Pediatric Critical Care Illness Severity Toolkit: Stata Commands for Calculation of Pediatric Index of Mortality and Pediatric Logistic Organ Dysfunction Scores.

Introduction: Illness severity scoring tools, such as PRISM III/IV, PIM-3, and PELOD-2, are widely used in pediatric critical care research. However, their application is hindered by complex calculation processes, privacy concerns with third-party online calculators, and challenges in accurate implementation within statistical packages. Methods: We have developed a comprehensive, open-source toolkit for implementing the PIM-3, Simplified PIM-3, and PELOD-2 scores. The toolkit includes the pim3 and pelod2 commands and is compatible with Stata versions 12 and above. It features robust data validation, error messaging, a graphical interface, and support for SI and Imperial units. The toolkit's accuracy was validated through unit testing and synthetic data, comparing results with existing implementations. Results: In performance tests, the toolkit exhibited a median processing time of 21.82 seconds for PELOD-2, 14.06 seconds for PIM-3, and 9.74 seconds for Simplified PIM-3, when applied to datasets of 10,000,000 records. It consistently achieved 100% accuracy in both synthetic data tests and manual spot checks. Conclusion: The toolkit decreases processing time and improves accuracy in calculating pediatric critical care severity scores such as PELOD-2, PIM-3, and Simplified PIM-3. Its application in large datasets and validation highlights its utility as a tool for streamlining pediatric critical care research.

A practical guide to adopting Bayesian analyses in clinical research.

Background: Bayesian statistical approaches are extensively used in new statistical methods but have not been adopted at the same rate in clinical and translational (C&T) research. The goal of this paper is to accelerate the transition of new methods into practice by improving the C&T researcher's ability to gain confidence in interpreting and implementing Bayesian analyses. Methods: We developed a Bayesian data analysis plan and implemented that plan for a two-arm clinical trial comparing the effectiveness of a new opioid in reducing time to discharge from the post-operative anesthesia unit and nerve block usage in surgery. Through this application, we offer a brief tutorial on Bayesian methods and exhibit how to apply four Bayesian statistical packages from STATA, SAS, and RStan to conduct linear and logistic regression analyses in clinical research. Results: The analysis results in our application were robust to statistical package and consistent across a wide range of prior distributions. STATA was the most approachable package for linear regression but was more limited in the models that could be fitted and easily summarized. SAS and R offered more straightforward documentation and data management for the posteriors. They also offered direct programming of the likelihood making them more easily extendable to complex problems. Conclusion: Bayesian analysis is now accessible to a broad range of data analysts and should be considered in more C&T research analyses. This will allow C&T research teams the ability to adopt and interpret Bayesian methodology in more complex problems where Bayesian approaches are often needed.

Methods for meta-analysis and meta-regression of binomial data: concepts and tutorial with Stata command metapreg.

BACKGROUND: Despite the widespread interest in meta-analysis of proportions, its rationale, certain theoretical and methodological concepts are poorly understood. The generalized linear models framework is well-established and provides a natural and optimal model for meta-analysis, network meta-analysis, and meta-regression of proportions. Nonetheless, generic methods for meta-analysis of proportions based on the approximation to the normal distribution continue to dominate. METHODS: We developed metapreg, a tool with advanced statistical procedures to perform a meta-analysis, network meta-analysis, and meta-regression of binomial proportions in Stata using binomial, logistic and logistic-normal models. First, we explain the rationale and concepts essential in understanding statistical methods for meta-analysis of binomial proportions and describe the models implemented in metapreg. We then describe and demonstrate the models in metapreg using data from seven published meta-analyses. We also conducted a simulation study to compare the performance of metapreg estimators with the existing estimators of the population-averaged proportion in metaprop and metan under a broad range of conditions including, high over-dispersion and small meta-analysis. CONCLUSION: metapreg is a flexible, robust and user-friendly tool employing a rigorous approach to evidence synthesis of binomial data that makes the most efficient use of all available data and does not require ad-hoc continuity correction or data imputation. We expect its use to yield higher-quality meta-analysis of binomial proportions.

