Biomedical subjects

David B Dunson

Publications and source records attributed to David B Dunson.

At least 37 records · Page 2Linked to original sources

PubMed2004

Bayesian estimation of survival functions under stochastic precedence.

When estimating the distributions of two random variables, X and Y, investigators often have prior information that Y tends to be bigger than X. To formalize this prior belief, one could potentially assume stochastic ordering between X and Y, which implies Pr(X < or = z) > or = Pr(Y < or = z) for all z in the domain of X and Y. Stochastic ordering is quite restrictive, though, and this article focuses instead on Bayesian estimation of the distribution functions of X and Y under the weaker stochastic precedence constraint, Pr(X < or = Y) > or = 0.5. We consider the case where both X and Y are categorical variables with common support and develop a Gibbs sampling algorithm for posterior computation. The method is then generalized to the case where X and Y are survival times. The proposed approach is illustrated using data on survival after tumor removal for patients with malignant melanoma.

Bayes Theorem↗

PubMed2004

Increased infertility with age in men and women.

OBJECTIVE: To estimate the effects of aging on the percentage of outwardly healthy couples who are sterile (completely unable to conceive without assisted reproduction) or infertile (unable to conceive within a year of unprotected intercourse). METHODS: A prospective fecundability study was conducted in a sample of 782 couples recruited from 7 European centers for natural family planning. Women aged 18-40 years were eligible. Daily intercourse records were used to adjust for timing and frequency of intercourse when estimating the per-menstrual-cycle probability of conception. The number of menstrual cycles required to conceive a clinical pregnancy and the probability of sterility and infertility were derived from the estimated fecundability distributions for men and women of different ages. RESULTS: Sterility was estimated at about 1%; this percent did not change with age. The percentage infertility was estimated at 8% for women aged 19-26 years, 13-14% for women aged 27-34 years and 18% for women aged 35-39 years. Starting in the late 30s, male age was an important factor, with the percentage failing to conceive within 12 cycles increasing from an estimated 18-28% between ages 35 and 40 years. The estimated percentage of infertile couples that would be able to conceive after an additional 12 cycles of trying varied from 43-63% depending on age. CONCLUSION: Increased infertility in older couples is attributable primarily to declines in fertility rates rather than to absolute sterility. Many infertile couples will conceive if they try for an additional year.

Adolescent↗

PubMed2004

Selecting factors predictive of heterogeneity in multivariate event time data.

In multivariate survival analysis, investigators are often interested in testing for heterogeneity among clusters, both overall and within specific classes. We represent different hypotheses about the heterogeneity structure using a sequence of gamma frailty models, ranging from a null model with no random effects to a full model having random effects for each class. Following a Bayesian approach, we define prior distributions for the frailty variances consisting of mixtures of point masses at zero and inverse-gamma densities. Since frailties with zero variance effectively drop out of the model, this prior allocates probability to each model in the sequence, including the overall null hypothesis of homogeneity. Using a counting process formulation, the conditional posterior distributions of the frailties and proportional hazards regression coefficients have simple forms. Posterior computation proceeds via a data augmentation Gibbs sampling algorithm, a single run of which can be used to obtain model-averaged estimates of the population parameters and posterior model probabilities for testing hypotheses about the heterogeneity structure. The methods are illustrated using data from a lung cancer trial.

Algorithms↗

PubMed2004

Bayesian isotonic regression and trend analysis.

In many applications, the mean of a response variable can be assumed to be a nondecreasing function of a continuous predictor, controlling for covariates. In such cases, interest often focuses on estimating the regression function, while also assessing evidence of an association. This article proposes a new framework for Bayesian isotonic regression and order-restricted inference. Approximating the regression function with a high-dimensional piecewise linear model, the nondecreasing constraint is incorporated through a prior distribution for the slopes consisting of a product mixture of point masses (accounting for flat regions) and truncated normal densities. To borrow information across the intervals and smooth the curve, the prior is formulated as a latent autoregressive normal process. This structure facilitates efficient posterior computation, since the full conditional distributions of the parameters have simple conjugate forms. Point and interval estimates of the regression function and posterior probabilities of an association for different regions of the predictor can be estimated from a single MCMC run. Generalizations to categorical outcomes and multiple predictors are described, and the approach is applied to an epidemiology application.

Bayes Theorem↗

PubMed2004

Bayesian multivariate logistic regression.

Bayesian analyses of multivariate binary or categorical outcomes typically rely on probit or mixed effects logistic regression models that do not have a marginal logistic structure for the individual outcomes. In addition, difficulties arise when simple noninformative priors are chosen for the covariance parameters. Motivated by these problems, we propose a new type of multivariate logistic distribution that can be used to construct a likelihood for multivariate logistic regression analysis of binary and categorical data. The model for individual outcomes has a marginal logistic structure, simplifying interpretation. We follow a Bayesian approach to estimation and inference, developing an efficient data augmentation algorithm for posterior computation. The method is illustrated with application to a neurotoxicology study.

