On a simple estimation of the proportional odds model under right truncation.

Retrospective sampling can be useful in epidemiological research for its convenience to explore an etiological association. One particular retrospective sampling is that disease outcomes of the time-to-event type are collected subject to right truncation, along with other covariates of interest. For regression analysis of the right-truncated time-to-event data, the so-called proportional reverse-time hazards model has been proposed, but the interpretation of its regression parameters tends to be cumbersome, which has greatly hampered its application in practice. In this paper, we instead consider the proportional odds model, an appealing alternative to the popular proportional hazards model. Under the proportional odds model, there is an embedded relationship between the reverse-time hazard function and the usual hazard function. Building on this relationship, we provide a simple procedure to estimate the regression parameters in the proportional odds model for the right truncated data. Weighted estimations are also studied.

Continued attendance in a PrEP program despite low adherence and non-protective drug levels among adolescent girls and young women in Kenya: Results from a prospective cohort study.

BACKGROUND: In sub-Saharan Africa (SSA), adolescent girls and young women (AGYW) ages 15 to 24 years represent <10% of the population yet account for 1 in 5 new HIV infections. Although oral pre-exposure prophylaxis (PrEP) with tenofovir disoproxil fumarate/emtricitabine (TDF/FTC) can be highly effective, low persistence in PrEP programs and poor adherence have limited its ability to reduce HIV incidence among women. METHODS AND FINDINGS: A total of 336 AGYW participating in the PEPFAR-funded DREAMS PrEP program in western Kenya were enrolled into a study of PrEP use conducted between 6/2019 to 1/2020. AGYW, who used daily oral TDF/FTC, completed interviews and provided dried blood spots (DBS) for measurement of tenofovir-diphosphate (TFV-DP) concentrations at enrollment and 3 months later, and 176/302 (58.3%, 95% confidence interval [95% CI 52.3 to 63.8]) met our definition of PrEP persistence: having expressed intention to use PrEP and attended both the second interview and an interim refill visit. Among AGYW with DBS taken at the second interview, only 9/197 (4.6%, [95% CI 1.6 to 7.5]) had protective TFV-DP levels (≥700 fmol/punch) and 163/197 (82.7%, [95% CI 77.5 to 88]) had levels consistent with no recent PrEP use (<10 fmol/punch). Perception of being at moderate-to-high risk for HIV if not taking PrEP was associated with persistence (adjusted odds ratio, 10.17 [95% CI 5.14 to 20.13], p < 0.001) in a model accounting for county of residence and variables that had p-value <0.1 in unadjusted analysis (age, being in school, initiated PrEP 2 to 3 months before the first interview, still active in DREAMS, having children, having multiple sex partners, partner aware of PrEP use, partner very supportive of PrEP use, partner has other partners, AGYW believes that a partner puts her at risk, male condom use, injectable contraceptive use, and implant contraceptive use). Among AGYW who reported continuing PrEP, >90% indicated they were using PrEP to prevent HIV, although almost all had non-protective TFV-DP levels. Limitations included short study duration and inclusion of only DREAMS participants. CONCLUSIONS: Many AGYW persisted in the PrEP program without taking PrEP frequently enough to receive benefit. Notably, AGYW who persisted had a higher self-perceived risk of HIV infection. These AGYW may be optimal candidates for long-acting PrEP.

Persistence of oral pre-exposure prophylaxis (PrEP) among adolescent girls and young women initiating PrEP for HIV prevention in Kenya.

