Computationally Efficient Estimation for the Generalized Odds Rate Mixture Cure Model With Interval-Censored Data

<p>For semiparametric survival models with interval-censored data and a cure fraction, it is often difficult to derive nonparametric maximum likelihood estimation due to the challenge in maximizing the complex likelihood function. In this article, we propose a computationally efficient EM algorithm, facilitated by a gamma-Poisson data augmentation, for maximum likelihood estimation in a class of generalized odds rate mixture cure (GORMC) models with interval-censored data. The gamma-Poisson data augmentation greatly simplifies the EM estimation and enhances the convergence speed of the EM algorithm. The empirical properties of the proposed method are examined through extensive simulation studies and compared with numerical maximum likelihood estimates. An R package “GORCure” is developed to implement the proposed method and its use is illustrated by an application to the Aerobic Center Longitudinal Study dataset. Supplementary material for this article is available online.</p

Illustrations of regions of statistical inference for GWA and NGS studies.

<p>The Signals (“S”), Indistinguishable (“I”), and Noise (“N”) regions are shown. False-positive control allows the selection of variants in the Signals region, whereas false-negative control selects from both the Signals and Indistinguishable regions. In NGS studies with rare variants, the Signals region often degenerates due to extremely low MAF and high dimensionality.</p

Comparisons across varying effect sizes and numbers of variants at <i>s</i> = 50.

<p>Performance of AFNC, FDR, and Bonferroni is evaluated in terms of sensitivity, specificity, and <i>g</i>-measure. Results are shown for <i>s</i> = 50 number of causal variants when <i>C</i> ≠ 0 and <i>n</i> = 2000 number of samples.</p

Annotation of AFNC-selected non-synonymous and splice-site variants in the analysis of CoLaus data.

<p>Annotation of AFNC-selected non-synonymous and splice-site variants in the analysis of CoLaus data.</p

Annotation of AFNC-selected variants of candidate genes in the analysis of CoLaus data.

<p>Annotation of AFNC-selected variants of candidate genes in the analysis of CoLaus data.</p

Number of variants selected in the analysis of CoLaus data at different control levels.

<p>Number of variants selected in the analysis of CoLaus data at different control levels.</p

Comparisons across varying sample sizes and numbers of causal variants at <i>C</i> = 0.5.

<p>Performance of AFNC, FDR, and Bonferroni is evaluated in terms of sensitivity, specificity, and <i>g</i>-measure. Results are shown for the effect-size multiplier <i>C</i> = 0.5 and <i>d</i> = 100,000 number of variants.</p

Classifications of variants under multiple testing control.

<p>Classifications of variants under multiple testing control.</p

Effects of incubation time on fluorescence intensities for 100 mg/L CRP, 10 ng/mLBNP, 100 ng/mL cTnI.

<p>The number of repeated experiments at any concentration was five. Error bars represent standard deviations.</p

Microscope image of three SCCBs with or without FITC-antibody conjuncted and optics property.

<p><b>A</b>) Three dimensional image of SCCBs composed of silica nanoparticles with the diameter of 290 nm. <b>B</b>) Three dimensional image of SCCBs composed of silica nanoparticles with the diameter of 314 nm. <b>C</b>) Three dimensional image of SCCBs composed of silica nanoparticles with the diameter of 375 nm. <b>D</b>) Black and white photograph of FITC-antibody conjuncted SCCBs composed of silica nanoparticles with the diameter of 290 nm. <b>E</b>) Color photograph of FITC-antibody conjuncted SCCBs composed of silica nanoparticles with the diameter of 290 nm. <b>F</b>) Reflection spectra of the three kinds of SCCBs.</p