Prediagnostic serum concentrations of per- and polyfluoroalkyl substances and risk of papillary thyroid cancer in the Finnish Maternity Cohort.

Human exposure to per- and polyfluoroalkyl substances (PFAS) occurs globally through contaminated food, dust, and drinking water. Studies of PFAS and thyroid cancer have been limited. We conducted a nested case-control study of prediagnostic serum levels of 19 PFAS and papillary thyroid cancer (400 cases, 400 controls) in the Finnish Maternity Cohort (pregnancies 1986-2010; follow-up through 2016), individually matched on sample year and age. We used conditional logistic regression to estimate odds ratios (OR) and 95% confidence intervals (CI) for log2 transformed and categorical exposures, overall and stratified by calendar period, birth cohort, and median age at diagnosis. We adjusted for other PFAS with Spearman correlation rho = 0.3-0.6. Seven PFAS, including perfluoroctanoic acid (PFOA), perfluorooctanesulfonic acid (PFOS), N-ethyl-perfluorooctane sulfonamidoacetic acid (EtFOSAA), perfluorononanoic acid (PFNA), perfluorodecanoic acid (PFDA), and perfluorohexane sulfonic acid (PFHxS) were detected in >50% of women. These PFAS were not associated with risk of thyroid cancer, except for PFHxS, which was inversely associated (OR log2 = 0.82, 95% CI: 0.70-0.97). We observed suggestive but imprecise increased risks associated with PFOA, PFOS, and EtFOSAA for those diagnosed at ages <40 years, whereas associations were null or inverse among those diagnosed at 40+ years (P-interaction: .02, .08, .13, respectively). There was little evidence of other interactions. These results show no clear association between PFAS and papillary thyroid cancer risk. Future work would benefit from evaluation of these relationships among those with higher exposure levels and during periods of early development when the thyroid gland may be more susceptible to environmental harms.

The landscape of etiological patterns of hepatocellular carcinoma and intrahepatic cholangiocarcinoma in Thailand.

Thailand is among countries with the highest global incidence and mortality rates of hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (iCCA). While viral hepatitis and liver fluke infections have been associated with HCC and iCCA, respectively, other environmental risk factors, overall risk factor commonality and combinatorial roles, and effects on survival have not been systematically examined. We conducted a TIGER-LC consortium-based population study covering all high-incidence areas of both malignancies across Thailand: 837 HCC, 1474 iCCA, and 1112 controls (2011-2019) were comprehensively queried on lifelong environmental exposures, lifestyle, and medical history. Multivariate logistic regression and Cox proportional hazards analyses were used to evaluate risk factors and associated survival patterns. Our models identified shared risk factors between HCC and iCCA, such as viral hepatitis infection, liver fluke infection, and diabetes, including novel and shared associations of agricultural pesticide exposure (OR range of 1.50; 95% CI: 1.06-2.11 to 2.91; 95% CI: 1.82-4.63) along with vulnerable sources of drinking water. Most patients had multiple risk factors, magnifying their risk considerably. Patients with lower risk levels had better survival in both HCC (HR 0.78; 95% CI: 0.64-0.96) and iCCA (HR 0.84; 95% CI: 0.70-0.99). Risk factor co-exposures and their common associations with HCC and iCCA in Thailand emphasize the importance for future prevention and control measures, especially in its large agricultural sector. The observed mortality patterns suggest ways to stratify patients for anticipated survivorship and develop plans to support medical care of longer-term survivors, including behavioral changes to reduce exposures.

Accommodating detection limits of multiple exposures in environmental mixture analyses: an overview of statistical approaches.

