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PubMed1992

Exact likelihood evaluation in a Markov mixture model for time series of seizure counts.

This paper provides an alternative to Albert's (1991), Biometrics 47, 1371-1381) approximation to the E-step when using the EM algorithm for parameter estimation in Markov mixture models. Use of a recursive algorithm of Baum et al. (1970, Annals of Mathematical Statistics 41, 164-171) results in exact evaluation of the likelihood, optimal parameter estimates, and very efficient computation. Applications to time series of seizure counts and fetal movements clearly show the advantages of this exact approach.

Algorithms↗
PubMed2001

Image recognition in the presence of non-Gaussian noise with unknown statistics.

We design receivers to detect a known pattern or a reference signal in the presence of very general and non-Gaussian types of noise. Three sources of input-noise degradation are considered: additive, multiplicative, and disjoint background. The detection process involves two steps: (1) estimation of the relevant noise parameters within the framework of hypothesis testing and (2) maximizing a certain metric that measures the likelihood of the target being at a given location. The parameter estimation portion is carried out by moment-matching techniques. Because of the number of unknown parameters and the fact that various types of input-noise processes are non-Gaussian, the methods that are used to estimate these parameters differ from the standard methods of maximizing the likelihood function. To verify the existence of the target at a certain location, we use l(p)-norm metric for p > or = 0 to measure the likelihood of the target being present at the location of interest. Computer simulations are used to show that for the images tested here, the receivers designed herein perform better than some existing receivers.

Journal Article↗
PubMed2004

SNAP: workbench management tool for evolutionary population genetic analysis.

UNLABELLED: The reconstruction of population processes from DNA sequence variation requires the coordinated implementation of several coalescent-based methods, each bound by specific assumptions and limitations. In practice, the application of these coalescent-based methods for parameter estimation is difficult because they make strict assumptions that must be verified a priori and their parameter-rich nature makes the estimation of all model parameters very complex and computationally intensive. A further complication is their distribution as console applications that require the user to navigate through console menus or specify complex command-line arguments. To facilitate the implementation of these coalescent-based tools we developed SNAP Workbench, a Java program that manages and coordinates a series of programs. The workbench enhances population parameter estimation by ensuring that the assumptions and program limitations of each method are met and by providing a step-by-step methodology for examining population processes that integrates both summary-statistic methods and coalescent-based population genetic models. AVAILABILITY: SNAP Workbench is freely available at http://snap.cifr.ncsu.edu. The workbench and tools can be downloaded for Mac, Windows and Unix operating systems. Each package includes installation instructions, program documentation and a sample dataset. SUPPLEMENTARY INFORMATION: A description of system requirements and installation instructions can be found at http://snap.cifr.ncsu.edu.

Computer Simulation↗
PubMed1999

A predictive model to determine the effects of pH, milkfat, and temperature on thermal inactivation of Listeria monocytogenes.

Listeria monocytogenes is a foodborne pathogen of significance because of its comparatively high heat resistance, zero tolerance in ready-to-eat foods, and growth at refrigeration temperatures. A 3 x 3 x 3 factorial study was done to determine the effects of milkfat (0%, 2.5%, 5.0%), pH (5.0, 6.0, 7.0), and processing temperature (55 degrees C, 60 degrees C, 65 degrees C) on the thermal resistance of L. monocytogenes in a formulated and homogenized milk system. Data were fit to a modified Gompertz equation where parameter estimates characterized three regions of a survival curve: the shoulder, maximum slope, and tail. Statistical analysis was done for each of the 27 individual treatment sets to visualize individual effects on parameter estimates and to evaluate how well the Gompertz equation represented the data. A regression model for the Gompertz equation was generated to predict the logarithmic surviving fraction of L. monocytogenes based on all 27 treatments and their single and interactive effects. The shoulder region of the survival curve was affected by pH; however, the maximum slope was affected by temperature, milkfat, and the interaction of temperature x milkfat. Validation of the model suggests that the predictions are best suited for processing above 62 degrees C. Trends over time for a 4-log reduction in cells (4D values) were evaluated using results from the 27 individual treatment sets, the regression model for the Gompertz equation, and a linear equation. At lower temperatures, 4D values by the three methods varied by twofold. At higher temperatures, all methods gave similar 4D values, suggesting that death became more linear. Based on this study all three factors affect heat resistance for specific regions of a survival curve, and a predictive model was developed that can be used as a preliminary estimate for L. monocytogenes inactivation.

Animals↗
PubMedDate not supplied

MHPG circadian rhythmicity in blood of healthy subjects.

