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At least 307 records · Page 17Linked to original sources
PubMed1998

Flexible assessment of trends in age-specific HIV incidence using two-dimensional penalized likelihood.

We present a method for estimating age- and time-specific HIV incidence using back-calculations of AIDS incidence data. Two-dimensional penalized likelihood is employed, using a flexible bivariate step function model of HIV incidence, together with a quadratic roughness penalty which leads to thin-plate spline smoothing. This allows incidence estimates to vary flexibly and smoothly in both age and time. We propose generalized cross-validation as a guide for choice of an appropriate level of smoothing and describe an EM algorithm for computing the estimates. We propose the method primarily for qualitative assessment of trends in age-specific incidence over time and apply it to a small Italian data set on men who have sex with men. The analysis suggests a trend over time of increasing relative incidence among younger individuals, consistent with incidence patterns observed in other countries.

Adult↗
PubMed1998

Modelling the cause of dependency with application to filaria infection.

A preliminary data set is analysed containing filaria specific IgG4 and IgE levels and the presence of microfilariae of 196 people from families of a village in Indonesia. Since filaria infected people may not be microfilaria positive, a filaria infection can easily be missed. First, the probabilities of a filaria infection are estimated from the IgG4 levels and the presence of microfilariae using the EM algorithm. By dichotomizing these probabilities, infection status is estimated for each person. Then for IgG4, IgE and infection status, the correlations between observations are modelled. Three causes for a correlation are considered, namely genetic, intra-uterine or environmental effects. The correlation structure of the genetic and the intra-uterine effects are quite similar and consequently it may be difficult to disentangle them. Empirical variograms are plotted and the various variance components are estimated by maximizing the log-likelihood. For infection status an environmental effect is found and for IgG4 and IgE levels genetic effects are found.

Family Health↗
PubMed1999

Using missing data methods in genetic studies with missing mutation status.

Because of current techniques of determining gene mutation, investigators are now interested in estimating the odds ratio between genetic status (mutation, no mutation) and an outcome variable such as disease cell type (A, B). In this paper we consider the mutation of the RAS genetic family. To determine if the genes have mutated, investigators look at five specific locations on the RAS gene. RAS mutated is a mutation in at least one of the five gene locations and RAS non-mutated is no mutation in any of the five locations. Owing to limited time and financial resources, one cannot obtain a complete genetic evaluation of all five locations on the gene for all patients. We propose the use of maximum likelihood (ML) with a 2(6) multinomial distribution formed by cross-classifying the binary mutation status at five locations by binary disease cell type. This ML method includes all patients regardless of completeness of data, treats the locations not evaluated as missing data, and uses the EM algorithm to estimate the odds ratio between genetic mutation status and the disease type. We compare the ML method to complete case estimates, and a method used by clinical investigators, which excludes patients with data on less than five locations who have no mutations on these sites.

Biometry↗
PubMed1999

Is the AIDS incubation time changing? A back-calculation approach.

Contradictory literature was recently published on possible changes in AIDS incubation time over the period 1978-1994. The purpose of this work was to test if a change in incubation time (shortening or lengthening) was observed in France, either globally or in specific transmission groups (homosexual-bisexual men, heterosexual subjects), using a back-calculation approach. An age dependent TSI model (time since infection), which took into account a temporary treatment effect and allowed us to test for a change in the incubation time, was applied to the French AIDS cases (Réseau National de Santé Publique). The EM algorithm was used to maximize the likelihood and the best model was selected on the basis of the likelihood ratio statistic. The analysis on all AIDS cases indicated a shortening of the AIDS incubation time estimated to begin in 1983 (95 per cent CI 1982-1984). This shortening of incubation time was also apparent when analysis was restricted to homosexual-bisexual men and to heterosexual subjects. This shortening corresponded to a median incubation time of 9.6 years (95 per cent CI 8.1-10.5) for people infected at 30 years of age in 1983, versus 12.7 years for people infected at 30 years of age before the change.

Acquired Immunodeficiency Syndrome↗
PubMed1999

Estimation and comparison of rates of change in longitudinal studies with informative drop-outs.

Many cohort studies and clinical trials have designs which involve repeated measurements of disease markers. One problem in such longitudinal studies, when the primary interest is to estimate and to compare the evolution of a disease marker, is that planned data are not collected because of missing data due to missing visits and/or withdrawal or attrition (for example, death). Several methods to analyse such data are available, provided that the data are missing at random. However, serious biases can occur when missingness is informative. In such cases, one needs to apply methods that simultaneously model the observed data and the missingness process. In this paper we consider the problem of estimation of the rate of change of a disease marker in longitudinal studies, in which some subjects drop out prematurely (informatively) due to attrition, while others experience a non-informative drop-out process (end of study, withdrawal). We propose a method which combines a linear random effects model for the underlying pattern of the marker with a log-normal survival model for the informative drop-out process. Joint estimates are obtained through the restricted iterative generalized least squares method which are equivalent to restricted maximum likelihood estimates. A nested EM algorithm is applied to deal with censored survival data. The advantages of this method are: it provides a unified approach to estimate all the model parameters; it can effectively deal with irregular data (that is, measured at irregular time points), a complicated covariance structure and a complex underlying profile of the response variable; it does not entail such complex computation as would be required to maximize the joint likelihood. The method is illustrated by modelling CD4 count data in a clinical trial in patients with advanced HIV infection while its performance is tested by simulation studies.

