A semiparametric approach to analysing dose-response data

Nottingham, Quinton; Birch, Jeffrey B.

doi:10.1002/(sici)1097-0258(20000215)19:3<389::aid-sim326>3.0.co;2-j

Cited by 22 publications

(11 citation statements)

References 19 publications

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“…A robust semiparametric regression model [86] implemented by R package drc [87] was used to model dose response relationships between the macrophage infectivity (as a percentage of TZM-bl infectivity) and IC50 concentration of maraviroc, sCD4 and b12. The dosages that caused 50% inhibition (IC50) were estimated as well as their 95% confidence intervals (not shown).…”

Section: Methodsmentioning

confidence: 99%

Independent evolution of macrophage-tropism and increased charge between HIV-1 R5 envelopes present in brain and immune tissue

et al. 2012

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BackgroundTransmitted HIV-1 clade B or C R5 viruses have been reported to infect macrophages inefficiently, while other studies have described R5 viruses in late disease with either an enhanced macrophage-tropism or carrying envelopes with an increased positive charge and fitness. In contrast, our previous data suggested that viruses carrying non-macrophage-tropic R5 envelopes were still predominant in immune tissue of AIDS patients. To further investigate the tropism and charge of HIV-1 viruses in late disease, we evaluated the properties of HIV-1 envelopes amplified from immune and brain tissues of AIDS patients with neurological complications.ResultsAlmost all envelopes amplified were R5. There was clear compartmentalization of envelope sequences for four of the five subjects. However, strong compartmentalization of macrophage-tropism in brain was observed even when brain and immune tissue envelope sequences were not segregated. R5 envelopes from immune tissue of four subjects carried a higher positive charge compared to brain envelopes. We also confirm a significant correlation between macrophage tropism and sensitivity to soluble CD4, a weak association with sensitivity to the CD4 binding site antibody, b12, but no clear relationship with maraviroc sensitivity.ConclusionsOur study shows that non-macrophage-tropic R5 envelopes carrying gp120s with an increased positive charge were predominant in immune tissue in late disease. However, highly macrophage-tropic variants with lower charged gp120s were nearly universal in the brain. These results are consistent with HIV-1 R5 envelopes evolving gp120s with an increased positive charge in immune tissue or sites outside the brain that likely reflect an adaptation for increased replication or fitness for CD4+ T-cells. Our data are consistent with the presence of powerful pressures in brain and in immune tissues selecting for R5 envelopes with very different properties; high macrophage-tropism, sCD4 sensitivity and low positive charge in brain and non-macrophage-tropism, sCD4 resistance and high positive charge in immune tissue.

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Section: Methodsmentioning

confidence: 99%

Independent evolution of macrophage-tropism and increased charge between HIV-1 R5 envelopes present in brain and immune tissue

et al. 2012

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“…A related and more robust approach is also based on Waldtype confidence intervals but combined with a transformation (e.g., Namata et al 2008). Yet another related approach is to derive confidence intervals for the fitted dose-response curve and use inverse regression to obtain confidence intervals on the dose axis (Nottingham and Birch 2000); the resulting estimate is referred to as the lower effective dose (Kimmel 1993). These approaches are directly applicable for the BMD calculation proposed above and moreover they do not require much additional computation.…”

Section: Calculation Of Bmdlmentioning

confidence: 99%

A Unified Framework for Benchmark Dose Estimation Applied to Mixed Models and Model Averaging

Ritz

Gerhard

Hothorn

2013

Statistics in Biopharmaceutical Research

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This article develops a framework for benchmark dose estimation that allows intrinsically nonlinear dose-response models to be used for continuous data in much the same way as is already possible for quantal data. This means that the same dose-response model equations may be applied to both continuous and quantal data, facilitating benchmark dose estimation in general for a wide range of candidate models commonly used in toxicology. Moreover, the proposed framework provides a convenient means for extending benchmark dose concepts through the use of model averaging and random effects modeling for hierarchical data structures, reflecting increasingly common types of assay data. We illustrate the usefulness of the methodology by means of a cytotoxicology example where the sensitivity of two types of assays are evaluated and compared. By means of a simulation study, we show that the proposed framework provides slightly conservative, yet useful, estimates of benchmark dose lower limit under realistic scenarios.

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“…Based on our results from the MC simulation, the df for the UCL for the T 2 i statistics utilizing the MMRPM estimated random effects is obtained as in Equation (27), as a convex combination from the P df and the NP df via the estimated mixing parameterl as given in Equation (18). df MMRPM ¼ 1 Àl Ã df P þl Ã df NP ¼ 1 À 0:98 ð ÞÃ2 þ 0:98 Ã 5 ¼ 4:94 (27) For this analysis, we choose a overall = 0.05 which corresponds to a ¼ 1 À 1 À a overall ð Þ 1 m ¼ 1 À 1 À 0:05 ð Þ 1 20 ¼ 0:00256…”

Section: The Automobile Engine Applicationmentioning

confidence: 99%

A Semiparametric Mixed Model Approach to Phase I Profile Monitoring

Abdel‐Salam

Birch

Jensen

2012

Quality & Reliability Eng

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Profile monitoring is an approach in quality control best used where the process data follow a profile (or curve). The majority of previous studies in profile monitoring focused on the parametric (P) modeling of either linear or nonlinear profiles, with both fixed and random effects, under the assumption of correct model specification. More recently, in the absence of an obvious P model, nonparametric (NP) methods have been employed in the profile monitoring context. For situations where a P model is adequate over part of the data but inadequate of other parts, we propose a semiparametric procedure that combines both P and NP profile fits. We refer to our semiparametric procedure as mixed model robust profile monitoring (MMRPM). These three methods (P, NP and MMRPM) can account for the autocorrelation within profiles and treat the collection of profiles as a random sample from a common population. For each approach, we propose a version of Hotelling's T 2 statistic for use in Phase I analysis to determine unusual profiles based on the estimated random effects and obtain the corresponding control limits. Simulation results show that our MMRPM method performs well in making decisions regarding outlying profiles when compared to methods based on a misspecified P model or based on NP regression. In addition, however, the MMRPM method is robust to model misspecification because it also performs well when compared to a correctly specified P model. The proposed chart is able to detect changes in Phase I data and has easily calculated control limits. We apply all three methods to the automobile engine data of Amiri et al. 5 and find that the NP and the MMRPM methods indicate signals that did not occur in a P approach.

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A semiparametric approach to analysing dose-response data

Cited by 22 publications

References 19 publications

Independent evolution of macrophage-tropism and increased charge between HIV-1 R5 envelopes present in brain and immune tissue

Independent evolution of macrophage-tropism and increased charge between HIV-1 R5 envelopes present in brain and immune tissue

A Unified Framework for Benchmark Dose Estimation Applied to Mixed Models and Model Averaging

A Semiparametric Mixed Model Approach to Phase I Profile Monitoring

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