An earlier study showed that plasma concentrations of total 3-methoxy-4-hydroxyphenylglycol (MHPG), the major metabolite of norepinephrine, display a circadian rhythm in 6 male healthy subjects. In the previous study, the period of the rhythm was not fixed to 24 h thereby undermining the reliability of the cosinor parameter estimates. The present study extends the findings to a larger group of 12 clinically healthy male volunteers. Plasma total MHPG concentrations were determined every 3h for one full day. The data were fitted to a cosinor model fixing the period of the putative MHPG rhythm at 24 h. Several estimation techniques were utilized including Fourier analysis and time domain analysis with 4 variations. It is concluded that a circadian rhythm indeed characterizes MHPG blood concentrations. The concordance among the various parameter estimates is discussed.

Adult↗
PubMed2006

Multiple imputation methods for modelling relative survival data.

In population-based cancer survival studies, the cause-specific survival measures the net survival (excess mortality) due to cancer when the cause of death information is available and reliable. In contrast, when the cause of death is uncertain or unavailable, relative survival, the ratio of the survival rate due to all causes to the expected survival rate, is more appropriate. There is a large body of work on the modelling and hypothesis testing of cause-specific survival, but many of these methods are not directly applicable to relative survival. In this paper, we extend the multiple imputation (MI) methods (Stat. Methods Med. Res. 1999; 8:3-15) to the case of relative survival data. The MI methodology is combined with relative survival to estimate the net survival by changing relative survival data to cause-specific data. This facilitates the direct application to statistical methods developed for the cause-specific survival to the special situation of relative survival. The parameter estimates and the log-rank statistics are obtained by combining the results from multiple imputed cause-specific data. The likelihood-based methods for modelling relative survival data have been implemented by a Windows application called CANSURV (Comput. Meth. Prog. Biomed 2005). Although these methods produce accurate parameter estimates, the choice for models and diagnostic tools is limited. The MI method is presented as a simpler alternative. The relative survival data for the colorectal cancer patients from Surveillance, Epidemiology, and End Results (SEER) program (SEER Cancer Statistics Review, 1973-1999. National Cancer Institute: Bethesda, 2002) is used as an illustration. The results are compared with those obtained from the likelihood-based relative survival analysis methods. A sample SAS macro for the MI method is provided.

Black People↗
PubMed2003

Pharmacokinetics and pharmacodynamics of mesna-mediated plasma cysteine depletion.

Cellular glutathione (GSH) levels are related to the resistance of tumor cells to platinum and alkylating agents, and depletion of GSH may enhance the activity of these drugs. The pharmacodynamic effects of mesna on depleting plasma cysteine, a GSH precursor, were evaluated in 22 patients as part of a Phase I study. Escalating doses of ifosfamide and mesna were administered; carboplatin was administered to achieve an AUC of 4 mg x min/mL. Plasma samples were collected and assayed by reverse-phase high-performance liquid chromatography (HPLC) for total mesna and total cysteine concentrations at 0, 1, 3, 6, 24, 25, 28, and 48 hours. A one-compartment pharmacokinetic model was fit to the mesna plasma concentrations, using M.A.P. Bayesian estimation (ADAPT II). Pharmacodynamics were evaluated by fitting an inhibitory Emax model to the cysteine concentration data. Both the pharmacokinetic (median R2 = 0.95; range = 0.85-0.98) and pharmacodynamic (median R2 = 0.96; range = 0.74-1.0) models fit the data well. Mean (coefficient of variation [CV%]) mesna pharmacokinetic parameter estimates were as follows: Vss of 15.3 (29) L/m2, CL of 4.6 (29) L/h/m2, and half-life of 2.2 (37) hours. Mean (CV%) pharmacodynamic parameter estimates were as follows: Emax of 31.7 (19) microg/mL and EC50 of 10.3 (52) microg/mL. Mesna produced a rapid, concentration-dependent reduction in plasma cysteine concentrations that could be adequately characterized by an inhibitory Emax pharmacodynamic model. The depletion of plasma cysteine was facilitated by ifosfamide, suggesting a pharmacodynamic interaction between these two agents. Further increases in mesna doses beyond those administered in this study would be unlikely to provide additional benefit.

Antineoplastic Agents, Alkylating↗
PubMed2004

A shared random effect parameter approach for longitudinal dementia data with non-ignorable missing data.

A significant source of missing data in longitudinal epidemiologic studies on elderly individuals is death. It is generally believed that these missing data by death are non-ignorable to likelihood based inference. Inference based on data only from surviving participants in the study may lead to biased results. In this paper we model both the probability of disease and the probability of death using shared random effect parameters. We also propose to use the Laplace approximation for obtaining an approximate likelihood function so that high dimensional integration over the distributions of the random effect parameters is not necessary. Parameter estimates can be obtained by maximizing the approximate log-likelihood function. Data from a longitudinal dementia study will be used to illustrate the approach. A small simulation is conducted to compare parameter estimates from the proposed method to the 'naive' method where missing data is considered at random.

