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Hypertabastic survival model.

Tabatabai MA, Bursac Z, Williams DK, Singh KP - Theor Biol Med Model (2007)

Bottom Line: We then demonstrate the application of the hypertabastic survival model by applying it to data from two motivating studies.The first one demonstrates the proportional hazards version of the model by applying it to a data set from multiple myeloma study.Based on the results from the simulation study and two applications, the proposed model shows to be a flexible and promising alternative to practitioners in this field.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biostatistics, University of Arkansas for Medical Sciences, Little Rock, AR, USA. mtabatabai@cameron.edu

ABSTRACT
A new two-parameter probability distribution called hypertabastic is introduced to model the survival or time-to-event data. A simulation study was carried out to evaluate the performance of the hypertabastic distribution in comparison with popular distributions. We then demonstrate the application of the hypertabastic survival model by applying it to data from two motivating studies. The first one demonstrates the proportional hazards version of the model by applying it to a data set from multiple myeloma study. The second one demonstrates an accelerated failure time version of the model by applying it to data from a randomized study of glioma patients who underwent radiotherapy treatment with and without radiosensitizer misonidazole. Based on the results from the simulation study and two applications, the proposed model shows to be a flexible and promising alternative to practitioners in this field.

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a) Hypertabastic hazard curve for 0 <β ≤ 0.25; b) Hypertabastic hazard curve for 0.25 <β < 1; c) Hypertabastic hazard curve for β = 1; d) Hypertabastic hazard curve for 1 ≤ β < 2; e) Hypertabastic hazard curve for β = 2; f) Hypertabastic hazard curve for β > 2.
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Figure 1: a) Hypertabastic hazard curve for 0 <β ≤ 0.25; b) Hypertabastic hazard curve for 0.25 <β < 1; c) Hypertabastic hazard curve for β = 1; d) Hypertabastic hazard curve for 1 ≤ β < 2; e) Hypertabastic hazard curve for β = 2; f) Hypertabastic hazard curve for β > 2.

Mentions: 1. The hypertabastic baseline hazard function is monotone decreasing if 0 <β ≤ 0.25 (Figure 1a). Clark et al. [21] analyzed data for 825 patients diagnosed with primary epithelial ovarian carcinoma and observed that the hazard rate was initially high after the diagnosis and gradually decreased afterwards.


Hypertabastic survival model.

Tabatabai MA, Bursac Z, Williams DK, Singh KP - Theor Biol Med Model (2007)

a) Hypertabastic hazard curve for 0 <β ≤ 0.25; b) Hypertabastic hazard curve for 0.25 <β < 1; c) Hypertabastic hazard curve for β = 1; d) Hypertabastic hazard curve for 1 ≤ β < 2; e) Hypertabastic hazard curve for β = 2; f) Hypertabastic hazard curve for β > 2.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2169222&req=5

Figure 1: a) Hypertabastic hazard curve for 0 <β ≤ 0.25; b) Hypertabastic hazard curve for 0.25 <β < 1; c) Hypertabastic hazard curve for β = 1; d) Hypertabastic hazard curve for 1 ≤ β < 2; e) Hypertabastic hazard curve for β = 2; f) Hypertabastic hazard curve for β > 2.
Mentions: 1. The hypertabastic baseline hazard function is monotone decreasing if 0 <β ≤ 0.25 (Figure 1a). Clark et al. [21] analyzed data for 825 patients diagnosed with primary epithelial ovarian carcinoma and observed that the hazard rate was initially high after the diagnosis and gradually decreased afterwards.

Bottom Line: We then demonstrate the application of the hypertabastic survival model by applying it to data from two motivating studies.The first one demonstrates the proportional hazards version of the model by applying it to a data set from multiple myeloma study.Based on the results from the simulation study and two applications, the proposed model shows to be a flexible and promising alternative to practitioners in this field.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biostatistics, University of Arkansas for Medical Sciences, Little Rock, AR, USA. mtabatabai@cameron.edu

ABSTRACT
A new two-parameter probability distribution called hypertabastic is introduced to model the survival or time-to-event data. A simulation study was carried out to evaluate the performance of the hypertabastic distribution in comparison with popular distributions. We then demonstrate the application of the hypertabastic survival model by applying it to data from two motivating studies. The first one demonstrates the proportional hazards version of the model by applying it to a data set from multiple myeloma study. The second one demonstrates an accelerated failure time version of the model by applying it to data from a randomized study of glioma patients who underwent radiotherapy treatment with and without radiosensitizer misonidazole. Based on the results from the simulation study and two applications, the proposed model shows to be a flexible and promising alternative to practitioners in this field.

Show MeSH
Related in: MedlinePlus