The Determined, Resilient, Empowered, AIDS-free, Mentored, and Safe (DREAMS) Initiative aims to reduce HIV infections among adolescent girls and young women (AGYW) in Africa. Oral pre-exposure prophylaxis (PrEP) is offered through DREAMS in Kenya to eligible AGYW in high burden counties including Kisumu and Homa Bay. This study examines PrEP persistence among AGYW in high burden community-based PrEP delivery settings. We evaluated PrEP persistence among AGYW in the DREAMS PrEP program in Kisumu and Homa Bay using survival analysis and programmatic PrEP refill data collected between March through December 2017. Among 1,259 AGYW who initiated PrEP during the study period, the median persistence time in the program was 56 days (95% CI: 49-58 days) and the proportion who persisted 3 months later was 37% (95% CI: 34-40%). Persistence varied by county (p < 0.001), age at PrEP initiation (p = 0.002), marital status (p = 0.008), transactional sex (p = 0.002), gender-based violence (GBV) experience (p = 0.009) and current school attendance (p = 0.001) at DREAMS enrollment. Persistence did not vary with orphan status, food insecurity, condom use, age at first sexual encounter or engagement in age-disparate sex at DREAMS enrollment. Targeted strategies are needed to improve AGYW retention in the PrEP program.

Methods for generalized change-point models: with applications to human immunodeficiency virus surveillance and diabetes data.

In many epidemiological and biomedical studies, the association between a response variable and some covariates of interest may change at one or several thresholds of the covariates. Change-point models are suitable for investigating the relationship between the response and covariates in such situations. We present change-point models, with at least one unknown change-point occurring with respect to some covariates of a generalized linear model for independent or correlated data. We develop methods for the estimation of the model parameters and investigate their finite-sample performances in simulations. We apply the proposed methods to examine the trends in the reported estimates of the annual percentage of new human immunodeficiency virus (HIV) diagnoses linked to HIV-related medical care within 3 months after diagnosis using HIV surveillance data from the HIV prevention trial network 065 study. We also apply our methods to a dataset from the Pima Indian diabetes study to examine the effects of age and body mass index on the risk of being diagnosed with type 2 diabetes.

On estimation of time-dependent attributable fraction from population-based case-control studies.

Population attributable fraction (PAF) is widely used to quantify the disease burden associated with a modifiable exposure in a population. It has been extended to a time-varying measure that provides additional information on when and how the exposure's impact varies over time for cohort studies. However, there is no estimation procedure for PAF using data that are collected from population-based case-control studies, which, because of time and cost efficiency, are commonly used for studying genetic and environmental risk factors of disease incidences. In this article, we show that time-varying PAF is identifiable from a case-control study and develop a novel estimator of PAF. Our estimator combines odds ratio estimates from logistic regression models and density estimates of the risk factor distribution conditional on failure times in cases from a kernel smoother. The proposed estimator is shown to be consistent and asymptotically normal with asymptotic variance that can be estimated empirically from the data. Simulation studies demonstrate that the proposed estimator performs well in finite sample sizes. Finally, the method is illustrated by a population-based case-control study of colorectal cancer.

On estimation of covariate-specific residual time quantiles under the proportional hazards model.

Estimation and inference in time-to-event analysis typically focus on hazard functions and their ratios under the Cox proportional hazards model. These hazard functions, while popular in the statistical literature, are not always easily or intuitively communicated in clinical practice, such as in the settings of patient counseling or resource planning. Expressing and comparing quantiles of event times may allow for easier understanding. In this article we focus on residual time, i.e., the remaining time-to-event at an arbitrary time t given that the event has yet to occur by t. In particular, we develop estimation and inference procedures for covariate-specific quantiles of the residual time under the Cox model. Our methods and theory are assessed by simulations, and demonstrated in analysis of two real data sets.

Hypothesis testing for an extended cox model with time-varying coefficients.

The log-rank test has been widely used to test treatment effects under the Cox model for censored time-to-event outcomes, though it may lose power substantially when the model's proportional hazards assumption does not hold. In this article, we consider an extended Cox model that uses B-splines or smoothing splines to model a time-varying treatment effect and propose score test statistics for the treatment effect. Our proposed new tests combine statistical evidence from both the magnitude and the shape of the time-varying hazard ratio function, and thus are omnibus and powerful against various types of alternatives. In addition, the new testing framework is applicable to any choice of spline basis functions, including B-splines, and smoothing splines. Simulation studies confirm that the proposed tests performed well in finite samples and were frequently more powerful than conventional tests alone in many settings. The new methods were applied to the HIVNET 012 Study, a randomized clinical trial to assess the efficacy of single-dose Nevirapine against mother-to-child HIV transmission conducted by the HIV Prevention Trial Network.

