application to AIDS prediction in England and Wales.

Back-calculation is a widely used method to estimate HIV incidence rates, and is commonly based on times of AIDS diagnosis. Following up earlier work, we extend this method to also incorporate knowledge of times of HIV diagnosis (first positive test). This is achieved through the use of a Markov model which describes the progress of an HIV infected person through various stages, and which allows causal connections between events to be explicitly modelled. Estimation is based on maximum likelihood, the likelihood being calculated within a discretized version of the Markov model. The effect of sampling uncertainty and model uncertainty (sensitivity) is evaluated simultaneously by means of a combined bootstrap and simulation procedure. At each replication we resample both the data and the model (from a set of possible models described by randomizing one or more parameters). For instance, uncertain knowledge about the incubation distribution affects the estimates of some parameters, but not others. The Markov approach is applied to the prediction of AIDS incidence for homosexuals in England and Wales up to the year 2000.

AIDS Serodiagnosis↗

PubMed1997

The latent class model for multiple binary screening tests.

Given multiple binary tests, such as repeated application of a blind screening test to each individual in a sample, we attempt to estimate the prevalence, sensitivity and specificity of the test without knowing the true disease status of those tested (gold standard). This problem is equivalent to finding the mixing distribution of a mixture of binomial distributions. We suggest a new method to determine the number of latent classes. Our simulations show that the coverage probabilities of the bootstrap confidence intervals of our estimates are correct. Our methods are illustrated by examples from published medical research.

Algorithms↗

PubMed1997

Resampling and cross-validation techniques: a tool to reduce bias caused by model building?

The process of model building involved in the analysis of many medical studies may lead to a considerable amount of over-optimism with respect to the predictive ability of the 'final' regression model. In this paper we illustrate this phenomenon in a simple cutpoint model and explore to what extent bias can be reduced by using cross-validation and bootstrap resampling. These computer intensive methods are compared to an ad hoc approach and to a heuristic method. Besides illustrating all proposals with the data from a breast cancer study we perform a simulation study in order to assess the quality of the methods.

Bias↗

PubMed1998

Development of a clinical prediction model for an ordinal outcome: the World Health Organization Multicentre Study of Clinical Signs and Etiological agents of Pneumonia, Sepsis and Meningitis in Young Infants. WHO/ARI Young Infant Multicentre Study Group.

This paper describes the methodologies used to develop a prediction model to assist health workers in developing countries in facing one of the most difficult health problems in all parts of the world: the presentation of an acutely ill young infant. Statistical approaches for developing the clinical prediction model faced at least two major difficulties. First, the number of predictor variables, especially clinical signs and symptoms, is very large, necessitating the use of data reduction techniques that are blinded to the outcome. Second, there is no uniquely accepted continuous outcome measure or final binary diagnostic criterion. For example, the diagnosis of neonatal sepsis is ill-defined. Clinical decision makers must identify infants likely to have positive cultures as well as to grade the severity of illness. In the WHO/ARI Young Infant Multicentre Study we have found an ordinal outcome scale made up of a mixture of laboratory and diagnostic markers to have several clinical advantages as well as to increase the power of tests for risk factors. Such a mixed ordinal scale does present statistical challenges because it may violate constant slope assumptions of ordinal regression models. In this paper we develop and validate an ordinal predictive model after choosing a data reduction technique. We show how ordinality of the outcome is checked against each predictor. We describe new but simple techniques for graphically examining residuals from ordinal logistic models to detect problems with variable transformations as well as to detect non-proportional odds and other lack of fit. We examine an alternative type of ordinal logistic model, the continuation ratio model, to determine if it provides a better fit. We find that it does not but that this model is easily modified to allow the regression coefficients to vary with cut-offs of the response variable. Complex terms in this extended model are penalized to allow only as much complexity as the data will support. We approximate the extended continuation ratio model with a model with fewer terms to allow us to draw a nomogram for obtaining various predictions. The model is validated for calibration and discrimination using the bootstrap. We apply much of the modelling strategy described in Harrell, Lee and Mark (Statist. Med. 15, 361-387 (1998)) for survival analysis, adapting it to ordinal logistic regression and further emphasizing penalized maximum likelihood estimation and data reduction.

Chi-Square Distribution↗

PubMed1999

Comparing two prevalence rates in a two-phase design study.

An epidemiological study often uses a two-phase design to estimate the prevalence rate of a mental disease. In a two-phase design study, the first phase assesses a large sample with an inexpensive screening test, and then the second phase selects a subsample for a more expensive diagnostic evaluation. Furthermore, disease status may not be ascertained for all subjects who are selected for disease verification because some subjects are unable to be clinically assessed, while others may refuse. Since not all screened subjects are selected for diagnostic assessments, there is potential for verification bias. In this paper, we propose the maximum likelihood (ML) and bootstrap methods to correct for verification bias for estimating and comparing the prevalence rates under the missing-at-random (MAR) assumption for the verification mechanism. We also propose a method to test this MAR assumption. Finally, we apply our methods to a large-scale prevalence study of dementia disorders.

Aged↗