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In this paper, we advocate and expand upon a previously described monitoring strategy for efficient and robust estimation of disease prevalence and case numbers within closed and enumerated populations such as schools, workplaces, or retirement communities. The proposed design relies largely on voluntary testing, which is notoriously biased (e.g. in the case of coronavirus disease 2019) due to nonrepresentative sampling. The approach yields unbiased and comparatively precise estimates with no assumptions about factors underlying selection of individuals for voluntary testing, building on the strength of what can be a small random sampling component. This component enables the use of a recently proposed "anchor stream" estimator, a well-calibrated alternative to classical capture-recapture (CRC) estimators based on 2 data streams. We show that this estimator is equivalent to a direct standardization based on "capture," that is, selection (or not) by the voluntary testing program, made possible by means of a key parameter identified by design. This equivalency simultaneously allows for novel 2-stream CRC-like estimation of general mean values (e.g. means of continuous variables like antibody levels or biomarkers). For inference, we propose adaptations of Bayesian credible intervals when estimating case counts and bootstrapping when estimating means of continuous variables. We use simulations to demonstrate significant precision benefits relative to random sampling alone. [ABSTRACT FROM AUTHOR]

Proteome analysis is increasingly being used in investigations elucidating the molecular basis of disease, identifying diagnostic and prognostic markers, and ultimately improving patient care.We appraised the current status of proteomic investigations using human samples, including the state of the art in proteomic technologies, from sample preparation to data evaluation approaches, as well as key epidemiologic, statistical, and translational issues.We systematically reviewed the most highly cited clinical proteomic studies published between January 2009 and March 2014 that included a minimum of 100 samples, as well as strategies that have been successfully implemented to enhance the translational relevance of proteomic investigations. Limited comparability between studies and lack of specification of biomarker context of use are frequently observed. Nevertheless, there are initial examples of successful biomarker discovery in cross-sectional studies followed by validation in high-risk longitudinal cohorts. Translational potential is currently hindered, as limitations in proteomic investigations are not accounted for. Interdisciplinary communication between proteomics experts, basic researchers, epidemiologists, and clinicians, an orchestrated assimilation of required resources, and a more systematic translational outlook for accumulation of evidence may augment the public health impact of proteomic investigations. [ABSTRACT FROM AUTHOR]

Second-generation assays for anti-cyclic citrullinated peptide (anti-CCP), a highly sensitive and specific marker for rheumatoid arthritis (RA), have redefined the epidemiology of RA. In the Women's Health Initiative (WHI) RA study (2009–2011), we evaluated the prevalence of anti-CCP positivity among 15,691 (10.2% of 161,808) WHI participants aged 50–79 years who reported RA. Using stored baseline specimens, we measured serum anti-CCP, rheumatoid factor (RF), and antinuclear antibody in a defined sample of 9,988 of black, white, and Hispanic women. In a subset of women, we measured plasma cytokine levels and number of copies of the human leukocyte antigen (HLA)-DRB1 (HLA-DRB1) shared epitope in DNA by means of Luminex polymerase chain reaction typing (Luminex Corporation, Austin, Texas). We validated classification of probable clinical RA in 2 clinics as anti-CCP positivity or self-reported validated use of disease-modifying antirheumatic drugs (DMARDs). The prevalence of anti-CCP positivity was 8.1%, and the prevalence of RF positivity was approximately 16.0%. DMARD use including prednisone was reported by 1,140 (11.4%) participants (841 excluding prednisone) but by 57.5% of anti-CCP-positive women. The prevalence of 2 shared epitopes was also much higher for anti-CCP-positive women (18.2%, as opposed to only 5.5% for women with anti-CCP-negative DMARD-positive RA and 6.6% for anti-CCP-negative, RF-negative DMARD nonusers). Median cytokine levels were much higher for anti-CCP-positive/RF-positive women. Women with anti-CCP-positive RA and anti-CCP-negative RA had different characteristics with regard to HLA shared epitope, cigarette smoking, and inflammation (cytokines). [ABSTRACT FROM AUTHOR]

The linear mixed model (LMM), which is routinely used to describe change in outcomes over time and its association with risk factors, assumes that a unit change in any predictor is associated with a constant change in the outcome. When it is used on psychometric tests, this assumption may not hold. Indeed, psychometric tests usually suffer from ceiling and/or floor effects and curvilinearity (i.e., varying sensitivity to change). The authors aimed to determine the consequences of such misspecification when evaluating predictors of cognitive decline. As an alternative to the LMM, they considered 2 mixed models based on latent processes that handle discrete and bounded outcomes. Model differences are illustrated here using data on 4 psychometric tests from the Personnes Agées QUID (PAQUID) Study (1989–2004). The type I error of the Wald test for risk-factor regression parameters was formally assessed in a simulation study. It demonstrated that type I errors in the LMM could be dramatically inflated for some tests, such that spurious associations with risk factors were found. In particular, confusion between effects on mean level and effects on change over time was highlighted. The authors recommend use of the alternative mixed models when studying psychometric tests and more generally quantitative scales (quality of life, activities of daily living). [ABSTRACT FROM AUTHOR]