BACKGROUND: Identifying the impact of environmental mixtures on human health is an important topic. However, such studies face challenges when exposure measurements lie below limit of detection (LOD). While various approaches for accommodating a single exposure subject to LOD have been used, their impact on mixture analysis has not been thoroughly investigated. Our study aims to understand the impact of five popular LOD accommodation approaches on mixture analysis results with multiple exposures subject to LOD, including omitting subjects with any exposures below LOD (complete case analysis); single imputations by LOD/ 2 , and by estimates from a censored accelerated failure time (AFT) model; and multiple imputation (MI) with or without truncation based on LOD. METHODS: In extensive simulation studies with high-dimensional and highly correlated exposures and a continuous health outcome, we examined the performance of each LOD approach on three mixture analysis methods: elastic net regression, weighted quantile sum regression (WQS) and Bayesian kernel machine regression (BKMR). We further analyzed data from the National Health and Nutrition Examination Survey (NHANES) on how persistent organic pollutants (POPs) influenced leukocyte telomere length (LTL). RESULTS: Complete case analysis was inefficient and could result in severe bias for some mixture methods. Imputation by LOD/ 2 showed unstable performance across mixture methods. Conventional MI was associated with consistent mild biases, which can be reduced by using a truncated distribution for imputation. Estimating censored values by AFT models had a minimal impact on the results. In the NHANES analysis, imputation by LOD/ 2 , truncated MI and censored AFT models performed similarly, with a positive overall effect of POPs on LTL while PCB126, PCB169 and furan 2,3,4,7,8-pncdf being the most important exposures. CONCLUSIONS: Our study favored using truncated MI and censored AFT models to accommodate values below LOD for the stability of downstream mixture analysis.

Nutritional Intake in Dichorionic Twin Pregnancies: A Descriptive Analysis of a Multisite United States Cohort.

INTRODUCTION: Twin gestations have greater nutritional demands than singleton gestations, yet dietary intakes of women with twin gestations have not been well described. METHODS: In a prospective, multi-site US study of 148 women with dichorionic twin gestations (2012-2013), we examined longitudinal changes in diet across pregnancy. Women completed a food frequency questionnaire during each trimester of pregnancy. We examined changes in means of total energy and energy-adjusted dietary components using linear mixed effects models. RESULTS: Mean energy intake (95% CI) across the three trimesters was 2010 kcal/day (1846, 2175), 2177 kcal/day (2005, 2349), 2253 kcal/day (2056, 2450), respectively (P = 0.01), whereas the Healthy Eating Index-2010 was 63.9 (62.1, 65.6), 64.5 (62.6, 66.3), 63.2 (61.1, 65.3), respectively (P = 0.53). DISCUSSION: Women with twin gestations moderately increased total energy as pregnancy progressed, though dietary composition and quality remained unchanged. These findings highlight aspects of nutritional intake that may need to be improved among women carrying twins.

A hidden Markov modeling approach for identifying tumor subclones in next-generation sequencing studies.

Allele-specific copy number alteration (ASCNA) analysis is for identifying copy number abnormalities in tumor cells. Unlike normal cells, tumor cells are heterogeneous as a combination of dominant and minor subclones with distinct copy number profiles. Estimating the clonal proportion and identifying mainclone and subclone genotypes across the genome are important for understanding tumor progression. Several ASCNA tools have recently been developed, but they have been limited to the identification of subclone regions, and not the genotype of subclones. In this article, we propose subHMM, a hidden Markov model-based approach that estimates both subclone region and region-specific subclone genotype and clonal proportion. We specify a hidden state variable representing the conglomeration of clonal genotype and subclone status. We propose a two-step algorithm for parameter estimation, where in the first step, a standard hidden Markov model with this conglomerated state variable is fit. Then, in the second step, region-specific estimates of the clonal proportions are obtained by maximizing region-specific pseudo-likelihoods. We apply subHMM to study renal cell carcinoma datasets in The Cancer Genome Atlas. In addition, we conduct simulation studies that show the good performance of the proposed approach. The R source code is available online at https://dceg.cancer.gov/tools/analysis/subhmm. Expectation-Maximization algorithm; Forward-backward algorithm; Somatic copy number alteration; Tumor subclones.

An imputation approach for a time-to-event analysis subject to missing outcomes due to noncoverage in disease registries.

