Group testing regression model estimation when case identification is a goal

Group testing is frequently used to reduce the costs of screening a large number of individuals for infectious diseases or other binary characteristics in small prevalence situations. In many applications, the goals include both identifying individuals as positive or negative and estimating the probability of positivity. The identification aspect leads to additional tests being performed, known as “retests”, beyond those performed for initial groups of individuals. In this paper, we investigate how regression models can be fit to estimate the probability of positivity while also incorporating the extra information from these retests. We present simulation evidence showing that significant gains in efficiency occur by incorporating retesting information, and we further examine which testing protocols are the most efficient to use. Our investigations also demonstrate that some group testing protocols can actually lead to more efficient estimates than individual testing when diagnostic tests are imperfect. The proposed methods are applied retrospectively to chlamydia screening data from the Infertility Prevention Project. We demonstrate that significant cost savings could occur through the use of particular group testing protocols.     

Simultaneous analysis of large-scale RNAi screens for pathogen entry

Background

Large-scale RNAi screening has become an important technology for identifying genes involved in biological processes of interest. However, the quality of large-scale RNAi screening is often deteriorated by off-targets effects. In order to find statistically significant effector genes for pathogen entry, we systematically analyzed entry pathways in human host cells for eight pathogens using image-based kinome-wide siRNA screens with siRNAs from three vendors. We propose a Parallel Mixed Model (PMM) approach that simultaneously analyzes several non-identical screens performed with the same RNAi libraries.

Results

We show that PMM gains statistical power for hit detection due to parallel screening. PMM allows incorporating siRNA weights that can be assigned according to available information on RNAi quality. Moreover, PMM is able to estimate a sharedness score that can be used to focus follow-up efforts on generic or specific gene regulators. By fitting a PMM model to our data, we found several novel hit genes for most of the pathogens studied.

Conclusions

Our results show parallel RNAi screening can improve the results of individual screens. This is currently particularly interesting when large-scale parallel datasets are becoming more and more publicly available. Our comprehensive siRNA dataset provides a public, freely available resource for further statistical and biological analyses in the high-content, high-throughput siRNA screening field.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-1162) contains supplementary material, which is available to authorized users.     

Optimal mix of screening and contact tracing for endemic diseases

Two common means of controlling infectious diseases are screening and contact tracing. Which should be used, and when? We consider the problem of determining the cheapest mix of screening and contact tracing necessary to achieve a desired endemic prevalence of a disease or to identify a specified number of cases. We perform a partial equilibrium analysis of small-scale interventions, assuming that prevalence is unaffected by the intervention; we develop a full equilibrium analysis where we compare the long-term cost of various combinations of screening and contact tracing needed to achieve a given equilibrium prevalence; and we solve the problem of minimizing the total costs of identifying and treating disease cases plus the cost of untreated disease cases. Our analysis provides several insights. First, contact tracing is only cost effective when prevalence is below a threshold value. This threshold depends on the relative cost per case found by screening versus contact tracing. Second, for a given contact tracing policy, the screening rate needed to achieve a given prevalence or identify a specified number of cases is a decreasing function of disease prevalence. As prevalence increases above the threshold (and contact tracing is discontinued), the screening rate jumps discontinuously to a higher level. Third, these qualitative results hold when we consider unchanged or changed prevalence, and short-term or long-term costs.     

The use of virtual screening and differential scanning fluorimetry for the rapid identification of fragments active against MEK1

We report the analysis of an in-house fragment screening campaign for the oncology target MEK1. The application of virtual screening (VS) as a primary fragment screening approach, followed by biophysical validation using differential screening fluorimetry (DSF), with resultant binding mode determination by X-ray crystallography (X-ray), is presented as the most time and cost-effective combination of in silico and in vitro methods to identify fragments. We demonstrate the effectiveness of the VS–DSF workflow for the early identification of fragments to both ‘jump-start’ the drug discovery project and to complement biochemical screening data.     

Hepatitis C virus screening trends: A 2016 update of the National Health Interview Survey

Background50% of liver cancer is caused by hepatitis C virus (HCV). Baby boomers are at increased risk and are recommended for one-time HCV screening. However, <13% of baby boomers were screened in 2015.Materials and methodsWe are updating a previous study using 2013–2015 NHIS data to examine HCV screening prevalence by birth cohort, with 2016 data. We used logistic regression to evaluate whether HCV screening prevalence changed over time, stratified by birth cohort.Results and discussionThe sample consisted of 132,742 participants from 2013–2016. Screening increased in baby boomers from 11.9 to 14.1%. Odds of HCV screening for baby boomers was significantly associated with age, gender, race/ethnicity, and other variables and increased significantly with each subsequent year (aOR = 1.21, aOR = 1.33, aOR = 1.42, consecutively). While HCV screening is increasing over time, there is still room for improvement and future interventions should focus on increasing HCV screening among groups demonstrating significantly lower screening prevalence.     

