In problems with a time to event outcome, subjects may experience competing events, which censor the outcome of interest. Cox's partial likelihood estimator treating competing events as independent censoring is commonly used to examine group differences in clinical trials but fails to adjust for omitted covariates and can bias the assessment of marginal benefit. A bivariate normal linear model generating latent data with dependent censoring is used to assess this bias. Our R-package bnc provides maximum penalized likelihood (MPL) parameter estimation using a novel EM algorithm. Using bnc, we study the properties of such MPL estimation. Simulation results for two-sample survival comparisons of time to an event of interest, with independent censoring accompanied by censoring from a correlated competing risk, are presented. Key parameters—means, hazard ratios, and correlation—are estimated. These results demonstrated that, despite ill-conditioning in models generating correlated competing risks, estimates of marginal effects are reliable. Bivariate normal models were fitted in a trial of head and neck cancer. Model fits help with clinical interpretation while also supplementing other standard methods for follow-up that are terminated by intervening risks.

Background and purposePrediction of chemoradiotherapy response (CRT) in locally advanced rectal cancer would enable stratification of management. The purpose was to prospectively evaluate multi-parametric magnetic resonance imaging (MRI) assessment of tumour heterogeneity combining diffusion weighted imaging (DWI) and dynamic contrast enhanced (DCE) MRI for the prediction of CRT response in locally advanced rectal cancer.Materials and methodsPatients with Stage II or III rectal adenocarcinoma undergoing neoadjuvant CRT and surgery underwent MRI (DWI and DCE) before, during (week 3), and after CRT (1 week before surgery). Patients with histopathology tumour regression grade (TRG) 0–1 were classified as responders, and TRG 2–3 were classified as non-responders. A whole tumour voxel-wise technique was used to produce apparent diffusion coefficient (ADC) and Ktrans (Tofts model) histograms derived from DWI and DCE-MRI, respectively. Logistic regression was used to predict response status for ADC and Ktrans quantiles.ResultsThirty-three patients were included in this analysis; 16 responders, and 17 non-responders. On heterogeneity analysis, odds of being a responder were significantly higher after CRT (before surgery) for higher ADC 75th (p = 0.049) and ADC 90th (p = 0.034) percentile values. The Ktrans quantiles were lower in non-responders than responders before and during CRT, and higher after CRT although no significant association with response status was observed (p ≥ 0.10).ConclusionsDWI-MRI after CRT (before surgery) incorporating a histogram analysis of whole tumour heterogeneity was predictive of CRT response in patients with locally advanced rectal cancer. DCE-MRI did not add value in response prediction.Clinical trial registrationAustralian New Zealand Clinical Trials Registry (ANZCTR) number ACTRN12616001690448.

Individuals with psychosis are over-represented in the criminal justice system and, as a group, are at elevated risk of re-offending. Recent studies have observed an association between increased contacts with mental health services and reduced re-offending, as well as reduced risk of re-offending in those who are ordered to mental health treatment rather than punitive sanctions. In furthering this work, this study examines the effect of disengagement from mental health treatment on probability of re-offence in offenders with psychosis over time. Data linkage was conducted with judicial, health and mortality datasets from New South Wales, Australia (2001–2015). The study population included 4960 offenders with psychosis who received non-custodial sentences and engaged with community-based mental health treatment. Risk factors for leaving treatment and/or reconviction were examined using multivariate cox regression. Further, a multi-state model was used to observe the probabilities associated with individuals moving between three states: conviction, disengagement from mental health treatment and subsequent re-conviction. A threefold increase was observed in the risk of re-offending for those who disengaged from treatment compared to those who did not (aHR = 2.76, 95% CI 1.65–4.62, p < 0.001). The median time until re-offence was 195 days, with the majority (67%) being convicted within one year of leaving treatment. A higher risk of leaving treatment was found for those born outside of Australia, with substance-related psychosis, and a history of violent offence. The findings argue for an emphasis on continued engagement with mental health services following release for offenders with psychosis and identify subgroups within this population for whom concentrated efforts regarding treatment retention should be targeted.