Disease incidence data in a national-based cohort study would ideally be obtained through a national disease registry. Unfortunately, no such registry currently exists in the United States. Instead, the results from individual state registries need to be combined to ascertain certain disease diagnoses in the United States. The National Cancer Institute has initiated a program to assemble all state registries to provide a complete assessment of all cancers in the United States. Unfortunately, not all registries have agreed to participate. In this article, we develop an imputation-based approach that uses self-reported cancer diagnosis from longitudinally collected questionnaires to impute cancer incidence not covered by the combined registry. We propose a two-step procedure, where in the first step a mover-stayer model is used to impute a participant's registry coverage status when it is only reported at the time of the questionnaires given at 10-year intervals and the time of the last-alive vital status and death. In the second step, we propose a semiparametric working model, fit using an imputed coverage area sample identified from the mover-stayer model, to impute registry-based survival outcomes for participants in areas not covered by the registry. The simulation studies show the approach performs well as compared with alternative ad hoc approaches for dealing with this problem. We illustrate the methodology with an analysis that links the United States Radiologic Technologists study cohort with the combined registry that includes 32 of the 50 states.

Metabolomic Profiling of an Ultraprocessed Dietary Pattern in a Domiciled Randomized Controlled Crossover Feeding Trial.

BACKGROUND: Objective markers of ultraprocessed foods (UPF) may improve the assessment of UPF intake and provide insight into how UPF influences health. OBJECTIVES: To identify metabolites that differed between dietary patterns (DPs) high in or void of UPF according to Nova classification. METHODS: In a randomized, crossover, controlled-feeding trial (clinicaltrials.govNCT03407053), 20 domiciled healthy participants (mean ± standard deviation: age 31 ± 7 y, body mass index [kg/m2] 22 ± 11.6) consumed ad libitum a UPF-DP (80% UPF) and an unprocessed DP (UN-DP; 0% UPF) for 2 wk each. Metabolites were measured using liquid chromatography with tandem mass spectrometry in ethylenediaminetetraacetic acid plasma, collected at week 2 and 24-h, and spot urine, collected at weeks 1 and 2, of each DP. Linear mixed models, adjusted for energy intake, were used to identify metabolites that differed between DPs. RESULTS: After multiple comparisons correction, 257 out of 993 plasma and 606 out of 1279 24-h urine metabolites differed between UPF-DP and UN-DP. Overall, 21 known and 9 unknown metabolites differed between DPs across all time points and biospecimen types. Six metabolites were higher (4-hydroxy-L-glutamic acid, N-acetylaminooctanoic acid, 2-methoxyhydroquinone sulfate, 4-ethylphenylsulfate, 4-vinylphenol sulfate, and acesulfame) and 14 were lower following the UPF-DP; pimelic acid, was lower in plasma but higher in urine following the UPF-DP. CONCLUSIONS: Consuming a DP high in, compared with 1 void of, UPF has a measurable impact on the short-term human metabolome. Observed differential metabolites could serve as candidate biomarkers of UPF intake or metabolic response in larger samples with varying UPF-DPs. This trial was registered at clinicaltrials.gov as NCT03407053 and NCT03878108.

A latent functional approach for modeling the effects of multidimensional exposures on disease risk.

Understanding the relationships between exposure and disease incidence is an important problem in environmental epidemiology. Typically, a large number of these exposures are measured, and it is found either that a few exposures transmit risk or that each exposure transmits a small amount of risk, but, taken together, these may pose a substantial disease risk. Further, these exposure effects can be nonlinear. We develop a latent functional approach, which assumes that the individual effect of each exposure can be characterized as one of a series of unobserved functions, where the number of latent functions is less than or equal to the number of exposures. We propose Bayesian methodology to fit models with a large number of exposures and show that existing Bayesian group LASSO approaches are a special case of the proposed model. An efficient Markov chain Monte Carlo sampling algorithm is developed for carrying out Bayesian inference. The deviance information criterion is used to choose an appropriate number of nonlinear latent functions. We demonstrate the good properties of the approach using simulation studies. Further, we show that complex exposure relationships can be represented with only a few latent functional curves. The proposed methodology is illustrated with an analysis of the effect of cumulative pesticide exposure on cancer risk in a large cohort of farmers.

