Internal validation of the GA118-24B Genetic Analyzer, a stable capillary electrophoresis system for forensic DNA identification.

The GA118-24B Genetic Analyzer (hereafter, "GA118-24B") is an independently developed capillary electrophoresis instrument. In the present research, we designed a series of validation experiments to test its performance at detecting DNA fragments compared to the Applied Biosystems 3500 Genetic Analyzer (hereafter, "3500"). Three commercially available autosomal short tandem repeat multiplex kits were used in this validation. The results showed that GA118-24B had acceptable spectral calibration for three kits. The results of accuracy and concordance studies were also satisfactory. GA118-24B showed excellent precision, with a standard deviation of less than 0.1 bp. Sensitivity and mixture studies indicated that GA118-24B could detect low-template DNA and complex mixtures as well as the results generated by 3500 in parallel experiments. Based on the experimental results, we set specific analytical and stochastic thresholds. Besides, GA118-24B showed superiority than 3500 within certain size ranges in the resolution study. Instead of conventional commercial multiplex kits, GA118-24B performed stably on a self-developed eight-dye multiplex system, which were not performed on 3500 Genetic Analyzer. We compared our validation results with those of previous research and found our results to be convincing. Overall, we conclude that GA118-24B is a stable and reliable genetic analyzer for forensic DNA identification.

[Development and Validation of a Risk Prediction Model for Prolonged Hospitalization in Patients With Diabetic Foot Ulcers]. / ç³–å°¿ç— è¶³æºƒç–¡æ‚£è€ ä½é™¢æ—¶é—´å»¶é•¿é£Žé™©é¢„æµ‹æ¨¡åž‹æž„å»ºåŠéªŒè¯.

Objective: To investigate the risk factors associated with prolonged hospitalization in patients diagnosed with diabetic foot ulcers (DFU), to develop a predictive model, and to conduct internal validation of the model. Methods: The clinical data of DFU patients admitted to West China Hospital, Sichuan University between January 2012 and December 2022 were retrospectively collected. The subjects were randomly assigned to a training cohort and a validation cohort at a ratio of 7 to 3. Hospital stays longer than 75th percentile were defined as prolonged length-of-stay. A thorough analysis of the risk factors was conducted using the training cohort, which enabled the development of an accurate risk prediction model. To ensure robustness, the model was internally validated using the validation cohort. Results: A total of 967 inpatients with DFU were included, among whom 245 patients were identified as having an extended length-of-stay. The training cohort consisted of 622 patients, while the validation cohort comprised 291 patients. Multivariate logistic regression analysis revealed that smoking history (odds ratio [OR]=1.67, 95% confidence interval [CI], 1.13 to 2.48, P=0.010), Wagner grade 3 or higher (OR=7.13, 95% CI, 3.68 to 13.83, P<0.001), midfoot ulcers (OR=1.99, 95% CI, 1.07 to 3.72, P=0.030), posterior foot ulcers (OR=3.68, 95% CI, 1.83 to 7.41, P<0.001), multisite ulcers (OR=2.91, 95% CI, 1.80 to 4.69, P<0.001), wound size≥3 cm2 (OR=2.00, 95% CI, 1.28-3.11, P=0.002), and white blood cell count (OR=1.11, 95% CI, 1.05 to 1.18, P<0.001) were associated with an increased risk of prolonged length of stay. Additionally, a nomogram was constructed based on the identified risk factors. The areas under the receiver operating characteristic (ROC) curves for both the training cohort and the validation cohort were 0.782 (95% CI, 0.745 to 0.820) and 0.756 (95% CI, 0.694 to 0.818), respectively, indicating robust predictive performance. Furthermore, the calibration plot demonstrated optimal concordance between the predicted probabilities and the observed outcomes in both the training and the validation cohorts. Conclusion: Smoking history, Wagner grade≥3, midfoot ulcers, posterior foot ulcers, multisite ulcers, ulcer area≥3 cm2, and elevated white blood cell count are identified as independent predictors of prolonged hospitalization. Therefore, it is imperative that clinicians conduct a comprehensive patient evaluation and implement appropriate diagnostic and therapeutic strategies to effectively shorten the length of stay for DFU patients.

There is no such thing as a validated prediction model.

