Sensitivity and Uncertainty Analysis for Two-stream Capture-Recapture Methods in Disease Surveillance.

Capture-recapture methods are widely applied in estimating the number ( ) of prevalent or cumulatively incident cases in disease surveillance. Here, we focus the bulk of our attention on the common case in which there are 2 data streams. We propose a sensitivity and uncertainty analysis framework grounded in multinomial distribution-based maximum likelihood, hinging on a key dependence parameter that is typically nonidentifiable but is epidemiologically interpretable.

A censored quantile regression approach for relative survival analysis: Relative survival quantile regression.

We propose a censored quantile regression model for the analysis of relative survival data. We create a hybrid data set consisting of the study observations and counterpart randomly sampled pseudopopulation observations imputed from population life tables that adjust for expected mortality. We then fit a censored quantile regression model to the hybrid data incorporating demographic variables (e.

Using Capture-Recapture Methodology to Enhance Precision of Representative Sampling-Based Case Count Estimates.

The application of serial principled sampling designs for diagnostic testing is often viewed as an ideal approach to monitoring prevalence and case counts of infectious or chronic diseases. Considering logistics and the need for timeliness and conservation of resources, surveillance efforts can generally benefit from creative designs and accompanying statistical methods to improve the precision of sampling-based estimates and reduce the size of the necessary sample. One option is to augment the analysis with available data from other surveillance streams that identify cases from the population of interest over the same timeframe, but may do so in a highly nonrepresentative manner.

Application of Bayesian spatial-temporal models for estimating unrecognized COVID-19 deaths in the United States.

In the United States, COVID-19 has become a leading cause of death since 2020. However, the number of COVID-19 deaths reported from death certificates is likely to represent an underestimate of the total deaths related to SARS-CoV-2 infections. Estimating those deaths not captured through death certificates is important to understanding the full burden of COVID-19 on mortality.

Calibrated Bayesian Credible Intervals for Binomial Proportions.

Drawbacks of traditional approximate (Wald test-based) and exact (Clopper-Pearson) confidence intervals for a binomial proportion are well-recognized. Alternatives include an interval based on inverting the score test, adaptations of exact testing, and Bayesian credible intervals derived from uniform or Jeffreys beta priors. We recommend a new interval intermediate between the Clopper-Pearson and Jeffreys in terms of both width and coverage.

An efficient design strategy for logistic regression using outcome- and covariate-dependent pooling of biospecimens prior to assay.

Potential reductions in laboratory assay costs afforded by pooling equal aliquots of biospecimens have long been recognized in disease surveillance and epidemiological research and, more recently, have motivated design and analytic developments in regression settings. For example, Weinberg and Umbach (1999, Biometrics 55, 718-726) provided methods for fitting set-based logistic regression models to case-control data when a continuous exposure variable (e.g.

A Discriminant Function Approach to Adjust for Processing and Measurement Error When a Biomarker is Assayed in Pooled Samples.

Pooling biological specimens prior to performing expensive laboratory assays has been shown to be a cost effective approach for estimating parameters of interest. In addition to requiring specialized statistical techniques, however, the pooling of samples can introduce assay errors due to processing, possibly in addition to measurement error that may be present when the assay is applied to individual samples. Failure to account for these sources of error can result in biased parameter estimates and ultimately faulty inference.

Numeric score-based conditional and overall change-in-status indices for ordered categorical data.

Planned interventions and/or natural conditions often effect change on an ordinal categorical outcome (e.g., symptom severity).

Approximate and Pseudo-Likelihood Analysis for Logistic Regression Using External Validation Data to Model Log Exposure.

A common goal in environmental epidemiologic studies is to undertake logistic regression modeling to associate a continuous measure of exposure with binary disease status, adjusting for covariates. A frequent complication is that exposure may only be measurable indirectly, through a collection of subject-specific variables assumed associated with it. Motivated by a specific study to investigate the association between lung function and exposure to metal working fluids, we focus on a multiplicative-lognormal structural measurement error scenario and approaches to address it when external validation data are available.

Reducing Bias and Mean Squared Error Associated With Regression-Based Odds Ratio Estimators.

Ratio estimators of effect are ordinarily obtained by exponentiating maximum-likelihood estimators (MLEs) of log-linear or logistic regression coefficients. These estimators can display marked positive finite-sample bias, however. We propose a simple correction that removes a substantial portion of the bias due to exponentiation.

Validation data-based adjustments for outcome misclassification in logistic regression: an illustration.

Misclassification of binary outcome variables is a known source of potentially serious bias when estimating adjusted odds ratios. Although researchers have described frequentist and Bayesian methods for dealing with the problem, these methods have seldom fully bridged the gap between statistical research and epidemiologic practice. In particular, there have been few real-world applications of readily grasped and computationally accessible methods that make direct use of internal validation data to adjust for differential outcome misclassification in logistic regression.

Sensitivity analysis for misclassification in logistic regression via likelihood methods and predictive value weighting.

The potential for bias due to misclassification error in regression analysis is well understood by statisticians and epidemiologists. Assuming little or no available data for estimating misclassification probabilities, investigators sometimes seek to gauge the sensitivity of an estimated effect to variations in the assumed values of those probabilities. We present an intuitive and flexible approach to such a sensitivity analysis, assuming an underlying logistic regression model.

A conditional expectation approach for associating ambient air pollutant exposures with health outcomes.