Uncovering circadian rhythms in metabolic longitudinal data: A Bayesian latent class modeling approach.

Researchers in biology and medicine have increasingly focused on characterizing circadian rhythms and their potential impact on disease. Understanding circadian variation in metabolomics, the study of chemical processes involving metabolites may provide insight into important aspects of biological mechanism. Of scientific importance is developing a statistical rigorous approach for characterizing different types of 24-hour patterns among high dimensional longitudinal metabolites. We develop a latent class approach to incorporate variation in 24-hour patterns across metabolites where profiles are modeled with finite mixtures of distinct shape-invariant circadian curves that themselves incorporate variation in amplitude and phase across metabolites. An efficient Markov chain Monte Carlo sampling is used to carry out Bayesian posterior computation. When the model was fit separately by individual to the data from a small group of participants, two distinct 24-hour rhythms were identified, with one being sinusoidal and the other being more complex with multiple peaks. Interestingly, the latent pattern associated with circadian variation (simple sinusoidal curve) had a similar phase across the three participants, while the more complex latent pattern reflecting diurnal variation differed across individual. The results suggested that this modeling framework can be used to separate 24-hour rhythms into an endogenous circadian and one or more exogenous diurnal patterns in describing human metabolism.

A Bayesian hierarchical sparse factor model for estimating simultaneous covariance matrices for gestational outcomes in consecutive pregnancies.

Covariance estimation for multiple groups is a key feature for drawing inference from a heterogeneous population. One should seek to share information about common features in the dependence structures across the various groups. In this paper, we introduce a novel approach for estimating the covariance matrices for multiple groups using a hierarchical latent factor model that shrinks the factor loadings across groups toward a global value. Using a sparse spike and slab model on these loading coefficients allows for a sparse formulation of our model. Parameter estimation is accomplished through a Markov chain Monte Carlo scheme, and a model selection approach is used to select the number of factors to use. We validate our model through extensive simulation studies. Finally, we apply our methodology to the NICHD Consecutive Pregnancies Study to estimate the correlations between birth weights and gestational ages of three consecutive birth within four different subgroups (underweight, normal, overweight, and obese) of women.

Combination of Fundal Height and Ultrasound to Predict Small for Gestational Age at Birth.

OBJECTIVE: The objective of the study was to determine whether adding longitudinal measures of fundal height (FH) to the standard cross-sectional FH to trigger third trimester ultrasound estimated fetal weight (EFW) would improve small for gestational age (SGA) prediction. STUDY DESIGN: We developed a longitudinal FH calculator in a secondary analysis of a prospective cohort study of 1,939 nonobese pregnant women who underwent serial FH evaluations at 12 U.S. clinical sites. We evaluated cross-sectional FH measurement ≤ -3 cm at visit 3 (mean: 32.0 ± 1.6 weeks) versus the addition of longitudinal FH up to and including visit 3 to trigger an ultrasound to diagnose SGA defined as birth weight <10th percentile. If the FH cut points were not met, the SGA screen was classified as negative. If FH cut points were met and EFW was <10th percentile, the SGA screen was considered positive. If EFW was ≥10th percentile, the SGA screen was also considered negative. Sensitivity, specificity, and positive predictive value (PPV) and negative predictive value (NPV) were computed. RESULTS: In a comparison of methods, 5.8% of women were classified as at risk of SGA by both cross-sectional and longitudinal classification methods; cross-sectional FH identified an additional 4.0%, and longitudinal fundal height identified a separate, additional 4.5%.Using cross-sectional FH as an ultrasound trigger, EFW had a PPV and NPV for SGA of 69 and 92%, respectively. After adding longitudinal FH, PPV increased to 74%, whereas NPV of 92% remained unchanged; however, the number of women who underwent triggered EFW decreased from 9.7 to 5.7%. CONCLUSION: An innovative approach for calculating longitudinal FH to the standard cross-sectional FH improved identification of SGA birth weight, while simultaneously reducing the number of triggered ultrasounds. As an essentially free-of-charge screening test, our novel method has potential to decrease costs as well as perinatal morbidity and mortality (through better prediction of SGA). KEY POINTS: · We have developed an innovative calculator for fundal height trajectory.. · Longitudinal fundal height improves detection of SGA.. · As a low cost screening test, the fundal height calculator may decrease costs and morbidity through better prediction of SGA..