BACKGROUND: Clinical prediction models should be validated before implementation in clinical practice. But is favorable performance at internal validation or one external validation sufficient to claim that a prediction model works well in the intended clinical context? MAIN BODY: We argue to the contrary because (1) patient populations vary, (2) measurement procedures vary, and (3) populations and measurements change over time. Hence, we have to expect heterogeneity in model performance between locations and settings, and across time. It follows that prediction models are never truly validated. This does not imply that validation is not important. Rather, the current focus on developing new models should shift to a focus on more extensive, well-conducted, and well-reported validation studies of promising models. CONCLUSION: Principled validation strategies are needed to understand and quantify heterogeneity, monitor performance over time, and update prediction models when appropriate. Such strategies will help to ensure that prediction models stay up-to-date and safe to support clinical decision-making.

Supervised application of internal validation measures to benchmark dimensionality reduction methods in scRNA-seq data.

A typical single-cell RNA sequencing (scRNA-seq) experiment will measure on the order of 20 000 transcripts and thousands, if not millions, of cells. The high dimensionality of such data presents serious complications for traditional data analysis methods and, as such, methods to reduce dimensionality play an integral role in many analysis pipelines. However, few studies have benchmarked the performance of these methods on scRNA-seq data, with existing comparisons assessing performance via downstream analysis accuracy measures, which may confound the interpretation of their results. Here, we present the most comprehensive benchmark of dimensionality reduction methods in scRNA-seq data to date, utilizing over 300 000 compute hours to assess the performance of over 25 000 low-dimension embeddings across 33 dimensionality reduction methods and 55 scRNA-seq datasets. We employ a simple, yet novel, approach, which does not rely on the results of downstream analyses. Internal validation measures (IVMs), traditionally used as an unsupervised method to assess clustering performance, are repurposed to measure how well-formed biological clusters are after dimensionality reduction. Performance was further evaluated over nearly 200 000 000 iterations of DBSCAN, a density-based clustering algorithm, showing that hyperparameter optimization using IVMs as the objective function leads to near-optimal clustering. Methods were also assessed on the extent to which they preserve the global structure of the data, and on their computational memory and time requirements across a large range of sample sizes. Our comprehensive benchmarking analysis provides a valuable resource for researchers and aims to guide best practice for dimensionality reduction in scRNA-seq analyses, and we highlight Latent Dirichlet Allocation and Potential of Heat-diffusion for Affinity-based Transition Embedding as high-performing algorithms.

Development and internal validation of a non-invasive clinical tool to predict sufficient omega-3 levels in early pregnancy.

BACKGROUND: Complications from preterm birth (PTB) are the leading cause of death and disability in those under five years. Whilst the role of omega-3 (n-3) supplementation in reducing PTB is well-established, growing evidence suggests supplementation use in those replete may increase the risk of early PTB. AIM: To develop a non-invasive tool to identify individuals with total n-3 serum levels above 4.3% of total fatty acids in early pregnancy. METHODS: We conducted a prospective observational study recruiting 331 participants from three clinical sites in Newcastle, Australia. Eligible participants (n = 307) had a singleton pregnancy between 8 and 20 weeks' gestation at recruitment. Data on factors associated with n-3 serum levels were collected using an electronic questionnaire; these included estimated intake of n-3 (including food type, portion size, frequency of consumption), n-3 supplementation, and sociodemographic factors. The optimal cut-point of estimated n-3 intake that predicted mothers with total serum n-3 levels likely above 4.3% was developed using multivariate logistic regression, adjusting for maternal age, body mass index, socioeconomic status, and n-3 supplementation use. Total serum n-3 levels above 4.3% was selected as previous research has demonstrated that mothers with these levels are at increased risk of early PTB if they take additional n-3 supplementation during pregnancy. Models were evaluated using various performance metrics including sensitivity, specificity, area under receiver operator characteristic (AUROC) curve, true positive rate (TPR) at 10% false positive rate (FPR), Youden Index, Closest to (0,1) Criteria, Concordance Probability, and Index of Union. Internal validation was performed using 1000-bootstraps to generate 95% confidence intervals for performance metrics generated. RESULTS: Of 307 eligible participants included for analysis, 58.6% had total n-3 serum levels above 4.3%. The optimal model had a moderate discriminative ability (AUROC 0.744, 95% CI 0.742-0.746) with 84.7% sensitivity, 54.7% specificity and 37.6% TPR at 10% FPR. CONCLUSIONS: Our non-invasive tool was a moderate predictor of pregnant women with total serum n-3 levels above 4.3%; however, its performance is not yet adequate for clinical use. TRIAL REGISTRATION: This trial was approved by the Hunter New England Human Research Ethics Committee of the Hunter New England Local Health District (Reference 2020/ETH00498 on 07/05/2020 and 2020/ETH02881 on 08/12/2020).