Prevalence of antibiotic resistance in clinical isolates of Mycobacterium kansasii: a systematic review and meta-analysis.

INTRODUCTION: The prevalence of diseases caused by non-tuberculous mycobacteria (NTM), including M. kansasii, is increasing, necessitating further information to guide prevention, control, and treatment strategies. AREAS COVERED: A comprehensive analysis of articles published until February 2023 was conducted on PubMed, Web of Science, and Scopus databases to investigate antibiotic resistance in M. kansasii species. Stata software version 17 was employed for all analyses. EXPERT OPINION: A total of 1647 articles were obtained through database search. After removing duplicates and unrelated studies, 17 cross-sectional studies that examined the breakpoints proposed by CLSI were included. The rates of resistance of M. kansasii to various antibiotics were as follows: clarithromycin (0%), rifampin (1%), amikacin (0%), ciprofloxacin (14%), linezolid (0%), moxifloxacin (0%), rifabutin (1%), doxycycline (96%), and SXT (49%). Our findings underscore the importance of managing and monitoring the use of these antibiotics, as well as the need for further studies to elucidate the exact mechanism of M. kansasii resistance to these antibiotics.

Dataset of the impact of food insecurity on health outcomes in sub-Saharan Africa.

OBJECTIVES: The 2030 Sustainable Development Goals (SDGs) have goals and targets, including food insecurity and health outcomes. Hence, information about socioeconomic variables that determine the health outcomes of people is essential for health-related research, planning, and policy development. Therefore, this data paper aims to present (describe) the dataset of eight socioeconomic variables for 31 sub-Saharan Africa (SSA) countries from 2001 to 2018. DATA DESCRIPTIONS: The dataset was official information obtained via open online sources from the Food and Agricultural Organization (FAO), the World Bank (WB), and the United Nations Development Programme (UNDP). It included 558 observations and eight variables, such as life expectancy (LEXP), infant mortality (INFMOR), the prevalence of undernourishment (PRUND), average dietary energy supply (AVRDES), Gross domestic product per capita (GDPPC), general government health expenditure (GOVEXP), urbanisation (URBAN), and mean years of schooling (MNSCHOOL). Moreover, all the data estimation is conducted by Statistical Software (STATA) version 15. This paper achieved its intended objective with a detailed and understandable description.

audit_cc: A Stata command for the analysis of matched case-control audits of cervical cancer screening.

Background and objectives: Cervical screening programmes are crucial for the early diagnosis and prevention of cancer of the cervix. Regular auditing is vital for ensuring that these programmes achieve their full potential and meet their objectives in practice. Unfortunately, the time and skills required for the statistical analysis of the data collected are often important limiting factors. Comparisons across countries and over time have also been particularly difficult due to a lack of standardized definitions and methodology. We aimed to overcome these problems. Methods: Using the statistical software Stata, we developed a new command called audit_cc for the analysis of matched case-control audits of cervical cancer screening. Analyses are reported for two measures of screening history: time since last test and time since last negative test. Results: The command carries out the data manipulation which is required for the analysis and allows to save the resulting data set in an external file for further investigations. It promotes consistent evaluations of screening programmes over time and across studies and facilitates the creation of automatic publication-quality reports, which are especially useful in the context of routine audits. Conclusions: audit_cc is a valid tool that not only simplifies the analysis and reporting of cervical screening audits but also allows meaningful international comparisons. Although it is specific for cervical cancer, it can be seen as an example of how the standardisation of exposure definitions and key methodological issues can enable consistent and comparable evaluations of screening programmes across different countries and settings.

Comparison and validation of metadta for meta-analysis of diagnostic test accuracy studies.

