Quantifying the impact of surgical teams on each stage of the operating room process.

Introduction: Operating room (OR) efficiency is a key factor in determining surgical healthcare costs. To enable targeted changes for improving OR efficiency, a comprehensive quantification of the underlying sources of variability contributing to OR efficiency is needed. Previous literature has focused on select stages of the OR process or on aggregate process times influencing efficiency. This study proposes to analyze the OR process in more fine-grained stages to better localize and quantify the impact of important factors. Methods: Data spanning from 2019-2023 were obtained from a surgery center at a large academic hospital. Linear mixed models were developed to quantify the sources of variability in the OR process. The primary factors analyzed in this study included the primary surgeon, responsible anesthesia provider, primary circulating nurse, and procedure type. The OR process was segmented into eight stages that quantify eight process times, e.g., procedure duration and procedure start time delay. Model selection was performed to identify the key factors in each stage and to quantify variability. Results: Procedure type accounted for the most variability in three process times and for 44.2% and 45.5% of variability, respectively, in procedure duration and OR time (defined as the total time the patient spent in the OR). Primary surgeon, however, accounted for the most variability in five of the eight process times and accounted for as much as 21.1% of variability. The primary circulating nurse was also found to be significant for all eight process times. Discussion: The key findings of this study include the following. (1) It is crucial to segment the OR process into smaller, more homogeneous stages to more accurately assess the underlying sources of variability. (2) Variability in the aggregate quantity of OR time appears to mostly reflect the variability in procedure duration, which is a subinterval of OR time. (3) Primary surgeon has a larger effect on OR efficiency than previously reported in the literature and is an important factor throughout the entire OR process. (4) Primary circulating nurse is significant for all stages of the OR process, albeit their effect is small.

Postmenopausal Shrinkage of Uterine Myomas: A Retrospective Study of 97 Cases Monitored Annually for 10 Years.

Introduction and aim Both patients and gynecologists are concerned about how much and how quickly myomas shrink after menopause. This study aimed to elucidate clinical findings that may be associated with postmenopausal shrinkage of uterine myomas. Materials and methods This study included 97 patients who underwent menopause by August 2012, had myoma nodules with the longest diameter between 50 mm and 160 mm, and visited our specialized myoma clinic annually for at least 10 years after menopause. They underwent transabdominal ultrasonography at least once per year. An experienced gynecologist measured the longest diameter of myoma nodules with a maximum diameter between 50 mm and 160 mm. The shrinkage rate of myoma diameters after menopause compared to premenopausal diameters was calculated each year for 10 years. The shrinkage rate of the longest diameter of the largest nodule 10 years after menopause (10-year shrinkage rate) and its relationship with clinical findings (the age at menopause, parity, body mass index {BMI}, number of nodules, MRI findings on T2-weighted image, location of the nodule, and longest diameter of the largest nodule before menopause) were analyzed. Additionally, we examined annual changes in shrinkage rate of myomas over a 10-year period after menopause (annual trend), and the relationship between annual trends and factors such as BMI and the number of nodules. Results In this examination of 10-year shrinkage rate, the group with a BMI of less than 25 showed a significantly greater shrinkage rate compared to the group with a BMI of 25 or more (25.0% vs 15.7%, p=0.023). Additionally, the group with a single nodule showed a significantly greater 10-year shrinkage rate compared to the group with four or more nodules (26.3% vs 15.2%, p=0.036). For annual trends, the rate of change in the first two years after menopause was significantly faster compared to the trend from the third to the 10th year (difference in slope: 3.888 points per year, p<0.001). When divided into two groups based on the number of nodules (one or two nodules group and three or more nodules group), the group with one or two nodules showed a significant difference in the shrinkage rate between up to two years after menopause and from the period from the third to the 10th year (difference in slope: 4.590 points per year, p<0.001). However, for the group with three or more nodules, there was no significant difference in the annual trend between the first two years after menopause and the rate from the third to the 10th year (difference in slope: 1.626 points per year, p=0.107). Conclusion BMI and the number of myoma nodules were significantly related to the 10-year shrinkage rate. Although myomas shrank significantly faster within the first two years after menopause compared to the later period, the early annual trend did not differ significantly from the trend in the later period when there were multiple nodules with a maximum diameter of 50 mm or more.

