عنوان

Parametric reversed hazards model for left censored data with application to HIV

Number

TL49242

.Language of Text, Soundtrack etc

انگلیسی

Title Proper

Parametric reversed hazards model for left censored data with application to HIV

General Material Designation

[Thesis]

First Statement of Responsibility

Farahnaz Islam

Subsequent Statement of Responsibility

Chakraborty, Hrishikesh

Name of Publisher, Distributor, etc.

University of South Carolina

Date of Publication, Distribution, etc.

2016

Specific Material Designation and Extent of Item

61

Text of Note

Committee members: Hussey, James; McLain, Alexander

Text of Note

Place of publication: United States, Ann Arbor; ISBN=978-1-369-56464-8

Dissertation or thesis details and type of degree

M.S.P.H.

Discipline of degree

Biostatistics

Body granting the degree

University of South Carolina

Text preceding or following the note

2016

Text of Note

Left censoring is generally a rare type of censoring in time-to-event data, however there are some fields such as HIV related studies where it commonly occurs. Currently, there is no clear recommendation in the literature on the optimal model and distribution to analyze left-censored data. Recommendations can help researchers apply more accurate models for this type of censoring. This study derives the Parametric Reversed Hazards (PRH) Model for a variety of distributions which may be appropriate for left censored data. The performance of these derived PRH models to analyze HIV viral load data are compared using extensive simulations and a guideline is established for which distribution/s are most appropriate. Each simulation setup is varied by sample size and proportion of censoring to find a consistently high performance distribution. The best distribution is determined using the information criteria: AIC, AICC, HQIC, and CAIC. The South Carolina Enhanced HIV/AIDS Reporting Surveillance System (SC eHARS) data were utilized and a bootstrap study provided further insights towards appropriateness of the distributions in analyzing HIV viral load data. Results from simulation studies point to the Generalized Inverse Weibull distribution to outperform all others across censoring rates and sample sizes. The bootstrap study, however, contradicts this and suggests the Marshal-Olkin distribution to be the superior performer. This disagreement may have resulted from the special heavy tail nature of viral load data that demands further attention. Application of the best performing models on the SC eHARS database revealed important effects explaining trends of viral load over time.

Biostatistics; Statistics; Public health

Subject Term

Pure sciences;Biological sciences;Health and environmental sciences

Islam, Md Azharul

Chakraborty, Hrishikesh

Subdivision

Biostatistics

University of South Carolina

Call Number

1874575504; 10193083

Electronic name

p

[Thesis]

276903

a

Y