Performance of goodness-of-fit tests for the Cox proportional hazards model with time-varying covariates.

There are few readily-implemented tests for goodness-of-fit for the Cox proportional hazards model with time-varying covariates. Through simulations, we assess the power of tests by Cox (J R Stat Soc B (Methodol) 34(2):187-220, 1972), Grambsch and Therneau (Biometrika 81(3):515-526, 1994), and Lin et al. (Biometrics 62:803-812, 2006). Results show that power is highly variable depending on the time to violation of proportional hazards, the magnitude of the change in hazard ratio, and the direction of the change. Because these characteristics are unknown outside of simulation studies, none of the tests examined is expected to have high power in real applications. While all of these tests are theoretically interesting, they appear to be of limited practical value.

Gasotransmitters in non-alcoholic fatty liver disease: just the tip of the iceberg.

Non-alcoholic fatty liver disease (NAFLD) is a clinicopathological syndrome characterized by fatty lesions and fat accumulation in hepatic parenchymal cells, which is in the absence of excessive alcohol consumption or definite liver damage factors. The exact pathogenesis of NAFLD is not fully understood, but it is now recognized that oxidative stress, insulin resistance, and inflammation are essential mechanisms involved in the development and treatment of NAFLD. NAFLD therapy aims to stop, delay or reverse disease progressions, as well as improve the quality of life and clinical outcomes of patients with NAFLD. Gasotransmitters are produced by enzymatic reactions, regulated through metabolic pathways in vivo, which can freely penetrate cell membranes with specific physiological functions and targets. Three gasotransmitters, nitric oxide, carbon monoxide, and hydrogen sulfide have been discovered. Gasotransmitters exhibit the effects of anti-inflammatory, anti-oxidant, vasodilatory, and cardioprotective agents. Gasotransmitters and their donors can be used as new gas-derived drugs and provide new approaches to the clinical treatment of NAFLD. Gasotransmitters can modulate inflammation, oxidative stress, and numerous signaling pathways to protect against NAFLD. In this paper, we mainly review the status of gasotransmitters research on NAFLD. It provides clinical applications for the future use of exogenous and endogenous gasotransmitters for the treatment of NAFLD.

Population-Level Effectiveness of an Inactivated Whole-Virion COVID-19 Vaccine: A Test Negative Case-Control Study in the Dominican Republic.

Background: A continuing nationwide vaccination campaign began in the Dominican Republic on February 16, 2021 to prevent severe consequences of acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Estimates of vaccine effectiveness under real-world conditions are needed to support policy decision making and inform further vaccine selection. Methods: We conducted a test-negative case-control study to assess the real-world effectiveness of nationwide coronavirus disease 2019 (COVID-19) vaccination program using an inactivated vaccine (CoronaVac) on preventing symptomatic SARS-CoV-2 infections and hospitalizations from August to November 2021 in the Dominican Republic. Participants were recruited from 10 hospitals in 5 provinces to estimate the effectiveness of full immunization (≥14 days after receipt of the second dose) and partial immunization (otherwise with at least 1 dose ≥14 days after receipt of the first dose). Results: Of 1078 adult participants seeking medical care for COVID-19-related symptoms, 395 (36.6%) had positive polymerase chain reaction (PCR) tests for SARS-CoV-2; 142 (13.2%) were hospitalized during 15 days of follow up, including 91 (23%) among 395 PCR-positive and 51 (7.5%) among 683 PCR-negative participants. Full vaccination was associated with 31% lower odds of symptomatic infection (odds ratio [OR], 0.69; 95% confidence interval [CI], 0.52-0.93) and partial vaccination was associated with 49% lower odds (OR, 0.51; CI, 0.30-0.86). Among 395 PCR-positive participants, full vaccination reduced the odds of COVID-19-related hospitalization by 85% (OR, 0.15; 95% CI, 0.08-0.25) and partial vaccination reduced it by 75% (OR, 0.25; 95% CI, 0.08-0.80); full vaccination was associated with reduced use of assisted ventilation by 73% (OR, 0.27; 95% CI, 0.15-0.49). Conclusions: Given the ancestral and delta viral variants circulating during this study period, our results suggest that the inactivated COVID-19 vaccine offered moderate protection against symptomatic SARS-CoV-2 infections and high protection against COVID-19-related hospitalizations and assisted ventilation. This is reassuring given that, as of August 2022, an estimated 2.6 billion inactivated CoronaVac vaccine doses had been administered worldwide. This vaccine will become a basis for developing multivalent vaccine against the currently circulating omicron variant.

