The early peak knee abduction moment waveform is a novel risk factor predicting anterior cruciate ligament injury in young athletes: A prospective study.

PURPOSE: In this study, prospective data were used to evaluate whether the early peak knee abduction moment waveform is associated with the risk of anterior cruciate ligament (ACL) injury. METHODS: Biomechanical data from 84 athletes who participated in the study as adolescents were analysed after cross-referencing national health registry data to confirm ACL reconstruction in the subsequent years. The knee abduction moment waveform shape was obtained with cluster analysis for the first 100 ms of a cutting manoeuvre (1776 trials in total) and classified as either containing an early peak knee abduction moment or not, and the odds ratio for later ACL injury was then calculated. Additionally, discrete kinematic and kinetic variables were extracted, and tested against the risk of ACL injury using mixed model logistic regression. RESULTS: Of 84 athletes, 8 (all female) sustained a total of 13 ACL injuries in the years after motion analysis data collection. Six clusters of knee abduction moment waveform shapes were identified. Two clusters containing 446 trials were classified as an early peak knee abduction waveform. This waveform was associated with a 7.2-fold increase in the risk of ACL injury (95% confidence interval: 2.4-24.6; p < 0.001). Of the kinematic and kinetic variables tested, only the knee abduction angle at initial contact was associated with an increased risk of ACL injury (p < 0.001). CONCLUSION: This is the first study to confirm the association between the early peak knee abduction moment waveform and the risk of ACL injury. Using waveforms, instead of discrete peak values of the knee abduction moment, may better represent risky movement patterns. Replicating these findings in a larger cohort will support the use of this method to screen athletes for risk and guide targeted preventive interventions and their efficacy. LEVEL OF EVIDENCE: Level II.

Integrated noninvasive diagnostics for prediction of survival in immunotherapy.

Background: Integrating complementary diagnostic data sources promises enhanced robustness in the predictive performance of artificial intelligence (AI) models, a crucial requirement for future clinical validation/implementation. In this study, we investigate the potential value of integrating data from noninvasive diagnostic modalities, including chest computed tomography (CT) imaging, routine laboratory blood tests, and clinical parameters, to retrospectively predict 1-year survival in a cohort of patients with advanced non-small-cell lung cancer, melanoma, and urothelial cancer treated with immunotherapy. Patients and methods: The study included 475 patients, of whom 444 had longitudinal CT scans and 475 had longitudinal laboratory data. An ensemble of AI models was trained on data from each diagnostic modality, and subsequently, a model-agnostic integration approach was adopted for combining the prediction probabilities of each modality and producing an integrated decision. Results: Integrating different diagnostic data demonstrated a modest increase in predictive performance. The highest area under the curve (AUC) was achieved by CT and laboratory data integration (AUC of 0.83, 95% confidence interval 0.81-0.85, P < 0.001), whereas the performance of individual models trained on laboratory and CT data independently yielded AUCs of 0.81 and 0.73, respectively. Conclusions: In our retrospective cohort, integrating different noninvasive data modalities improved performance.

Durability of annuloplasty in patients with atrial functional mitral regurgitation associated with atrial fibrillation.

OBJECTIVE: To determine the durability of mitral valve repair (MVr) with complete ring or flexible band annuloplasty in patients with atrial functional mitral regurgitation (AFMR) due to atrial fibrillation (AF) and identify risk factors associated with postoperative recurrence of mitral regurgitation. METHODS: Between January 1, 2000, and January 1, 2023, 194 adults with a history of AF underwent MVr with annuloplasty alone for moderate/severe AFMR. Exclusion criteria were prior cardiac surgery, additional repair techniques, ejection fraction <45%, ischemic heart disease, aortic valve disease, mitral annular calcification, and concomitant procedures other than surgical ablation or tricuspid repair/replacement. The durability of annuloplasty was assessed using longitudinal analysis of postoperative echocardiographic data. RESULTS: Complete ring annuloplasty was performed in 126 of 194 patients (65%); partial ring (posterior band) in the other 68 (35%). Concomitantly, 124 of the 194 patients underwent tricuspid valve surgery, and 173 (89%) had a procedure for AF, including biatrial Cox-Maze III/IV lesion set in 152 (88%) and pulmonary vein isolation in 21 (12%). All patients were discharged with no/trace MR. Freedom from moderate/severe MR after repair with annuloplasty alone was 89% at 10 years, and no significant differences were noted between complete and partial ring annuloplasty (early, P = .41; late, P = .92). Forty-eight percent of patients developed AF at 3 months or longer after surgery, and the presence of postoperative AF was not associated with a greater likelihood of recurrence of MR (P = .15). Freedom from mitral reintervention was 96% at 10 years. CONCLUSIONS: In appropriate patients with AFMR, the long-term durability of annuloplasty is excellent with complete ring and posterior band annuloplasty techniques.