Algorithms↗

PubMed2004

Modeling the effects of a bidirectional latent predictor from multivariate questionnaire data.

Researchers often measure stress using questionnaire data on the occurrence of potentially stress-inducing life events and the strength of reaction to these events, characterized as negative or positive and assigned an ordinal ranking. In studying the health effects of stress, one needs to obtain measures of an individual's negative and positive stress levels to be used as predictors. Motivated by data of this type, we propose a latent variable model, which is characterized by event-specific negative and positive reaction scores. If the positive reaction score dominates the negative reaction score for an event, then the individual's reported response to that event will be positive, with an ordinal ranking determined by the value of the score. Measures of overall positive and negative stress can be obtained by summing the reactivity scores across the events that occur for an individual. By incorporating these measures as predictors in a regression model and fitting the stress and outcome models jointly using Bayesian methods, inferences can be conducted without the need to assume known weights for the different events. We propose an MCMC algorithm for posterior computation and apply the approach to study the effects of stress on preterm delivery.

Algorithms↗

PubMed2004

Methodologic and statistical approaches to studying human fertility and environmental exposure.

Although there has been growing concern about the effects of environmental exposures on human fertility, standard epidemiologic study designs may not collect sufficient data to identify subtle effects while properly adjusting for confounding. In particular, results from conventional time to pregnancy studies can be driven by the many sources of bias inherent in these studies. By prospectively collecting detailed records of menstrual bleeding, occurrences of intercourse, and a marker of ovulation day in each menstrual cycle, precise information on exposure effects can be obtained, adjusting for many of the primary sources of bias. This article provides an overview of the different types of study designs, focusing on the data required, the practical advantages and disadvantages of each design, and the statistical methods required to take full advantage of the available data. We conclude that detailed prospective studies allowing inferences on day-specific probabilities of conception should be considered as the gold standard for studying the effects of environmental exposures on fertility.

Data Collection↗

PubMed2003

Vulvar mucus observations and the probability of pregnancy.

OBJECTIVE: To assess the day-specific and cycle-specific probabilities of conception leading to clinical pregnancy, in relation to the timing of intercourse and vulvar mucus observations. METHODS: This was a retrospective cohort study of women beginning use of the Creighton Model Fertility Care System in Missouri, Nebraska, Kansas, and California. Data were abstracted from Creighton Model Fertility Care System records, including women's daily standardized vulvar observations of cervical mucus discharge, days of intercourse, and clinically evident pregnancy (conception). Established statistical models were used to estimate day-specific probabilities of conception. RESULTS: Data were analyzed from 1681 cycles with 81 conceptions from 309 normally fertile couples (initially seeking to avoid pregnancy) and from 373 cycles with 30 conceptions from 117 subfertile couples (who were initially trying to achieve pregnancy). The highest probability of pregnancy occurred on the peak day of vulvar mucus observation (.38 for normally fertile couples and.14 for subfertile couples). The probability of pregnancy was greater than.05 for normally fertile couples from 3 days before to 2 days after the peak, and for subfertile couples from 1 day before to 1 day after the peak. The cycle-specific probability of conception correlated with the quality of mucus discharge in normally fertile couples but not in subfertile couples. CONCLUSION: Standardized vulvar observations of vaginal mucus discharge identify the days with the greatest likelihood of conception from intercourse in normal fertility and subfertility and provide an indicator of the overall potential for conception in a given menstrual cycle in normal fertility.

Cervix Mucus↗

PubMed2003

High cumulative incidence of uterine leiomyoma in black and white women: ultrasound evidence.

OBJECTIVE: Uterine leiomyoma, or fibroid tumors, are the leading indication for hysterectomy in the United States, but the proportion of women in whom fibroid tumors develop is not known. This study screened for fibroid tumors, independently of clinical symptoms, to estimate the age-specific proportion of black and white women in whom fibroid tumors develop. STUDY DESIGN: Randomly selected members of an urban health plan who were 35 to 49 years old participated (n = 1364 women). Medical records and self-report were used to assess fibroid status for those women who were no longer menstruating (most of whom had had hysterectomies). Premenopausal women were screened by ultrasonography. We estimated the age-specific cumulative incidence of fibroid tumors for black and white women. RESULTS: Thirty-five percent of premenopausal women had a previous diagnosis of fibroid tumors. Fifty-one percent of the premenopausal women who had no previous diagnosis had ultrasound evidence of fibroid tumors. The estimated cumulative incidence of tumors by age 50 was >80% for black women and nearly 70% for white women. The difference between the age-specific cumulative incidence curves for black and white women was highly significant (odds ratio, 2.9; 95% CI, 2.5-3.4; P <.001). CONCLUSION: The results of this study suggest that most black and white women in the United States develop uterine fibroid tumors before menopause and that uterine fibroid tumors develop in black women at earlier ages than in white women.