We developed metadta, a flexible, robust, and user-friendly statistical procedure that fuses established and innovative statistical methods for meta-analysis, meta-regression, and network meta-analysis of diagnostic test accuracy studies in Stata. Using data from published meta-analyses, we validate metadta by comparing and contrasting its features and output to popular procedures dedicated to the meta-analysis of diagnostic test accuracy studies; (midas [Stata], metandi [Stata], metaDTA [web application], mada [R], and MetaDAS [SAS]). We also demonstrate how to perform network meta-analysis with metadta, for which no alternative procedure is dedicated to network meta-analysis of diagnostic test accuracy data in the frequentist framework. metadta generated consistent estimates in simple and complex diagnostic test accuracy data sets. We expect its availability to stimulate better statistical practice in the evidence synthesis of diagnostic test accuracy studies.

Clinicopathological features and prognosis of colonic and rectal gastrointestinal stromal tumors: A propensity score matching analysis.

Background: Colonic gastrointestinal stromal tumor (cGIST) and rectal gastrointestinal stromal tumor (rGIST) are two rare subtypes of gastrointestinal stromal tumor (GIST). The view that colonic and rectal carcinoma are different is generally accepted; however, whether there is a difference between cGIST and rGIST is still unknown. Here, we aimed to provide evidence for future clinical management and research by comparing the differences between the two types of GIST in the above-mentioned aspects. Methods: Patients were enrolled from three medical centers in China and published literature was collected following the inclusion and exclusion criteria. Propensity score matching was used to eliminate differences between cohorts. Results: Between cGIST and rGIST patients, significant differences were observed in age, tumor size, mitotic index, NIH risk category, growth pattern, and symptoms. Adjuvant therapy is used in a high proportion of cGIST patients, and neoadjuvant therapy is used in a high proportion of rGIST patients. Although local resection is the main surgical method in both cohorts, the proportion is higher in cGIST patients. The overall survival of rGIST patients was better than that of the cGIST patients before propensity score matching (PSM). Interestingly, no significant differences in prognosis were observed after PSM. Conclusions: Although there were significant differences between cGIST and rGIST patients in baseline characteristics, clinicopathological features, treatment choice, and overall survival rate before PSM, no significant differences in long-term survival were observed between the two groups after PSM. In our study, there may be no differences in the tumor entity between cGIST and rGIST.

Knowledge of intensive care nurses' towards prevention of ventilator-associated pneumonia in North West Ethiopia referral hospitals, 2021: A multicenter, cross-sectional study.

Introduction: Ventilator-associated pneumonia is a common nosocomial infection that occurs in critically ill patients who are on intubation and mechanical ventilation. Nurses' lack of knowledge may be a barrier to adherence to evidence-based guidelines for preventing ventilator-associated pneumonia. This study aimed to assess the knowledge of intensive care nurses' towards the prevention of ventilator-associated pneumonia. Methods: A multicenter cross-sectional study was conducted among nurses working in the intensive care unit from April to July 2021. A pre-tested and structured questionnaire was used to collect data. All intensive care nurses working in the study area were included in the study. Data was entered into Epi-data 4.1 version (EpiData Association, Denmark) and transferred to STATA version 14 (College Station, Texas 77845-4512 USA) statistical software for analysis. Both bi-variable and multivariable binary logistic regression analysis was used to identify factors associated with knowledge of intensive care unit nurse. Variables with a p-value less than <0.2 in the bi-variable analysis were fitted into the multivariable logistic regression analysis. Both Crude and Adjusted Odds Ratio with the corresponding 95% Confidence Interval was calculated to show the strength of association. In multivariable analysis, variables with a p-value of <0.05 were considered statistically significant. Result: A total of 213 intensive care nurses were included in the study, with a response rate of 204(95.77%). The mean knowledge score of intensive care nurses regarding the prevention of ventilator-associated pneumonia out of 20 questions is (10.1 ± 2.41). There are 98 (48.04%) of the participants have been found to have good knowledge and 106 (51.96%) of them are rendered poor knowledge about the overall knowledge related to the prevention of ventilator-associated pneumonia. Higher academic qualifications and taking intensive care unit training were significantly associated with good knowledge of ventilator-associated pneumonia prevention in multi-variable logistic regression. Conclusion: Our study indicates that the knowledge of intensive care nurses about ventilator-associated pneumonia prevention is not sufficient. Higher academic qualifications and taking intensive care unit training are significantly associated with a good level of knowledge. Therefore it shows the necessity for thorough training and education.