Non-invasive prediction of axillary lymph node dissection exemption in breast cancer patients post-neoadjuvant therapy: A radiomics and deep learning analysis on longitudinal DCE-MRI data.

PURPOSE: In breast cancer (BC) patients with clinical axillary lymph node metastasis (cN+) undergoing neoadjuvant therapy (NAT), precise axillary lymph node (ALN) assessment dictates therapeutic strategy. There is a critical demand for a precise method to assess the axillary lymph node (ALN) status in these patients. MATERIALS AND METHODS: A retrospective analysis was conducted on 160 BC patients undergoing NAT at Fujian Medical University Union Hospital. We analyzed baseline and two-cycle reassessment dynamic contrast-enhanced MRI (DCE-MRI) images, extracting 3668 radiomic and 4096 deep learning features, and computing 1834 delta-radiomic and 2048 delta-deep learning features. Light Gradient Boosting Machine (LightGBM), Support Vector Machine (SVM), RandomForest, and Multilayer Perceptron (MLP) algorithms were employed to develop risk models and were evaluated using 10-fold cross-validation. RESULTS: Of the patients, 61 (38.13 %) achieved ypN0 status post-NAT. Univariate and multivariable logistic regression analyses revealed molecular subtypes and Ki67 as pivotal predictors of achieving ypN0 post-NAT. The SVM-based "Data Amalgamation" model that integrates radiomic, deep learning features, and clinical data, exhibited an outstanding AUC of 0.986 (95 % CI: 0.954-1.000), surpassing other models. CONCLUSION: Our study illuminates the challenges and opportunities inherent in breast cancer management post-NAT. By introducing a sophisticated, SVM-based "Data Amalgamation" model, we propose a way towards accurate, dynamic ALN assessments, offering potential for personalized therapeutic strategies in BC.

Biomarker data with measurement error in medical research: A literature review.

A biomarker is a measurable indicator of the severity or presence of a disease or medical condition in biomedical or epidemiological research. Biomarkers may help in early diagnosis and prevention of diseases. Several biomarkers have been identified for many diseases such as carbohydrate antigen 19-9 for pancreatic cancer. However, biomarkers may be measured with errors due to many reasons such as specimen collection or day-to-day within-subject variability of the biomarker, among others. Measurement error in the biomarker leads to bias in the regression parameter estimation for the association of the biomarker with disease in epidemiological studies. In addition, measurement error in the biomarkers may affect standard diagnostic measures to evaluate the performance of biomarkers such as the receiver operating characteristic (ROC) curve, area under the ROC curve, sensitivity, and specificity. Measurement error may also have an effect on how to combine multiple cancer biomarkers as a composite predictor for disease diagnosis. In follow-up studies, biomarkers are often collected intermittently at examination times, which may be sparse and typically biomarkers are not observed at the event times. Joint modeling of longitudinal and time-to-event data is a valid approach to account for measurement error in the analysis of repeatedly measured biomarkers and time-to-event outcomes. In this article, we provide a literature review on existing methods to correct for estimation in regression analysis, diagnostic measures, and joint modeling of longitudinal biomarkers and survival outcomes when the biomarkers are measured with errors. This article is categorized under: Statistical and Graphical Methods of Data Analysis > Robust MethodsStatistical and Graphical Methods of Data Analysis > EM AlgorithmStatistical Models > Survival Models.

Modeling longitudinal data using matrix completion.

