Point-source modelling using matched case-control data

Diggle, Peter J.; Morris, Sara; Wakefield, Jon

doi:10.1093/biostatistics/1.1.89

Cited by 52 publications

(39 citation statements)

References 32 publications

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“…Following imaging at 100 kV in an FEI Tecnai G2 transmission electron microscope, digital images were analyzed for spatial mapping by Ripley's univariate K function to determine the extent of clustering within the immunogold patterns (44). Bootstrap tests to examine differences between replicated point patterns were constructed exactly as described earlier (12); statistical variance was evaluated against 1,000 bootstrap samples.…”

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confidence: 99%

Raft Protein Clustering Alters N-Ras Membrane Interactions and Activation Pattern

Eisenberg

Beckett

Prior

et al. 2011

Molecular and Cellular Biology

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The trafficking, membrane localization, and lipid raft association of Ras proteins, which are crucial oncogenic mediators, dictate their isoform-specific biological responses. Accordingly, their spatiotemporal dynamics are tightly regulated. While extensively studied for H-and K-Ras, such information on N-Ras, an etiological oncogenic factor, is limited. Here, we report a novel mechanism regulating the activationdependent spatiotemporal organization of N-Ras, its modulation by biologically relevant stimuli, and isoform-specific effects on signaling. We combined patching/immobilization of another membrane protein with fluorescence recovery after photobleaching (patch-FRAP) and FRAP beam size analysis to investigate N-Ras membrane interactions. Clustering of raft-associated proteins, either glycosylphosphatidylinositolanchored influenza virus hemagglutinin (HA-GPI) or fibronectin receptors, selectively enhanced the plasma membrane-cytoplasm exchange of N-Ras-GTP (preferentially associated with raft domains) in a cholesterol-dependent manner. Electron microscopy (EM) analysis showed N-Ras-GTP localization in cholesterol-sensitive clusters, from which it preferentially detached upon HA-GPI cross-linking. HA-GPI clustering enhanced the Golgi compartment (GC) accumulation and signaling of epidermal growth factor (EGF)-stimulated N-Ras-GTP. Notably, the cross-linking-mediated enhancement of N-Ras-GTP exchange and GC accumulation depended strictly on depalmitoylation. We propose that the N-Ras activation pattern (e.g., by EGF) is altered by raft protein clustering, which enhances N-Ras-GTP raft localization and depalmitoylation, entailing its exchange and GC accumulation following repalmitoylation. This mechanism demonstrates a functional signaling role for the activation-dependent differential association of Ras isoforms with raft nanodomains.

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confidence: 99%

Raft Protein Clustering Alters N-Ras Membrane Interactions and Activation Pattern

Eisenberg

Beckett

Prior

et al. 2011

Molecular and Cellular Biology

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“…The parameters ( α , β ) in model have a natural interpretation: α is proportional to the disease odds at the point source ( α = [ r (0) ∕ ρ ] − 1); β measures the rate of decay with increasing distance from the point source, in the unit of distance x . Under this model setting, as x → ∞ , we have f ( x ) → 1 and the risk function p ( x ) = P ( Y = 1 | x ) = ρf ( x ) ∕ (1 + ρf ( x )) → ρ ∕ (1 + ρ ), that is, the background risk in the case–control population . Note that, if f ( x ) = exp ( βx ) is chosen with r ( x ) = ρf ( x ) in model , then one would have that log( r ( x )) = log( ρ ) + βx , which becomes the usual logistic regression model that assumes a linear distance‐odds relationship with log odds ratio β and intercept log( ρ ).…”

Section: Model Formulationmentioning

confidence: 99%

“…For a 1: M matched case–control study with N matched pairs, the risk of disease for an individual at distance x in the i th stratum can be expressed as

P_{i} (Y MathClass-rel= 1 MathClass-rel| x) MathClass-rel= \frac{r_{i} (x)}{1 MathClass-bin+ r_{i} (x)} MathClass-rel= \frac{ρ_{i} f (x)}{1 MathClass-bin+ ρ_{i} f (x)} MathClass-punc, i MathClass-rel= 1 MathClass-punc, MathClass-op\dots MathClass-punc, N MathClass-punc,

where the baseline odds ρ i for the i th stratum can potentially vary across matched pairs under the matched case–control design. The conditional likelihood, given the exposure vector at distance x i = ( x i 1 , x i 2 , … , x i ( M + 1) ) for the i th stratum, that the case is at distance x i 1 is

\begin{matrix} aligned \\ right L_{i} MathClass-open(α, β MathClass-close) & left = P MathClass-open(Y_{i 1} = 1, Y_{i 2} = \dots = Y_{i MathClass-open(M + 1 MathClass-close)} = 0 | Y_{i 1} + Y_{i 2} + \dots + Y_{i MathClass-open(M + 1 MathClass-close)} = 1, x_{i} MathClass-close) & right \\ right & left = \frac{ρ_{i} f MathClass-open(x_{i 1} MathClass-close)}{\prod_{}} \end{matrix}

…”

Section: Model Formulationmentioning

confidence: 99%

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Point source modeling of matched case–control data with multiple disease subtypes

Mukherjee

Batterman

2012

Statistics in Medicine

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In this paper, we propose nonlinear distance-odds models investigating elevated odds around point sources of exposure, under a matched case-control design where there are subtypes within cases. We consider models analogous to the polychotomous logit models and adjacent-category logit models for categorical outcomes and extend them to the nonlinear distance-odds context. We consider multiple point sources as well as covariate adjustments. We evaluate maximum likelihood, profile likelihood, iteratively reweighted least squares, and a hierarchical Bayesian approach using Markov chain Monte Carlo techniques under these distance-odds models. We compare these methods using an extensive simulation study and show that with multiple parameters and a nonlinear model, Bayesian methods have advantages in terms of estimation stability, precision, and interpretation. We illustrate the methods by analyzing Medicaid claims data corresponding to the pediatric asthma population in Detroit, Michigan, from 2004 to 2006.

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“…(See Breslow and Day 1980;Breslow 1996 for background and references.) With no missing data, there is a complementary Bayesian literature for ordinary and matched case-control studies (see Zelen and Parker 1986;Müller and Roeder 1997;Diggle, Morris, and Wakefield 2000;Seaman and Richardson 2001;Ghosh and Chen 2002).…”

Section: Introductionmentioning

confidence: 99%

Semiparametric Bayesian Analysis of Matched Case-Control Studies With Missing Exposure

Sinha

Mukherjee

Ghosh

et al. 2005

Journal of the American Statistical Association

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This article considers Bayesian analysis of matched case-control problems when one of the covariates is partially missing. Within the likelihood context, the standard approach to this problem is to posit a fully parametric model among the controls for the partially missing covariate as a function of the covariates in the model and the variables making up the strata. Sometimes the strata effects are ignored at this stage. Our approach differs not only in that it is Bayesian, but, far more importantly, in the manner in which it treats the strata effects. We assume a Dirichlet process prior with a normal base measure for the stratum effects and estimate all of the parameters in a Bayesian framework. Three matched case-control examples and a simulation study are considered to illustrate our methods and the computing scheme.

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Point-source modelling using matched case-control data

Cited by 52 publications

References 32 publications

Raft Protein Clustering Alters N-Ras Membrane Interactions and Activation Pattern

Raft Protein Clustering Alters N-Ras Membrane Interactions and Activation Pattern

Point source modeling of matched case–control data with multiple disease subtypes

Semiparametric Bayesian Analysis of Matched Case-Control Studies With Missing Exposure

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