Our research focuses on the association between exposure to an airborne pollutant and counts of emergency department visits attributed to a specific chronic illness. The motivating example for this analysis of measurement error in time series studies of air pollution and acute health outcomes was a study of emergency department visits from a 20-county Atlanta metropolitan statistical area from 1993-1999. The research presented illustrates the impact of using various surrogates for unobserved measurements of ambient concentrations at the zip code level.

Greater years of maternal schooling and higher scores on academic achievement tests are independently associated with improved management of child diarrhea by rural Guatemalan mothers.

Appropriate home management can alleviate many of the consequences of diarrhea including malnutrition, impaired development, growth faltering, and mortality. Maternal cognitive ability, years of schooling, and acquired academic skills are hypothesized to improve child health by improving maternal child care practices, such as illness management. Using information collected longitudinally in 1996-1999 from 466 rural Guatemalan women with children <36 months, we examined the independent associations between maternal years of schooling, academic skills, and scores on the Raven's Progressive Matrices and an illness management index (IMI).

Nonlinear model-based estimates of IC(50) for studies involving continuous therapeutic dose-response data.

We present statistical details for estimating an in vitro 50% inhibitory concentration (IC(50)), based on several models for continuous response data fit to bone-marrow endothelial cell lines replicated in vehicle and at several dose increments. Nonlinear models are fit via maximum likelihood assuming normal errors, and primary attention is given to exponential, Gompertz, and scaled logistic dose-response curves that admit increasing or decreasing monotonic and sigmoidal patterns. Careful consideration is given to dose axis scaling, comparative model fit via mean squared error and graphical assessment, analogues to weighted least squares analysis to address heterogeneity of variance across doses, and potential hormetic effects.

Combining internal and external validation data to correct for exposure misclassification: a case study.

Internal validation data offer a well-recognized means to help correct for exposure misclassification or measurement error. When available, external validation data offer the advantage of cost-effectiveness. However, external data are a generally inefficient source of information about misclassification parameters.

A practical approach to computing power for generalized linear models with nominal, count, or ordinal responses.

Data analysts facing study design questions on a regular basis could derive substantial benefit from a straightforward and unified approach to power calculations for generalized linear models. Many current proposals for dealing with binary, ordinal, or count outcomes are conceptually or computationally demanding, limited in terms of accommodating covariates, and/or have not been extensively assessed for accuracy assuming moderate sample sizes. Here, we present a simple method for estimating conditional power that requires only standard software for fitting the desired generalized linear model for a non-continuous outcome.

Improving point predictions of random effects for subjects at high risk.

The prediction of random effects corresponding to subject-specific characteristics (e.g. means or rates of change) can be very useful in medical and epidemiologic research.

Inference for case-control studies when exposure status is both informatively missing and misclassified.

In case-control studies, it is common for a categorical exposure variable to be misclassified. It is also common for exposure status to be informatively missing for some individuals, in that the probability of missingness may be related to exposure. Procedures for addressing the bias due to misclassification via validation data have been extensively studied, and related methods have been proposed for dealing with informative missingness based on supplemental sampling of some of those with missing data.

Longitudinal estimates of puberty timing in Senegalese adolescent girls.

This study aimed to assess the timing of sexual maturation (breast development and menarche occurrence) among sub-Saharan African adolescent girls from rural areas. In the framework of a longitudinal study of growth at puberty, the stages of pubertal development (Tanner classification) and menarche occurrence were recorded at intervals between 1995 and 2000 in a sample of 406 Senegalese adolescent girls from a rural area. Nutritional status was estimated during infancy, childhood, and adolescence within this sample, and body composition was estimated only during adolescence.

Rationale for a follow-up study focusing on economic productivity.

Past studies of nutrition, human capital formation, and economic productivity have been limited by the fact that biomedical researchers and economists work largely in isolation, with loss of complementarity. Biomedical researchers are faulted for not adequately addressing bias and measurement issues and for naive analyses and interpretation of results, whereas economists are criticized for using simplistic nutrition and physiological measures and for relying on statistical methods rather than experimental designs. To avoid these problems, a multidisciplinary team of biomedical investigators and economists undertook a follow-up study in 2002-04 of a cohort of young men and women, who participated as young children in a randomized community trial of nutrition supplementation carried out from 1969-77 Previous studies, particularly the original trial and a 1988-89 follow- up, are described to provide an overview of the data available for linkage with the 2002-04 follow-up.

Extending McNemar's test: estimation and inference when paired binary outcome data are misclassified.

McNemar's test is popular for assessing the difference between proportions when two observations are taken on each experimental unit. It is useful under a variety of epidemiological study designs that produce correlated binary outcomes. In studies involving outcome ascertainment, cost or feasibility concerns often lead researchers to employ error-prone surrogate diagnostic tests.

Design and analytic considerations for single-armed studies with misclassification of a repeated binary outcome.

Due to logistics or prohibitive costs, clinical studies often rely upon a potentially misclassified binary outcome variable for assessing an intervention effect. We consider noncomparative single-armed studies that are sometimes necessary for ethical reasons, and we focus on the situation in which subjects are selected to receive the intervention contingent upon a positive screening test. Both initial misclassification at screening and a regression phenomenon impacting the error-prone follow-up outcome measure contribute to bias in the typical treatment effect estimate.

A global sensitivity analysis of performance of a medical diagnostic test when verification bias is present.

Current advances in technology provide less invasive or less expensive diagnostic tests for identifying disease status. When a diagnostic test is evaluated against an invasive or expensive gold standard test, one often finds that not all patients undergo the gold standard test. The sensitivity and specificity estimates based only on the patients with verified disease are often biased.