Background Guidelines recommend identifying in early pregnancy women at elevated risk of pre-eclampsia. The aim of this study was to develop and validate a pre-eclampsia risk prediction model for nulliparous women attending routine antenatal care “the Western Sydney (WS) model”; and to compare its performance with the National Institute of Health and Care Excellence (NICE) risk factor-list approach for classifying women as high-risk. Methods This retrospective cohort study included all nulliparous women who gave birth in three public hospitals in the Western-Sydney-Local-Health-District, Australia 2011-2014. Using births from 2011-2012, multivariable logistic regression incorporated established maternal risk factors to develop and internally validate the WS model. The WS model was then externally validated using births from 2013-2014, assessing its discrimination and calibration. We fitted the final WS model for all births from 2011-2014, and compared its accuracy in predicting pre-eclampsia with the NICE approach. Results Among 12,395 births to nulliparous women in 2011-2014, there were 293 (2.4%) pre-eclampsia events. The WS model included: maternal age, body mass index, ethnicity, multiple pregnancy, family history of pre-eclampsia, autoimmune disease, chronic hypertension and chronic renal disease. In the validation sample (6201 births), the model c-statistic was 0.70 (95% confidence interval 0.65–0.75). The observed:expected ratio for pre-eclampsia was 0.91, with a Hosmer-Lemeshow goodness-of-fit test p-value of 0.20. In the entire study sample of 12,395 births, 374 (3.0%) women had a WS model-estimated pre-eclampsia risk ≥8%, the pre-specified risk-threshold for considering aspirin prophylaxis. Of these, 54 (14.4%) developed pre-eclampsia (sensitivity 18% (14–23), specificity 97% (97–98)). Using the NICE approach, 1173 (9.5%) women were classified as high-risk, of which 107 (9.1%) developed pre-eclampsia (sensitivity 37% (31-42), specificity 91% (91–92)). The final model showed similar accuracy to the NICE approach when using lower risk-threshold of ≥4% to classify women as high-risk for pre-eclampsia. Conclusion The WS risk model that combines readily-available maternal characteristics achieved modest performance for prediction of pre-eclampsia in nulliparous women. The model did not outperform the NICE approach, but has the advantage of providing individualised absolute risk estimates, to assist with counselling, inform decisions for further testing, and consideration of aspirin prophylaxis.

Background: An elevated neutrophil-lymphocyte ratio (NLR) is associated with poor prognosis in advanced renal cell carcinoma (RCC). We examined whether the addition of NLR improves the risk reclassification of advanced RCC using current prognostic tools from the Memorial Sloan Kettering Cancer Center (MSKCC) and International Metastatic Renal Cell Carcinoma Database Consortium (IMDC). Methods: Using randomised data from the COMPARZ trial of first-line pazopanib vs. sunitinib in advanced RCC, we constructed multivariable models containing MSKCC and IMDC predictor variables with and without NLR. We evaluated model discrimination using the concordance index (C-index). We computed net reclassification improvement to quantify patient reclassification into low/intermediate/poor risk groups with the addition of NLR. Results: Of 1102 patients, NLR ≥ 5 (16%) was associated with shorter survival adjusting for MSKCC variables (adjusted HR 1.89, p < .001). Adding NLR to MSKCC variables increased the C-index by 0.01. Among patients who died before 24 months (N = 415), adding NLR reclassified 8% and 2% to a higher and lower risk category, respectively. Among those alive at 24 months (N = 636), adding NLR reclassified 4% and 1% to a higher and lower risk category, respectively. This finding translates to a net benefit of eight additional patients who die within 24 months correctly identified as poor risk per 1000 patients tested. We obtained similar results when evaluating NLR with IMDC variables. Conclusions: NLR does not substantially improve risk reclassification over pre-existing prognostic tools. MSKCC and IMDC classifications remain the standard for guiding risk-directed therapy and trial stratification of patients with advanced RCC.