Quality indices for topic model selection and evaluation: a literature review and case study.

BACKGROUND: Topic models are a class of unsupervised machine learning models, which facilitate summarization, browsing and retrieval from large unstructured document collections. This study reviews several methods for assessing the quality of unsupervised topic models estimated using non-negative matrix factorization. Techniques for topic model validation have been developed across disparate fields. We synthesize this literature, discuss the advantages and disadvantages of different techniques for topic model validation, and illustrate their usefulness for guiding model selection on a large clinical text corpus. DESIGN, SETTING AND DATA: Using a retrospective cohort design, we curated a text corpus containing 382,666 clinical notes collected between 01/01/2017 through 12/31/2020 from primary care electronic medical records in Toronto Canada. METHODS: Several topic model quality metrics have been proposed to assess different aspects of model fit. We explored the following metrics: reconstruction error, topic coherence, rank biased overlap, Kendall's weighted tau, partition coefficient, partition entropy and the Xie-Beni statistic. Depending on context, cross-validation and/or bootstrap stability analysis were used to estimate these metrics on our corpus. RESULTS: Cross-validated reconstruction error favored large topic models (K ≥ 100 topics) on our corpus. Stability analysis using topic coherence and the Xie-Beni statistic also favored large models (K = 100 topics). Rank biased overlap and Kendall's weighted tau favored small models (K = 5 topics). Few model evaluation metrics suggested mid-sized topic models (25 ≤ K ≤ 75) as being optimal. However, human judgement suggested that mid-sized topic models produced expressive low-dimensional summarizations of the corpus. CONCLUSIONS: Topic model quality indices are transparent quantitative tools for guiding model selection and evaluation. Our empirical illustration demonstrated that different topic model quality indices favor models of different complexity; and may not select models aligning with human judgment. This suggests that different metrics capture different aspects of model goodness of fit. A combination of topic model quality indices, coupled with human validation, may be useful in appraising unsupervised topic models.

Development and internal validation of the HIV In-hospital Mortality Prediction (HIV-IMP) risk score.

BACKGROUND: Despite advances in availability and access to antiretroviral therapy (ART), HIV still ranks as a major cause of global mortality. Hence, the aim of this study was to develop and internally validate a risk score capable of accurately predicting in-hospital mortality in HIV-positive patients requiring hospital admission. METHODS: Consecutive HIV-positive patients presenting to the Charlotte Maxeke Johannesburg Academic Hospital adult emergency department between 7 July 2017 and 18 October 2018 were prospectively enrolled. Multivariate logistic regression was used to determine parameters for inclusion in the final risk score. Discrimination and calibration were assessed by means of the area under the receiver operating curve (AUROC) and the Hosmer-Lemeshow goodness-of-fit test, respectively. Internal validation was conducted using the regular bootstrap technique. RESULTS: The overall in-hospital mortality rate was 13.6% (n = 166). Eight predictors were included in the final risk score: ART non-adherence or not yet on ART, Glasgow Coma Scale < 15, respiratory rate > 20 breaths/min, oxygen saturation < 90%, white cell count < 4 × 109 /L, creatinine > 120 µmol/L, lactate > 2 mmol/L and albumin < 35 g/L. After internal validation, the risk score maintained good discrimination [AUROC 0.83, 95% confidence interval (CI): 0.78-0.88] and calibration (Hosmer-Lemeshow χ2 = 2.26, p = 0.895). CONCLUSION: The HIV In-hospital Mortality Prediction (HIV-IMP) risk score has overall good discrimination and calibration and is relatively easy to use. Further studies should be aimed at externally validating the score in varying clinical settings.

