Weighted Kaplan-Meier estimators motivating to estimate HIV-1 RNA reduction censored by a limit of detection.
Ismaïl AhmedPhilippe FlandrePublished in: Statistics in medicine (2020)
Measuring the magnitude of reduction in HIV-1 RNA levels accurately is difficult because many patients have a censored reduction due to the limit of detection (LOD) of the virologic assay being employed. The use of censored methods has improved the analysis of such reductions compared with crude methods but implies independent censoring. For HIV-1 RNA reduction data, the value at which a patient's HIV-1 RNA reduction becomes censored is mainly determined by the patient's baseline HIV-1 RNA level. We suggest two possibilities based on modification of the redistribution to the right algorithm to handle the situation of dependence either from a single continuous marker, that is, the baseline HIV-1 RNA level, or from multiple markers. Two series of simulation, one in the HIV-1 RNA setting and one in the classical censoring setting, compared performance of the previous methods with our suggestions. Our proposed estimators show good performances when the dependent censoring is due to LOD. Overall, in the classical censoring setting, our suggestions perform as well as other methods including the Inverse Probability of Censoring Weighted and the Kaplan-Meier imputation with Bootstrap. We applied those estimators to estimate the HIV-1 RNA reduction at week 8 of 502 patients who received a raltegravir-containing regimen and to data from the Mayo Clinic trial in primary biliary cirrhosis.
Keyphrases
- antiretroviral therapy
- hiv positive
- hiv infected
- hiv testing
- human immunodeficiency virus
- hiv aids
- hepatitis c virus
- men who have sex with men
- hiv infected patients
- nucleic acid
- south africa
- randomized controlled trial
- clinical trial
- case report
- machine learning
- magnetic resonance
- primary care
- ejection fraction
- study protocol
- newly diagnosed
- big data
- high throughput
- label free
- patient reported outcomes
- network analysis