A fast virtual screening approach to identify structurally diverse inhibitors of trypanothione reductase

Trypanothione reductase (TryR) is one of the favorite targets for those designing drugs for the treatment of Chagas disease. We present the application of a fast virtual screening approach for designing hit compounds active against TryR. Our protocol combines information derived from structurally known inhibitors and from the TryR receptor structure. Five structurally diverse hit compounds active against TryR and holding promise for the treatment of Chagas disease are reported.     

Model-free conditional screening for ultrahigh-dimensional survival data via conditional distance correlation

How to select the active variables that have significant impact on the event of interest is a very important and meaningful problem in the statistical analysis of ultrahigh-dimensional data. In many applications, researchers often know that a certain set of covariates are active variables from some previous investigations and experiences. With the knowledge of the important prior knowledge of active variables, we propose a model-free conditional screening procedure for ultrahigh dimensional survival data based on conditional distance correlation. The proposed procedure can effectively detect the hidden active variables that are jointly important but are weakly correlated with the response. Moreover, it performs well when covariates are strongly correlated with each other. We establish the sure screening property and the ranking consistency of the proposed method and conduct extensive simulation studies, which suggests that the proposed procedure works well for practical situations. Then, we illustrate the new approach through a real dataset from the diffuse large-B-cell lymphoma study S1 .     

Screening for elder mistreatment in dental and medical clinics

doi: 10.1111/j.1741‐2358.2010.00405.x
Screening for elder mistreatment in dental and medical clinics Objective: Elder mistreatment (EM) is a potentially fatal and largely unrecognised problem in the United States. The purpose of this study was to determine the prevalence of EM in busy clinics and specifically, we report on the feasibility of screening for EM as well as the appropriate instrumentation for screening. Background: Prevalence estimates for elder mistreatment vary, but recent data from a national sample of community‐residing adults over 60 years of age indicate that 11.4% of older adults report some form of elder mistreatment. There is a paucity of research related to screening in dental and medical clinics to understand the prevalence in such practice settings. Methods: A cross‐sectional study was conducted from January 2008 to March 2009. We enrolled 241 patients at two clinics: a medical clinic (n = 102) and dental clinics (n = 139). A mini‐mental status exam was conducted with a minimum of 18 or better for inclusion. An elder mistreatment screen was next used [elder assessment instrument (EAI‐R) for medical and Hwalek–Sengstock elder abuse screening test (HS‐EAST) for dental]. Results: For the 241 patients, we were able to compare data from the EAI‐R with the HS‐EAST. This pilot work demonstrates the feasibility of screening for EM in busy clinics since we documented patient enrolment of 20% in the medical clinics and 66% in dental clinics. Patients are willing to answer extremely‐sensitive questions related to elder mistreatment and are also willing to use computer technology for interviewing. Conclusion: Dental and medical clinics are important practice venues to screen for elder mistreatment.     

An Exact Confidence Interval for the Ratio of Two Related Proportions

I propose an exact confidence interval for the ratio of two proportions when the proportions are not independent. One application is to estimate the population prevalence using a screening test with perfect specificity but imperfect sensitivity. The population prevalence is the ratio of the observed prevalence divided by the test's sensitivity. I describe a method to calculate exact confidence intervals for this problem and compare these results with approximate confidence intervals given previously.     

Uncovering Symptom Progression History from Disease Registry Data with Application to Young Cystic Fibrosis Patients

Summary The growing availability of various disease registry data has brought precious opportunities to epidemiologists to understand the natural history of the registered diseases. It also presents challenges to the traditional data analysis techniques because of complicated censoring/truncation schemes and temporal dynamics of covariate influences. In a case study of the Cystic Fibrosis Foundation Patient Registry data, we propose analyses of progressive symptoms using temporal process regressions, as an alternative to the commonly employed proportional hazards models. Two endpoints are considered, the prevalence of ever positive and currently positive for Pseudomonas aeruginosa (PA) infection in the lungs, which capture different aspect of the disease process. The analysis of ever PA positive via a time‐varying coefficient model demonstrates the lack of fit, as well as the potential loss of information, in the standard proportional hazards analysis. The analysis of currently PA positive yields results that are clinically meaningful and have not previously been reported in the cystic fibrosis literature. Our analyses demonstrate that prenatal/neonatal screening results in lower prevalence of PA infection compared to traditional diagnosis via signs and symptoms, but this benefit attenuates with age. Calendar years of diagnosis also affect the risk of PA infection; patients diagnosed in more recent cohort show higher prevalence of ever PA positive but lower prevalence of currently PA positive.     

