The proliferation marker Ki67, but not neuroendocrine expression, is an independent factor in the prediction of prognosis of primary prostate cancer patients

Background. Neuroendocrine markers, which could indicate for aggressive variants of prostate cancer and Ki67 (a well-known marker in oncology for defining tumor proliferation), have already been associated with clinical outcome in prostate cancer. The aim of this study was to investigate the prognostic value of those markers in primary prostate cancer patients. Patients and methods. NSE (neuron specific enolase), ChrA (chromogranin A), Syp (Synaptophysin) and Ki67 stain- ing were performed by immunohistochemistry. Then, the prognostic impact of their expression on overall survival was investigated in 166 primary prostate cancer patients by univariate and multivariate analyses. Results. NSE, ChrA, Syp and Ki67 were positive in 50, 45, 54 and 146 out of 166 patients, respectively. In Kaplan-Meier analysis only diffuse NSE staining (negative vs associated with overall survival. Ki67 expression, but not NSE, resulted as an independent prognostic factor for overall survival in multivariate analysis. Conclusions. A prognostic model incorporating Ki67 expression with clinical-pathological covariates could provide additional prognostic information. Ki67 may thus improve prediction of prostate cancer outcome based on standard clinical-pathological parameters improving prognosis and management of prostate cancer patients

MIXTURE MODELS FOR INTERVAL CENSORED OUTCOMES

Silent events such as the first detectable HIV infection, the onset of Type 2 diabetes and prostate cancer progression are often ascertained by diagnostic tests and/or self-reports that are scheduled periodically. In such applications, we only observe the time to the event of interest to lie between the times of last negative and the first positive tests, resulting in interval-censored observations. In addition, in some medical studies, a substantial proportion of participants may experience the events before the study, so-called prevalent cases, or participants may never experience the event, that is regarded as non-susceptible cases (or indolent cancer or long-term survivor). In this dissertation, I develop mixture models for the analysis of heterogeneous survival data subject to interval-censoring. The first chapter of this dissertation is motivated by a study of the effects of maternal and infant antiretroviral therapy on the sensitivity of DNA PCR diagnostic tests in detecting HIV infection in infants born to HIV-positive mothers. We apply a mixture model to evaluate the association of a set of predictors with an interval-censored time to first detectable DNA PCR test, while accounting for the subset of infants who test positive at birth. The mixture model is applied to data from the Pediatric AIDS Collaborative Transmission Study and the Women and Infants Transmission Study to evaluate the effects of maternal/infant antiretroviral therapy in HIV subtype B infected mother-infant pairs. In Chapter 2, we propose a parametric mixture model for interval censored time to event outcomes, while relaxing the commonly used proportional hazards assumption. The proposed model is applied to data collected in the National Health and Nutrition Examination Survey to evaluate risk factors of Type 2 diabetes. Chapter 3 is motivated by a Canary Prostate Active Surveillance Study (PASS) where the time to cancer progression (i.e., biopsy upgrade) is of primary interest. We propose a semiparametric mixture model to handle misclassification of progressed cancer at baseline and non-susceptible cases (or, indolent cancer). In addition, we account for imperfect diagnostic tests at each visit and risk factors that change over time in the proposed model. Extensive simulation studies are conducted to assess the performance of the proposed approaches with/without mixture components. The proposed approach is applied to the Canary Prostate Active Surveillance Study to evaluate the effects of factors on the risk of cancer progression and estimate the indolent fraction under a range of sensitivity rates of biopsy

Estimation of extended mixed models using latent classes and latent processes: the R package lcmm

The R package lcmm provides a series of functions to estimate statistical models based on linear mixed model theory. It includes the estimation of mixed models and latent class mixed models for Gaussian longitudinal outcomes (hlme), curvilinear and ordinal univariate longitudinal outcomes (lcmm) and curvilinear multivariate outcomes (multlcmm), as well as joint latent class mixed models (Jointlcmm) for a (Gaussian or curvilinear) longitudinal outcome and a time-to-event that can be possibly left-truncated right-censored and defined in a competing setting. Maximum likelihood esimators are obtained using a modified Marquardt algorithm with strict convergence criteria based on the parameters and likelihood stability, and on the negativity of the second derivatives. The package also provides various post-fit functions including goodness-of-fit analyses, classification, plots, predicted trajectories, individual dynamic prediction of the event and predictive accuracy assessment. This paper constitutes a companion paper to the package by introducing each family of models, the estimation technique, some implementation details and giving examples through a dataset on cognitive aging

Acute and midterm outcomes of the post-approval MELODY Registry: a multicentre registry of transcatheter pulmonary valve implantation