BackgroundRates of pre‐eclampsia vary between countries and certain ethnic groups. However, there is limited evidence about the impact of ethnicity on risk of pre‐eclampsia, beyond established clinical risk factors.AimsTo assess the association between ethnicity and pre‐eclampsia in Australia's diverse multi‐ethnic population.Materials and MethodsWe conducted a retrospective cohort study using the ObstetriX database. We included all women with a birth between January 2011 and December 2014, at Auburn, Blacktown/Mount‐Druitt and Westmead Hospitals in the Western Sydney Local Health District. We estimated the pre‐eclampsia rate overall, and by maternal ethnic group, defined by country of birth and primary language. We developed multivariable logistic regression models to estimate odds ratios (OR) and 95% confidence intervals (CIs) for pre‐eclampsia, adjusting for maternal age, body mass index, autoimmune disease, chronic hypertension, chronic renal disease, diabetes mellitus (type 1 or 2), and multiple pregnancy. A secondary analysis was restricted to nulliparous women.ResultsThere were 40 824 women evaluated, including 12 743 nulliparous women. Of these, 1448 (3.5%) developed pre‐eclampsia (range: Australian/New Zealand‐born English speakers 735/15 422 (4.8%); North‐East Asian women 51/4470 (1.1%)). Relative to Australian/New Zealand‐born English speakers, immigrants had a lower risk of pre‐eclampsia overall (adjusted OR 0.67; 95% CI 0.60–0.75); as did the three largest immigrant groups examined: Southern Asian (0.73; 0.62–0.85), Middle‐Eastern/African (0.55; 0.47–0.66) and North‐East Asian (0.33; 0.25–0.45) women. Findings were similar for nulliparous women.ConclusionsCertain immigrant groups are at lower risk of pre‐eclampsia than Australian/New Zealand‐born English‐speaking women. Understanding why this is so may lead to better screening and preventive strategies in higher‐risk women.

Tumor deposits are independent poor prognostic factors in LARC patients following neo-CRT and surgery.The N1c category is also applicable in lymph nodes negative patients.However, it needs further studies to investigate whether one positive TD could be considered as one positive lymph node.