Plasma Proteomic Biomarkers in Alzheimer's Disease and Cardiovascular Disease: A Longitudinal Study.

The co-occurrence of Alzheimer's disease (AD) and cardiovascular diseases (CVDs) in older adults highlights the necessity for the exploration of potential shared risk factors. A total of 566 adults were selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, including 111 individuals with AD, 383 with mild cognitive impairment (MCI), and 410 with CVD. The multivariable linear mixed model (LMM) was used to investigate the associations of AD and CVD with longitudinal changes in 146 plasma proteomic biomarkers (measured at baseline and the 12-month follow-up). The LMM showed that 48 biomarkers were linked to AD and 46 to CVD (p < 0.05). Both AD and CVD were associated with longitudinal changes in 14 biomarkers (α1Micro, ApoH, ß2M, BNP, complement C3, cystatin C, KIM1, NGAL, PPP, TIM1, THP, TFF3, TM, and VEGF), and both MCI and CVD were associated with 12 biomarkers (ApoD, AXL, BNP, Calcitonin, CD40, C-peptide, pM, PPP, THP, TNFR2, TTR, and VEGF), suggesting intricate connections between cognitive decline and cardiovascular health. Among these, the Tamm Horsfall Protein (THP) was associated with AD, MCI, CVD, and APOE-ε4. This study provides valuable insights into shared and distinct biological markers and mechanisms underlying AD and CVD.

Lifestyle factors associated with episodic memory in middle-aged and older adults: evidence from a 9-year longitudinal study.

BACKGROUND: Episodic memory naturally deteriorates with age, and its deficits are widely recognized as the most significant feature and the most sensitive indicator of cognitive decline. It has been suggested that adopting a healthy lifestyle can play a protective role in preserving episodic memory. This study aimed to systematically examine the relationship between lifestyle factors (social activities, leisure activities, physical activities, internet use, smoking, alcohol drinking, and sleep quality) and episodic memory in middle-aged and older adults. METHODS: The current study included 10,392 participants from the Chinese Health and Retirement Longitudinal Survey. A linear mixed model was used to explore the associations between lifestyle factors and episodic memory performance and the age- and sex-specific effects of the association. RESULTS: Low-frequency alcohol drinking, higher engagement in social, leisure, and physical activities, increased internet use, and improved sleep quality were associated with better episodic memory performance in middle-aged and older adults. Stratified analyses demonstrated that internet use significantly correlated with episodic memory performance in middle-aged adults but not in older adults. On the other hand, sleep quality showed a significant association with episodic memory performance in women but not in men. CONCLUSIONS: This study highlights the association between various lifestyle factors and episodic memory performance, with variations observed based on age and sex. Adopting healthy lifestyle factors can have positive effects on episodic memory in middle-aged adults, emphasizing the importance of adhering to healthy lifestyles from middle age onwards to counteract episodic memory decline.

Incorporating genetic similarity of auxiliary samples into eGene identification under the transfer learning framework.

BACKGROUND: The term eGene has been applied to define a gene whose expression level is affected by at least one independent expression quantitative trait locus (eQTL). It is both theoretically and empirically important to identify eQTLs and eGenes in genomic studies. However, standard eGene detection methods generally focus on individual cis-variants and cannot efficiently leverage useful knowledge acquired from auxiliary samples into target studies. METHODS: We propose a multilocus-based eGene identification method called TLegene by integrating shared genetic similarity information available from auxiliary studies under the statistical framework of transfer learning. We apply TLegene to eGene identification in ten TCGA cancers which have an explicit relevant tissue in the GTEx project, and learn genetic effect of variant in TCGA from GTEx. We also adopt TLegene to the Geuvadis project to evaluate its usefulness in non-cancer studies. RESULTS: We observed substantial genetic effect correlation of cis-variants between TCGA and GTEx for a larger number of genes. Furthermore, consistent with the results of our simulations, we found that TLegene was more powerful than existing methods and thus identified 169 distinct candidate eGenes, which was much larger than the approach that did not consider knowledge transfer across target and auxiliary studies. Previous studies and functional enrichment analyses provided empirical evidence supporting the associations of discovered eGenes, and it also showed evidence of allelic heterogeneity of gene expression. Furthermore, TLegene identified more eGenes in Geuvadis and revealed that these eGenes were mainly enriched in cells EBV transformed lymphocytes tissue. CONCLUSION: Overall, TLegene represents a flexible and powerful statistical method for eGene identification through transfer learning of genetic similarity shared across auxiliary and target studies.

