Predicting Individual Risk of Advanced Adenoma Based on the Interval-Censored Recurrent Adenoma Event and Informative Screening Time.

Joint models for recurrent and terminal events have not been yet developed for clustered data. The goals of our study are to develop a statistical framework for modelling clustered recurrent and terminal events and to perform dynamic predictions of the terminal event in family studies. We propose a joint nested frailty model for colonoscopy screening visits and colorectal cancer onset in Lynch Syndrome families. The screening and disease processes could each depend on individuals' screening history and other measured covariates and be correlated within families; our approach allows for familial correlations to affect both the visit process and the terminal event and the dependence between the two processes is specified through frailty distributions. We provide dynamic predictions of colorectal cancer risk for an individual conditional on his/her own screening history, his/her family history of screening and disease and other important clinical covariates. We apply our model to 18 Lynch Syndrome families from Newfoundland for individualized dynamic predictions of colorectal cancer risks. We demonstrate that the screening visits are non-ignorable for estimating the disease risks, and the joint nested frailty model improves dynamic prediction accuracies compared to existing joint frailty models after accounting for familial and individual screening and cancer histories.

Muitivariate failure time data are common in medical research; com¬monly used statistical models for such correlated failure-time data include frailty and marginal models. Both types of models most often assume pro¬portional hazards (Cox, 1972); but the Cox model may not fit the data well This article presents a class of linear transformation frailty models that in¬cludes, as a special case, the proportional hazards model with frailty. We then propose approximate procedures to derive the best linear unbiased es¬timates and predictors of the regression parameters and frailties. We apply the proposed methods to analyze results of a clinical trial of different dose levels of didansine (ddl) among HIV-infected patients who were intolerant of zidovudine (ZDV). These methods yield estimates of treatment effects and of frailties corresponding to patient groups defined by clinical history prior to entry into the trial.

Panel count data occur in many fields including clinical, demographical and industrial studies and an extensive literature has been established for their regression analysis. However, most of the existing methods apply only to the situations where both covariates and their effects are constant or one of them may be time-dependent. In the first part of this dissertation, we consider a situation where both covariates and their effects may be time-dependent and an estimating equation-based approach is developed for estimating those time-varying effects. In the method, B-spline functions are employed to approximate time-dependent coefficients and the asymptotic properties of the proposed estimators are established. To assess the performance of the proposed approach, an extensive simulation study is conducted and suggests that it works well in practical situations. An application to the China Health and Nutrition Survey (CHNS) study is provided. In practice, there could exist more than one type of event of interest, such as two types of tumor recurrence, leading to multivariate panel count data. The second part of this dissertation considers marginal mean model for multivariate panel count data with time-dependent coefficient and covariate effects, which has limited previous research. Based on the conditional estimating equation method developed for time-dependent covariates, we approximate the coefficients by B-splines, hence allow both coefficients and covariates to be time-dependent. Simulation studies show that the proposed estimation procedures work well for practical situations. The methodology is again applied to the China Health and Nutrition Survey (CHNS) study. When we consider time-varying covariates and coefficients effects, most of the previous study focused on the proportional mean model because the likelihood function under the rate model involves intractable integration. However, the rate model is more realistic and efficient. Hence, in the third part of this dissertation, we propose a semi-parametric MLE method under the rate model for panel count data with time-dependent covariates and time-varying effects. B-spline functions are employed again to approximate time-dependent coefficients and an efficient Expectation-Maximization-type algorithm is developed to overcome the computational difficulty. The resulting estimators are shown to be consistent and asymptotically efficient. Monte Carlo simulation studies demonstrate that the proposed method enjoys desirable finite-sample properties. An application to The Young Women's Project (YWP) is provided.

Human papillomavirus vaccination and subsequent cervical cancer screening in a large integrated healthcare system

Recurrent gap times are analyzed with diverse methods under several assumptions such as a marginal model or a frailty model. Several resampling techniques have been recently suggested to estimate the covariate effect; however, these approaches can be applied with a time-fixed covariate. According to simulation results, these methods result in biased estimates for a time-varying covariate which is often observed in a longitudinal study. In this paper, we extend a resampling method by incorporating new weights and sampling scheme. Simulation studies are performed to compare the suggested method with previous resampling methods. The proposed method is applied to estimate the effect of an educational program on traffic conviction data where a program participation occurs in the middle of the study.