Online interactive tools for exploring and communicating the technically useful and clinically important measures of test Background Personal experience and a growing body of empirical studies (initi- ated by Gerd Gigerenzer) show that people generally find it hard to understand statistical measures of test accuracy. Sensitivity and spe- cificity are technically useful for comparing assay performance because they are (mathematically at least) independent of study design and disease prevalence. For patients and clinicians, the clinically important measures for decision-making are predictive values and their relation to decision thresholds that depend on the personal values (positive and negative) placed on outcomes. Objectives To help people develop an intuitive understanding of diagnostic ac- curacy measures and their technical and clinical application. Methods We introduce the concepts of technical accuracy (sensitivity and spe- cificity) and clinical accuracy (predictive values) to distinguish between the two main applications of test performance measures, and to be used alongside the concept of clinical utility . We developed two free interactive tools using the RStudio applica- tion “ Shiny ” that allow users to quantitatively and visually explore the effects on technical and clinical accuracy of true and false test re- sults and prevalence. Results Using a point of care test for Clostridium difficile, we demonstrate the effect of prevalence and distinct clinical scenarios on the clinical accuracy and utility of the test in the UK NHS. Conclusions applying test Conclusion Model performance for predicting early-onset pre-eclampsia this validation population. Determining appropriate thresholds for assessment of clinical performance will be for ongoing model development. time points, before and after red cell transfusion. MitoPO 2 mea- surements were performed using a COMET monitor (Photonics Healthcare, Utrecht, The Netherlands) on skin primed during 4 hours with an ALA containing patch (Alacare, Photonamic, We-del, Germany) for induction of mitochondrial PpIX. Reported values are a mean mitoPO 2 of 5 consecutive measures at each time point. Results : A mitoPO 2 measurement was obtained in all but 1 participant, most likely due to excessive chlorhexidin at the meas- urement site. All measurements were above the signal-to-noise ratio of 25, irrespective of severity of critical illness assessed via APACHE IV score (range 49-171). The median and interquartile ranges of mitoPO 2 before and after transfusion were 66.9 mmHg (IQR 61.5-77.7 mmHg), and 65.8 mmHg (IQR 57.5-87.2) mmHg, respectively. Median within-subject variability was lim- ited during the first 3 hours after transfusion (3.96 (IQR 2.1-11.4)mmHg), but increased considerably after 24 hours (7.9 (IQR 4.3-13.9) mmHg). Conclusion : It is feasible to measure mitochondrial oxygen tension in critically ill patients. The measurements seem to be most reliable in the first 3 hours after patch removal. Interestingly, mitoPO 2 values in our study population were higher than those previously reported in healthy volunteers. Background: Biological markers able to predict the benefit of a given treatment vs. another one are essential in precision BACKGROUND : Risk prediction models which incorporate the FOBT with other colorectal cancer risk factors have demonstrated increased sensitivity compared with FOBT alone. EHRs from primary care have a rich level Background : Performance of cardiovascular disease (CVD) risk predic- tion models for the general female population in women with a history of hypertensive disorders of pregnancy (HDP) is not established. Objectives : Assess predictive performance of the Framingham Risk Score (FRS), Pooled Cohort Equations (PCE) and Systematic Coronary Risk Evaluation model (SCORE) in women with a history of HDP, compare these to women without, and determine the effects of models ’ recalibration or refitting on predictive performance. Methods : We included 29,751 women of whom 6,302 had a history of HDP and 17,369 had not. Model performance was assessed with calibration (calibration curves, Expected:Observed (E:O) ratios) and discrimination (C-statistics) for the original, recalibrated and refitted FRS, PCE and SCORE models. All three models predict a form of CVD, include classical CVD risk factors as predictors, and have a 10-year prediction horizon. Results : In women with and without HDP, calibration showed an overprediction for FRS and PCE, which decreased after recalibration, whereas the original SCORE model slightly underpredicted, which improved after recalibration. This study provides comprehensive explanation and R syntax for a toolbox of approaches to obtain updated predictions of a binary outcome based on newly available measurements. adjustments to be made during the study. Within study monitoring of prevalence is required during studies of test accuracy and impact. Background: Linked evidence or model based evaluations of tests are recommended and frequently used to predict health benefit and assess cost effectiveness. The validity of a linked evidence assess- ment depends on whether it appropriately models the mechanisms by which tests impact on patient outcomes. Objective: To assess the extent to which model based evaluations of point-of-care tests appropriately account for the effects and impact of changes in timing of tests on patient health and costs. Method: We reviewed model based evaluations of point-of-care tests published between 2004 to 2017 identified by systematic searches of MEDLINE, EMBASE, CINAHL, NHS EED, PsychInfo and HEED. Each model was evaluated for the patient outcomes considered, whether the model estimated the impact of reduced time to diagnosis on health status and costs, and whether societal costs were included. Results: 74 model based evaluations met the inclusion criteria, of which 54 compared point-of-care tests with a slower laboratory counterpart. Of these, only 39% assessed the economic benefits and 37% the health benefits of faster diagnosis. Only 32% assessed the impact on patient health; intermediate outcomes such as rates of correct diagnosis were used instead. 95% of models did incorporate evidence on test accuracy and consider the impact of false positive and false negative results. Discussion: Many model based evaluations fail to capture the effects of point of care tests related to advancing the time to diagnosis and treatment, reduced anxiety and potential cost impact for and the system. Neither do The objective of this research is to evaluate the predictive perform- ance of regression methods to develop clinical risk prediction models using multicenter data, and provide guidelines for practice. To this end, we compared the predictive performance of standard logistic regression, generalized estimating equations, random inter- cepts logistic regression and fixed effects logistic regression. First, we presented a case study on the diagnosis of ovarian cancer using data from the International Ovarian Tumor Analysis group (IOTA). Subse-quently, a simulation study investigated the performance of the different models as a function of the amount of clustering, development sample size, distribution of center-specific intercepts, the pres- ence of a center-predictor interaction and the presence of a dependency between center effects and predictors. During valid- ation, both new patients from centers in the development dataset and from new centers were included. The results showed that sufficiently large sample sizes lead to cali-brated predictions under conditional models and miscalibrated predic- tions under marginal models. Small sample sizes led to overfitting and unreliable predictions. This miscalibration was worse with more heavily clustered data. Calibration of random intercepts logistic regression was better than that of standard logistic regression even when center- specific intercepts were not normally distributed, a center-predictor interaction was present, center effects and predictors were dependent, or when the model was applied in a new center. In conclusion, to make reliable predictions in a specific center, we recommend random intercepts logistic regression. Background: Response evaluation with PET/CT has potential in personalizing cancer treatment and evaluating treatment response. PET/CT is a powerful assessor due to its ability to differ- entiate between anatomical and physiological response, but designing clinical studies is challenging; there is no consensus on which response criteria to follow, which time points to choose for scans, and how to establish rules for differentiating between responders and non-responders. of designs response PET/CT basic methodological characteristics. Background : Clinical prediction models (CPMs) are widely used in healthcare to support medical decision-making based on patient-specific characteristics. Over the past decade, there has been an ex- ponential growth of peer-reviewed articles regarding the development of CPMs. However, the majority of these models lack proper validation and transparent description. As a result, CPMs often do not reach their full potential. Objectives: We aimed to develop an open online platform enabling users to create, validate, integrate, and apply CPMs in daily medical practice. Methods: An innovative platform was developed to enable regis- tered users to add CPMs and describe them with maximum transparency in concordance with published guidelines (TRIPOD statement). On the Evidencio platform, researchers and healthcare professionals can create CPMs based on pre-specified regression formulas, custom formulas, or run R code formulas. Tools were developed to facilitate validation of online CPMs (model discrimination and calibration) based on institutional patient data. Model developers maintain own-ership of their data and intellectual property of created CPMs. Methods to integrate CPMs using an applicatio