Fixed and random effect selections in generalized linear mixed models.

Generalized linear mixed models are commonly used to describe relationships between correlated responses and covariates in medical research. In this paper, we propose a simple and easily implementable regularized estimation approach to select both fixed and random effects in generalized linear mixed model. Specifically, we propose to construct and optimize the objective functions using the confidence distributions of model parameters, as opposed to using the observed data likelihood functions, to perform effect selections. Two estimation methods are developed. The first one is to use the joint confidence distribution of model parameters to perform simultaneous fixed and random effect selections. The second method is to use the marginal confidence distributions of model parameters to perform the selections of fixed and random effects separately. With a proper choice of regularization parameters in the adaptive LASSO framework, we show the consistency and oracle properties of the proposed regularized estimators. Simulation studies have been conducted to assess the performance of the proposed estimators and demonstrate computational efficiency. Our method has also been applied to two longitudinal cancer studies to identify demographic and clinical factors associated with patient health outcomes after cancer therapies.

Sample size requirements for testing treatment effect heterogeneity in cluster randomized trials with binary outcomes.

Cluster randomized trials (CRTs) refer to a popular class of experiments in which randomization is carried out at the group level. While methods have been developed for planning CRTs to study the average treatment effect, and more recently, to study the heterogeneous treatment effect, the development for the latter objective has currently been limited to a continuous outcome. Despite the prevalence of binary outcomes in CRTs, determining the necessary sample size and statistical power for detecting differential treatment effects in CRTs with a binary outcome remain unclear. To address this methodological gap, we develop sample size procedures for testing treatment effect heterogeneity in two-level CRTs under a generalized linear mixed model. Closed-form sample size expressions are derived for a binary effect modifier, and in addition, a computationally efficient Monte Carlo approach is developed for a continuous effect modifier. Extensions to multiple effect modifiers are also discussed. We conduct simulations to examine the accuracy of the proposed sample size methods. We present several numerical illustrations to elucidate features of the proposed formulas and to compare our method to the approximate sample size calculation under a linear mixed model. Finally, we use data from the Strategies and Opportunities to Stop Colon Cancer in Priority Populations (STOP CRC) CRT to illustrate the proposed sample size procedure for testing treatment effect heterogeneity.

Can We Predict Skeletal Lesion on Bone Scan Based on Quantitative PSMA PET/CT Features?

OBJECTIVE: The increasing use of PSMA-PET/CT for restaging prostate cancer (PCa) leads to a patient shift from a non-metastatic situation based on conventional imaging (CI) to a metastatic situation. Since established therapeutic pathways have been designed according to CI, it is unclear how this should be translated to the PSMA-PET/CT results. This study aimed to investigate whether PSMA-PET/CT and clinical parameters could predict the visibility of PSMA-positive lesions on a bone scan (BS). METHODS: In four different centers, all PCa patients with BS and PSMA-PET/CT within 6 months without any change in therapy or significant disease progression were retrospectively selected. Up to 10 non-confluent clear bone metastases were selected per PSMA-PET/CT and SUVmax, SUVmean, PSMAtot, PSMAvol, density, diameter on CT, and presence of cortical erosion were collected. Clinical variables (age, PSA, Gleason Score) were also considered. Two experienced double-board physicians decided whether a bone metastasis was visible on the BS, with a consensus readout for discordant findings. For predictive performance, a random forest was fit on all available predictors, and its accuracy was assessed using 10-fold cross-validation performed 10 times. RESULTS: A total of 43 patients were identified with 222 bone lesions on PSMA-PET/CT. A total of 129 (58.1%) lesions were visible on the BS. In the univariate analysis, all PSMA-PET/CT parameters were significantly associated with the visibility on the BS (p < 0.001). The random forest reached a mean accuracy of 77.6% in a 10-fold cross-validation. CONCLUSIONS: These preliminary results indicate that there might be a way to predict the BS results based on PSMA-PET/CT, potentially improving the comparability between both examinations and supporting decisions for therapy selection.