Biomarkers↗
PubMed1999

Evaluating the exposure and disease relationship with adjustment for different types of exposure misclassification: a regression approach.

Misclassification of exposure can lead to biased results in the epidemiologic research. Available methods accounting for misclassification often require the use of a gold standard or assume non-differential misclassification of exposure. We present a regression approach which can detect and account for different types of misclassification when estimating the exposure and disease relationship. This approach uses two imperfect measures of a dichotomous exposure and does not require a gold standard. Standard statistical packages with a logistic regression module can be used for estimation of parameters through the EM algorithm process. Two examples are used to illustrate the methodology.

Algorithms↗
PubMed1999

Monitoring epidemiologic surveillance data using hidden Markov models.

The analysis of routinely collected surveillance data is an important challenge in public health practice. We present a method based on a hidden Markov model for monitoring such time series. The model characterizes the sequence of measurements by assuming that its probability density function depends on the state of an underlying Markov chain. The parameter vector includes distribution parameters and transition probabilities between the states. Maximum likelihood estimates are obtained with a modified EM algorithm. Extensions are provided to take into account trend and seasonality in the data. The method is demonstrated on two examples: the first seeks to characterize influenza-like illness incidence rates with a mixture of Gaussian distributions, and the other, poliomyelitis counts with mixture of Poisson distributions. The results justify a wider use of this method for analysing surveillance data.

Disease Outbreaks↗
PubMed2000

Patient-specific analysis of sequential haematological data by multiple linear regression and mixture distribution modelling.

Automated storage and analysis of the results of serial haematologic studies are now technically feasible with present-day laboratory instruments and devices for data storage and processing. In current practice, physicians mentally compare a laboratory result with previous values and use their clinical judgement to determine the significance of any change. To provide a statistical basis for this process, we describe a new approach for the detection of changes in patient-specific sequential measurements of standard haematologic laboratory tests. These methods include hierarchical multiple regression modelling, with a weighted minimum risk criteria for model selection, to choose models indicating changes in mean values over time. This study is the first to analyse sequential patient-specific distributions of laboratory measurements, utilizing mixture distribution modelling with systematic selection of starting values for the EM algorithm. To evaluate these statistical methods under controlled conditions, we studied 11 healthy human volunteers who were depleted of iron by serial phlebotomy to iron-deficiency anaemia, then treated with oral iron supplements to replete iron stores and correct the anaemia. Application of sequential patient-specific analyses of haemoglobin, haematocrit, and mean cell volume showed that significant departures from past values could be identified, in many cases, even when values were still within the population reference ranges. Additionally, for all subjects sequential alterations in red blood cell volume distributions during development of iron-deficiency anaemia could be characterized and quantified. These methods promise to provide more sensitive techniques for improved diagnostic evaluation of developing anaemia and serial monitoring of response to therapy.

Administration, Oral↗
PubMed2000

Joint analysis of HIV and AIDS surveillance data in back-calculation.

AIDS surveillance data are the main source of information to perform back-calculation of HIV incidence. We propose a method to incorporate additional information gained by linkage with an HIV surveillance system, containing data on the time of first positive HIV test. In this paper we generalize an earlier method that was developed to use HIV testing data available only for AIDS cases. The new method also makes use of cases with an HIV positive test who have not yet developed AIDS, typically a substantial proportion of the HIV-infected population. Furthermore, we use a more realistic model for the HIV testing rate, incorporating dependence on both time since infection and calendar time. The method makes use of an EM algorithm with generalized additive model smoothing, and is applied to data from Veneto, a region of northern Italy. Our results show that HIV incidence in Veneto peaked in the late 1980s, and decreased thereafter. Importantly, the HIV incidence estimates based on joint analysis of HIV and AIDS surveillance data are more efficient than estimates based on AIDS surveillance data alone. Our estimates also show a decreasing trend in the HIV testing rate over time, which leads to the conclusion that the interval between HIV infection and first positive test has lengthened over time. Furthermore, it is found that for infected individuals, the probability of seeking on HIV test is highest soon after infection.

AIDS Serodiagnosis↗
PubMed2000

Statistical models for longitudinal biomarkers of disease onset.