BackgroundPersistent infections with high-risk types of human papillomavirus (HPV) are associated with the development of cervical neoplasia. Compared to cytology HPV testing is more sensitive in detecting high-grade cervical cancer precursors, but with lower specificity. HPV based primary screening for cervical cancer is currently discussed in Germany. Decisions should be based on a systematic evaluation of the long-term effectiveness and cost-effectiveness of HPV based primary screening.Research questionsWhat is the long-term clinical effectiveness (reduction in lifetime risk of cervical cancer and death due to cervical cancer, life years gained) of HPV testing and what is the cost-effectiveness in Euro per life year gained (LYG) of including HPV testing in primary cervical cancer screening in the German health care context? How can the screening program be improved with respect to test combination, age at start and end of screening and screening interval and which recommendations should be made for the German health care context?MethodsA previously published and validated decision-analytic model for the German health care context was extended and adapted to the natural history of HPV infection and cervical cancer in order to evaluate different screening strategies that differ by screening interval, and tests, including cytology alone, HPV testing alone or in combination with cytology, and HPV testing with cytology triage for HPV-positive women. German clinical, epidemiological and economic data were used. In the absence of individual data, screening adherence was modelled independently from screening history. Test accuracy data were retrieved from international meta-analyses. Predicted outcomes included reduction in lifetime-risk for cervical cancer cases and deaths, life expectancy, lifetime costs, and discounted incremental cost-effectiveness ratios (ICER). The perspective of the third party payer and 3% annual discount rate were adopted. Extensive sensitivity analyses were performed in order to evaluate the robustness of results and identify areas of future research.ResultsIn the base case analysis screening resulted in a 53% to 97% risk reduction for cervical cancer with a discounted ICER between 2,600 Euro/LYG (cytology alone every five years) and 155,500 Euro/LYG (Annual cytology age 20 to 29 years, and annual HPV age 30 years and older). Annual cytology, the current recommended screening strategy in Germany, was dominated. In sensitivity analyses variation in the relative increase in the sensitivity of HPV testing as compared to cytology, HPV test costs, screening adherence, HPV incidence, and annual discount rate influenced the ICER results. Variation in the screening start age also influenced the ICER. All cytology strategies were dominated by HPV screening strategies, when relative sensitivity increase by HPV testing compared to cytology was higher (scenario analysis with data for test accuracy from German studies). HPV testing every one, two or three years was more effective than annual cytology. With increased screening adherence a longer screening interval and with low screening adherence a shorter interval would be more cost-effective. With a reduction in HPV incidence of more than 70% triennial HPV screening in women aged 30 years and older (and biennial Pap screening in women aged 20 to 29 years) is cost-effective. The discounted ICER increases with increasing annual discount rate. Increasing screening start age to 25 years had no relevant loss in effectiveness but resulted in lower costs. An optimal strategy may be biennial HPV testing age 30 years and older with biennial cytology at age 25 to 29 years (ICER of 23,400 Euro/LYG).ConclusionsBased on these results, HPV-based cervical cancer screening is more effective than cytology and could be cost-effective if performed at intervals of two years or greater. Increasing the age at screening start to 25 years causes no relevant loss in effectiveness but saves resources. In the German context an optimal screening strategy could be biennial HPV testing at age 30 years and older with biennial cytology at the age of 25 to 29 years. An extension to a three-yearly screening interval requires substantially improved screening adherence or a higher relative increase in the sensitivity of HPV testing as compared to cytology. The implementation of an organised screening program for quality-controlled introduction of HPV-screening and -vaccination with continued systematic outcome evaluation is recommended.

The aim of the study was to identify factors associated with nonattendance in a Danish nationwide screening program for diabetic retinopathy among people with type 2 diabetes. A retrospective observational study linking individual-level register data was performed. First, we compared characteristics of 156,878 people with type 2 diabetes divided into attenders and never-attenders on the basis of their screening history over a 6-year period. Second, we assessed 230,173 screening intervals within the same 6-year period. Mixed-effects models were used to investigate the effect of sociodemographic and health-related factors on the likelihood of having a nonattender interval (i.e., failing to attend screening within the recommended interval). A total of 42,068 (26.8%) people were identified as never-attenders, having no registered eye screening over a 6-year period. Compared with attenders, never-attenders were more frequently divorced/widowed, lived in the Capital Region of Denmark, and had poorer health. A total of 62,381 (27.1%) screening intervals were identified as nonattender intervals. Both sociodemographic and health-related factors were significantly associated with the likelihood of having a nonattender interval. The largest odds ratios for nonattendance were seen for mental illness, nonwestern descent, divorce, comorbidity, and place of residence. Our findings suggest that never- and nonattendance of screening for diabetic retinopathy are more common among people who are divorced/widowed and of poorer health. Additionally, nonattendance is more frequent among people of nonwestern decent. These population subgroups may benefit from targeted interventions aimed at increasing participation in diabetic retinopathy screening.