Predictors of cervical tumour size for outpatients with cervical cancer at the University of Gondar referral hospital: a retrospective study design.

BACKGROUND: Cervical cancer is one of the most serious threats to women's lives. Modelling the change in tumour size over time for outpatients with cervical cancer was the study's main goal. METHODS: A hospital conducted a retrospective cohort study with outpatients who had cervical cancer. The information about the tumour size was taken from the patient's chart and all patient data records between May 20, 2017, and May 20, 2021. The data cover 322 cervical cancer outpatients' basic demographic and medical information. When analysing longitudinal data, the linear mixed effect model and the connection between tumour sizes in outpatients were taken into consideration. A linear mixed model, a random intercept model, and a slope model were used to fit the data. RESULT: A sample of 322 cervical cancer outpatients was examined, and 148 (or 46% of the outpatients) tested positive for HIV. The linear mixed model with a first-order autoregressive covariance structure revealed that a change in time of one month led to a 0.009 cm2 reduction in tumour size. For every kilogramme more in weight, the tumour size change in cervical cancer patients decreased considerably by 0.0098 cm2. The tumour size change in the cervical cancer patient who was HIV-positive was 0.4360 cm squared greater than that in the HIV-negative outpatients. CONCLUSION: As a consequence, there was a significant association between the longitudinal change in tumour size and the predictor variables visit time, therapy, patient weight, cancer stage, HIV, oral contraceptive use, history of abortion, and smoking status.

Evaluation of Primary Allied Health Care in Patients Recovering From COVID-19 at 6-Month Follow-up: Dutch Nationwide Prospective Cohort Study.

BACKGROUND: Patients recovering from COVID-19 often experience persistent problems in their daily activities related to limitations in physical, nutritional, cognitive, and mental functioning. To date, it is unknown what treatment is needed to support patients in their recovery from COVID-19. OBJECTIVE: This study aimed to evaluate the primary allied health care of patients recovering from COVID-19 at 6-month follow-up and to explore which baseline characteristics are associated with changes in the scores of outcomes between baseline and 6-month follow-up. METHODS: This Dutch nationwide prospective cohort study evaluated the recovery of patients receiving primary allied health care (ie, dietitians, exercise therapists, occupational therapists, physical therapists, and speech and language therapists) after COVID-19. All treatments offered by primary allied health professionals in daily practice were part of usual care. Patient-reported outcome measures on participation, health-related quality of life, fatigue, physical functioning, and psychological well-being were assessed at baseline and at 3- and 6-month follow-up. Linear mixed model analyses were used to evaluate recovery over time, and uni- and multivariable linear regression analyses were used to examine the association between baseline characteristics and recovery. RESULTS: A total of 1451 adult patients recovering from COVID-19 and receiving treatment from 1 or more primary allied health professionals were included. For participation (Utrecht Scale for Evaluation of Rehabilitation-Participation range 0-100), estimated mean differences of at least 2.3 points were observed at all time points. For the health-related quality of life (EuroQol Visual Analog Scale, range 0-100), the mean increase was 12.3 (95% CI 11.1-13.6) points at 6 months. Significant improvements were found for fatigue (Fatigue Severity Scale, range 1-7): the mean decrease was -0.7 (95% CI -0.8 to -0.6) points at 6 months. However, severe fatigue was reported by 742/929 (79.9%) patients after 6 months. For physical functioning (Patient-Reported Outcomes Measurement Information System-Physical Function Short Form 10b, range 13.8-61.3), the mean increase was 5.9 (95% CI 5.9-6.4) points at 6 months. Mean differences of -0.8 (95% CI -1.0 to -0.5) points for anxiety (Hospital Anxiety and Depression Scale range 0-21) and -1.6 (95% CI -1.8 to -1.3) points for depression were found after 6 months. A worse baseline score, hospital admission, and male sex were associated with greater improvement between baseline and 6-month follow-up, whereas age, the BMI, comorbidities, and smoking status were not associated with mean changes in any outcome measures. CONCLUSIONS: Patients recovering from COVID-19 who receive primary allied health care make progress in recovery but still experience many limitations in their daily activities after 6 months. Our findings provide reference values to health care providers and health care policy makers regarding what to expect from the recovery of patients who receive health care from 1 or more primary allied health professionals. TRIAL REGISTRATION: ClinicalTrials.gov NCT04735744; https://tinyurl.com/3vf337pn. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2340/jrm.v54.2506.