We consider the analysis of serial biomarkers to screen and monitor individuals in a given population for onset of a specific disease of interest. The biomarker readings are subject to error. We survey some of the existing literature and concentrate on two recently proposed models. The first is a fully Bayesian hierarchical structure for a mixed effects segmented regression model. Posterior estimates of the changepoint (onset time) distribution are obtained by Gibbs sampling. The second is a hidden changepoint model in which the onset time distribution is estimated by maximum likelihood using the EM algorithm. Both methods lead to a dynamic index that represents a strength of evidence that onset has occurred by the current time in an individual subject. The methods are applied to some large data sets concerning prostate specific antigen (PSA) as a serial marker for prostate cancer. Rules based on the indices are compared to standard diagnostic criteria through the use of ROC curves adapted for longitudinal data.

Algorithms↗
PubMed2000

Tests for gaussian repeated measures with missing data in small samples.

For small samples of Gaussian repeated measures with missing data, Barton and Cramer recommended using the EM algorithm for estimation and reducing the degrees of freedom for an analogue of Rao's F approximation to Wilks' test. Computer simulations led to the conclusion that the modified test was slightly conservative for total sample size of N=40. Here we consider additional methods and smaller sample sizes, Nin¿12,24¿. We describe analogues of the Pillai-Bartlett trace, Hotelling-Lawley trace and Geisser-Greenhouse corrected univariate tests which allow for missing data. Eleven sample size adjustments were examined which replace N by some function of the numbers of non-missing pairs of responses in computing error degrees of freedom. Overall, simulation results allowed concluding that an adjusted test can always control test size at or below the nominal rate, even with as few as 12 observations and up to 10 per cent missing data. The choice of method varies with the test statistic. Replacing N by the mean number of non-missing responses per variable works best for the Geisser-Greenhouse test. The Pillai-Bartlett test requires the stronger adjustment of replacing N by the harmonic mean number of non-missing pairs of responses. For Wilks' and Hotelling-Lawley, an even more aggressive adjustment based on the minimum number of non-missing pairs must be used.

Choline↗
PubMedDate not supplied

Estimating odds ratios adjusting for misclassification in Alzheimer's disease risk factor assessment.

Epidemiological studies of Alzheimer's disease and dementia are often two-phase studies including a screening phase and a clinical assessment phase. It is common to interview a relative of the subject at each of these phases to obtain information about the subject's exposure to risk factors. This can result in a misclassification error when assessing risk factors, as the two responses of the relative often differ. This is especially a problem for risk factors involving life-style and family history which cannot be confirmed using the subject's medical records. A naive analysis using data from each phase separately would give two different estimates of the odds ratio; both estimates could be biased. In this paper, we extend the estimation methods adjusting for misclassification developed by Liu and Liang to data collected through two-phase sampling. We first use a latent class analysis and the EM algorithm to estimate the misclassification parameters. We then derive the maximum pseudo-likelihood estimators, conditional on the misclassification parameters, to estimate the odds ratios accounting for the complex sampling study design. We propose to use the jack-knife estimator for estimation of the variances. We apply the above method to data collected in the Indianapolis-Ibadan Dementia Study to estimate the odds ratio for smoking adjusting for misclassification error.

Black or African American↗
PubMedDate not supplied

Estimating the incidence of dementia from two-phase sampling with non-ignorable missing data.

Two-phase sampling designs have been used in the field of psychiatry to estimate prevalence and incidence of a rare disease such as dementia and Alzheimer's disease. In a longitudinal study on dementia, since the repeated two-phase sampling is conducted several years after the baseline wave, some subjects may die before the follow-up wave, thus their disease status prior to death is missing. There are reasons to suggest that the missing due to death is non-ignorable. Estimation of disease incidence from longitudinal dementia study has to appropriately adjust for data missing by death as well as the sampling design used at each study wave. In this paper we adopt a selection model approach to model the missing data by death and use a likelihood approach to derive incidence estimates. A modified EM algorithm is used to deal with data from sampling selection. The non-parametric jack-knife variance estimator is used to derive variance estimates for the model parameters and the incidence estimates. The proposed approaches are applied to data from the Indianapolis-Ibadan Dementia Study.

Black or African American↗
PubMed1998

Modeling hazard functions in families.

A genetic frailty model is presented for censored age of onset data in nuclear families where individuals carrying a genetic susceptibility gene have an increased risk of becoming affected. We use maximum likelihood via the EM algorithm to estimate the genetic relative risk and the allele frequency under a dominant susceptibility type and a proportional hazards model. When sampling is from a disease registry, likelihood corrections are necessary for reducing bias in the parameter estimates. In these biased samples, the full conditional likelihood is approximated by a likelihood conditional on the proband's age of onset. For unbiased samples, simulations show the distributions of the estimates are similar under both a semiparametric and the correctly specified parametric likelihoods. For biased samples, simulations under the approximate conditional likelihood show the median estimates of the allele frequency and genetic relative risk tend to under- and overestimate, respectively, the true values; however, the approximation is better for rarer allele frequencies (0.0033 vs. 0.01). In practice, large samples or more complex ascertainment corrections are recommended. Using the approximate conditional likelihood on familial breast cancer onset data collected as part of a case-control study at the Fred Hutchinson Cancer Research Center in Seattle, Washington, we estimate an allele frequency of 0.0009 (approximate 95% CI 0.0006-0.002) and a genetic relative risk of 104 (approximate 95% CI 55-181).