Metadta: a Stata command for meta-analysis and meta-regression of diagnostic test accuracy data - a tutorial.

BACKGROUND: Although statistical procedures for pooling of several epidemiological metrics are generally available in statistical packages, those for meta-analysis of diagnostic test accuracy studies including options for multivariate regression are lacking. Fitting regression models and the processing of the estimates often entails lengthy and tedious calculations. Therefore, packaging appropriate statistical procedures in a robust and user-friendly program is of great interest to the scientific community. METHODS: metadta is a statistical program for pooling of diagnostic accuracy test data in Stata. It implements both the bivariate random-effects and the fixed-effects model, allows for meta-regression, and presents the results in tables, a forest plot and/or summary receiver operating characteristic (SROC) plot. For a model without covariates, it quantifies the unexplained heterogeneity due to between-study variation using an I2 statistic that accounts for the mean-variance relationship and the correlation between sensitivity and specificity. To demonstrate metadta, we applied the program on two published meta-analyses on: 1) the sensitivity and specificity of cytology and other markers including telomerase for primary diagnosis of bladder cancer, and 2) the accuracy of human papillomavirus (HPV) testing on self-collected versus clinician-collected samples to detect cervical precancer. RESULTS: Without requiring a continuity correction, the pooled sensitivity and specificity generated by metadta of telomerase for the diagnosis of primary bladder cancer was 0.77 [95% CI, 0.70, 0.82] and 0.91 [95% CI, 0.75, 0.97] respectively. Metadta also allowed to assess the relative accuracy of HPV testing on self- versus clinician-taken specimens using data from comparative studies conducted in different clinical settings. The analysis showed that HPV testing with target-amplification assays on self-samples was as sensitive as on clinician-samples in detecting cervical pre-cancer irrespective of the clinical setting. CONCLUSION: The metadta program implements state of art statistical procedures in an attempt to close the gap between methodological statisticians and systematic reviewers. We expect the program to popularize the use of appropriate statistical methods for diagnostic meta-analysis further.

Software to Conduct a Meta-Analysis and Network Meta-Analysis.

Statistical software for meta-analysis (MA) and network meta-analysis (NMA) have become indispensable for researchers. The aim of this chapter is to introduce key features of MA and NMA software to compare the effectiveness of interventions. Commonly used or routinely maintained statistical software are reviewed, including commercial and open-sourced programs such as Stata, R and Excel plug-ins. It does not provide a comprehensive overview of all features available in the software covered. Rather, it focuses on the essential features required to carry out an MA or NMA . This chapter begins with a review of key considerations when implementing an MA or NMA , then presents a summary of the software. Key features of each software option are discussed.

Employment Status and Its Association With Psychological Distress and Alcohol Consumption.

PURPOSE: The objective of this analysis was to find an association between employment status, psychological distress, and alcohol consumption. METHODS: Data from the 2020 Health Information National Trends Survey (HINTS) data was used. Patient health questionnaire-4 (PHQ-4) data and an average number of drinks per week were used to assess psychological distress and drinking status. RESULTS: Out of the 3865 people who answered the survey in the year 2020, 1890 (59.11%) were employed in one or multiple jobs during the time of the survey. The sample included 1561 men and 2204 women with an average age of 48.4 years. More than half (58.7%) of them were Non-Hispanic White followed by Hispanic and Non-Hispanic Black at 15.73% and 10.32%, respectively. Bivariate analysis showed a significant association between employment, psychological distress (p value=0.032), and alcohol drinking (p value=0.002); 60.59% of participants reporting severe distress (PHQ-4 score of 9-12) were unemployed. Alternatively, 73.1% of the employed respondents reported no stress (PHQ-4 score of 0-2). While more than half (75.97%) of those who were unemployed consumed only 1-2 drinks per week on an average, 7.27% consumed >13 drinks per week on an average. After adjusting for covariates, the regression analysis showed a highly significant association (p value< 0.001) between unemployment and psychological distress (OR=1.55; 95% CI 1.03, 2.31), and alcohol consumption (OR=0.67; 95% CI 0.48, 0.92). CONCLUSION: Unemployment is associated with outcomes like psychological distress and alcohol consumption. However, being employed was found to be more strongly associated with alcohol drinking. We do not know if the coronavirus disease 2019 (COVID-19) pandemic was a risk factor for the given outcomes.

