Biomedical subjects

M P Wand

Publications and source records attributed to M P Wand.

6 recordsLinked to original sources

PubMed2002

A local likelihood proportional hazards model for interval censored data.

We discuss the use of local likelihood methods to fit proportional hazards regression models to right and interval censored data. The assumed model allows for an arbitrary, smoothed baseline hazard on which a vector of covariates operates in a proportional manner, and thus produces an interpretable baseline hazard function along with estimates of global covariate effects. For estimation, we extend the modified EM algorithm suggested by Betensky, Lindsey, Ryan and Wand. We illustrate the method with data on times to deterioration of breast cosmeses and HIV-1 infection rates among haemophiliacs.

Algorithms↗

PubMed2001

Simple incorporation of interactions into additive models.

Often, the functional form of covariate effects in an additive model varies across groups defined by levels of a categorical variable. This structure represents a factor-by-curve interaction. This article presents penalized spline models that incorporate factor-by-curve interactions into additive models. A mixed model formulation for penalized splines allows for straightforward model fitting and smoothing parameter selection. We illustrate the proposed model by applying it to pollen ragweed data in which seasonal trends vary by year.

Biometry↗

PubMed2000

Negative binomial additive models.

The generalized additive model is extended to handle negative binomial responses. The extension is complicated by the fact that the negative binomial distribution has two parameters and is not in the exponential family. The methodology is applied to data involving DNA adduct counts and smoking variables among ex-smokers with lung cancer. A more detailed investigation is made of the parametric relationship between the number of adducts and years since quitting while retaining a smooth relationship between adducts and the other covariates.

Algorithms↗

PubMed2000

Polymorphism of the beta(2)-adrenergic receptor gene and desensitization in human airway smooth muscle.

We examined the influence of two common polymorphic forms of the beta(2)-adrenergic receptor (beta(2)AR): the Gly16 and Glu27 alleles, on acute and long-term beta(2)AR desensitization in human airway smooth muscle (HASM) cells. In cells from 15 individuals, considered without respect to genotype, pretreatment with Isoproterenol (ISO) at 10(-7) M for 1 h or 24 h caused approximately 25% and 64% decreases in the ability of subsequent ISO (10(-6) M) stimulation to reduce HASM cell stiffness as measured by magnetic twisting cytometry. Similar results were obtained with ISO-induced cyclic adenosine monophosphate (cAMP) as the outcome indicator. Data were then stratified post hoc by genotype. Cells containing at least one Glu27 allele (equivalent to presence of the Gly16Glu27 haplotype) showed significantly greater acute desensitization than did cells with no Glu27 allele, whether ISO-induced cell stiffness (34% versus 19%, p < 0.03) or cAMP formation (58% versus 11%, p < 0.02) was measured. Likewise, cells with any Glu27 allele showed greater long-term desensitization of cell stiffness and cAMP formation responses than did cells without the Glu27 allele. The distribution of genotypes limited direct conclusions about the influence of the Gly16 allele. However, presence of the Gly16Gln27 haplotype was associated with less acute and long-term desensitization of ISO-induced cAMP formation than was seen in cells without the Gly16Gln27 haplotype (14% versus 47%, p < 0.09 for short-term desensitization; 32% versus 84%, p < 0.01 for long-term desensitization), suggesting that the influence of Glu27 is not through its association with Gly16. The Glu27 allele was in strong linkage disequilibrium with the Arg19 allele, a polymorphic form of the beta(2)AR upstream peptide of the 5'-leader cistron of the beta(2)AR, and this polymorphism in the beta(2)AR 5'-flanking region may explain the effects of the Glu27 allele. Cells with any Arg19 allele showed significantly greater acute and long-term desensitization of ISO-induced cAMP formation than did cells without the Arg19 allele (54% versus 2%, p < 0.01 for short-term desensitization; 73% versus 35%, p < 0.05 for long-term desensitization). Similar results were obtained for ISO-induced changes in cell stiffness. Thus, the presence of the Glu27 allele is associated with increased acute and long-term desensitization in HASM.

Adrenergic beta-Agonists↗

PubMed1999

Local EM estimation of the hazard function for interval-censored data.

We propose a smooth hazard estimator for interval-censored survival data using the method of local likelihood. The model is fit using a local EM algorithm. The estimator is more descriptive than traditional empirical estimates in regions of concentrated information and takes on a parametric flavor in regions of sparse information. We derive two different standard error estimates for the smooth curve, one based on asymptotic theory and the other on the bootstrap. We illustrate the local EM method for times to breast cosmesis deterioration (Finkelstein, 1986, Biometrics 42, 845-854) and for times to HIV-1 infection for individuals with hemophilia (Kroner et al., 1994, Journal of AIDS 7, 279-286). Our hazard estimates for each of these data sets show interesting structures that would not be found using a standard parametric hazard model or empirical survivorship estimates.

Algorithms↗

PubMed1999

Kriging with nonparametric variance function estimation.

A method for fitting regression models to data that exhibit spatial correlation and heteroskedasticity is proposed. It is well known that ignoring a nonconstant variance does not bias least-squares estimates of regression parameters; thus, data analysts are easily lead to the false belief that moderate heteroskedasticity can generally be ignored. Unfortunately, ignoring nonconstant variance when fitting variograms can seriously bias estimated correlation functions. By modeling heteroskedasticity and standardizing by estimated standard deviations, our approach eliminates this bias in the correlations. A combination of parametric and nonparametric regression techniques is used to iteratively estimate the various components of the model. The approach is demonstrated on a large data set of predicted nitrogen runoff from agricultural lands in the Midwest and Northern Plains regions of the U.S.A. For this data set, the model comprises three main components: (1) the mean function, which includes farming practice variables, local soil and climate characteristics, and the nitrogen application treatment, is assumed to be linear in the parameters and is fitted by generalized least squares; (2) the variance function, which contains a local and a spatial component whose shapes are left unspecified, is estimated by local linear regression; and (3) the spatial correlation function is estimated by fitting a parametric variogram model to the standardized residuals, with the standardization adjusting the variogram for the presence of heteroskedasticity. The fitting of these three components is iterated until convergence. The model provides an improved fit to the data compared with a previous model that ignored the heteroskedasticity and the spatial correlation.

Agriculture↗