Timing of testing and treatment for asymptomatic diseases

Many papers in the medical literature analyze the cost-effectiveness of screening for diseases by comparing a limited number of a priori testing policies under estimated problem parameters. However, this may be insufficient to determine the best timing of the tests or incorporate changes over time. In this paper, we develop and solve a Markov Decision Process (MDP) model for a simple class of asymptomatic diseases in order to provide the building blocks for analysis of a more general class of diseases. We provide a computationally efficient method for determining a cost-effective dynamic intervention strategy that takes into account (i) the results of the previous test for each individual and (ii) the change in the individual’s behavior based on awareness of the disease. We demonstrate the usefulness of the approach by applying the results to screening decisions for Hepatitis C (HCV) using medical data, and compare our findings to current HCV screening recommendations.     

A novel and efficient ligand-based virtual screening approach using the HWZ scoring function and an enhanced shape-density model

In this work, we extend our previous ligand shape-based virtual screening approach by using the scoring function Hamza–Wei–Zhan (HWZ) score and an enhanced molecular shape-density model for the ligands. The performance of the method has been tested against the 40 targets in the Database of Useful Decoys and compared with the performance of our previous HWZ score method. The virtual screening results using the novel ligand shape-based approach demonstrated a favorable improvement (area under the receiver operator characteristics curve AUC?=?.89?±?.02) and effectiveness (hit rate HR^1%?=?53.0%?±?6.3 and HR^10%?=?71.1%?±?4.9). The comparison of the overall performance of our ligand shape-based method with the highest ligand shape-based virtual screening approach using the data fusion of multi queries showed that our strategy takes into account deeper the chemical information of the set of active ligands. Furthermore, the results indicated that our method are suitable for virtual screening and yields superior prediction accuracy than the other study derived from the data fusion using five queries. Therefore, our novel ligand shape-based screening method constitutes a robust and efficient approach to the 3D similarity screening of small compounds and open the door to a whole new approach to drug design by implementing the method in the structure-based virtual screening.     

Estimating disease prevalence in two-phase studies

Disease prevalence is ideally estimated using a 'gold standard' to ascertain true disease status on all subjects in a population of interest. In practice, however, the gold standard may be too costly or invasive to be applied to all subjects, in which case a two-phase design is often employed. Phase 1 data consisting of inexpensive and non-invasive screening tests on all study subjects are used to determine the subjects that receive the gold standard in the second phase. Naive estimates of prevalence in two-phase studies can be biased (verification bias). Imputation and re-weighting estimators are often used to avoid this bias. We contrast the forms and attributes of the various prevalence estimators. Distribution theory and simulation studies are used to investigate their bias and efficiency. We conclude that the semiparametric efficient approach is the preferred method for prevalence estimation in two-phase studies. It is more robust and comparable in its efficiency to imputation and other re-weighting estimators. It is also easy to implement. We use this approach to examine the prevalence of depression in adolescents with data from the Great Smoky Mountain Study.     

Cancer screening barriers and facilitators for under and never screened populations: A mixed methods study

BackgroundCancer screening is below targets in Ontario, Canada. Our objective was to identify and quantify the barriers and facilitators for breast, cervical and colorectal cancer screening for under and never screened (UNS) residents living in Ontario between 2011 and 2013.MethodsWe used a multi-phased mixed methods study design. Results from thematic analysis of focus group discussions with health care providers and UNS community members were used to develop an on-line, province-wide, cross-sectional survey to estimate the prevalence of barriers and facilitators for the provincial population. Adjusted prevalence odds ratios and 95% confidence intervals were estimated for UNS compared to regularly screened participants using logistic regression.ResultsFour focus groups were held with health service providers and sixteen with UNS community members. Top barriers and facilitators themed around provider-patient communication, fear and embarrassment, history of physical or sexual abuse, social determinants of health (including low literacy, lack of awareness, and health insurance), symptoms appearing, and family and friends. 3075 participants completed the online survey. Compared to regularly screened participants, UNS had significantly higher odds of reporting: no regular health care provider; not feeling comfortable talking about screening; or the Doctor or Nurse Practitioner not suggesting screening. UNS also had significantly higher odds of reporting the facilitators: the test being less scary/painful or uncomfortable; friend/family insisting on getting screened; starting to have symptoms; or an easier test that could be done at home.ConclusionsInterventions addressing fear through individual, interpersonal and structural facilitators may increase cancer screening.     

A gold nanoparticle-based strategy for label-free and colorimetric screening of DNA triplex binders

A label-free colorimetric assay, using non-crosslinking AuNP aggregation, has been developed for the screening of specific triplex DNA binders. The relative binding affinities can be simultaneously determined. Our novel assay is simple in design and fast in operation, avoiding either AuNPs modification or oligonucleotide labeling, and easy to implement for visual detection. This strategy may offer a new approach for developing low cost, sensitive and high-throughput screening platform that is likely to be highly useful in a wide range of applications.     