A Retrospective Observational Study: Is Absolute Lymphocyte Count a Prognostic Marker in COVID-19?

Aim Our study aimed to find a correlation between low absolute lymphocyte count and COVID-19-related mortality. Methods This study followed a retrospective observational cohort design to analyze the data of patients who presented with symptoms and signs of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), at the Conquest Hospital and Eastbourne District General Hospital in East Sussex, United Kingdom, between February 10, 2020 and May 1, 2020, retrospectively. Survival and mortality for the first 30 days and comorbidities were analyzed for all patients who were tested for COVID-19 irrespective of swab results and had blood lymphocyte levels taken at the time of their visit to the ED and their data were analyzed for statistical significance. Results A total of 1226 patients had SARS-CoV-2 RNA identification swabs taken between February 10, 2020 and May 1, 2020. A cohort of 742 patients of these patients tested for COVID-19 also had blood lymphocyte levels measured. Overall, the lymphocyte count did not differ significantly between patients suspected to have COVID-19 infection with either positive or negative COVID-19 swab results. The lymphocyte count, however, was significantly lower in those who died from COVID-19 (p < 0.001) but when comorbidities were analyzed, we found an association between an increased number of comorbidities and a significantly decreased lymphocyte count. Conclusion Once adjusted for comorbidities, the lymphocyte count had no association with COVID-19 infection and mortality.

Trends in the Usage of Statistical Software and Their Associated Study Designs in Health Sciences Research: A Bibliometric Analysis.

Background The development of statistical software in research has transformed the way scientists and researchers conduct their statistical analysis. Despite these advancements, it was not clear which statistical software is mainly used for which research design thereby creating confusion and uncertainty in choosing the right statistical tools. Therefore, this study aimed to review the trend of statistical software usage and their associated study designs in articles published in health sciences research. Methods This bibliometric analysis study reviewed 10,596 articles published in PubMed in three 10-year intervals (1997, 2007, and 2017). The data were collected through Google sheet and were analyzed using SPSS software. This study described the trend and usage of currently available statistical tools and the different study designs that are associated with them. Results Of the statistical software mentioned in the retrieved articles, SPSS was the most common statistical tool used (52.1%) in the three-time periods followed by SAS (12.9%) and Stata (12.6%). WinBugs was the least used statistical software with only 40(0.6%) of the total articles. SPSS was mostly associated with observational (61.1%) and experimental (65.3%) study designs. On the other hand, Review Manager (43.7%) and Stata (38.3%) were the most statistical software associated with systematic reviews and meta-analyses. Conclusion In this study, SPSS was found to be the most widely used statistical software in the selected study periods. Observational studies were the most common health science research design. SPSS was associated with observational and experimental studies while Review Manager and Stata were mostly used for systematic reviews and meta-analysis.

Transient receptor potential vanilloid 4 activation inhibits the delayed rectifier potassium channels in hippocampal pyramidal neurons: An implication in pathological changes following pilocarpine-induced status epilepticus.

