Parametric frailty models for clustered data with arbitrary censoring: application to effect of male circumcision on HPV clearance

Analysis of longitudinal multivariate outcome data from couples cohort studies: application to HPV transmission dynamics

We consider a specific situation of correlated data where multiple outcomes are repeatedly measured on each member of a couple. Such multivariate longitudinal data from couples may exhibit multi-faceted correlations which can be further complicated if there are polygamous partnerships. An example is data from cohort studies on human papillomavirus (HPV) transmission dynamics in heterosexual couples. HPV is a common sexually transmitted disease with 14 known oncogenic types causing anogenital cancers. The binary outcomes on the multiple types measured in couples over time may introduce inter-type, intra-couple, and temporal correlations. Simple analysis using generalized estimating equations or random effects models lacks interpretability and cannot fully utilize the available information. We developed a hybrid modeling strategy using Markov transition models together with pairwise composite likelihood for analyzing such data. The method can be used to identify risk factors associated with HPV transmission and persistence, estimate difference in risks between male-to-female and female-to-male HPV transmission, compare type-specific transmission risks within couples, and characterize the inter-type and intra-couple associations. Applying the method to HPV couple data collected in a Ugandan male circumcision (MC) trial, we assessed the effect of MC and the role of gender on risks of HPV transmission and persistence.

Multilevel mixed effects parametric survival models using adaptive Gauss-Hermite quadrature with application to recurrent events and individual participant data meta-analysis

Multilevel mixed effects survival models are used in the analysis of clustered survival data, such as repeated events, multicenter clinical trials, and individual participant data (IPD) meta-analyses, to investigate heterogeneity in baseline risk and covariate effects. In this paper, we extend parametric frailty models including the exponential, Weibull and Gompertz proportional hazards (PH) models and the log logistic, log normal, and generalized gamma accelerated failure time models to allow any number of normally distributed random effects. Furthermore, we extend the flexible parametric survival model of Royston and Parmar, modeled on the log-cumulative hazard scale using restricted cubic splines, to include random effects while also allowing for non-PH (time-dependent effects). Maximum likelihood is used to estimate the models utilizing adaptive or nonadaptive Gauss-Hermite quadrature. The methods are evaluated through simulation studies representing clinically plausible scenarios of a multicenter trial and IPD meta-analysis, showing good performance of the estimation method. The flexible parametric mixed effects model is illustrated using a dataset of patients with kidney disease and repeated times to infection and an IPD meta-analysis of prognostic factor studies in patients with breast cancer. User-friendly Stata software is provided to implement the methods.

Sample size determination in shared frailty models for multivariate time-to-event data

The frailty model is increasingly popular for analyzing multivariate time-to-event data. The most common model is the shared frailty model. Although study design consideration is as important as analysis strategies, sample size determination methodology in studies with multivariate time-to-event data is greatly lacking in the literature. In this article, we develop a sample size determination method for the shared frailty model to investigate the treatment effect on multivariate event times. We analyzed the data using both a parametric model and a piecewise model with unknown baseline hazard, and compare the empirical power with the calculated power. Last, we discuss the formula for testing the treatment effect on recurrent events.

Sexual behaviour and less frequent bathing are associated with higher human papillomavirus incidence in a cohort study of uncircumcised Kenyan men

OBJECTIVES

Data on the acquisition of human papillomavirus (HPV) infection in men are limited, especially from developing regions including Africa. The objective of this study was to characterise and determine the risk factors of HPV acquisition among a cohort of uncircumcised men participating in a randomised controlled trial (RCT) of male circumcision in Kisumu, Kenya.

METHODS

Penile exfoliated cell specimens were collected at baseline, 6- and 12-month follow-up visits from the glans/coronal sulcus and shaft of men enrolled in the control arm of the RCT between 2002 and 2005. All participants were HIV seronegative, aged 17-24 years at baseline and remained uncircumcised over follow-up. Specimens were tested with GP5+/6+ PCR to detect 44 HPV types. Parametric frailty models were used to assess risk factors of HPV incidence.

RESULTS

The median age of 966 participants was 20 years. The median follow-up time was 12.1 months. The incidence rate (IR) of any HPV infection was 49.3/1000 person-months with HPV16 having the highest IR (10.9/1000 person-months). The strongest risk factors for overall HPV incidence were bathing less frequently than daily (adjusted HR=2.6; 95% CI 1.0 to 6.5) and having ≥ 2 female sexual partners in the past year (adjusted HR=1.6; 95% CI 1.2 to 2.1).

CONCLUSIONS

HPV IRs were notably high in this cohort of high-risk, uncircumcised men from Kisumu, Kenya, with the number of sexual partners and bathing frequency being the strongest risk factors.