Adult↗

PubMed2003

Why is parity protective for uterine fibroids?

Uterine fibroids are benign tumors, the etiology of which is not understood. Symptoms can be debilitating, and the primary treatment is surgery, usually hysterectomy. Epidemiologic data show that pregnancy is associated with reduced risk of fibroids. We hypothesize that this association is attributable to a protective effect of postpartum involution of the uterus. After each pregnancy the uterus rapidly returns to prepregnancy size by dramatic remodeling of the tissue. We hypothesize that small fibroids are eliminated during this process. We present preliminary epidemiologic evidence that is consistent with this hypothesis. If the hypothesis is supported by more direct evidence, it may have broader implications, supporting the idea that tissue remodeling may be a general mechanism for limiting tumor development.

Disease Susceptibility↗

PubMed2003

Bayesian modeling of time-varying and waning exposure effects.

In epidemiologic studies, there is often interest in assessing the association between exposure history and disease incidence. For many diseases, incidence may depend not only on cumulative exposure, but also on the ages at which exposure occurred. This article proposes a flexible Bayesian approach for modeling age-varying and waning exposure effects. The Cox model is generalized to allow the hazard of disease to depend on an integral, across the exposed ages, of a piecewise polynomial function of age, multiplied by an exponential decay term. Linearity properties of the model facilitate posterior computation via a Gibbs sampler, which generalizes previous algorithms for Cox regression with time-dependent covariates. The approach is illustrated by an application to the study of protective effects of breastfeeding on incidence of childhood asthma.

Adult↗

PubMed2003

Bayesian inference on order-constrained parameters in generalized linear models.

In biomedical studies, there is often interest in assessing the association between one or more ordered categorical predictors and an outcome variable, adjusting for covariates. For a k-level predictor, one typically uses either a k-1 degree of freedom (df) test or a single df trend test, which requires scores for the different levels of the predictor. In the absence of knowledge of a parametric form for the response function, one can incorporate monotonicity constraints to improve the efficiency of tests of association. This article proposes a general Bayesian approach for inference on order-constrained parameters in generalized linear models. Instead of choosing a prior distribution with support on the constrained space, which can result in major computational difficulties, we propose to map draws from an unconstrained posterior density using an isotonic regression transformation. This approach allows flat regions over which increases in the level of a predictor have no effect. Bayes factors for assessing ordered trends can be computed based on the output from a Gibbs sampling algorithm. Results from a simulation study are presented and the approach is applied to data from a time-to-pregnancy study.

Adult↗

PubMed2003

Bayesian latent variable models for median regression on multiple outcomes.

Often a response of interest cannot be measured directly and it is necessary to rely on multiple surrogates, which can be assumed to be conditionally independent given the latent response and observed covariates. Latent response models typically assume that residual densities are Gaussian. This article proposes a Bayesian median regression modeling approach, which avoids parametric assumptions about residual densities by relying on an approximation based on quantiles. To accommodate within-subject dependency, the quantile response categories of the surrogate outcomes are related to underlying normal variables, which depend on a latent normal response. This underlying Gaussian covariance structure simplifies interpretation and model fitting, without restricting the marginal densities of the surrogate outcomes. A Markov chain Monte Carlo algorithm is proposed for posterior computation, and the methods are applied to single-cell electrophoresis (comet assay) data from a genetic toxicology study.

Algorithms↗

PubMed2003

A Bayesian approach for joint modeling of cluster size and subunit-specific outcomes.

In applications that involve clustered data, such as longitudinal studies and developmental toxicity experiments, the number of subunits within a cluster is often correlated with outcomes measured on the individual subunits. Analyses that ignore this dependency can produce biased inferences. This article proposes a Bayesian framework for jointly modeling cluster size and multiple categorical and continuous outcomes measured on each subunit. We use a continuation ratio probit model for the cluster size and underlying normal regression models for each of the subunit-specific outcomes. Dependency between cluster size and the different outcomes is accommodated through a latent variable structure. The form of the model facilitates posterior computation via a simple and computationally efficient Gibbs sampler. The approach is illustrated with an application to developmental toxicity data, and other applications, to joint modeling of longitudinal and event time data, are discussed.

Animals↗

PubMed2003

Random effects selection in linear mixed models.