Development and validation of a prediction model for early mortality after transcatheter aortic valve implantation (TAVI) based on the Netherlands Heart Registration (NHR): The TAVI-NHR risk model.

BACKGROUND: The currently available mortality prediction models (MPM) have suboptimal performance when predicting early mortality (30-days) following transcatheter aortic valve implantation (TAVI) on various external populations. We developed and validated a new TAVI-MPM based on a large number of predictors with recent data from a national heart registry. METHODS: We included all TAVI-patients treated in the Netherlands between 2013 and 2018, from the Netherlands Heart Registration. We used logistic-regression analysis based on the Akaike Information Criterion for variable selection. We multiply imputed missing values, but excluded variables with >30% missing values. For internal validation, we used ten-fold cross-validation. For temporal (prospective) validation, we used the 2018-data set for testing. We assessed discrimination by the c-statistic, predicted probability accuracy by the Brier score, and calibration by calibration graphs, and calibration-intercept and calibration slope. We compared our new model to the updated ACC-TAVI and IRRMA MPMs on our population. RESULTS: We included 9144 TAVI-patients. The observed early mortality was 4.0%. The final MPM had 10 variables, including: critical-preoperative state, procedure-acuteness, body surface area, serum creatinine, and diabetes-mellitus status. The median c-statistic was 0.69 (interquartile range [IQR] 0.646-0.75). The median Brier score was 0.038 (IQR 0.038-0.040). No signs of miscalibration were observed. The c-statistic's temporal-validation was 0.71 (95% confidence intervals 0.64-0.78). Our model outperformed the updated currently available MPMs ACC-TAVI and IRRMA (p value < 0.05). CONCLUSION: The new TAVI-model used additional variables and showed fair discrimination and good calibration. It outperformed the updated currently available TAVI-models on our population. The model's good calibration benefits preprocedural risk-assessment and patient counseling.

Nomogram predicting cesarean delivery undergoing induction of labor among high-risk nulliparous women at term: a retrospective study.

BACKGROUND: Our aim was to create and validate a nomogram predicting cesarean delivery after induction of labor among nulliparous women at term. METHODS: Data were obtained from medical records from Nanjing Drum Tower Hospital. Nulliparous women with singleton pregnancies undergoing induction of labor at term were involved. A total of 2950 patients from Jan. 2014 to Dec. 2015 were served as derivation cohort. A nomogram was constructed by multivariate logistic regression using maternal, fetal and pregnancy characteristics. The predictive accuracy and discriminative ability of the nomogram were internal validated by 1000-bootstrap resampling, followed by external validation of a new dataset from Jan. 2016 to Dec. 2016. RESULTS: Logistic regression revealed nine predictors of cesarean delivery, including maternal height, age, uterine height, abdominal circumference, estimated fetal weight, indications for induction of labor, initial cervical consistency, cervical effacement and station. Nomogram was well calibrated and had an AUC of 0.73 (95% confidence interval [CI], 0.70-0.75) after bootstrap resampling for internal validation. The AUC in external validation reached 0.67, which was significantly higher than that of three models published previously (P<0.05). CONCLUSIONS: This validated nomogram, constructed by variables that were obtained form medical records, can help estimate risk of cesarean delivery before induction of labor.

Development of a questionnaire to measure teachers' student-centred perspectives based on the Onion Model.