Activation of transient receptor potential vanilloid 4 (TRPV4) can increase hippocampal neuronal excitability. TRPV4 has been reported to be involved in the pathogenesis of epilepsy. Voltage-gated potassium channels (VGPCs) play an important role in regulating neuronal excitability and abnormal VGPCs expression or function is related to epilepsy. Here, we examined the effect of TRPV4 activation on the delayed rectifier potassium current (IK ) in hippocampal pyramidal neurons and on the Kv subunits expression in male mice. We also explored the role of TRPV4 in changes in Kv subunits expression in male mice following pilocarpine-induced status epilepticus (PISE). Application of TRPV4 agonists, GSK1016790A and 5,6-EET, markedly reduced IK in hippocampal pyramidal neurons and shifted the voltage-dependent inactivation curve to the hyperpolarizing direction. GSK1016790A- and 5,6-EET-induced inhibition of IK was blocked by TRPV4 specific antagonists, HC-067047 and RN1734. GSK1016790A-induced inhibition of IK was markedly attenuated by calcium/calmodulin-dependent kinase II (CaMKII) antagonist. Application of GSK1016790A for up to 1 hr did not change the hippocampal protein levels of Kv1.1, Kv1.2, or Kv2.1. Intracerebroventricular injection of GSK1016790A for 3 d reduced the hippocampal protein levels of Kv1.2 and Kv2.1, leaving that of Kv1.1 unchanged. Kv1.2 and Kv2.1 protein levels as well as IK reduced markedly in hippocampi on day 3 post PISE, which was significantly reversed by HC-067047. We conclude that activation of TRPV4 inhibits IK in hippocampal pyramidal neurons, possibly by activating CaMKII. TRPV4-induced decrease in Kv1.2 and Kv2.1 expression and IK may be involved in the pathological changes following PISE.

gsem: A Stata command for parametric joint modelling of longitudinal and accelerated failure time models.

BACKGROUND: The number of studies using joint modelling of longitudinal and survival data have increased in the past two decades, but analytical techniques and software shortcomings have remained. A joint model is often used for analysis of a combination of longitudinal sub-model and survival sub-model using shared random effects. Cox regression commonly referring to the survival sub-model, should not be used when proportional hazards assumptions are not satisfied. In such cases, the parametric survival model is preferable. METHODS: We describe different parametric survival models for survival sub-model of joint modelling. We demonstrate how these models can be fit using gsem command (used for generalized structural equation model) in Stata that allows the model to be jointly continuous longitudinal and parametric survival data. With this code, linear mixed effect model is used for the longitudinal sub-model of the joint model, allowing random and fixed effects of the time. In gsem command for survival sub-models, there are five different choices: exponential, Weibull, log-normal, log-logistic and gamma accelerated failure time models. RESULTS: In this paper, we have described properties of gsem command for parametric joint modelling and have shown an application for parametric joint models on the 312 patients with primary biliary cirrhosis, which is a major health problem in the western world. CONCLUSIONS: We showed how parametric joint models can be used with the gsem command which has been the only Stata code in the literature to fit the parametric joint models, for the generalized structural equation model, and we used the primary biliary cirrhosis dataset for the detailed application of the command. Thus, the gsem command becomes more useful for fitting parametric joint models.

Metodologías cuantitativas: Cálculo del tamaño de muestra con STATA y R / Quantitative methods: Sample size calculation with STATA and R

El cálculo de tamaño de muestra es un aspecto esencial del diseño de estudios cuantitativos. Un adecuado tamaño de muestra nos permite determinar cuál es la mínima cantidad de participantes necesarios para probar nuestra hipótesis de interés. De esta manera, podemos reducir costos, maximizar el uso de nuestros recursos de investigación y garantizar la factibilidad del estudio. Contradictoriamente, a pesar de su relevancia muy pocos investigadores dominan esta habilidad. Esta revisión tiene por objeto revisar los conceptos básicos para realizar un cálculo de tamaño de muestra y compartir códigos de Stata y R específicamente diseñados para facilitar estos cálculos.

The calculation of sample size is an essential aspect of the design of quantitative studies. An adequate sample size allows us to determine the minimum number of participants necessary to test our hypothesis of interest. Hence, we can reduce costs, maximize the use of our research resources and guarantee the feasibility of the study. Contradictorily, despite its relevance, very few researchers dominate this skill. This review aims to review the basics of sample size calculation and share Stata and R codes specifically designed to facilitate these calculations.

