title: Estimation of intervention effect using paired interval-censored data with clumping below lower detection limit.
abstract: Outcome variables that are semicontinuous with clumping at zero are commonly seen in biomedical research. In addition, the outcome measurement is sometimes subject to interval censoring and a lower detection limit (LDL). This gives rise to interval-censored observations with clumping below the LDL. Level of antibody against influenza virus measured by the hemagglutination inhibition assay is an example. The interval censoring is due to the assay's technical procedure. The clumping below LDL is likely a result of the lack of prior exposure in some individuals such that they either have zero level of antibodies or do not have detectable level of antibodies. Given a pair of such measurements from the same subject at two time points, a binary 'fold-increase' endpoint can be defined according to the ratio of these two measurements, as it often is in vaccine clinical trials. The intervention effect or vaccine immunogenicity can be assessed by comparing the binary endpoint between groups of subjects given different vaccines or placebos. We introduce a two-part random effects model for modeling the paired interval-censored data with clumping below the LDL. Based on the estimated model parameters, we propose to use Monte Carlo approximation for estimation of the 'fold-increase' endpoint and the intervention effect. Bootstrapping is used for variance estimation. The performance of the proposed method is demonstrated by simulation. We analyze antibody data from an influenza vaccine trial for illustration.
authors.affiliation: Centre for Quantitative Medicine, Duke-NUS Graduate Medical School, Singapore.
authors.forename: Ying
authors.initials: Y
authors.lastname: Xu
authors.forename: K F
authors.initials: KF
authors.lastname: Lam
authors.forename: Benjamin J
authors.initials: BJ
authors.lastname: Cowling
authors.forename: Yin Bun
authors.initials: YB
authors.lastname: Cheung
mesh_terms: D000293:Adolescent; D000704:Analysis of Variance; D000914:Antibodies, Viral / Q000097:blood; D035843:Biomedical Research / Q000379:methods* / Q000706:statistics & numerical data; D002648:Child; D003198:Computer Simulation; D006723:Hong Kong; D006801:Humans; D053118:Influenza A Virus, H1N1 Subtype / Q000187:drug effects / Q000276:immunology; D007252:Influenza Vaccines / Q000008:administration & dosage / Q000276:immunology*; D007251:Influenza, Human / Q000097:blood / Q000276:immunology* / Q000517:prevention & control / Q000821:virology; D016013:Likelihood Functions; D015233:Models, Statistical; D009010:Monte Carlo Method; D017063:Outcome Assessment, Health Care / Q000379:methods* / Q000706:statistics & numerical data; D016032:Randomized Controlled Trials as Topic / Q000379:methods* / Q000706:statistics & numerical data
chemical_list: D000914:Antibodies, Viral; D007252:Influenza Vaccines
keywords: fold-increase; interval censoring; lower detection limit; semicontinuous data; two-part random effects model