AIMS The post-approval MELODY Registry aimed to obtain multicentre registry data after transcatheter pulmonary valve implantation (TPVI) with the Melody™ valve (Medtronic plc.) in a large-scale cohort of patients with congenital heart disease (CHD). METHODS AND RESULTS Retrospective analysis of multicentre registry data after TPVI with the Melody™ valve. Eight hundred and forty-five patients (mean age: 21.0 ± 11.1 years) underwent TPVI in 42 centres between December 2006 and September 2013 and were followed-up for a median of 5.9 years (range: 0-11.0 years). The composite endpoint of TPVI-related events during follow-up (i.e. death, reoperation, or reintervention >48 h after TPVI) showed an incidence rate of 4.2% per person per year [95% confidence interval (CI) 3.7-4.9]. Transcatheter pulmonary valve implantation infective endocarditis (I.E.) showed an incidence rate of 2.3% per person per year (95% CI 1.9-2.8) and resulted in significant morbidity and in nine deaths. In multivariable Cox proportional hazard models, the invasively measured residual right ventricle (RV)-to-pulmonary artery (PA) pressure gradient (per 5 mmHg) was associated with the risk of the composite endpoint (adjusted hazard ratio: 1.21, 95% CI 1.12-1.30; P 2 improved significantly from 36 [interquartile range (IQR) 24-47] to 12 (IQR 7-17) mmHg and 47 to 1%, respectively (P < 0.001 for each). CONCLUSION The post-approval MELODY Registry confirms the efficacy of TPVI with the Melody™ valve in a large-scale cohort of CHD patients. The residual invasively measured RV-to-PA pressure gradient may serve as a target for further improvement in the composite endpoint and TPVI I.E. However, TPVI I.E. remains a significant concern causing significant morbidity and mortality

Class III obesity is a risk factor for the development of acute on chronic liver failure in patients with decompensated cirrhosis

BACKGROUND AND AIMS: Acute on chronic liver failure (ACLF) is a syndrome of systemic inflammation and organ failures. Obesity, also characterized by chronic inflammation, is a risk factor among patients with cirrhosis for decompensation, infection, and mortality. Our aim was to test the hypothesis that obesity predisposes to ACLF development in patients with decompensated cirrhosis. METHODS: We examined the United Network for Organ Sharing (UNOS) database, from 2005-2016, characterizing patients at wait-listing as non-obese (BMI < 30), obese class I-II (BMI 30-39.9) and obese class III (BMI≥40). ACLF was determined based on the CANONIC study definition. We used Cox proportional hazards regression to assess the association between obesity and ACLF development at liver transplantation (LT). We confirmed our findings using the Nationwide Inpatient Sample (NIS), years 2009-2013, using validated diagnostic coding algorithms to identify obesity, hepatic decompensation and ACLF. Logistic regression evaluated the association between obesity and ACLF occurrence. RESULTS: Among 387,884 with decompensated cirrhosis, 116,704 patients (30.1%) were identified as having ACLF in both databases. Multivariable modeling from the UNOS database revealed class III obesity to be an independent risk factor for ACLF at LT (HR=1.24, 95% CI 1.09-1.41, p<0.001). This finding was confirmed using the NIS (OR=1.30, 95% CI 1.25-1.35, p<0.001). Regarding specific organ failures, analysis of both registries demonstrated patients with class I-II and class III obesity had greater prevalence of renal failure. CONCLUSION: Class III obesity is a newly identified risk factor for ACLF development in patients with decompensated cirrhosis. Obese patients have a particularly higher prevalence of renal failure as a component of ACLF. These findings have important implications regarding stratifying risk and preventing the occurrence of ACLF. LAY SUMMARY: In this study, we identify that among patients with decompensated cirrhosis, class III obesity is a modifiable risk factor for the development of acute on chronic liver failure (ACLF). We further demonstrate that regarding the specific organ failures associated with ACLF, renal failure is significantly more prevalent among obese patients, particularly class III obesity. These findings underscore the importance of weight management in cirrhosis, to reduce the risk of ACLF. Patients with class III obesity should be monitored closely for the development of renal failure