Aged↗
PubMed1999

Fitting logistic IRT models: small wonder.

State-of-the-art item response theory (IRT) models use logistic functions exclusively as their item response functions (IRFs). Logistic functions meet the requirements that their range is the unit interval and that they are monotonically increasing, but they impose a parameter space whose dimensions can only be assigned a metaphorical interpretation in the context of testing. Applications of IRT models require obtaining the set of values for logistic function parameters that best fit an empirical data set. However, success in obtaining such set of values does not guarantee that the constructs they represent actually exist, for the adequacy of a model is not sustained by the possibility of estimating parameters. This article illustrates how mechanical adoption of off-the-shelf logistic functions as IRFs for IRT models can result in off-the-shelf parameter estimates and fits to data. The results of a simulation study are presented, which show that logistic IRT models can fit a set of data generated by IRFs other than logistic functions just as well as they fit logistic data, even though the response processes and parameter spaces involved in each case are substantially different. An explanation of why logistic functions work as they do is offered, the theoretical and practical consequences of their behavior are discussed, and a testable alternative to logistic IRFs is commented upon.

Humans↗
PubMed1998

Rasch measurement for reducing the items of the Nottingham Health Profile.

The present study aimed to develop a short form of the Spanish version of the Nottingham Health Profile (NHP) by means of Rasch analysis. Data from several Spanish studies that included the NHP since 1987 were collected in a common database. Forty-five different studies were included, covering a total of 9,419 subjects both from the general population and with different clinical pathologies. The overall questionnaire (38 items) was simultaneously analyzed using the dichotomous response model. Parameter estimates, model-data fit and separation statistics were computed. The items of the NHP were additionally regrouped into two different scales: Physical (19 items) and Psychological (19 items). Separated Physical and Psychological parameter estimates were produced using the simultaneous item calibrations as anchor values. Misfitting items were deleted, resulting in a 22 item final short form (NHP22)-11 Physical and 11 Psychological-. The evaluation of the item hierarchies confirmed the construct validity of the new questionnaire. To demonstrate the invariance of the NHP22 item calibrations, Rasch analyses were performed separately for each study included in the sample and for several sociodemographic and health status variables. Results confirmed the validity of using the NHP22 item calibrations to measure different groups of people categorized by gender, clinical and health status.

Health Status Indicators↗
PubMed2004

Algorithms for calculation of kinetic parameters from T1-weighted dynamic contrast-enhanced magnetic resonance imaging.

PURPOSE: To quantify the errors involved in calculating dynamic parameters (K(trans) and ve) from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) scans, and to develop alternative analyses to improve accuracy or increase processing speed. MATERIALS AND METHODS: This paper presents three different ways of handling the discrete samples of the arterial input and tissue response data with increasing fidelity, with which this continuous arterial input function (AIF) is represented. Also, a new noniterative approach to parameter estimation was developed from one used previously for analysis of radioactive tracer concentrations in radioangiography. The analysis methods were tested using simulated data. RESULTS: The more sophisticated schemes for data processing give more accurate parameter estimates when data are sparsely sampled, at least for the AIF that we modeled. The noniterative algorithm is very rapid in execution, but was more susceptible to measurement errors. CONCLUSION: The improved algorithms presented should be useful when the AIF and tissue response are sparsely sampled. The noniterative approach may be suitable for semiquantitative visualization, or where the AIF and tissue response are sampled accurately and with a small time interval between samples.

Algorithms↗
PubMed1997

Regional perfusion parameters from pulmonary microfocal angiograms.

An indicator-dilution model was developed to describe transport of vascular contrast medium through an organ during acquisition of vascular dynamic contrast images. The model provides the theoretical basis for methods of determining regional blood flow, blood volume, and mean transit time from time-absorbance curves acquired from the images of tissue regions of interest (ROI) distal from the inlet site. The robustness of these methods was evaluated using a computer-simulated vessel network, which simulated the passage of a bolus of contrast medium through arterioles, networks of capillaries, and venules. The network was used to evaluate the reliability of ROI parameter estimation methods when the underlying model assumptions are violated. The shape of the ROI inlet concentration curve and moderate amounts of random noise did not affect the ability of the method to recover accurate parameter estimates. The estimates of ROI flow and transit time were degraded in the presence of significant dispersion of the inlet concentration curve as it traveled through arteries upstream from the microvascular ROI or when the flow was redistributed within the ROI. The estimates of ROI volume were relatively robust. The method was applied to image data of the dog pulmonary vasculature obtained using microfocal X-ray angiography to show that the results obtained from the simulations are consistent with actual data.

