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Observational databases provide unprecedented opportunities for secondary use in biomedical research. However, these data can be error‐prone and must be validated before use. It is usually unrealistic to validate the whole database because of resource constraints. A cost‐effective alternative is a two‐phase design that validates a subset of records enriched for information about a particular research question. We consider odds ratio estimation under differential outcome and exposure misclassification and propose optimal designs that minimize the variance of the maximum likelihood estimator. Our adaptive grid search algorithm can locate the optimal design in a computationally feasible manner. Because the optimal design relies on unknown parameters, we introduce a multiwave strategy to approximate the optimal design. We demonstrate the proposed design's efficiency gains through simulations and two large observational studies. [ABSTRACT FROM AUTHOR]

Missing data is a common challenge when analyzing epidemiological data, and imputation is often used to address this issue. Here, we investigate the scenario where a covariate used in an analysis has missingness and will be imputed. There are recommendations to include the outcome from the analysis model in the imputation model for missing covariates, but it is not necessarily clear if this recommendation always holds and why this is sometimes true. We examine deterministic imputation (i.e. single imputation with fixed values) and stochastic imputation (i.e. single or multiple imputation with random values) methods and their implications for estimating the relationship between the imputed covariate and the outcome. We mathematically demonstrate that including the outcome variable in imputation models is not just a recommendation but a requirement to achieve unbiased results when using stochastic imputation methods. Moreover, we dispel common misconceptions about deterministic imputation models and demonstrate why the outcome should not be included in these models. This article aims to bridge the gap between imputation in theory and in practice, providing mathematical derivations to explain common statistical recommendations. We offer a better understanding of the considerations involved in imputing missing covariates and emphasize when it is necessary to include the outcome variable in the imputation model. [ABSTRACT FROM AUTHOR]

People living with HIV on antiretroviral therapy often have undetectable virus levels by standard assays, but "latent" HIV still persists in viral reservoirs. Eliminating these reservoirs is the goal of HIV cure research. The quantitative viral outgrowth assay (QVOA) is commonly used to estimate the reservoir size, that is, the infectious units per million (IUPM) of HIV-persistent resting CD4+ T cells. A new variation of the QVOA, the ultra deep sequencing assay of the outgrowth virus (UDSA), was recently developed that further quantifies the number of viral lineages within a subset of infected wells. Performing the UDSA on a subset of wells provides additional information that can improve IUPM estimation. This paper considers statistical inference about the IUPM from combined dilution assay (QVOA) and deep viral sequencing (UDSA) data, even when some deep sequencing data are missing. Methods are proposed to accommodate assays with wells sequenced at multiple dilution levels and with imperfect sensitivity and specificity, and a novel bias-corrected estimator is included for small samples. The proposed methods are evaluated in a simulation study, applied to data from the University of North Carolina HIV Cure Center, and implemented in the open-source R package SLDeepAssay. [ABSTRACT FROM AUTHOR]

Persons living with HIV engage in routine clinical care, generating large amounts of data in observational HIV cohorts. These data are often error‐prone, and directly using them in biomedical research could bias estimation and give misleading results. A cost‐effective solution is the two‐phase design, under which the error‐prone variables are observed for all patients during Phase I, and that information is used to select patients for data auditing during Phase II. For example, the Caribbean, Central, and South America network for HIV epidemiology (CCASAnet) selected a random sample from each site for data auditing. Herein, we consider efficient odds ratio estimation with partially audited, error‐prone data. We propose a semiparametric approach that uses all information from both phases and accommodates a number of error mechanisms. We allow both the outcome and covariates to be error‐prone and these errors to be correlated, and selection of the Phase II sample can depend on Phase I data in an arbitrary manner. We devise a computationally efficient, numerically stable EM algorithm to obtain estimators that are consistent, asymptotically normal, and asymptotically efficient. We demonstrate the advantages of the proposed methods over existing ones through extensive simulations. Finally, we provide applications to the CCASAnet cohort. [ABSTRACT FROM AUTHOR]

Background: Studies in women have shown an increased risk of human immunodeficiency virus (HIV) acquisition with prior human papilloma virus (HPV) infection; however, few studies have been conducted among men. Our objective was to assess whether HPV-related external genital lesions (EGLs) increase risk of HIV seroconversion among men.Methods: A total of 1379 HIV-negative men aged 18 to 70 years from the United States, Mexico, and Brazil were followed for up to 7 years and underwent clinical examination for EGLs and blood draws every 6 months. Human immunodeficiency virus seroconversion was assessed in archived serum. Cox proportional hazards and marginal structural models assessed the association between EGL status and time to HIV seroconversion.Results: Twenty-nine participants HIV seroconverted during follow-up. Older age was associated with a lower hazard of HIV seroconversion. We found no significant difference in the risk of HIV seroconversion between men with and without EGLs (adjusted hazard ratio, 0.94; 95% confidence interval, 0.32-2.74). Stratified analyses focusing on men that have sex with men found no association between EGLs and HIV seroconversion risk (hazards ratio, 0.63; 95% confidence interval, 0.00-1.86).Conclusions: External genital lesions were not associated with higher risk for HIV seroconversion in this multinational population, although statistical power was limited as there were few HIV seroconversions. Results may differ in populations at higher risk for HIV. [ABSTRACT FROM AUTHOR]

In modern observational studies using electronic health records or other routinely collected data, both the outcome and covariates of interest can be error‐prone and their errors often correlated. A cost‐effective solution is the two‐phase design, under which the error‐prone outcome and covariates are observed for all subjects during the first phase and that information is used to select a validation subsample for accurate measurements of these variables in the second phase. Previous research on two‐phase measurement error problems largely focused on scenarios where there are errors in covariates only or the validation sample is a simple random sample of study subjects. Herein, we propose a semiparametric approach to general two‐phase measurement error problems with a quantitative outcome, allowing for correlated errors in the outcome and covariates and arbitrary second‐phase selection. We devise a computationally efficient and numerically stable expectation‐maximization algorithm to maximize the nonparametric likelihood function. The resulting estimators possess desired statistical properties. We demonstrate the superiority of the proposed methods over existing approaches through extensive simulation studies, and we illustrate their use in an observational HIV study. [ABSTRACT FROM AUTHOR]

Child maltreatment is a global public health issue that has been linked with multiple negative health and life outcomes. This study evaluates the association between children placed in out-of-home care and neighborhood-level factors using eight years of administrative data. Between 2011–2018, 33,890 unique instances of child welfare involvement were captured in a department of child and family services database in a southern state in the United States. Removal addresses were geocoded and linked to the U.S. Census Bureau's American Community Survey to obtain census tract socioeconomic factors. Incidence overall and stratified by individual and neighborhood-level factors was computed. Rate ratios, relative indexes of inequality, and concentration curves quantified disparities in incidence of child welfare involvement by neighborhood-level factors. Incidence of children less than 19 years old placed into out-of-home care was 255 per 100,000 person-years (95 % CI: 252, 258). At the individual level, incidence was highest among children <5 and 15–17 years old, comparable between male and female children, and higher among Black children. At the neighborhood level, incidence was highest in census tracts with lower median household incomes, higher percentages of households below poverty or of female-headed or single-parent households, higher unemployment rates, and fewer residents with some college education or health insurance. Incidence of children placed into out-of-home care is disproportionally higher for those who live in disadvantaged communities. Understanding neighborhood-level risk factors that may be linked to child welfare involvement can help inform policy and target prevention efforts. [ABSTRACT FROM AUTHOR]