We address the important practical problem of how to select the random effects component in a linear mixed model. A hierarchical Bayesian model is used to identify any random effect with zero variance. The proposed approach reparameterizes the mixed model so that functions of the covariance parameters of the random effects distribution are incorporated as regression coefficients on standard normal latent variables. We allow random effects to effectively drop out of the model by choosing mixture priors with point mass at zero for the random effects variances. Due to the reparameterization, the model enjoys a conditionally linear structure that facilitates the use of normal conjugate priors. We demonstrate that posterior computation can proceed via a simple and efficient Markov chain Monte Carlo algorithm. The methods are illustrated using simulated data and real data from a study relating prenatal exposure to polychlorinated biphenyls and psychomotor development of children.

Algorithms↗

PubMed2003

Bayesian inferences in the Cox model for order-restricted hypotheses.

In studying the relationship between an ordered categorical predictor and an event time, it is standard practice to include dichotomous indicators of the different levels of the predictor in a Cox model. One can then use a multiple degree-of-freedom score or partial likelihood ratio test for hypothesis testing. Often, interest focuses on comparing the null hypothesis of no difference to an order-restricted alternative, such as a monotone increase across levels of a predictor. This article proposes a Bayesian approach for addressing hypotheses of this type. We reparameterize the Cox model in terms of a cumulative product of parameters having conjugate prior densities, consisting of mixtures of point masses at one, and truncated gamma densities. Due to the structure of the model, posterior computation can proceed via a simple and efficient Gibbs sampling algorithm. Posterior probabilities for the global null hypothesis and subhypotheses, comparing the hazards for specific groups, can be calculated directly from the output of a single Gibbs chain. The approach allows for level sets across which a predictor has no effect. Generalizations to multiple predictors are described, and the method is applied to a study of emergency medical treatment for stroke.

Bayes Theorem↗

PubMed2002

Deficiency of either cyclooxygenase (COX)-1 or COX-2 alters epidermal differentiation and reduces mouse skin tumorigenesis.

Nonsteroidal anti-inflammatory drugs are widely reported to inhibit carcinogenesis in humans and in rodents. These drugs are believed to act by inhibiting one or both of the known isoforms of cyclooxygenase (COX). However, COX-2, and not COX-1, is the isoform most frequently reported to have a key role in tumor development. Here we report that homozygous deficiency of either COX-1 or COX-2 reduces skin tumorigenesis by 75% in a multistage mouse skin model. Reduced tumorigenesis was observed even though the levels of stable 7,12-dimethylbenz(a)anthracene-DNA adducts were increased about 2-fold in the COX-deficient mice compared with wild-type mice. The premature onset of keratinocyte terminal differentiation appeared to be the cellular event leading to the reduced tumorigenesis because keratin 1 and keratin 10, two keratins that indicate the commitment of keratinocytes to differentiate, were expressed 8-13-fold and 10-20-fold more frequently in epidermal basal cells of the COX-1-deficient and COX-2-deficient mice, respectively, than in wild-type mice. Papillomas on the COX-deficient mice also displayed the premature onset of keratinocyte terminal differentiation. However, loricrin, a late marker of epidermal differentiation, was not significantly altered, suggesting that it was the early stages of keratinocyte differentiation that were primarily affected by COX deficiency. Because keratin 5, a keratin associated with basal cells, was detected differently in papillomas of COX-1-deficient as compared with COX-2-deficient mice, it appears that the isoforms do not have identical roles in papilloma development. Interestingly, apoptosis, a cellular process associated with nonsteroidal anti-inflammatory drug-induced inhibition of tumorigenesis, was not significantly altered in the epidermis or in papillomas of the COX-deficient mice. Thus, both COX-1 and COX-2 have roles in keratinocyte differentiation, and we propose that the absence of either isoform causes premature terminal differentiation of initiated keratinocytes and reduced tumor formation.

9,10-Dimethyl-1,2-benzanthracene↗

PubMed2002

Mutational fingerprints of aging.

Using a lacZ plasmid transgenic mouse model, spectra of spontaneous point mutations were determined in brain, heart, liver, spleen and small intestine in young and old mice. While similar at a young age, the mutation spectra among these organs were significantly different in old age. In brain and heart G:C-->A:T transitions at CpG sites were the predominant mutation, suggesting that oxidative damage is not a major mutagenic event in these tissues. Other base changes, especially those affecting A:T base pairs, positively correlated with increasing proliferative activity of the different tissues. A relatively high percentage of base changes at A:T base pairs and compound mutants were found in both spleen and spontaneous lymphoma, suggesting a possible role of the hypermutation process in splenocytes in carcinogenesis. The similar mutant spectra observed at a young age may reflect a common mutation mechanism for all tissues that could be driven by the rapid cell division that takes place during development. However, the spectra of the young tissues did not resemble that of the most proliferative aged tissue, implying that replicative history per se is not the underlying causal factor of age-related organ-specific differences in mutation spectra. Rather, differences in organ function, possibly in association with replicative history, may explain the divergence in mutation spectra during aging.

Aging↗