Ascertainment of Incident Cancer by US Population-Based Cancer Registries Versus Self-Reports and Death Certificates in a Nationwide Cohort Study, the US Radiologic Technologists Study.

Follow-up of US cohort members for incident cancer is time-consuming, is costly, and often results in underascertainment when the traditional methods of self-reporting and/or medical record validation are used. We conducted one of the first large-scale investigations to assess the feasibility, methods, and benefits of linking participants in the US Radiologic Technologists (USRT) Study (n = 146,022) with the majority of US state or regional cancer registries. Follow-up of this cohort has relied primarily on questionnaires (mailed approximately every 10 years) and linkage with the National Death Index. We compared the level of agreement and completeness of questionnaire/death-certificate-based information with that of registry-based (43 registries) incident cancer follow-up in the USRT cohort. Using registry-identified first primary cancers from 1999-2012 as the gold standard, the overall sensitivity was 46.5% for self-reports only and 63.0% for both self-reports and death certificates. Among the 37.0% false-negative reports, 27.8% were due to dropout, while 9.2% were due to misreporting. The USRT cancer reporting patterns differed by cancer type. Our study indicates that linkage to state cancer registries would greatly improve completeness and accuracy of cancer follow-up in comparison with questionnaire self-reporting. These findings support ongoing development of a national US virtual pooled registry with which to streamline cohort linkages.

Multistate models for the natural history of cancer progression.

BACKGROUND: Multistate models can be effectively used to characterise the natural history of cancer. Inference from such models has previously been useful for setting screening policies. METHODS: We introduce the basic elements of multistate models and the challenges of applying these models to cancer data. Through simulation studies, we examine (1) the impact of assuming time-homogeneous Markov transition intensities when the intensities depend on the time since entry to the current state (i.e., the process is time-inhomogenous semi-Markov) and (2) the effect on precancer risk estimation when observation times depend on an unmodelled intermediate disease state. RESULTS: In the settings we examined, we found that misspecifying a time-inhomogenous semi-Markov process as a time-homogeneous Markov process resulted in biased estimates of the mean sojourn times. When screen-detection of the intermediate disease leads to more frequent future screening assessments, there was minimal bias induced compared to when screen-detection of the intermediate disease leads to less frequent screening. CONCLUSIONS: Multistate models are useful for estimating parameters governing the process dynamics in cancer such as transition rates, sojourn time distributions, and absolute and relative risks. As with most statistical models, to avoid incorrect inference, care should be given to use the appropriate specifications and assumptions.

Using group testing in a two-phase epidemiologic design to identify the effects of a large number of antibody reactions on disease risk.

BACKGROUND: The role of immunological responses to exposed bacteria on disease incidence is increasingly under investigation. With many bacterial species, and many potential antibody reactions to a particular species, the large number of assays required for this type of discovery can make it prohibitively expensive. We propose a two-phase group testing design to more efficiently screen numerous antibody effects in a case-control setting. METHODS: Phase 1 uses group testing to select antibodies that are differentially expressed between cases and controls. The selected antibodies go on to Phase 2 individual testing. RESULTS: We evaluate the two-phase group testing design through simulations and example data and find that it substantially reduces the number of assays required relative to standard case-control and group testing designs, while maintaining similar statistical properties. CONCLUSION: The proposed two-phase group testing design can dramatically reduce the number of assays required, while providing comparable results to a case-control design.