BACKGROUND: Teachers with a teacher-centred perspective have difficulties applying student-centred approaches in Problem Based Learning (PBL) because they are inclined to show teacher-centred behaviours. The six aspects explained in Korthagen's Onion Model (environment, behaviour, competencies, beliefs, identity, and mission) are assumed to contribute to teachers' perspectives, showing that both the environment and personal characteristics influence behaviours. For teachers to function properly in PBL, those six aspects should reflect a student-centred perspective. Previous instruments to measure teaching perspectives focused on only a few of these relevant aspects. Therefore, we developed the Student-Centred Perspective of Teachers (SCPT) questionnaire with subscales for each aspect in the Onion Model. This study aimed to provide evidence for its internal and external validity. METHODS: The SCPT was distributed in a survey to 795 teachers from 20 medical schools. For the internal validation, Confirmatory Factor Analysis was performed to analyse theoretical fit model validation, convergent validation, and discriminant validation. For the external validation, teachers' perspective scores were compared among three groups of amount of PBL training using Analysis of Variance (ANOVA) and post-hoc Least Significant Difference (LSD) tests. The p-value for all tests was set at .05. RESULTS: A total of 543 out of 795 teachers (68.3%) participated. Confirmatory Factor Analysis showed the evidence of the SCPT's internal validation with acceptable fit for the six subscales measured by 19 items and the following Composite Reliability scores: environment (.72), behaviour (.74), competencies (.63), beliefs (.55), identity (.76), and mission (.60). All items' factors loadings reached a good standard (.5 or greater). Only the environment subscale had the Average Variance Extracted (AVE) score higher than .5 and the Maximum Shared Variance score lower than the AVE score. ANOVA and Post-hoc LSD tests showed that teachers who participated in more PBL training showed significantly higher student-centred perspectives, providing evidence for external validity. CONCLUSION: The SCPT is a reliable and valid instrument to measure teaching perspectives. Identifying aspects that do not represent the adoption of a student-centred perspective may provide valuable input for faculty development in the context of PBL.

Sampling Strategies for Internal Validation Samples for Exposure Measurement-Error Correction: A Study of Visceral Adipose Tissue Measures Replaced by Waist Circumference Measures.

Statistical correction for measurement error in epidemiologic studies is possible, provided that information about the measurement error model and its parameters are available. Such information is commonly obtained from a randomly sampled internal validation sample. It is however unknown whether randomly sampling the internal validation sample is the optimal sampling strategy. We conducted a simulation study to investigate various internal validation sampling strategies in conjunction with regression calibration. Our simulation study showed that for an internal validation study sample of 40% of the main study's sample size, stratified random and extremes sampling had a small efficiency gain over random sampling (10% and 12% decrease on average over all scenarios, respectively). The efficiency gain was more pronounced in smaller validation samples of 10% of the main study's sample size (i.e., a 31% and 36% decrease on average over all scenarios, for stratified random and extremes sampling, respectively). To mitigate the bias due to measurement error in epidemiologic studies, small efficiency gains can be achieved for internal validation sampling strategies other than random, but only when measurement error is nondifferential. For regression calibration, the gain in efficiency is, however, at the cost of a higher percentage bias and lower coverage.

Internal validation and improvement of mitochondrial genome sequencing using the Precision ID mtDNA Whole Genome Panel.

With the recent advances in next-generation sequencing (NGS), mitochondrial whole-genome sequencing has begun to be applied to the field of the forensic biology as an alternative to the traditional Sanger-type sequencing (STS). However, experimental workflows, commercial solutions, and output data analysis must be strictly validated before being implemented into the forensic laboratory. In this study, we performed an internal validation for an NGS-based typing of the entire mitochondrial genome using the Precision ID mtDNA Whole Genome Panel (Thermo Fisher Scientific) on the Ion S5 sequencer (Thermo Fisher Scientific). Concordance, repeatability, reproducibility, sensitivity, and heteroplasmy detection analyses were assessed using the 2800 M and 9947A standard control DNA as well as typical casework specimens, and results were compared with conventional Sanger sequencing and another NGS sequencer in a different laboratory. We discuss the strengths and limitations of this approach, highlighting some issues regarding noise thresholds and heteroplasmy detection, and suggesting solutions to mitigate these effects and improve overall data interpretation. Results confirmed that the Precision ID Whole mtDNA Genome Panel is highly reproducible and sensitive, yielding useful full mitochondrial DNA sequences also from challenging DNA specimens, thus providing further support for its use in forensic practice.

The milestone for preventing post-ERCP pancreatitis using novel simplified predictive scoring system: a propensity score analysis.