In clinical practice and biomedical research, measurements are often collected sparsely and irregularly in time, while the data acquisition is expensive and inconvenient. Examples include measurements of spine bone mineral density, cancer growth through mammography or biopsy, a progression of defective vision, or assessment of gait in patients with neurological disorders. Practitioners often need to infer the progression of diseases from such sparse observations. A classical tool for analyzing such data is a mixed-effect model where time is treated as both a fixed effect (population progression curve) and a random effect (individual variability). Alternatively, researchers use Gaussian processes or functional data analysis, assuming that observations are drawn from a certain distribution of processes. While these models are flexible, they rely on probabilistic assumptions, require very careful implementation, and tend to be slow in practice. In this study, we propose an alternative elementary framework for analyzing longitudinal data motivated by matrix completion. Our method yields estimates of progression curves by iterative application of the Singular Value Decomposition. Our framework covers multivariate longitudinal data, and regression and can be easily extended to other settings. As it relies on existing tools for matrix algebra, it is efficient and easy to implement. We apply our methods to understand trends of progression of motor impairment in children with Cerebral Palsy. Our model approximates individual progression curves and explains 30% of the variability. Low-rank representation of progression trends enables identification of different progression trends in subtypes of Cerebral Palsy.

Longitudinal Image Data for Outcome Modeling.

In oncology, medical imaging is crucial for diagnosis, treatment planning and therapy execution. Treatment responses can be complex and varied and are known to involve factors of treatment, patient characteristics and tumor microenvironment. Longitudinal image analysis is able to track temporal changes, aiding in disease monitoring, treatment evaluation, and outcome prediction. This allows for the enhancement of personalized medicine. However, analyzing longitudinal 2D and 3D images presents unique challenges, including image registration, reliable segmentation, dealing with variable imaging intervals, and sparse data. This review presents an overview of techniques and methodologies in longitudinal image analysis, with a primary focus on outcome modeling in radiation oncology.

Bayesian two-stage modeling of longitudinal and time-to-event data with an integrated fractional Brownian motion covariance structure.

It is difficult to characterize complex variations of biological processes, often longitudinally measured using biomarkers that yield noisy data. While joint modeling with a longitudinal submodel for the biomarker measurements and a survival submodel for assessing the hazard of events can alleviate measurement error issues, the continuous longitudinal submodel often uses random intercepts and slopes to estimate both between- and within-patient heterogeneity in biomarker trajectories. To overcome longitudinal submodel challenges, we replace random slopes with scaled integrated fractional Brownian motion (IFBM). As a more generalized version of integrated Brownian motion, IFBM reasonably depicts noisily measured biological processes. From this longitudinal IFBM model, we derive novel target functions to monitor the risk of rapid disease progression as real-time predictive probabilities. Predicted biomarker values from the IFBM submodel are used as inputs in a Cox submodel to estimate event hazard. This two-stage approach to fit the submodels is performed via Bayesian posterior computation and inference. We use the proposed approach to predict dynamic lung disease progression and mortality in women with a rare disease called lymphangioleiomyomatosis who were followed in a national patient registry. We compare our approach to those using integrated Ornstein-Uhlenbeck or conventional random intercepts-and-slopes terms for the longitudinal submodel. In the comparative analysis, the IFBM model consistently demonstrated superior predictive performance.

The chronically ill in the labour market - are they hierarchically sorted by education?

BACKGROUND: The chronically ill as a group has on average lower probability of employment compared to the general population, a situation that has persisted over time in many countries. Previous studies have shown that the prevalence of chronic diseases is higher among those with lower levels of education. We aim to quantify the double burden of low education and chronic illness comparing the differential probabilities of employment between the chronically ill with lower, medium, and high levels of education and how their employment rates develop over time. METHODS: Using merged Norwegian administrative data over a 11-year period (2008-2018), our estimations are based on multivariable regression with labour market and time fixed effects. To reduce bias due to patients' heterogeneity, we included a series of covariates that may influence the association between labour market participation and level of education. To explicitly explore the 'shielding effect' of education over time, the models include the interaction effects between chronic illness and level of education and year. RESULTS: The employment probabilities are highest for the high educated and lowest for chronically ill individuals with lower education, as expected. The differences between educational groups are changing over time, though, driven by a revealing development among the lower-educated chronically ill. That group has a significant reduction in employment probabilities both in absolute terms and relative to the other groups. The mean predicted employment probabilities for the high educated chronic patient is not changing over time indicating that the high educated as a group is able to maintain labour market participation over time. Additionally, we find remarkable differences in employment probabilities depending on diagnoses. CONCLUSION: For the chronically ill as a group, a high level of education seems to "shield" against labour market consequences. The magnitude of the shielding effect is increasing over time leaving chronically ill individuals with lower education behind. However, the shielding effect varies in size between types of chronic diseases. While musculoskeletal, cardiovascular and partly cancer patients are "sorted" hierarchically according to level of education, diabetes, respiratory and mental patients are not.