Impact of Population Growth and Aging on Estimates of Excess U.S. Deaths During the COVID-19 Pandemic, March to August 2020.

BACKGROUND: Excess death estimates quantify the full impact of the coronavirus disease 2019 (COVID-19) pandemic. Widely reported U.S. excess death estimates have not accounted for recent population changes, especially increases in the population older than 65 years. OBJECTIVE: To estimate excess deaths in the United States in 2020, after accounting for population changes. DESIGN: Surveillance study. SETTING: United States, March to August 2020. PARTICIPANTS: All decedents. MEASUREMENTS: Age-specific excess deaths in the United States from 1 March to 31 August 2020 compared with 2015 to 2019 were estimated, after changes in population size and age were taken into account, by using Centers for Disease Control and Prevention provisional death data and U.S. Census Bureau population estimates. Cause-specific excess deaths were estimated by month and age. RESULTS: From March through August 2020, 1 671 400 deaths were registered in the United States, including 173 300 COVID-19 deaths. An average of 1 370 000 deaths were reported over the same months during 2015 to 2019, for a crude excess of 301 400 deaths (128 100 non-COVID-19 deaths). However, the 2020 U.S. population includes 5.04 million more persons aged 65 years and older than the average population in 2015 to 2019 (a 10% increase). After population changes were taken into account, an estimated 217 900 excess deaths occurred from March through August 2020 (173 300 COVID-19 and 44 600 non-COVID-19 deaths). Most excess non-COVID-19 deaths occurred in April, July, and August, and 34 900 (78%) were in persons aged 25 to 64 years. Diabetes, Alzheimer disease, and heart disease caused the most non-COVID-19 excess deaths. LIMITATION: Provisional death data are underestimated because of reporting delays. CONCLUSION: The COVID-19 pandemic resulted in an estimated 218 000 excess deaths in the United States between March and August 2020, and 80% of those deaths had COVID-19 as the underlying cause. Accounting for population changes substantially reduced the excess non-COVID-19 death estimates, providing important information for guiding future clinical and public health interventions. PRIMARY FUNDING SOURCE: National Cancer Institute.

Racial and Ethnic Disparities in Excess Deaths During the COVID-19 Pandemic, March to December 2020.

BACKGROUND: Although racial/ethnic disparities in U.S. COVID-19 death rates are striking, focusing on COVID-19 deaths alone may underestimate the true effect of the pandemic on disparities. Excess death estimates capture deaths both directly and indirectly caused by COVID-19. OBJECTIVE: To estimate U.S. excess deaths by racial/ethnic group. DESIGN: Surveillance study. SETTING: United States. PARTICIPANTS: All decedents. MEASUREMENTS: Excess deaths and excess deaths per 100 000 persons from March to December 2020 were estimated by race/ethnicity, sex, age group, and cause of death, using provisional death certificate data from the Centers for Disease Control and Prevention (CDC) and U.S. Census Bureau population estimates. RESULTS: An estimated 2.88 million deaths occurred between March and December 2020. Compared with the number of expected deaths based on 2019 data, 477 200 excess deaths occurred during this period, with 74% attributed to COVID-19. Age-standardized excess deaths per 100 000 persons among Black, American Indian/Alaska Native (AI/AN), and Latino males and females were more than double those in White and Asian males and females. Non-COVID-19 excess deaths also disproportionately affected Black, AI/AN, and Latino persons. Compared with White males and females, non-COVID-19 excess deaths per 100 000 persons were 2 to 4 times higher in Black, AI/AN, and Latino males and females, including deaths due to diabetes, heart disease, cerebrovascular disease, and Alzheimer disease. Excess deaths in 2020 resulted in substantial widening of racial/ethnic disparities in all-cause mortality from 2019 to 2020. LIMITATIONS: Completeness and availability of provisional CDC data; no estimates of precision around results. CONCLUSION: There were profound racial/ethnic disparities in excess deaths in the United States in 2020 during the COVID-19 pandemic, resulting in rapid increases in racial/ethnic disparities in all-cause mortality between 2019 and 2020. PRIMARY FUNDING SOURCE: National Institutes of Health Intramural Research Program.