Adolescent↗
PubMed1998

Semiparametric estimation of major gene effects for age of onset.

Analysis of age of onset is a key factor in the segregation and linkage analysis of some complex genetic traits. Previous work in the genetics literature has used parametric distributional assumptions on age of onset. In this paper, a Cox model with latent major gene effects is used: a semiparametric model with unspecified baseline hazard. A Monte Carlo EM procedure is used to obtain maximum likelihood estimates. Markov chain Monte Carlo is used to realize genotypic configurations from the posterior distribution given the current model and the observed data, and these genotypic configurations are used to estimate the expectations in the EM algorithm. Simulated data sets indicate that the parameters can be estimated well, and one real data set shows the practical applicability of the proposed method.

Age Distribution↗
PubMed2000

A joint analysis of quality of life and survival using a random effect selection model.

In studies of patients with advanced disease, longitudinal quality of life data may be truncated as a result of early death. Since survival and quality of life are likely to be related, modelling of the quality of life response needs to account for these different survival patterns. Here we discuss the application of a random effect selection model, in the form of a trivariate Normal model for the joint analysis of quality of life response (intercept and slope) and log survival time. Under certain assumptions this can give an unbiased description of the quality of life responses and valid inferences comparing treatment strategies in a clinical trial. It also indicates how quality of life and survival are related, by estimating the expected quality of life responses conditional on different survival times. Model parameters can be estimated using a restricted iterative generalized least-squares (RIGLS) procedure within standard software, extended to handle censoring of survival outcome using an EM algorithm. The model is applied to a physical quality of life score and survival data from a trial of treatment for patients with colorectal hepatic metastases. Survival differed between the treatment groups, and quality of life repsonse tended to be worse, both in initial level and change over time, for those patients who died earlier. The parameter estimates obtained agreed well with those from analysing the extended trial data set with complete survival information. Residual diagnostics used to check the necessary underlying assumptions of the model are exemplified. We conclude that such models can give an informative description of longitudinal responses when these are truncated by differential survival patterns.

Algorithms↗
PubMed2000

Proportional hazards model with random effects.

We propose a general proportional hazards model with random effects for handling clustered survival data. This generalizes the usual frailty model by allowing a multivariate random effect with arbitrary design matrix in the log relative risk, in a way similar to the modelling of random effects in linear, generalized linear and non-linear mixed models. The distribution of the random effects is generally assumed to be multivariate normal, but other (preferably symmetrical) distributions are also possible. Maximum likelihood estimates of the regression parameters, the variance components and the baseline hazard function are obtained via the EM algorithm. The E-step of the algorithm involves computation of the conditional expectations of functions of the random effects, for which we use Markov chain Monte Carlo (MCMC) methods. Approximate variances of the estimates are computed by Louis' formula, and posterior expectations and variances of the individual random effects can be obtained as a by-product of the estimation. The inference procedure is exemplified on two data sets.

Algorithms↗
PubMed2001

Association between the ancestral haplotype HLA A30B18DR3 and multiple sclerosis in central Sardinia.

Association and linkage studies have established the importance of the major histocompatibility complex (MHC) in the susceptibility for multiple sclerosis (MS). We carried out a case-control study to investigate the ancestral haplotype A30B18DR3 and MS in the Nuoro population of Sardinia, which is isolated and genetically distinct from other populations in the Mediterranean basin and characterized by genetic homogeneity, high level of inbreeding, low migration, high prevalence of MS, high frequency of the relevant haplotype, and high past malaria prevalence. Cases and controls were serologically typed for the currently recognized HLA-A, B, and DR antigens. We used a log-linear approach to fit a wide class of models. We tested our hypothesis comparing different models via a likelihood ratio test. We overcame the complication due to unknown gametic phase using expectation-maximization (EM) algorithm as the estimation method. We estimated confidence intervals for odds ratio by using a profile likelihood approach. We found that: (1) the ancestral haplotype A30B18DR3 was associated to MS after allowing for a possible stratification in cases and controls; (2) DR3 allele was conditional independent on disease status, given A30B18 haplotype; (3) there was a tendency for ORs for the high-risk haplotypes to be higher in the high malaria strata; however, this indication did not achieve statistical significance (P = 0.11).

Alleles↗