Joint modeling of forced vital capacity measures with time to onset of polycythemia among chronic obstructive pulmonary outpatients follows-up: A case of University of Gondar Referral Hospital.

Background and Aims: Chronic obstructive pulmonary disease (COPD) causes airflow obstruction and respiratory problems. Thus, the main objective of this study was to determine the risk factors for the progression of COPD using longitudinally measured forced vital capacity with time to onset of polycythemia outpatients follow-up. Methods: A retrospective study design was used to gather the related data on longitudinal change of forced vital capacity and time to onset of polycythemia from the medical charts. The joint model consists of a longitudinal submodel for the change of forced vital capacity and a survival submodel for the time to onset of polycythemia of chronic obstructive pulmonary patients. Results: From the total of 266 patient's estimated value of forced vital capacity of chronic obstructive pulmonary patients was 74.45 years with a standard deviation of 8.59. The estimated value of the association parameter was -0.006, which indicates that the lower value for a forced vital capacity measure was associated with the higher risk of polycythemia and vice versa "Based on the joint model analysis found that the predictor smoking, comorbidities, marital status, weight, and HIV" jointly affected the two responses, which are change of forced vital capacity and time to onset of polycythemia among chronic obstructive pulmonary patients. Conclusion: The overall performance of separate and joint models, joint modeling of longitudinal measures with the time-to-event outcome was the best model due to smaller standard errors and statistical significance of both the association parameters.

Effects of Web-Based and Mobile Self-Care Support in Addition to Standard Care in Patients After Radical Prostatectomy: Randomized Controlled Trial.

BACKGROUND: Prostate cancer is a common form of cancer that is often treated with radical prostatectomy, which can leave patients with urinary incontinence and sexual dysfunction. Self-care (pelvic floor muscle exercises and physical activity) is recommended to reduce the side effects. As more and more men are living in the aftermath of treatment, effective rehabilitation support is warranted. Digital self-care support has the potential to improve patient outcomes, but it has rarely been evaluated longitudinally in randomized controlled trials. Therefore, we developed and evaluated the effects of digital self-care support (electronic Patient Activation in Treatment at Home [ePATH]) on prostate-specific symptoms. OBJECTIVE: This study aimed to investigate the effects of web-based and mobile self-care support on urinary continence, sexual function, and self-care, compared with standard care, at 1, 3, 6, and 12 months after radical prostatectomy. METHODS: A multicenter randomized controlled trial with 2 study arms was conducted, with the longitudinal effects of additional digital self-care support (ePATH) compared with those of standard care alone. ePATH was designed based on the self-determination theory to strengthen patients' activation in self-care through nurse-assisted individualized modules. Men planned for radical prostatectomy at 3 county hospitals in southern Sweden were included offline and randomly assigned to the intervention or control group. The effects of ePATH were evaluated for 1 year after surgery using self-assessed questionnaires. Linear mixed models and ordinal regression analyses were performed. RESULTS: This study included 170 men (85 in each group) from January 2018 to December 2019. The participants in the intervention and control groups did not differ in their demographic characteristics. In the intervention group, 64% (53/83) of the participants used ePATH, but the use declined over time. The linear mixed model showed no substantial differences between the groups in urinary continence (ß=-5.60; P=.09; 95% CI -12.15 to -0.96) or sexual function (ß=-.12; P=.97; 95% CI -7.05 to -6.81). Participants in the intervention and control groups did not differ in physical activity (odds ratio 1.16, 95% CI 0.71-1.89; P=.57) or pelvic floor muscle exercises (odds ratio 1.51, 95% CI 0.86-2.66; P=.15). CONCLUSIONS: ePATH did not affect postoperative side effects or self-care but reflected how this support may work in typical clinical conditions. To complement standard rehabilitation, digital self-care support must be adapted to the context and individual preferences for use and effect. TRIAL REGISTRATION: ISRCTN Registry ISRCTN18055968; https://www.isrctn.com/ISRCTN18055968. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/11625.