On a Statistical Transmission Model in Analysis of the Early Phase of COVID-19 Outbreak.

Since December 2019, a disease caused by a novel strain of coronavirus (COVID-19) had infected many people and the cumulative confirmed cases have reached almost 180,000 as of 17, March 2020. The COVID-19 outbreak was believed to have emerged from a seafood market in Wuhan, a metropolis city of more than 11 million population in Hubei province, China. We introduced a statistical disease transmission model using case symptom onset data to estimate the transmissibility of the early-phase outbreak in China, and provided sensitivity analyses with various assumptions of disease natural history of the COVID-19. We fitted the transmission model to several publicly available sources of the outbreak data until 11, February 2020, and estimated lock down intervention efficacy of Wuhan city. The estimated R 0 was between 2.7 and 4.2 from plausible distribution assumptions of the incubation period and relative infectivity over the infectious period. 95% confidence interval of R 0 were also reported. Potential issues such as data quality concerns and comparison of different modelling approaches were discussed.

Semiparametric regression in size-biased sampling.

Size-biased sampling arises when a positive-valued outcome variable is sampled with selection probability proportional to its size. In this article, we propose a semiparametric linear regression model to analyze size-biased outcomes. In our proposed model, the regression parameters of covariates are of major interest, while the distribution of random errors is unspecified. Under the proposed model, we discover that regression parameters are invariant regardless of size-biased sampling. Following this invariance property, we develop a simple estimation procedure for inferences. Our proposed methods are evaluated in simulation studies and applied to two real data analyses.

On a Shape-Invariant Hazard Regression Model with application to an HIV Prevention Study of Mother-to-Child Transmission.

In survival analysis, Cox model is widely used for most clinical trial data. Alternatives include the additive hazard model, the accelerated failure time (AFT) model and a more general transformation model. All these models assume that the effects for all covariates are on the same scale. However, it is possible that for different covariates, the effects are on different scales. In this paper, we propose a shape-invariant hazard regression model that allows us to estimate the multiplicative treatment effect with adjustment of covariates that have non-multiplicative effects. We propose moment-based inference procedures for the regression parameters. We also discuss the risk prediction and the goodness of fit test for our proposed model. Numerical studies show good finite sample performance of our proposed estimator. We applied our method to the HIVNET 012 study, a milestone trial of single-dose nevirapine in prevention of mother-to-child transmission of HIV. From the HIVNET 012 data analysis, single-dose nevirapine treatment is shown to improve 18-month infant survival significantly with appropriate adjustment of the maternal CD4 counts and the virus load.

Measuring Surrogacy in Clinical Research: With an application to studying surrogate markers for HIV Treatment-as-Prevention.

In clinical research, validated surrogate markers are highly desirable in study design, monitoring, and analysis, as they do not only reduce the required sample size and follow-up duration, but also facilitate scientific discoveries. However, challenges exist to identify a reliable marker. One particular statistical challenge arises on how to measure and rank the surrogacy of potential markers quantitatively. We review the main statistical methods for evaluating surrogate markers. In addition, we suggest a new measure, the so-called "population surrogacy fraction of treatment effect," or simply the p-measure, in the setting of clinical trials. The p-measure carries an appealing population impact interpretation and supplements the existing statistical measures of surrogacy by providing "absolute" information. We apply the new measure along with other prominent measures to the HIV Prevention Trial Network 052 Study, a landmark trial for HIV/AIDS treatment-as-prevention.

