Evidence map›Paper›PMID 24850445›Full record

ArticleCPT: pharmacometrics & systems pharmacology2014

Model-based meta-analysis for quantifying Paclitaxel dose response in cancer patients.

D Lu, A Joshi, H Li, N Zhang, M M Ren, Y Gao, R Wada, J Y Jin

Abstract read
In one paragraph

Article in CPT: pharmacometrics & systems pharmacology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
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  5. Review
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  9. Review
  10. A Six-Stage Workflow for Robust Application of Systems Pharmacology.CPT: pharmacometrics & systems pharmacology · 2016
    Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

D LuDepartment of Clinical Pharmacology, Genentech, South San Francisco, California, USA.
A JoshiDepartment of Clinical Pharmacology, Genentech, South San Francisco, California, USA.
H LiDrug Development Consulting Services, Quantitative Solutions, Menlo Park, California, USA.
N ZhangDrug Development Consulting Services, Quantitative Solutions, Menlo Park, California, USA.
M M RenDepartment of Clinical Pharmacology, Genentech, South San Francisco, California, USA.
Y GaoDrug Development Consulting Services, Quantitative Solutions, Menlo Park, California, USA.
R WadaDrug Development Consulting Services, Quantitative Solutions, Menlo Park, California, USA.
J Y JinDepartment of Clinical Pharmacology, Genentech, South San Francisco, California, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Model-based meta-analysis of dose response is a sophisticated method to guide dose and regimen selection. In this report, the effects of paclitaxel dose and regimen (weekly or every 3 weeks) on the efficacy and safety in cancer patients were quantified by model-based meta-analysis of 29 monotherapy trials. Logistic regression models were developed to assess the relationship between dose and objective response rate or neutropenia rate. Survival models were developed to assess the relationship between dose and overall survival or progression-free survival. Paclitaxel efficacy (e.g., objective response rate, median overall survival, and progression-free survival) is correlated with average dose per week (mg/m(2)/week), whereas safety (e.g., neutropenia rate) is correlated with dose per administration (mg/m(2)). Weekly paclitaxel regimen at 65-80 mg/m(2) is supported to have comparable to better efficacy and lower neutropenia incidence than an every-3-week regimen at 175 mg/m(2).

Identifiers

PMID24850445
PMCPMC4055787

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.