A Bayesian zero-inflated beta-binomial model for longitudinal data with group-specific changepoints.

Timeline followback (TLFB) is often used in addiction research to monitor recent substance use, such as the number of abstinent days in the past week. TLFB data usually take the form of binomial counts that exhibit overdispersion and zero inflation. Motivated by a 12-week randomized trial evaluating the efficacy of varenicline tartrate for smoking cessation among adolescents, we propose a Bayesian zero-inflated beta-binomial model for the analysis of longitudinal, bounded TLFB data. The model comprises a mixture of a point mass that accounts for zero inflation and a beta-binomial distribution for the number of days abstinent in the past week. Because treatment effects appear to level off during the study, we introduce random changepoints for each study group to reflect group-specific changes in treatment efficacy over time. The model also includes fixed and random effects that capture group- and subject-level slopes before and after the changepoints. Using the model, we can accurately estimate the mean trend for each study group, test whether the groups experience changepoints simultaneously, and identify critical windows of treatment efficacy. For posterior computation, we propose an efficient Markov chain Monte Carlo algorithm that relies on easily sampled Gibbs and Metropolis-Hastings steps. Our application shows that the varenicline group has a short-term positive effect on abstinence that tapers off after week 9.

Proposal for a new non-linear model to describe growth curves

This study was developed with longitudinal data measurements of Norfolk rabbits from birth to 119 days of age to estimate the average growth curve, with the primary objective of proposing a non-linear model. It also selected the most appropriate sigmoidal model to describe the growth of Norfolk rabbits. The adjustments provided by the logistic, von Bertalanffy, Gompertz, Brody, Richards, and proposed models were compared. The parameters were estimated using the "nls" function of the "stats" package in R software, the least-squares method, and the Gauss-Newton convergence algorithm. The goodness-of-fit comparison was based on the following criteria: adjusted coefficient of determination (), mean square error (MSE), mean absolute deviation (MAD), Akaike information criterion (AIC), and Bayesian information criterion (BIC). Cluster analysis helped select and classify the non-linear growth models, considering the other goodness-of-fit criteria results. The proposed non-linear, von Bertalanffy, Gompertz, and Richards models described the growth curve of Norfolk rabbits satisfactorily, providing parameters with practical interpretations. The goodness-of-fit criteria showed that the proposed and von Bertalanffy models best represented the growth of rabbits.

Longitudinal trajectories of depression and quality of life in a cohort of cancer survivors and individuals without cancer in Europe.

BACKGROUND: The long-term effects of cancer on psychological symptoms and quality of life (QoL) have been widely reported, but they were seldom examined over time compared to the general population. AIMS: To investigate trajectories of depression and QoL over time among cancer survivors compared to individuals without cancer throughout Europe and identify associated factors. METHODS: Data from five waves of the Survey of Health, Ageing and Retirement in Europe study were used. The study sample featured 1066 cancer survivors and 9655 individuals without cancer from 13 European countries. Group-based trajectory modeling was used to identify depression and QoL trajectories, and a linear mixed-effects model was used to characterize their correlates. RESULTS: Four depression trajectories-stable low, stable high, increasing, and decreasing-and four QoL trajectories were identified. All QoL trajectories were stable over time, but differed in their levels: low, low-medium, medium-high, and high. Depression and QoL trajectories were similar between cancer survivors and individuals without cancer. However, significantly more cancer survivors had high-depression and low-QoL trajectories. Further, better perceived health, activities of daily living, physical activity, and income adequacy levels were significantly associated with changes in depression and QoL levels over time. CONCLUSIONS: Although depression and QoL trajectories did not differ between cancer survivors and individuals without cancer, more cancer survivors were characterized by high-depression and low-QoL life trajectories. IMPLICATIONS FOR CANCER SURVIVORS: Providers should be aware and screen for cancer survivors with elevated depression and low QoL, and promote relevant psychosocial interventions. Modifiable factors associated with depression and QoL can be targets for cancer survivors' long-term care plans.

A composite semiparametric homogeneity test for the distributions of multigroup interval-bounded longitudinal data.