IFN-λ4 is associated with increased risk and earlier occurrence of several common infections in African children.

Genetic polymorphisms within the IFNL3/IFNL4 genomic region, which encodes type III interferons, have been strongly associated with clearance of hepatitis C virus. We hypothesized that type III interferons might be important for the immune response to other pathogens as well. In a cohort of 914 Malian children, we genotyped functional variants IFNL4-rs368234815, IFNL4-rs117648444, and IFNL3-rs4803217 and analyzed episodes of malaria, gastrointestinal, and respiratory infections recorded at 30,626 clinic visits from birth up to 5 years of age. Compared to children with the rs368234815-TT/TT genotype (IFN-λ4-Null), rs368234815-dG allele was most strongly associated with an earlier time-to-first episode of gastrointestinal infections (p = 0.003). The risk of experiencing an infection episode during the follow-up was also significantly increased with rs368234815-dG allele, with OR = 1.53, 95%CI (1.13-2.07), p = 0.005 for gastrointestinal infections and OR = 1.30, 95%CI (1.02-1.65), p = 0.033 for malaria. All the associations for the moderately linked rs4803217 (r2 = 0.78 in this set) were weaker and lost significance after adjusting for rs368234815. We also analyzed all outcomes in relation to IFN-λ4-P70S groups. Our results implicate IFN-λ4 and not IFN-λ3 as the primary functional cause of genetic associations with increased overall risk and younger age at first clinical episodes but not with recurrence or intensity of several common pediatric infections.

Is group testing ready for prime-time in disease identification?

Large-scale disease screening is a complicated process in which high costs must be balanced against pressing public health needs. When the goal is screening for infectious disease, one approach is group testing in which samples are initially tested in pools and individual samples are retested only if the initial pooled test was positive. Intuitively, if the prevalence of infection is small, this could result in a large reduction of the total number of tests required. Despite this, the use of group testing in medical studies has been limited, largely due to skepticism about the impact of pooling on the accuracy of a given assay. While there is a large body of research addressing the issue of testing errors in group testing studies, it is customary to assume that the misclassification parameters are known from an external population and/or that the values do not change with the group size. Both of these assumptions are highly questionable for many medical practitioners considering group testing in their study design. In this article, we explore how the failure of these assumptions might impact the efficacy of a group testing design and, consequently, whether group testing is currently feasible for medical screening. Specifically, we look at how incorrect assumptions about the sensitivity function at the design stage can lead to poor estimation of a procedure's overall sensitivity and expected number of tests. Furthermore, if a validation study is used to estimate the pooled misclassification parameters of a given assay, we show that the sample sizes required are so large as to be prohibitive in all but the largest screening programs.

Hidden mover-stayer model for disease progression accounting for misclassified and partially observed diagnostic tests: Application to the natural history of human papillomavirus and cervical precancer.

Hidden Markov models (HMMs) have been proposed to model the natural history of diseases while accounting for misclassification in state identification. We introduce a discrete time HMM for human papillomavirus (HPV) and cervical precancer/cancer where the hidden and observed state spaces are defined by all possible combinations of HPV, cytology, and colposcopy results. Because the population of women undergoing cervical cancer screening is heterogeneous with respect to sexual behavior, and therefore risk of HPV acquisition and subsequent precancers, we use a mover-stayer mixture model that assumes a proportion of the population will stay in the healthy state and are not subject to disease progression. As each state is a combination of three distinct tests that characterize the cervix, partially observed data arise when at least one but not every test is observed. The standard forward-backward algorithm, used for evaluating the E-step within the E-M algorithm for maximum-likelihood estimation of HMMs, cannot incorporate time points with partially observed data. We propose a new forward-backward algorithm that considers all possible fully observed states that could have occurred across a participant's follow-up visits. We apply our method to data from a large management trial for women with low-grade cervical abnormalities. Our simulation study found that our method has relatively little bias and out preforms simpler methods that resulted in larger bias.