Adjusted time-varying population attributable hazard in case-control studies.

Population attributable fraction is a widely used measure for quantifying the disease burden associated with a modifiable exposure of interest at the population level. It has been extended to a time-varying measure, population attributable hazard function, to provide additional information on when and how the exposure's impact varies over time. However, like the classic population attributable fraction, the population attributable hazard is generally biased if confounders are present. In this article, we provide a natural definition of adjusted population attributable hazard to take into account the effects of confounders, and its alternative that is identifiable from case-control studies under the rare disease assumption. We propose a novel estimator, which combines the odds ratio estimator from logistic regression model, and the conditional density function estimator of the exposure and confounding variables distribution given the failure times of cases or the current times of controls from a kernel smoother. We show that the proposed estimators are consistent and asymptotically normal with variance that can be estimated empirically from the data. Simulation studies demonstrate that the proposed estimators perform well in finite sample sizes. Finally, we illustrate the method by an analysis of a case-control study of colorectal cancer. Supplementary materials for this article are available online.

Trial designs for evaluating combination HIV prevention approaches.

BACKGROUND: Combination HIV prevention approaches that include both biomedical and non-biomedical interventions often hold greater promise to improve health outcomes and reduce the risk of HIV transmission. OBJECTIVES: Evaluate the relative properties of four leading candidate trial designs - 'single-factor', 'multi-arm', 'all-in-one', and 'factorial' designs - for assessing individual and/or combination prevention intervention approaches. METHODS: Monte-Carlo simulations are conducted, assuming a putative combination approach could choose its components from two candidate biomedical interventions, i.e. Treatment-as-Prevention (TasP) and Pre-exposure Prophylaxis (PrEP), and three candidate behavioral interventions, i.e. linkage-to-care, counseling, and use of condoms. Various scenarios for individual components' effect sizes, their possible interaction, and the sample size based on real clinical studies are considered. RESULTS: The all-in-one and factorial designs used to assess a combination approach and the multi-arm design used to assess multiple individual components are consistently more powerful than single-factor designs. The all-in-one design is powerful when the individual components are effective without negative interaction, while the factorial design is more consistently powerful across a broad array of settings. CONCLUSIONS: The multi-arm design is useful for evaluating single factor regimens, while the all-in-one and factorial designs are sensitive in assessing the overall efficacy when there is interest in combining individual component regimens anticipated to have complementary mechanisms. The factorial design is a preferred approach when assessing combination regimens due to its favorable power properties and since it is the only design providing direct insights about the contribution of individual components to the combination approach's overall efficacy and about potential interactions.

Variable Selection in Threshold Regression Model with Applications to HIV Drug Adherence Data.

The threshold regression model is an effective alternative to the Cox proportional hazards regression model when the proportional hazards assumption is not met. This paper considers variable selection for threshold regression. This model has separate regression functions for the initial health status and the speed of degradation in health. This flexibility is an important advantage when considering relevant risk factors for a complex time-to-event model where one needs to decide which variables should be included in the regression function for the initial health status, in the function for the speed of degradation in health, or in both functions. In this paper, we extend the broken adaptive ridge (BAR) method, originally designed for variable selection for one regression function, to simultaneous variable selection for both regression functions needed in the threshold regression model. We establish variable selection consistency of the proposed method and asymptotic normality of the estimator of non-zero regression coefficients. Simulation results show that our method outperformed threshold regression without variable selection and variable selection based on the Akaike information criterion. We apply the proposed method to data from an HIV drug adherence study in which electronic monitoring of drug intake is used to identify risk factors for non- adherence.