Motivated by comparing the distribution of longitudinal quality of life (QoL) data among different treatment groups from a cancer clinical trial, we propose a semiparametric test statistic for the homogeneity of the distributions of multigroup longitudinal measurements, which are bounded in a closed interval with excess observations taking the boundary values. Our procedure is based on a three-component mixed density ratio model and a composite empirical likelihood for the longitudinal data taking values inside the interval. A nonparametric bootstrap method is applied to calculate the p-value of the proposed test. Simulation studies are conducted to evaluate the proposed procedure, which show that the proposed test is effective in controlling type I errors and more powerful than the procedure which ignores the values on the boundaries. It is also robust to the model mispecification than the parametric test. The proposed procedure is also applied to compare the distributions of the scores of Physical Function subscale and Global Heath Status between the patients randomized to two treatment groups in a cancer clinical trial.

Development and evaluation of a method to assess breast cancer risk using a longitudinal history of mammographic density: a cohort study.

BACKGROUND: Women with dense breasts have an increased risk of breast cancer. However, breast density is measured with variability, which may reduce the reliability and accuracy of its association with breast cancer risk. This is particularly relevant when visually assessing breast density due to variation in inter- and intra-reader assessments. To address this issue, we developed a longitudinal breast density measure which uses an individual woman's entire history of mammographic density, and we evaluated its association with breast cancer risk as well as its predictive ability. METHODS: In total, 132,439 women, aged 40-73 yr, who were enrolled in Kaiser Permanente Washington and had multiple screening mammograms taken between 1996 and 2013 were followed up for invasive breast cancer through 2014. Breast Imaging Reporting and Data System (BI-RADS) density was assessed at each screen. Continuous and derived categorical longitudinal density measures were developed using a linear mixed model that allowed for longitudinal density to be updated at each screen. Predictive ability was assessed using (1) age and body mass index-adjusted hazard ratios (HR) for breast density (time-varying covariate), (2) likelihood-ratio statistics (ΔLR-χ2) and (3) concordance indices. RESULTS: In total, 2704 invasive breast cancers were diagnosed during follow-up (median = 5.2 yr; median mammograms per woman = 3). When compared with an age- and body mass index-only model, the gain in statistical information provided by the continuous longitudinal density measure was 23% greater than that provided by BI-RADS density (follow-up after baseline mammogram: ΔLR-χ2 = 379.6 (degrees of freedom (df) = 2) vs. 307.7 (df = 3)), which increased to 35% (ΔLR-χ2 = 251.2 vs. 186.7) for follow-up after three mammograms (n = 76,313, 2169 cancers). There was a sixfold difference in observed risk between densest and fattiest eight-category longitudinal density (HR = 6.3, 95% CI 4.7-8.7), versus a fourfold difference with BI-RADS density (HR = 4.3, 95% CI 3.4-5.5). Discriminatory accuracy was marginally greater for longitudinal versus BI-RADS density (c-index = 0.64 vs. 0.63, mean difference = 0.008, 95% CI 0.003-0.012). CONCLUSIONS: Estimating mammographic density using a woman's history of breast density is likely to be more reliable than using the most recent observation only, which may lead to more reliable and accurate estimates of individual breast cancer risk. Longitudinal breast density has the potential to improve personal breast cancer risk estimation in women attending mammography screening.

Incorporating longitudinal history of risk factors into atherosclerotic cardiovascular disease risk prediction using deep learning.

Background: It is increasingly clear that longitudinal risk factor levels and trajectories are related to risk for atherosclerotic cardiovascular disease (ASCVD) above and beyond single measures. Currently used in clinical care, the Pooled Cohort Equations (PCE) are based on regression methods that predict ASCVD risk based on cross-sectional risk factor levels. Deep learning (DL) models have been developed to incorporate longitudinal data for risk prediction but its benefit for ASCVD risk prediction relative to the traditional Pooled Cohort Equations (PCE) remain unknown. Objective: To develop a ASCVD risk prediction model that incorporates longitudinal risk factors using deep learning. Methods: Our study included 15,565 participants from four cardiovascular disease cohorts free of baseline ASCVD who were followed for adjudicated ASCVD. Ten-year ASCVD risk was calculated in the training set using our benchmark, the PCE, and a longitudinal DL model, Dynamic-DeepHit. Predictors included those incorporated in the PCE: sex, race, age, total cholesterol, high density lipid cholesterol, systolic and diastolic blood pressure, diabetes, hypertension treatment and smoking. The discrimination and calibration performance of the two models were evaluated in an overall hold-out testing dataset. Results: Of the 15,565 participants in our dataset, 2,170 (13.9%) developed ASCVD. The performance of the longitudinal DL model that incorporated 8 years of longitudinal risk factor data improved upon that of the PCE [AUROC: 0.815 (CI: 0.782-0.844) vs 0.792 (CI: 0.760-0.825)] and the net reclassification index was 0.385. The brier score for the DL model was 0.0514 compared with 0.0542 in the PCE. Conclusion: Incorporating longitudinal risk factors in ASCVD risk prediction using DL can improve model discrimination and calibration.