Bioaccumulation and translocation of Hg and Cr by tobacco in Sichuan Province, China: understanding the influence of soil pH.

The present study investigated the bioaccumulation and translocation of mercury (Hg) and chromium (Cr) in Yunyan 87 flue-cured tobacco (Nicotiana tabacum) and assessed the influence of soil pH on the metal uptake by plant organs at the field scale. The study was conducted in 4 different regions selected from Sichuan Province, China: Guangyuan, Luzhou, Panzhihua, and Yibin. The results revealed that Hg highly contaminated Yibin soils at 0.29 mg kg-1 and by Cr at 147 mg kg-1, which is above the permissible limit. The levels of Hg in tobacco plant organs were predominantly in the order of leaves > root > stem. The overall trend for Cr contents in tobacco organs was in the order of root > leaves > stem. The results of an index of bioaccumulation (IBA) and translocation factor (TF) showed that the values observed in Panzhihua and Guangyuan tobacco leaves were generally higher, despite the low levels of soil contamination. The linear mixed model (LMM) demonstrated that the log of Hg IBA in tobacco organs was likely to decrease with soil pH increase, whereas the log of Cr IBA only decreased in the root but gradually increased in the aerial parts with soil pH increase. The total random variation in the log of metals' IBA due to regions indicated that for Hg, 33.42% of the variation was explained by regional differences, while for Cr, only 13% was accounted. The results suggested that Yibin and Luzhou need to correct the soil acidity if they are set to reduce Hg contamination in tobacco-growing soils. Guangyuan and Panzhihua need efforts to keep the soil pH on track to avoid high contamination levels, and effective measures of soil nutrients supply are required to produce high tobacco leaf quality free from heavy metal content. The findings of this study may be used to ascertain regional differences in heavy metals, particularly Hg and Cr uptake by tobacco plant organs, and to prevent the cultivation areas contamination through soil pH monitoring.

Candidate biomarkers and persistent transcriptional responses after low and high dose ionizing radiation at high dose rate.

PURPOSE: Development of an integrated time and dose model to explore the dynamics of gene expression alterations and identify biomarkers for biodosimetry following low- and high-dose irradiations at high dose rate. MATERIAL AND METHODS: We utilized multiple transcriptome datasets (GSE8917, GSE43151, and GSE23515) from Gene Expression Omnibus (GEO) for identifying candidate biological dosimeters. A linear mixed-effects model with random intercept was used to explore the dose-time dynamics of transcriptional responses and to functionally characterize the time- and dose-dependent changes in gene expression. RESULTS: We identified genes that are correlated with dose and time and discovered two clusters of genes that are either positively or negatively correlated with both dose and time based on the parameters of the model. Genes in these two clusters may have persistent transcriptional alterations. Twelve potential transcriptional markers for dosimetry-ARHGEF3, BAX, BBC3, CCDC109B, DCP1B, DDB2, F11R, GADD45A, GSS, PLK3, TNFRSF10B, and XPC were identified. Of these genes, BAX, GSS, and TNFRSF10B are positively associated with both dose and time course, have a persistent transcriptional response, and might be better biological dosimeters. CONCLUSIONS: With the proposed approach, we may identify candidate biomarkers that change monotonically in relation to dose, have a persistent transcriptional response, and are reliable over a wide dose range.