An Efficient Genome-Wide Multilocus Epistasis Search

There has been a continuing interest in approaches that analyze pairwise locus-by-locus (epistasis) interactions using multilocus association models in genome-wide data sets. In this paper, we suggest an approach that uses sure independence screening to first lower the dimension of the problem by considering the marginal importance of each interaction term within the huge loop. Subsequent multilocus association steps are executed using an extended Bayesian least absolute shrinkage and selection operator (LASSO) model and fast generalized expectation-maximization estimation algorithms. The potential of this approach is illustrated and compared with PLINK software using data examples where phenotypes have been simulated conditionally on marker data from the Quantitative Trait Loci Mapping and Marker Assisted Selection (QTLMAS) Workshop 2008 and real pig data sets.     

Monitoring and evaluating the performance of the UK NHS Cervical Screening Programme: monitoring performance by using cytology outcomes adjusted for population characteristics

Current quality assurance measures used in the NHS cervical screening programme (NHSCSP) include a review of laboratories with percentages of moderate/severe and borderline/mild smear results outside the 10th-90th percentiles. The method is limited by the fact that many of these outlier smear percentages may reflect laboratories covering populations with low or high risk and/or short or long average screening intervals. This paper outlines a new approach to aid the detection of outlier laboratories, by using data collected at the primary care trust (PCT) or health authority (HA) level and making allowances for population characteristics and screening interval. The setting is the NHSCSP in England using annual data provided by HAs. Data from the screening year 2000-01 is used to illustrate the methodology, although the methods can also be applied to data at the PCT level (now being collected for 2002-03 onwards). Percentages of smear results have been analysed against a series of explanatory variables using logistic regression models. These explanatory variables include Townsend deprivation index, uptake-corrected ethnic minority composition, a measure of screening interval, area type and region. An expected percentage of borderline/mild and moderate/severe smears is estimated from the models and an observed : predicted ratio (OPRmod/sev and OPRbord/mild) calculated. Low values are suggestive of relative undercalling and high values overcalling, after allowance for population characteristics. Analysis of data for 2000-01 showed that the OPRmod/sev for the 99 HAs varied from 0.68 to 1.44. Laboratories with low percentages of moderate/severe smears, but associated with PCTs or HAs with OPRmod/sev values closer to unity may not need to be investigated as their observed rates are consistent with predicted rates based on population characteristics. The method could also be directly applied to laboratories if further information on the population covered by each laboratory were routinely collected.     

Power calculations for a general class of family-based association tests: dichotomous traits

Using large-sample theory, we present a unified approach to power calculations for family-based association tests. Currently available methods for power calculations are restricted to special designs or require approximations or simulations. Our analytical approach to power calculations is broadly applicable in many settings. We discuss power calculations for two scenarios that have high practical relevance and in which power previously could only be assessed by simulation studies or by approximations: (1) studies using both affected and unaffected offspring and (2) studies with missing parental information. When the population prevalence is high, it can be worthwhile to genotype unaffected offspring. For many scenarios, high power can be achieved with reasonable sample sizes, even when no parental information is available.     

Can Data Science Inform Environmental Justice and Community Risk Screening for Type 2 Diabetes?

Background

Having the ability to scan the entire country for potential “hotspots” with increased risk of developing chronic diseases due to various environmental, demographic, and genetic susceptibility factors may inform risk management decisions and enable better environmental public health policies.

Objectives

Develop an approach for community-level risk screening focused on identifying potential genetic susceptibility hotpots.

Methods

Our approach combines analyses of phenotype-genotype data, genetic prevalence of single nucleotide polymorphisms, and census/geographic information to estimate census tract-level population attributable risks among various ethnicities and total population for the state of California.

Results

We estimate that the rs13266634 single nucleotide polymorphism, a type 2 diabetes susceptibility genotype, has a genetic prevalence of 56.3%, 47.4% and 37.0% in Mexican Mestizo, Caucasian, and Asian populations. Looking at the top quintile for total population attributable risk, 16 California counties have greater than 25% of their population living in hotspots of genetic susceptibility for developing type 2 diabetes due to this single genotypic susceptibility factor.

Conclusions

This study identified counties in California where large portions of the population may bear additional type 2 diabetes risk due to increased genetic prevalence of a susceptibility genotype. This type of screening can easily be extended to include information on environmental contaminants of interest and other related diseases, and potentially enables the rapid identification of potential environmental justice communities. Other potential uses of this approach include problem formulation in support of risk assessments, land use planning, and prioritization of site cleanup and remediation actions.