In patients with aplastic anemia (AA), infection-related complications are the leading cause of mortality. However, limited knowledge about the predictive factors for infection in these patients exists. Thus, this study aimed to evaluate risk factors for infection and develop a risk prediction model for the occurrence of infection in patients with AA. Between January 2004 and December 2020, 206 patients with AA ≥15 years of age were included in this study. Survival analysis using recurrent event methodologies was conducted to identify predictive factors associated with infection, including the Anderson and Gill model; Prentice, Williams, and Peterson (PWP) Total Time model; PWP Gap Time model; marginal model; and frailty models. The best model was determined using backward stepwise regression, and internal validation was performed using the bootstrapping method with 500 re-samplings. With a median follow-up of 2.95 years, the incidence rate of infection among patients with AA was 32.8 events per 100 person-years. The PWP Total Time model revealed that cirrhosis comorbidity, lymphocytes ≥80%, and previous infection increased the risk of infection, while bone marrow cellularity ≥20% offered protection. The bone marrow cellularity, lymphocyte percentage, previous infection, cirrhosis, and hematocrit (BLICH) model was generated to predict the risk of infection. The internal validation showed a good calibration of this model. Cirrhosis, lymphocytes ≥80%, previous infection, and bone marrow cellularity <20% are risk factors for infection in patients with AA. The BLICH model can predict the risk of infection in these patients.

Risk-Stratified Screening for Colorectal Cancer Using Genetic and Environmental Risk Factors: A Cost-Effectiveness Analysis Based on Real-World Data

BackgroundInterval breast cancers (BC) are those diagnosed within 24 months of a negative mammogram. This study estimates the odds of being diagnosed with high-severity BC among screen-detected, interval, and other symptom-detected BC (no screening history within 2 years); and explores factors associated with being diagnosed with interval BC.MethodsTelephone interviews and self-administered questionnaires were conducted among women (n = 3,326) diagnosed with BC in 2010–2013 in Queensland. Respondents were categorised into screen-detected, interval, and other symptom-detected BCs. Data were analysed using logistic regressions with multiple imputation.ResultsCompared with screen-detected BC, interval BC had higher odds of late-stage (OR = 3.50, 2.9–4.3), high-grade (OR = 2.36, 1.9–2.9) and triple-negative cancers (OR = 2.55, 1.9–3.5). Compared with other symptom-detected BC, interval BC had lower odds of late stage (OR = 0.75, 0.6–0.9), but higher odds of triple-negative cancers (OR = 1.68, 1.2–2.3). Among women who had a negative mammogram (n = 2,145), 69.8% were diagnosed at their next mammogram, while 30.2% were diagnosed with an interval cancer. Those with an interval cancer were more likely to have healthy weight (OR = 1.37, 1.1–1.7), received hormone replacement therapy (2–10 years: OR = 1.33, 1.0–1.7; > 10 years: OR = 1.55, 1.1–2.2), conducted monthly breast self-examinations (BSE) (OR = 1.66, 1.2–2.3) and had previous mammogram in a public facility (OR = 1.52, 1.2–2.0).ConclusionThese results highlight the benefits of screening even among those with an interval cancer. Women-conducted BSE were more likely to have interval BC which may reflect their increased ability to notice symptoms between screening intervals.

While most experts agree that cervical screening is effective, there remains controversy over the most appropriate screening interval. Annual screening is common in North America. In England, some argue for 3-yearly screening while others believe 5-yearly screening is adequate, and the frequency varies from one part of the country to another. Screening histories of 1305 women aged 20–69 years, diagnosed with frankly invasive cervical cancer and 2532 age-matched controls were obtained from UK screening programme databases. Data were analysed in terms of time since last negative, and time since last screening smear. Five-yearly screening offers considerable protection (83%) against cancer at ages 55–69 years and even annual screening offers only modest additional protection (87%). Three-yearly screening offers additional protection (84%) over 5-yearly screening (73%) for cancers at ages 40–54 years, but is almost as good as annual screening (88%). In women aged 20–39 years, even annual screening is not as effective (76%) as 3-yearly screening in older women, and 3 years after screening cancer rates return to those in unscreened women. This calls into question the policy of having a uniform screening interval from age 20 to 64 years and stresses the value of screening in middle-aged women.

536 Oncogenic Ki-RAS-miR-21 Signaling Promotes AKT Activation in Pancreatic Cancer Cells Through a Novel AKT-Interacting Protein Bsta

Human papillomavirus and Papanicolaou tests screening interval recommendations in the United States

History of screening by BreastScreen New South Wales of women with invasive breast cancer

There are several different topics that can be addressed with multivariate failure time regression data. Data analysis methods are needed that are suited to each such topic. Specifically, marginal hazard rate models are well suited to the analysis of exposures or treatments in relation to individual failure time outcomes, when failure time dependencies are themselves of little or no interest. On the other hand semiparametric copula models are well suited to analyses where interest focuses primarily on the magnitude of dependencies between failure times. These models overlap with frailty models, that seem best suited to exploring the details of failure time clustering. Recently proposed multivariate marginal hazard methods, on the other hand, are well suited to the exploration of exposures or treatments in relation to single, pairwise, and higher dimensional hazard rates. Here these methods will be briefly described, and the final method will be illustrated using the Women's Health Initiative hormone therapy trial data.