Intrauterine growth discordance across gestation and birthweight discordance in dichorionic twins.

BACKGROUND: Although intertwin size difference is an important measure of fetal growth, the appropriate cut point to define discordance is unclear. Few studies have assessed intertwin differences in estimated fetal weight longitudinally or in relation to size differences at birth. OBJECTIVES: The objectives of the study were to estimate the magnitude of percentage differences in estimated fetal weight across gestation in dichorionic twins in relation to a fixed discordance cut point and compare classification of aberrant fetal growth by different measures (estimated fetal weight differences, birthweight discordance, small for gestational age). STUDY DESIGN: Women aged 18-45 years from 8 US centers with dichorionic twin pregnancies at 8 weeks 0 days to 13 weeks 6 days gestation planning to deliver in participating hospitals were recruited into the Eunice Kennedy Shriver National Institute of Child Health and Human Development Fetal Growth Studies-Dichorionic Twins study and followed through delivery (n = 140; 2012-2013). Ultrasounds were conducted at 6 targeted study visits to obtain fetal biometrics and calculate estimated fetal weight. Percent estimated fetal weight and birthweight differences were calculated: ([weightlarger - weightsmaller]/weightlarger)*100; discordance was defined as ≥18% for illustration. Birth sizes for gestational age (both, 1, or neither small for gestational age) were determined; twins were categorized into combined birthweight plus small for gestational age groups: birthweight discordance ≥18% (yes, no) with both, 1, or neither small for gestational age. Linear mixed-models estimated percentiles of estimated fetal weight percent differences across gestation and compared estimated fetal weight differences between combined birthweight discordance and small for gestational age groups. A Fisher exact test compared birthweight discordance and small for gestational age classifications. RESULTS: Median estimated fetal weight percentage difference increased across gestation (5.9% at 15.0, 8.4% at 38.0 weeks), with greater disparities at higher percentiles (eg, 90th percentile: 15.6% at 15.0, 26.3% at 38.0 weeks). As gestation advanced, an increasing percentage of pregnancies were classified as discordant using a fixed cut point: 10% at 27.0, 15% at 34.0, and 20% at 38.0 weeks. Birthweight discordance and small for gestational age classifications differed (P = .002); for birthweight discordance ≥18% vs <18%: 44% vs 71% had neither small for gestational age; 56% vs 18% had 1 small for gestational age; no cases (0%) vs 11% had both small for gestational age, respectively. Estimated fetal weight percent difference varied across gestation by birthweight discordance plus small for gestational age classification (P = .040). Estimated fetal weight percentage difference increased with birthweight discordance ≥18% (neither small for gestational age: 0.46%/week [95% confidence interval, 0.08-0.84]; 1 small for gestational age: 0.57%/week [95% confidence interval, 0.25-0.90]) but less so without birthweight discordance (neither small for gestational age: 0.17%/week [95% confidence interval, 0.06-0.28]; 1 small for gestational age: 0.03%/week [95% confidence interval, -0.17 to 0.24]); both small for gestational age: 0.10%/week [95% confidence interval, -0.15 to 0.36]). CONCLUSION: The percentage of dichorionic pregnancies exceeding a fixed discordance cut point increased over gestation. A fixed cut point for defining twin discordance would identify an increasing percentage of twins as discordant as gestation advances. Small for gestational age and percentage weight differences assess distinct aspects of dichorionic twin growth. A percentile cut point may be more clinically useful for defining discordance, although further study is required to assess whether any specific percentile cut point correlates to adverse outcomes.