Angiography↗
PubMed1993

A longitudinal study of growth and development of stature among prenatally exposed atomic bomb survivors.

Growth retardation due to exposure to the atomic bomb has been evaluated using repeated measurements of stature from 10 to 18 years of age. A highly significant growth retardation due to the DS86 uterine dose was observed in all trimesters combined and in the first and second trimesters of pregnancy. In the first trimester, all parameter estimates based on a linear (L) or linear-quadratic (L-Q) dose-response relationship were negative in relation to the DS86 uterine dose. The positive dose estimate in the second trimester is small and close to the control level. Statistically significant difference was determined by a multivariate test statistic examining whether or not a set of two- or three-parameter estimates including a constant term related to an L or L-Q dose-response relationship was different from zero. A radiation-related growth retardation was demonstrable as a longitudinal result of the repeated measurements of stature. The dose effect in the third trimester was not significant under either the L or the L-Q model. The relationship between birth weights and repeated measurements of stature in adolescence is discussed based on the results obtained by a growth curve analysis.

Adolescent↗
PubMed1998

Role of metabolic gases in bubble formation during hypobaric exposures.

Our hypothesis is that metabolic gases play a role in the initial explosive growth phase of bubble formation during hypobaric exposures. Models that account for optimal internal tensions of dissolved gases to predict the probability of occurrence of venous gas emboli were statistically fitted to 426 hypobaric exposures from National Aeronautics and Space Administration tests. The presence of venous gas emboli in the pulmonary artery was detected with an ultrasound Doppler detector. The model fit and parameter estimation were done by using the statistical method of maximum likelihood. The analysis results were as follows. 1) For the model without an input of noninert dissolved gas tissue tension, the log likelihood (in absolute value) was 255.01. 2) When an additional parameter was added to the model to account for the dissolved noninert gas tissue tension, the log likelihood was 251.70. The significance of the additional parameter was established based on the likelihood ratio test (P < 0.012). 3) The parameter estimate for the dissolved noninert gas tissue tension participating in bubble formation was 19. 1 kPa (143 mmHg). 4) The additional gas tissue tension, supposedly due to noninert gases, did not show an exponential decay as a function of time during denitrogenation, but it remained constant. 5) The positive sign for this parameter term in the model is characteristic of an outward radial pressure of gases in the bubble. This analysis suggests that dissolved gases other than N2 in tissues may facilitate the initial explosive bubble-growth phase.

Air Pressure↗
PubMed1994

A stable, multivariate extension of the log-normal survival model.

When applied to survival data from a population of cancer patients, the log-normal model provides estimates of three important parameters: cured fraction, mean log survival time, and standard deviation log survival time. In the original model, however, these parameters are unrelated to prognostic covariates. Furthermore, the original algorithm is computationally unstable and highly dependent on initial parameter estimates. We have developed an extension of the log-normal model that stabilizes computation and expresses survival parameters as functions of prognostic covariates. We have also developed an ancillary algorithm that provides reliable initial estimates.

Algorithms↗
PubMed2002

Ascertainment adjustment in complex diseases.

Genetic studies of complex diseases must confront two statistically difficult issues simultaneously. First, in many settings, to minimize the number of individuals to be genotyped, families enriched for disease must be oversampled. Also, statistical models in family studies should allow for residual association. This association will represent unmeasured genetic and environmental factors influencing disease risk. Dealing with these features simultaneously is both compelling and challenging. Burton et al. [2000] (Am. J. Hum. Gen. 69: 1505-14) recently discussed this issue and suggested that ascertainment corrections may lead to problematic parameter estimation. We revisit the issues and examples of Burton et al. [2000] (Am. J. Hum. Genet. 69: 1505-14) and present a more optimistic assessment. Estimation in this context is conceptually straightforward, but may be more problematic in practice. Specifically, we find that even slight misspecification of the random effects distribution in ascertainment-adjusted likelihood can yield severely biased parameter estimates. This result should make scientists wary when interpreting results from ascertainment-adjusted variance-component models. .

Bias↗
PubMed1985

JANA: a new iterative polyexponential curve stripping program.

A new iterative polyexponential curve stripping technique for the provision of initial pharmacokinetic parameter estimates has been developed. Such estimates are required for nonlinear least-squares curve fitting. In contrast to conventional curve stripping, this new technique does not make any assumption about the relative magnitudes of the exponential rate constants. Hence, the parameter estimates which it provides are free of the bias which may arise in conventional curve stripping. A BASIC program called JANA has been developed to implement the new curve stripping procedure.

Computers↗