Uncovering Alterations in Cancer Epigenetics via Trans-Dimensional Markov Chain Monte Carlo and Hidden Markov Models.

Epigenetic alterations are key drivers in the development and progression of cancer. Identifying differentially methylated cytosines (DMCs) in cancer samples is a crucial step toward understanding these changes. In this paper, we propose a trans-dimensional Markov chain Monte Carlo (TMCMC) approach that uses hidden Markov models (HMMs) with binomial emission, and bisulfite sequencing (BS-Seq) data, called DMCTHM, to identify DMCs in cancer epigenetic studies. We introduce the Expander-Collider penalty to tackle under and over-estimation in TMCMC-HMMs. We address all known challenges inherent in BS-Seq data by introducing novel approaches for capturing functional patterns and autocorrelation structure of the data, as well as for handling missing values, multiple covariates, multiple comparisons, and family-wise errors. We demonstrate the effectiveness of DMCTHM through comprehensive simulation studies. The results show that our proposed method outperforms other competing methods in identifying DMCs. Notably, with DMCTHM, we uncovered new DMCs and genes in Colorectal cancer that were significantly enriched in the Tp53 pathway.

Refined expression quantitative trait locus analysis on adenocarcinoma at the gastroesophageal junction reveals susceptibility and prognostic markers.

Objectives: This study aimed to explore cell type level expression quantitative trait loci (eQTL) in adenocarcinoma at the gastroesophageal junction (ACGEJ) and identify susceptibility and prognosis markers. Methods: Whole-genome sequencing (WGS) was performed on 120 paired samples from Chinese ACGEJ patients. Germline mutations were detected by GATK tools. RNA sequencing (RNA-seq) data on ACGEJ samples were taken from our previous studies. Public single-cell RNA sequencing (scRNA-seq) data were used to produce the proportion of epithelial cells. Matrix eQTL and a linear mixed model were used to identify condition-specific cis-eQTLs. The R package coloc was used to perform co-localization analysis with the public data of genome-wide association studies (GWASs). Log-rank and Cox regression tests were used to identify survival-associated eQTL and genes. Functions of candidate risk loci were explored by experimental validation. Results: Refined eQTL analyses of paired ACGEJ samples were performed and 2,036 potential ACGEJ-specific eQTLs with East Asian specificity were identified in total. ACGEJ-gain eQTLs were enriched at promoter regions more than ACGEJ-loss eQTLs. rs658524 was identified as the top eQTL close to the transcription start site of its paired gene (CTSW). rs2240191-RASAL1, rs4236599-FOXP2, rs4947311-PSORS1C1, rs13134812-LOC391674, and rs17508585-CDK13-DT were identified as ACGEJ-specific susceptibility eQTLs. rs309483-LINC01355 was associated with the overall survival of ACGEJ patients. We explored functions of candidate eQTLs such as rs658524, rs309483, rs2240191, and rs4947311 by experimental validation. Conclusion: This study provides new risk loci for ACGEJ susceptibility and effective disease prognosis biomarkers.

CA-125 Early Dynamics to Predict Overall Survival in Women with Newly Diagnosed Advanced Ovarian Cancer Based on Meta-Analysis Data.

(1) Background: Cancer antigen 125 (CA-125) is a protein produced by ovarian cancer cells that is used for patients' monitoring. However, the best ways to analyze its decline and prognostic role are poorly quantified. (2) Methods: We leveraged individual patient data from the Gynecologic Cancer Intergroup (GCIG) meta-analysis (N = 5573) to compare different approaches summarizing the early trajectory of CA-125 before the prediction time (called the landmark time) at 3 or 6 months after treatment initiation in order to predict overall survival. These summaries included observed and estimated measures obtained by a linear mixed model (LMM). Their performances were evaluated by 10-fold cross-validation with the Brier score and the area under the ROC (AUC). (3) Results: The estimated value and the last observed value at 3 months were the best measures used to predict overall survival, with an AUC of 0.75 CI 95% [0.70; 0.80] at 24 and 36 months and 0.74 [0.69; 0.80] and 0.75 [0.69; 0.80] at 48 months, respectively, considering that CA-125 over 6 months did not improve the AUC, with 0.74 [0.68; 0.78] at 24 months and 0.71 [0.65; 0.76] at 36 and 48 months. (4) Conclusions: A 3-month surveillance provided reliable individual information on overall survival until 48 months for patients receiving first-line chemotherapy.