BACKGROUND: Post-ERCP pancreatitis (PEP) with trans-papillary approach remains a major issue, and the multi-factorial etiology can lead to the development of unpredictable PEP. Therefore, the early identification of PEP is highly desirable to assist with the health cost containment, the reduction in unnecessary admissions, earlier appropriate primary care, and intensive care for preventing progression of severe pancreatitis. This study aimed to establish a simplified predictive scoring system for PEP. METHODS: Between January 1, 2012, and December 31, 2019, 3362 consecutive trans-papillary ERCP procedures were retrospectively analyzed. Significant risk factors were extracted by univariate, multivariate, and propensity score analyses, and the probability of PEP in the combinations of each factor were quantified using propensity score analysis. The results were internally validated using bootstrapping resampling. RESULTS: In the scoring system with four stratifications using combinations of only five extracted risk factors, the very high-risk group showed 28.79% (95% confidence interval [CI], 18.30%-41.25%; P < 0.001) in the predicted incidence rate of PEP, and 9.09% (95% CI, 3.41%-18.74%; P < 0.001) in that of severe PEP; although the adjusted prevalence revealed 3.74% in PEP and 0.90% in severe PEP, respectively. The prediction model had an area under the curve of 0.86 (95% CI, 0.82-0.89) and the optimism-corrected model as an internal validation had an area under the curve of 0.81 (95% CI, 0.77-0.86). CONCLUSIONS: We established and validated a simplified predictive scoring system for PEP using five risk factors immediately after ERCP to assist with the early identification of PEP.

Real-Time Clinical Decision Support Based on Recurrent Neural Networks for In-Hospital Acute Kidney Injury: External Validation and Model Interpretation.

BACKGROUND: Acute kidney injury (AKI) is commonly encountered in clinical practice and is associated with poor patient outcomes and increased health care costs. Despite it posing significant challenges for clinicians, effective measures for AKI prediction and prevention are lacking. Previously published AKI prediction models mostly have a simple design without external validation. Furthermore, little is known about the process of linking model output and clinical decisions due to the black-box nature of neural network models. OBJECTIVE: We aimed to present an externally validated recurrent neural network (RNN)-based continuous prediction model for in-hospital AKI and show applicable model interpretations in relation to clinical decision support. METHODS: Study populations were all patients aged 18 years or older who were hospitalized for more than 48 hours between 2013 and 2017 in 2 tertiary hospitals in Korea (Seoul National University Bundang Hospital and Seoul National University Hospital). All demographic data, laboratory values, vital signs, and clinical conditions of patients were obtained from electronic health records of each hospital. We developed 2-stage hierarchical prediction models (model 1 and model 2) using RNN algorithms. The outcome variable for model 1 was the occurrence of AKI within 7 days from the present. Model 2 predicted the future trajectory of creatinine values up to 72 hours. The performance of each developed model was evaluated using the internal and external validation data sets. For the explainability of our models, different model-agnostic interpretation methods were used, including Shapley Additive Explanations, partial dependence plots, individual conditional expectation, and accumulated local effects plots. RESULTS: We included 69,081 patients in the training, 7675 in the internal validation, and 72,352 in the external validation cohorts for model development after excluding cases with missing data and those with an estimated glomerular filtration rate less than 15 mL/min/1.73 m2 or end-stage kidney disease. Model 1 predicted any AKI development with an area under the receiver operating characteristic curve (AUC) of 0.88 (internal validation) and 0.84 (external validation), and stage 2 or higher AKI development with an AUC of 0.93 (internal validation) and 0.90 (external validation). Model 2 predicted the future creatinine values within 3 days with mean-squared errors of 0.04-0.09 for patients with higher risks of AKI and 0.03-0.08 for those with lower risks. Based on the developed models, we showed AKI probability according to feature values in total patients and each individual with partial dependence, accumulated local effects, and individual conditional expectation plots. We also estimated the effects of feature modifications such as nephrotoxic drug discontinuation on future creatinine levels. CONCLUSIONS: We developed and externally validated a continuous AKI prediction model using RNN algorithms. Our model could provide real-time assessment of future AKI occurrences and individualized risk factors for AKI in general inpatient cohorts; thus, we suggest approaches to support clinical decisions based on prediction models for in-hospital AKI.

Development and internal validation of prediction models for colorectal cancer survivors to estimate the 1-year risk of low health-related quality of life in multiple domains.