Clustering functional data with application to electronic medication adherence monitoring in HIV prevention trials.

Maintaining high medication adherence is essential for achieving desired efficacy in clinical trials, especially prevention trials. However, adherence is traditionally measured by self-reports that are subject to reporting biases and measurement error. Recently, electronic medication dispenser devices have been adopted in several HIV pre-exposure prophylaxis prevention studies. These devices are capable of collecting objective, frequent, and timely drug adherence data. The device opening signals generated by such devices are often represented as regularly or irregularly spaced discrete functional data, which are challenging for statistical analysis. In this paper we focus on clustering the adherence monitoring data from such devices. We first pre-process the raw discrete functional data into smoothed functional data. Parametric mixture models with change-points, as well as several non-parametric and semi-parametric functional clustering approaches are adapted and applied to the smoothed adherence data. Simulation studies were conducted to evaluate finite sample performances, on the choices of tuning parameters in the pre-processing step as well as the relative performance of different clustering algorithms. We applied these methods to the HIV Prevention Trials Network(HPTN) 069 study for identifying subgroups with distinct adherence behavior over the study period.

Risk factors for late postnatal transmission of human immunodeficiency virus type 1 in sub-Saharan Africa.

BACKGROUND: We conducted secondary data analyses of a clinical trial (HIVNET 024) to assess risk factors for late postnatal transmission (LPT) of human immunodeficiency virus type 1 (HIV-1) through breast-feeding. METHODS: Data regarding live born, singleton infants of HIV-1-infected mothers were analyzed. The timing of HIV-1 transmission through 12 months after birth was defined as: in utero (positive HIV-1 RNA results at birth), perinatal/early postnatal (negative results at birth, positive at 4-6 week visit), or LPT (negative results through the 4-6 week visit, but positive assays thereafter through the 12-month visit). HIV-1-uninfected infants were those with negative HIV-1 enzyme immunoassay results at 12 months of age, or infants with negative HIV-1 RNA results throughout follow-up. RESULTS: Of 2292 HIV-1-infected enrolled women, 2052 mother/infant pairs met inclusion criteria. Of 1979 infants with HIV-1 tests, 404 were HIV-1-infected, and 382 had known timing of infection (LPT represented 22% of transmissions). Further analyses of LPT included infants who were breast-feeding at the 4-6 week visit (with negative HIV-1 results at that visit) revealed 6.9% of 1317 infants acquired HIV-1 infection through LPT by 12 months of age. More advanced maternal HIV-1 disease at enrollment (lower CD4 counts, higher plasma viral loads) were the factors associated with LPT in adjusted analyses. CONCLUSIONS: In this breast-feeding population, 6.9% of infants uninfected at 6 weeks of age acquired HIV-1 infection by 12 months. Making interventions to decrease the risk of LPT of HIV-1 available and continuing research regarding the mechanisms of LPT (so as to develop improved interventions to reduce such transmission) remain essential.

Estimating a Treatment Effect in Residual Time Quantiles under the Additive Hazards Model.

For randomized clinical trials where the endpoint of interest is a time-to-event subject to censoring, estimating the treatment effect has mostly focused on the hazard ratio from the Cox proportional hazards model. Since the model's proportional hazards assumption is not always satisfied, a useful alternative, the so-called additive hazards model, may instead be used to estimate a treatment effect on the difference of hazard functions. Still, the hazards difference may be difficult to grasp intuitively, particularly in a clinical setting of, e.g., patient counseling, or resource planning. In this paper, we study the quantiles of a covariate's conditional survival function in the additive hazards model. Specifically, we estimate the residual time quantiles, i.e., the quantiles of survival times remaining at a given time t, conditional on the survival times greater than t, for a specific covariate in the additive hazards model. We use the estimates to translates the hazards difference into the difference in residual time quantiles, which allows a more direct clinical interpretation. We determine the asymptotic properties, assess the performance via Monte-Carlo simulations, and demonstrate the use of residual time quantiles in two real randomized clinical trials.