Simulating and estimating agreement in the presence of multiple raters and covariates.

Cohen's and Fleiss's kappa are popular estimators for assessing agreement among two and multiple raters, respectively, for a binary response. While additional methods have been developed to account for multiple raters and covariates, they are not always applicable, rarely used, and none simplify to Cohen's kappa. Furthermore, there are no methods to simulate Bernoulli observations under the kappa agreement structure such that the developed methods could be adequately assessed. This manuscript overcomes these shortfalls. First, we developed a model-based estimator for kappa that accommodates multiple raters and covariates through a generalized linear mixed model and encompasses Cohen's kappa as a special case. Second, we created a framework to simulate dependent Bernoulli observations that upholds all 2-tuple pair of rater's kappa agreement structure and includes covariates. We used this framework to assess our method when kappa was nonzero. Simulations showed that Cohen's and Fleiss's kappa estimates were inflated unlike our model-based kappa. We analyzed an Alzheimer's disease neuroimaging study and the classic cervical cancer pathology study. The proposed model-based kappa and advancement in simulation methodology demonstrates that the popular approaches of Cohen's and Fleiss's kappa are poised to yield invalid conclusions while our work overcomes shortfalls, leading to improved inferences.

When a joint model should be preferred over a linear mixed model for analysis of longitudinal health-related quality of life data in cancer clinical trials.

BACKGROUND: Patient-reported outcomes such as health-related quality of life (HRQoL) are increasingly used as endpoints in randomized cancer clinical trials. However, the patients often drop out so that observation of the HRQoL longitudinal outcome ends prematurely, leading to monotone missing data. The patients may drop out for various reasons including occurrence of toxicities, disease progression, or may die. In case of informative dropout, the usual linear mixed model analysis will produce biased estimates. Unbiased estimates cannot be obtained unless the dropout is jointly modeled with the longitudinal outcome, for instance by using a joint model composed of a linear mixed (sub)model linked to a survival (sub)model. Our objective was to investigate in a clinical trial context the consequences of using the most frequently used linear mixed model, the random intercept and slope model, rather than its corresponding joint model. METHODS: We first illustrate and compare the models on data of patients with metastatic pancreatic cancer. We then perform a more formal comparison through a simulation study. RESULTS: From the application, we derived hypotheses on the situations in which biases arise and on their nature. Through the simulation study, we confirmed and complemented these hypotheses and provided general explanations of the bias mechanisms. CONCLUSIONS: In particular, this article reveals how the linear mixed model fails in the typical situation where poor HRQoL is associated with an increased risk of dropout and the experimental treatment improves survival. Unlike the joint model, in this situation the linear mixed model will overestimate the HRQoL in both arms, but not equally, misestimating the difference between the HRQoL trajectories of the two arms to the disadvantage of the experimental arm.

Tutorial: statistical methods for the meta-analysis of diagnostic test accuracy studies.

This tutorial shows how to perform a meta-analysis of diagnostic test accuracy studies (DTA) based on a 2 × 2 table available for each included primary study. First, univariate methods for meta-analysis of sensitivity and specificity are presented. Then the use of univariate logistic regression models with and without random effects for e.g. sensitivity is described. Diagnostic odds ratios (DOR) are then introduced to combine sensitivity and specificity into one single measure and to assess publication bias. Finally, bivariate random effects models using the exact binomial likelihood to describe within-study variability and a normal distribution to describe between-study variability are presented as the method of choice. Based on this model summary receiver operating characteristic (sROC) curves are constructed using a regression model logit-true positive rate (TPR) over logit-false positive rate (FPR). Also it is demonstrated how to perform the necessary calculations with the freely available software R. As an example a meta-analysis of DTA studies using Procalcitonin as a diagnostic marker for sepsis is presented.