A Bayesian approach for mixed effects state-space models under skewness and heavy tails.

Human immunodeficiency virus (HIV) dynamics have been the focus of epidemiological and biostatistical research during the past decades to understand the progression of acquired immunodeficiency syndrome (AIDS) in the population. Although there are several approaches for modeling HIV dynamics, one of the most popular is based on Gaussian mixed-effects models because of its simplicity from the implementation and interpretation viewpoints. However, in some situations, Gaussian mixed-effects models cannot (a) capture serial correlation existing in longitudinal data, (b) deal with missing observations properly, and (c) accommodate skewness and heavy tails frequently presented in patients' profiles. For those cases, mixed-effects state-space models (MESSM) become a powerful tool for modeling correlated observations, including HIV dynamics, because of their flexibility in modeling the unobserved states and the observations in a simple way. Consequently, our proposal considers an MESSM where the observations' error distribution is a skew-t. This new approach is more flexible and can accommodate data sets exhibiting skewness and heavy tails. Under the Bayesian paradigm, an efficient Markov chain Monte Carlo algorithm is implemented. To evaluate the properties of the proposed models, we carried out some exciting simulation studies, including missing data in the generated data sets. Finally, we illustrate our approach with an application in the AIDS Clinical Trial Group Study 315 (ACTG-315) clinical trial data set.

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.

Bayesian semiparametric joint model of multivariate longitudinal and survival data with dependent censoring.

We consider a novel class of semiparametric joint models for multivariate longitudinal and survival data with dependent censoring. In these models, unknown-fashion cumulative baseline hazard functions are fitted by a novel class of penalized-splines (P-splines) with linear constraints. The dependence between the failure time of interest and censoring time is accommodated by a normal transformation model, where both nonparametric marginal survival function and censoring function are transformed to standard normal random variables with bivariate normal joint distribution. Based on a hybrid algorithm together with the Metropolis-Hastings algorithm within the Gibbs sampler, we propose a feasible Bayesian method to simultaneously estimate unknown parameters of interest, and to fit baseline survival and censoring functions. Intensive simulation studies are conducted to assess the performance of the proposed method. The use of the proposed method is also illustrated in the analysis of a data set from the International Breast Cancer Study Group.

Contrasting socio-economic influences on colorectal cancer incidence and survival in England and Wales.

Colorectal cancer (CRC) is the third most commonly diagnosed cancer in the world and second most common cause of cancer death. The relationship between socio-economic deprivation and CRC incidence is unclear and previous findings have been inconsistent. There is stronger evidence of an association between area-level deprivation and CRC survival; however, few studies have investigated the association between individual-level socio-economic status (SES) and CRC survival. Data from the Office for National Statistics Longitudinal Study (LS) in England and Wales was used. LS members aged 50+ were stratified by individual-level educational attainment, social class, housing tenure and area deprivation quintile, measured at the 2001 Census. Time-to-event analysis examined associations between indicators of SES and CRC incidence and survival (all-cause and CRC death), over a 15-year follow-up period. Among 178116 LS members, incidence of CRC was lower among those with a degree, compared to those with no degree and higher among those employed in manual occupations compared to non-manual occupations. No clear relationship was observed between CRC incidence and the area-based measure of deprivation. Disparities were greater for survival. Among 5016 patients diagnosed with CRC aged 50+, probability of death from all-causes was lower among those with a degree, compared to no degree and higher among those employed in manual occupations, compared to non-manual occupations and among those living in social-rented housing, compared to owner-occupiers. Individual indicators of SES were also associated with probability of death from CRC. Those living in the most deprived areas had a higher probability of death (from all-causes and CRC) compared to those in the least deprived areas. Both individual and area-based indicators of SES were associated with CRC survival, and the relationships were stronger than those observed for CRC incidence. These findings could help inform more effective targeting of public health interventions for CRC.