Objective: To assess the accuracy of the National Institute of Health and Care Excellence (NICE) and United States Preventive Services Task Force (USPSTF) guidelines for predicting pre-eclampsia in pregnancy to guide aspirin prophylaxis. Study design: We conducted an individual participant data meta-analysis using the Perinatal Antiplatelet Review of International Studies (PARIS) dataset. This dataset includes randomised controlled trials (RCTs) of antiplatelet therapy for primary prevention of pre-eclampsia conducted in international antenatal care settings. RCTs were eligible if they enrolled pregnant women up to 28 weeks'gestation, reported risk factors, and assessed pre-eclampsia. Women assigned to the control arm (no antiplatelet agent) were included. Both guidelines recommend aspirin if >= 1 high-risk factors or >= 2 moderate-risk factors. Two moderate-risk factors (body mass index and pregnancy interval) were unavailable. Pre-eclampsia was the primary outcome. The secondary outcomes were pre-eclampsia defined by gestational age at delivery (<37 weeks versus >= 37 weeks; <34 weeks versus >= 34 weeks). We assessed the performance of the NICE and USPSTF approaches for parous and nulliparous women by estimating sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for predicting pre-eclampsia, the number-needed-to-screen (NNS) and the number-needed-to-treat (NNT) to prevent one pre-eclampsia event. Results: Three RCTs were eligible (4524 women, 221 pre-eclampsia cases). Using the NICE guidelines, 9.4% of 1020 parous women were classified as screen-positive with a sensitivity of 26.4% (95% confidence interval 16.4-39.6%), specificity 91.5% (89.6-93.1%), PPV 14.6% (8.9-23.0%) and NPV 95.8% (94.3-96.9%). The NNS was 729 and NNT 69. For 3504 nulliparous women, 3% were classified as screen-positive with a sensitivity of 8.9% (5.5-14.4%), specificity 97.2% (96.6-97.8%), PPV 14.2% (8.7-21.9%), NPV 95.5% (94.8-96.1%). The NNS was 2336 and NNT 71. The USPSTF approach demonstrated similar performance. Conclusion: The NICE and USPSTF guidelines offer a simple and specific approach for recommending aspirin prophylaxis for women at high-risk of pre-eclampsia where more advanced screening methods are not available. However, the low detection rate limits its value in clinical practice, in particular for nulliparous women, and raises the need for development of an improved simple risk prediction tool. (C)2018 Elsevier By, All rights reserved.

Background: Various patient reported quality-of-life indicators are independently prognostic for survival in metastatic breast cancer and other cancers. The same measures recorded at first diagnosis of early breast cancer carry no corresponding prognostic information. The present study aims to assess at what time in the disease evolution the prognostic association appears. Methods: Among 8024 patients enrolled in one of seven randomized controlled trials in early-stage breast cancer 3247 had a breast cancer relapse after a median follow-up of 12.1 years. Of these 677 had completed QL indicator assessments within defined windows 1, 2 or 3 months prior to relapse. We performed Cox regression analyses using these assessments and using identical instruments after relapse. All analyses were stratified by trial and adjusted for baseline clinicopathologic factors. Results: QL indicators in the months before relapse were not significantly prognostic for subsequent survival with the possibly chance exception of mood at the second month before relapse. After relapse, physical well-being was statistically significantly associated with survival (P < 0.001). This prognostic significance increased in later post-relapse assessments. Similar findings were observed using patient-reported indicators for nausea and vomiting, appetite, coping effort, and health perception. Conclusions: Before cancer relapse, QL indicators were not generally prognostic for subsequent survival. After relapse, QL indicators substantially predicted OS, with a stronger association later in the course of relapsed disease. Simple patient perception of disease burden seems unlikely to explain this sudden change: rather the patient's awareness of disease relapse must contribute.

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