Beyond Parameter Estimation: Analysis of the Case-Cohort Design in Cox Models

Cohort studies allow for powerful analysis, but an exposure may be too expensive to measure in the whole cohort. The case-cohort design measures covariates in a random sample (subcohort) of the full cohort, as well as in all cases that emerge, regardless of their initial presence in the subcohort. It is an increasingly popular method, particularly for medical and biological research, due to its efficiency and flexibility. However, the case-cohort design poses a number of challenges for estimation and post-estimation procedures. Cases are over-represented in the dataset, and hence estimation of coefficients in this design requires weighting of observations. This results in a pseudopartial likelihood, and standard post-estimation methods may not be readily transferable to the case-cohort design. This thesis presents theory and simulation studies for application of estimation and post-estimation methods in the case-cohort design. In the majority of extant literature considering methods for the case-cohort design, simulation studies generally consider full cohort sizes, sampling fractions, and case percentages that are dissimilar to those seen in practice. In this thesis the design of the simulation studies aims to provide circumstances which are similar to those encountered when using case-cohort designs in practice. Further, these methods are applied to the InterAct dataset, and practical advice and sample code for STATA is presented. Estimation of Coefficients & Cumulative Baseline Hazard: For estimation of coefficients, Prentice weighting and Barlow weighting are the most commonly used (Sharp et al, 2014). Inverse Probability Weighting (IPW), in this context, refers to methods where the entire case-cohort sample at risk is used in the analysis, as opposed to Prentice and Barlow weighting systems, where cases outside the subcohort sample are only included in risk sets just prior to their time of failure. This thesis assesses bias and precision of Prentice, Barlow and IPW weighting methods in the case-cohort design. Simulation studies show IPW, Prentice and Barlow weighting to have similar low bias. Where case percentage is high, IPW weighting shows an increase in precision over Prentice and Barlow, though this improvement is small. Checks of Model Assumptions: Appropriateness of covariate functional form in the standard Cox model can be assessed graphically by smoothed martingale residuals against various other values, such as time and covariates of interest (Therneau et al, 1990). The over-representation of cases in the case-cohort data, as compared to the full cohort, distorts the properties of such residuals. Methods related to IPW that adapt such plots to the case-cohort design are presented. Detection of non-proportional hazards by use of Schoenfeld residuals, scaled Schoenfeld residuals, and inclusion of time-varying covariates in the model are assessed and compared by simulation studies, finding that where risk set sizes are not overly variable, all three methods are appropriate for use in the case-cohort design, with similar power. Where case-cohort risk set sizes are more variable, methods based on Schoenfeld residuals and scaled Schoenfeld residuals show high Type 1 error rate. Model Comparison & Variable Selection: The methods of Lumley & Scott (2013, 2015) for modification of the Likelihood Ratio test (dLR), AIC (dAIC) and BIC (dBIC) in complex survey sampling are applied to case-cohort data and assessed in simulation studies. In the absence of sparse data, dLR is found to have similar power to robust Wald tests, with Type 1 error rate approximately 5%. In the presence of sparse data, the dLR is superior to robust Wald tests. In the absence of sparse data dBIC shows little difference from the naieve use of the pseudo-log-likelihood in the standard BIC formula (pBIC). In the presence of sparse data dBIC shows reduced power to select the true model, and pBIC is superior. dAIC shows improvement in power to select the true model over naieve methods. Where subcohort size and number of cases is not overly small, loss of power from the full cohort for dAIC, dBIC and pBIC is not substantial.The EPIC-InterAct study received funding from the European Union (Integrated Project LSHM-CT-2006-037197 in the Framework Programme 6 of the European Community). I thank all participants and staff for their contribution to this study. I thank the EPIC-InterAct PI, management team and wider consortium for their permission to use the data, and Nicola Kerrison (MRC Epidemiology Unit, University of Cambridge) for preparing the dataset which I used in Chapters 2 and 7. I acknowledge personal financial support from the UK Medical Research Council and St John's College, Cambridge

Polygenic and clinical risk scores and their impact on age at onset and prediction of cardiometabolic diseases and common cancers

Polygenic risk scores (PRSs) have shown promise in predicting susceptibility to common diseases1,2,3. We estimated their added value in clinical risk prediction of five common diseases, using large-scale biobank data (FinnGen; n = 135,300) and the FINRISK study with clinical risk factors to test genome-wide PRSs for coronary heart disease, type 2 diabetes, atrial fibrillation, breast cancer and prostate cancer. We evaluated the lifetime risk at different PRS levels, and the impact on disease onset and on prediction together with clinical risk scores. Compared to having an average PRS, having a high PRS contributed 21% to 38% higher lifetime risk, and 4 to 9 years earlier disease onset. PRSs improved model discrimination over age and sex in type 2 diabetes, atrial fibrillation, breast cancer and prostate cancer, and over clinical risk in type 2 diabetes, breast cancer and prostate cancer. In all diseases, PRSs improved reclassification over clinical thresholds, with the largest net reclassification improvements for early-onset coronary heart disease, atrial fibrillation and prostate cancer. This study provides evidence for the additional value of PRSs in clinical disease prediction. The practical applications of polygenic risk information for stratified screening or for guiding lifestyle and medical interventions in the clinical setting remain to be defined in further studies.Peer reviewe