BACKGROUND: Many colorectal cancer (CRC) survivors experience persisting health problems post-treatment that compromise their health-related quality of life (HRQoL). Prediction models are useful tools for identifying survivors at risk of low HRQoL in the future and for taking preventive action. Therefore, we developed prediction models for CRC survivors to estimate the 1-year risk of low HRQoL in multiple domains. METHODS: In 1458 CRC survivors, seven HRQoL domains (EORTC QLQ-C30: global QoL; cognitive, emotional, physical, role, social functioning; fatigue) were measured prospectively at study baseline and 1 year later. For each HRQoL domain, scores at 1-year follow-up were dichotomized into low versus normal/high. Separate multivariable logistic prediction models including biopsychosocial predictors measured at baseline were developed for the seven HRQoL domains, and internally validated using bootstrapping. RESULTS: Average time since diagnosis was 5 years at study baseline. Prediction models included both non-modifiable predictors (age, sex, socio-economic status, time since diagnosis, tumor stage, chemotherapy, radiotherapy, stoma, micturition, chemotherapy-related, stoma-related and gastrointestinal complaints, comorbidities, social inhibition/negative affectivity, and working status) and modifiable predictors (body mass index, physical activity, smoking, meat consumption, anxiety/depression, pain, and baseline fatigue and HRQoL scores). Internally validated models showed good calibration and discrimination (AUCs: 0.83-0.93). CONCLUSIONS: The prediction models performed well for estimating 1-year risk of low HRQoL in seven domains. External validation is needed before models can be applied in practice.

A pseudo-likelihood method for estimating misclassification probabilities in competing-risks settings when true-event data are partially observed.

Outcome misclassification occurs frequently in binary-outcome studies and can result in biased estimation of quantities such as the incidence, prevalence, cause-specific hazards, cumulative incidence functions, and so forth. A number of remedies have been proposed to address the potential misclassification of the outcomes in such data. The majority of these remedies lie in the estimation of misclassification probabilities, which are in turn used to adjust analyses for outcome misclassification. A number of authors advocate using a gold-standard procedure on a sample internal to the study to learn about the extent of the misclassification. With this type of internal validation, the problem of quantifying the misclassification also becomes a missing data problem as, by design, the true outcomes are only ascertained on a subset of the entire study sample. Although, the process of estimating misclassification probabilities appears simple conceptually, the estimation methods proposed so far have several methodological and practical shortcomings. Most methods rely on missing outcome data to be missing completely at random (MCAR), a rather stringent assumption which is unlikely to hold in practice. Some of the existing methods also tend to be computationally-intensive. To address these issues, we propose a computationally-efficient, easy-to-implement, pseudo-likelihood estimator of the misclassification probabilities under a missing at random (MAR) assumption, in studies with an available internal-validation sample. We present the estimator through the lens of studies with competing-risks outcomes, though the estimator extends beyond this setting. We describe the consistency and asymptotic distributional properties of the resulting estimator, and derive a closed-form estimator of its variance. The finite-sample performance of this estimator is evaluated via simulations. Using data from a real-world study with competing-risks outcomes, we illustrate how the proposed method can be used to estimate misclassification probabilities. We also show how the estimated misclassification probabilities can be used in an external study to adjust for possible misclassification bias when modeling cumulative incidence functions.

[Vascular damage-related risk factors for erectile dysfunction in patients with type 2 diabetes mellitus: Analysis based on a nomogram model].

OBJECTIVE: To analyze vascular damage-related risk factors for ED in patients with type 2 diabetes mellitus (DM) and develop a nomogram for the prediction of the factors. METHODS: A total of 181 patients with type 2 DM were included for sexual function assessment, and the clinical data on vascular damage were retrieved from the patients system. After preprocessing, the data were described by the number and percentage of different types of cases and subjected to statistical analysis with the R software. The Lasso regression model was used to optimize feature selection. On the premise of the sample size required for logistic regression analysis according to the number of events per variable, multivariable logistic regression analysis was performed on the selected variables and a nomogram was developed for diabetes-induced erectile dysfunction (DIED). Then, the performance of the nomogram was evaluated with respect to its calibration, discrimination and clinical utility using Harrell's concordance index (C-index), the calibration plot and decision curve analysis, as well as bootstrapping for internal validation. RESULTS: ED was diagnosed in 90 (49.7%) of the 181 patients. The risk factors subjected to logistic regression analysis included the duration of DM (OR = 4.440, 95% CI: 1.594－13.105; OR = 7.667, 95% CI: 1.444－48.733), status of carotid intima-media thickness (c-IMT) (OR = 3.767, 95% CI: 1.194－12.691), diabetic retinopathy (DR) (OR = 5.382, 95% CI: 1.373－28.301), diabetic kidney disease (DKD) (OR = 4.959, 95% CI: 1.156－27.728), low-density lipoprotein cholesterol (LDL-C) (OR = 8.210, 95% CI: 2.027－43.507), red blood cell distribution width (RDW) (OR = 2.418, 95% CI: 1.021－5.826), and plasma fibrinogen (Fbg) (OR = 4.649, 95% CI: 2.001－11.339). The C-index of the DIED model was 0.911 (95% CI: 0.869－0.954). The curve representing the performance of the nomogram fit in well with that representing a perfect prediction by the calibration plot. Decision curve analysis indicated that the nomogram was clinically useful for predicting DIED in the type 2 DM patients at the possibility threshold of 6% to 93%. CONCLUSIONS: A nomogram was preliminarily developed for predicting the risk of DIED in type 2 DM patients with respect to the seven independent influencing factors, including the duration of DM, status of c-IMT, DR, DKD, LDL-C, RDW, and Fbg.