Key considerations and current perspectives of epidemiological studies on human papillomavirus persistence, the intermediate phenotype to cervical cancer

Persistent infection with human papillomavirus (HPV) causes essentially all precancerous cervical lesions and cervical cancer in females and thus is an important intermediate phenotype to cervical cancer. A majority of infected individuals naturally clear HPV viral infection, but the virus persists in a subset of infected hosts and the mechanism for this differential outcome is not well described. Most of the epidemiological studies have been cross-sectional in nature, and even with longitudinal studies, the definition of HPV persistence or clearance has not been well defined. There is no consensus on the correct time interval between HPV DNA tests, or how to utilize HPV persistence information in clinical management because there is no treatment for HPV. While most studies are performed with the endpoint of cancer, the intermediate phenotype has been overlooked. Epidemiological studies of HPV persistence suffer with several challenges in definitions, study designs, and analyses that undermine its importance in identifying and understanding the interactions between the viral and host genomes in the process of HPV infection pathogenesis. We have evaluated the current status of HPV persistence and provide perspectives on how the field would benefit from a research focus on intermediate phenotype in epidemiological studies.

Vaccine-relevant human papillomavirus (HPV) infections and future acquisition of high-risk HPV types in men

BACKGROUND

Little is known about type-specific associations between prevalent human papillomavirus (HPV) infections and risk of acquiring other HPV types in men. Data on natural clustering of HPV types are needed as a prevaccine distribution to which postvaccine data can be compared.

METHODS

Using data from a randomized controlled trial of male circumcision in Kisumu, Kenya, adjusted mean survival ratios were estimated for acquisition of any-HPV, high-risk (HR) HPV, and individual HR-HPV types among men uninfected as compared to those infected with vaccine-relevant HPV types 16, 18, 31, 45, 6, or 11 at baseline.

RESULTS

Among 1097 human immunodeficiency virus-negative, uncircumcised men, 2303 incident HPV infections were detected over 2534 person-years of follow-up. Although acquisition of individual HR-HPV types varied by baseline HPV type, there was no clear evidence of shorter times to acquisition among men without vaccine-relevant HPV-16, -18, -31, -45, -6, or -11 infections at baseline, as compared to men who did have these infections at baseline.

CONCLUSIONS

These prospective data on combinations of HPV infections over time do not suggest the potential for postvaccination HPV type replacement. Future surveillance studies are needed to definitely determine whether elimination of HPV types by vaccination will alter the HPV type distribution in the population.

Transitional probability-based model for HPV clearance in HIV-1-positive adolescent females

Background

HIV-1-positive patients clear the human papillomavirus (HPV) infection less frequently than HIV-1-negative. Datasets for estimating HPV clearance probability often have irregular measurements of HPV status and risk factors. A new transitional probability-based model for estimation of probability of HPV clearance was developed to fully incorporate information on HIV-1-related clinical data, such as CD4 counts, HIV-1 viral load (VL), highly active antiretroviral therapy (HAART), and risk factors (measured quarterly), and HPV infection status (measured at 6-month intervals).

Methodology and Findings

Data from 266 HIV-1-positive and 134 at-risk HIV-1-negative adolescent females from the Reaching for Excellence in Adolescent Care and Health (REACH) cohort were used in this study. First, the associations were evaluated using the Cox proportional hazard model, and the variables that demonstrated significant effects on HPV clearance were included in transitional probability models. The new model established the efficacy of CD4 cell counts as a main clearance predictor for all type-specific HPV phylogenetic groups. The 3-month probability of HPV clearance in HIV-1-infected patients significantly increased with increasing CD4 counts for HPV16/16-like (p<0.001), HPV18/18-like (p<0.001), HPV56/56-like (p = 0.05), and low-risk HPV (p<0.001) phylogenetic groups, with the lowest probability found for HPV16/16-like infections (21.60±1.81% at CD4 level 200 cells/mm³, p<0.05; and 28.03±1.47% at CD4 level 500 cells/mm³). HIV-1 VL was a significant predictor for clearance of low-risk HPV infections (p<0.05). HAART (with protease inhibitor) was significant predictor of probability of HPV16 clearance (p<0.05). HPV16/16-like and HPV18/18-like groups showed heterogeneity (p<0.05) in terms of how CD4 counts, HIV VL, and HAART affected probability of clearance of each HPV infection.

Conclusions

This new model predicts the 3-month probability of HPV infection clearance based on CD4 cell counts and other HIV-1-related clinical measurements.

A modeling framework for the analysis of HPV incidence and persistence: a semi-parametric approach for clustered binary longitudinal data analysis

Human papillomavirus (HPV) infection is a common sexually transmitted disease of growing public health importance, and over 40 genotypes have been identified in genital infections. Current HPV cohort studies often follow participants at pre-determined visits, such as every 6 months, and data generated from such epidemiology studies can be described as clustered longitudinal binary data where correlation arises in two ways: the directionless clustering due to the multiple genotypes tested within an individual, and the temporal correlation among the repeated measurements on the same genotype along time. Current analyses for identification of risk factors associated with HPV incidence and persistence often either do not fully utilize information in the data set or ignore the correlation between the multiple genotypes. Given the scientific definition of incidence and persistence, conditional probability modeling provides us a natural mathematical tool. We thus present a semi-parametric regression model for such data where full specification of the joint multivariate binary distribution is avoided by using conditioning argument to handle the temporal correlation and GEE to account for the correlation between the multiple genotypes. The model is applied to the HPV data from the Rakai male circumcision (MC) trial to evaluate the as-treated efficacy of MC and also identify modifiable risk factors for incidence and persistence of oncogenic HPV types in men. A simulation study is performed to provide empirical information on the number of individuals that is needed for satisfactory power and estimation accuracy of the association parameter estimates in future studies.