Investigating heterogeneity of effects and associations using interaction terms.

Effect heterogeneity, the variability of an association or exposure across subgroups, usually warrants further investigation. The aim of this deeper analysis is to identify effect modifiers (or moderators) and quantify their relationship with the exposure. We explain why it is better to harness interaction effects within a single analytic model than to use separate models to analyze each subgroup. Using examples, we demonstrate a practical approach to modeling and interpretation with interaction terms from various measurement scales (categorical by categorical; categorical by continuous; and continuous by continuous).

Late-phase immune responses limiting oocyst survival are independent of TEP1 function yet display strain specific differences in Anopheles gambiae.

BACKGROUND: There is emerging evidence that mosquito anti-Plasmodium immunity is multimodal with distinct mechanisms for killing malaria parasites at either the ookinete or oocyst stages. Early-phase responses targeting the ookinete require complement-like components circulating in the mosquito hemolymph that result in TEP1-mediated lysis or melanization. Additional responses mediated by the LL3 and STAT pathways limit oocyst survival through unknown mechanisms that require mosquito hemocyte function. While previous experiments argue that these mechanisms of parasite killing are independent, the transient nature of gene-silencing has rendered these experiments inconclusive. To address this issue, we outline experiments using a TALEN-derived TEP1 mutant line to examine the role of TEP1 in the Anopheles gambiae late-phase immune response. RESULTS: Despite higher early oocyst numbers in the TEP1 mutant line, no differences in oocyst survival were observed when compared to control mosquitoes, suggesting that TEP1 function is independent of the late-phase immune response. To further validate this phenotype in the TEP1 mutant, oocyst survival was evaluated in the TEP1 mutant background by silencing either LL3 or STAT-A. Surprisingly, only STAT-A silenced mosquitoes were able to reconstitute the late-phase immune phenotype increasing oocyst survival in the TEP1 mutant line. Additional experiments highlight significant differences in LL3 expression in the M/S hybrid genetic background of the TEP1 mutant line compared to that of the Keele strain (M form) of An. gambiae, and demonstrate that LL3 is not required for granulocyte differentiation in the M/S hybrid G3 genetic background in response to malaria parasite infection. CONCLUSIONS: Through the combination of genetic experiments utilizing genetic mutants and reverse genetic approaches, new information has emerged regarding the mechanisms of mosquito late-phase immunity. When combined with previously published experiments, the body of evidence argues that Plasmodium oocyst survival is TEP1 independent, thus establishing that the mechanisms of early- and late-phase immunity are distinct. Moreover, we identify that the known components that mediate oocyst survival are susceptible to strain-specific differences depending on their genetic background and provide further evidence that the signals that promote hemocyte differentiation are required to limit oocyst survival. Together, this study provides new insights into the mechanisms of oocyst killing and the importance of genetics in shaping mosquito vector competence.

Introduction to bifactor polytomous item response theory analysis.

A bifactor item response theory model can be used to aid in the interpretation of the dimensionality of a multifaceted questionnaire that assumes continuous latent variables underlying the propensity to respond to items. This model can be used to describe the locations of people on a general continuous latent variable as well as on continuous orthogonal specific traits that characterize responses to groups of items. The bifactor graded response (bifac-GR) model is presented in contrast to a correlated traits (or multidimensional GR model) and unidimensional GR model. Bifac-GR model specification, assumptions, estimation, and interpretation are demonstrated with a reanalysis of data (Campbell, 2008) on the Shared Activities Questionnaire. We also show the importance of marginalizing the slopes for interpretation purposes and we extend the concept to the interpretation of the information function. To go along with the illustrative example analyses, we have made available supplementary files that include command file (syntax) examples and outputs from flexMIRT, IRTPRO, R, Mplus, and STATA. Supplementary data to this article can be found online at http://dx.doi.org/10.1016/j.jsp.2016.11.001. Data needed to reproduce analyses in this article are available as supplemental materials (online only) in the Appendix of this article.