Phenotype validation in electronic health records based genetic association studies.

The linkage between electronic health records (EHRs) and genotype data makes it plausible to study the genetic susceptibility of a wide range of disease phenotypes. Despite that EHR-derived phenotype data are subjected to misclassification, it has been shown useful for discovering susceptible genes, particularly in the setting of phenome-wide association studies (PheWAS). It is essential to characterize discovered associations using gold standard phenotype data by chart review. In this work, we propose a genotype stratified case-control sampling strategy to select subjects for phenotype validation. We develop a closed-form maximum-likelihood estimator for the odds ratio parameters and a score statistic for testing genetic association using the combined validated and error-prone EHR-derived phenotype data, and assess the extent of power improvement provided by this approach. Compared with case-control sampling based only on EHR-derived phenotype data, our genotype stratified strategy maintains nominal type I error rates, and result in higher power for detecting associations. It also corrects the bias in the odds ratio parameter estimates, and reduces the corresponding variance especially when the minor allele frequency is small.

A validation sampling approach for consistent estimation of adverse drug reaction risk with misclassified right-censored survival data.

Patient electronic health records, viewed as continuous-time right-censored survival data, can be used to estimate adverse drug reaction risk. Temporal outcome misclassification may occur as a result of errors in follow-up. These errors can be due to a failure to observe the incidence time of the adverse event of interest (due to misdiagnosis or nonreporting, etc) or an actual misdiagnosis of a competing adverse event. As the misclassifying event is often unobservable in the original data, we apply an internal validation sampling approach to produce consistent estimation in the presence of such errors. We introduce a univariate survival model and a cause-specific hazards model in which misclassification may also manifest as a diagnosis of an alternate adverse health outcome other than that of interest. We develop a method of maximum likelihood estimation of the model parameters and establish consistency and asymptotic normality of the estimators using standard results. We also conduct simulation studies to numerically investigate the finite sample properties of these estimators and the impact of ignoring the misclassification error.

Analyzing self-controlled case series data when case confirmation rates are estimated from an internal validation sample.

Vaccine safety studies are often electronic health record (EHR)-based observational studies. These studies often face significant methodological challenges, including confounding and misclassification of adverse event. Vaccine safety researchers use self-controlled case series (SCCS) study design to handle confounding effect and employ medical chart review to ascertain cases that are identified using EHR data. However, for common adverse events, limited resources often make it impossible to adjudicate all adverse events observed in electronic data. In this paper, we considered four approaches for analyzing SCCS data with confirmation rates estimated from an internal validation sample: (1) observed cases, (2) confirmed cases only, (3) known confirmation rate, and (4) multiple imputation (MI). We conducted a simulation study to evaluate these four approaches using type I error rates, percent bias, and empirical power. Our simulation results suggest that when misclassification of adverse events is present, approaches such as observed cases, confirmed case only, and known confirmation rate may inflate the type I error, yield biased point estimates, and affect statistical power. The multiple imputation approach considers the uncertainty of estimated confirmation rates from an internal validation sample, yields a proper type I error rate, largely unbiased point estimate, proper variance estimate, and statistical power.