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Biomedical subjects

Hakan Demirtas

Publications and source records attributed to Hakan Demirtas.

8 recordsLinked to original sources
PubMed2007

Gaussianization-based quasi-imputation and expansion strategies for incomplete correlated binary responses.

New quasi-imputation and expansion strategies for correlated binary responses are proposed by borrowing ideas from random number generation. The core idea is to convert correlated binary outcomes to multivariate normal outcomes in a sensible way so that re-conversion to the binary scale, after performing multiple imputation, yields the original specified marginal expectations and correlations. This conversion process ensures that the correlations are transformed reasonably which in turn allows us to take advantage of well-developed imputation techniques for Gaussian outcomes. We use the phrase 'quasi' because the original observations are not guaranteed to be preserved. We argue that if the inferential goals are well-defined, it is not necessary to strictly adhere to the established definition of multiple imputation. Our expansion scheme employs a similar strategy where imputation is used as an intermediate step. It leads to proportionally inflated observed patterns, forcing the data set to a complete rectangular format. The plausibility of the proposed methodology is examined by applying it to a wide range of simulated data sets that reflect alternative assumptions on complete data populations and missing-data mechanisms. We also present an application using a data set from obesity research. We conclude that the proposed method is a promising tool for handling incomplete longitudinal or clustered binary outcomes under ignorable non-response mechanisms.

Adolescent↗
PubMed2006

Demolition of high-rise public housing increases particulate matter air pollution in communities of high-risk asthmatics.

Public housing developments across the United States are being demolished, potentially increasing local concentrations of particulate matter (PM) in communities with high burdens of severe asthma. Little is known about the impact of demolition on local air quality. At three public housing developments in Chicago, IL, PM with an aerodynamic diameter < 10 microm (PM10) and < 2.5 microm were measured before and during high-rise demolition. Additionally, size-selective sampling and real-time monitoring were concurrently performed upwind and downwind of one demolition site. The concentration of particulates attributable to demolition was estimated after accounting for background urban air pollution. Particle microscopy was performed on a small number of samples. Substantial increases of PM10 occurred during demolition, with the magnitude of that increase varying based on sampler distance, wind direction, and averaging time. During structural demolition, local concentrations of PM10 42 m downwind of a demolition site increased 4- to 9-fold above upwind concentrations (6-hr averaging time). After adjusting for background PM10, the presence of dusty conditions was associated with a 74% increase in PM10 100 m downwind of demolition sites (24-hr averaging times). During structural demolition, short-term peaks in real-time PM10 (30-sec averaging time) occasionally exceeded 500 microg/m(3). The median particle size downwind of a demolition site (17.3 microm) was significantly larger than background (3 microm). Specific activities are associated with realtime particulate measures. Microscopy did not identify asbestos or high concentrations of mold spores. In conclusion, individuals living near sites of public housing demolition are at risk for exposure to high particulate concentrations. This increase is characterized by relatively large particles and high short-term peaks in PM concentration.

Air Pollutants↗
PubMed2006

Mood states and milk output in lactating mothers of preterm and term infants.

The purpose of this study is to compare psychological distress via both negative and positive mood states between 2 different groups of lactating mothers during the first 6 weeks postpartum with a large sample. Mood states were measured using the Multiple Affect Adjective Check List-Revised by a convenience sample of newly delivered mothers from 4 tertiary care units in Illinois. Preterm mothers' negative mood states of anxiety, depression, hostility, and dysphoria were significantly greater than those reported for term mothers. For the positive mood states of positive affect and PASS (positive affect + sensation seeking), preterm mothers had significantly lower scores than term mothers; there were no differences for the positive mood state, Sensation Seeking. Maternal perceived mood states had no apparent effect upon lactation as measured by milk volume produced. Further study is warranted to determine what factors influence milk output in mothers of preterm and term infants who are at risk for lactation failure.

Adult↗
PubMed2005

Multiple imputation under Bayesianly smoothed pattern-mixture models for non-ignorable drop-out.

Conventional pattern-mixture models can be highly sensitive to model misspecification. In many longitudinal studies, where the nature of the drop-out and the form of the population model are unknown, interval estimates from any single pattern-mixture model may suffer from undercoverage, because uncertainty about model misspecification is not taken into account. In this article, a new class of Bayesian random coefficient pattern-mixture models is developed to address potentially non-ignorable drop-out. Instead of imposing hard equality constraints to overcome inherent inestimability problems in pattern-mixture models, we propose to smooth the polynomial coefficient estimates across patterns using a hierarchical Bayesian model that allows random variation across groups. Using real and simulated data, we show that multiple imputation under a three-level linear mixed-effects model which accommodates a random level due to drop-out groups can be an effective method to deal with non-ignorable drop-out by allowing model uncertainty to be incorporated into the imputation process.

Antipsychotic Agents↗
PubMed2005

Bayesian analysis of hierarchical pattern-mixture models for clinical trials data with attrition and comparisons to commonly used ad-hoc and model-based approaches.

This article addresses the problem of making scientifically sound inferences from clinical trials data with attrition, when reasons of attrition seem related to the outcomes of interest. The problem is particularly difficult when the effect of a covariate is to be estimated and if the dropout mechanism appears to operate differently at different levels of the covariate. In this context, multiple imputation under a multilevel pattern-mixture model that allows random variation across dropout groups is presented. Using simulated data generated around an alcoholic hepatitis trial, we compare the performance of this model and commonly used ad-hoc and model-based approaches.

Algorithms↗
PubMed2004

Assessment of relative improvement due to weights within generalized estimating equations framework for incomplete clinical trials data.

The generalized estimating equations (GEE) approach has been popular for analyzing longitudinal clinical trials data with missing values. The GEE methodology allows one to obtain unbiased estimates only when the data are missing completely at random. The use of weights into the estimating equations has been proposed as an adjustment for differential probabilities of nonresponse to accomodate more realistic missingness mechanisms. This article addresses the problem of assessing the relative improvement due to weights using simulated data generated around an alcoholic hepatitis trial. We argue that weights yield improved results in terms of bias, coverage rate, and efficiency only when the underlying missingness mechanism is correctly specified.

Algorithms↗
PubMed2003

On the performance of random-coefficient pattern-mixture models for non-ignorable drop-out.

Random-coefficient pattern-mixture models (RCPMMs) have been proposed for longitudinal data when drop-out is thought to be non-ignorable. An RCPMM is a random-effects model with summaries of drop-out time included among the regressors. The basis of every RCPMM is extrapolation. We review RCPMMs, describe various extrapolation strategies, and show how analyses may be simplified through multiple imputation. Using simulated and real data, we show that alternative RCPMMs that fit equally well may lead to very different estimates for parameters of interest. We also show that minor model misspecification can introduce biases that are quite large relative to standard errors, even in fairly small samples. For many scientific applications, where the form of the population model and nature of the drop-out are unknown, interval estimates from any single RCPMM may suffer from undercoverage because uncertainty about model specification is not taken into account.

Antipsychotic Agents↗