Background/Objectives: Worldwide, men experience a higher incidence of pancreatic cancer (PC) than women. Methods: To increase understanding of the underlying reasons for this sex-related difference, we analysed general and sex-related risk factors for PC in the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort (women/men No. = 293,682/136,728; 717/577 PC-cases). Results: Cox proportional hazards models showed a 1.31-fold higher risk of developing PC for men compared to women (HR, 95% CI 1.15–1.49) after adjustment for age, smoking history, BMI, diabetes, and alcohol consumption. Associations of PC with established risk factors did not differ between men and women, with the exception of a greater risk of PC among women with greater attained body height, meat consumption and cigarettes smoked (1.12 (1.05–1.19) per 5 cm, 1.18 (1.02–1.36) per 100 g/d, 1.42 (1.27–1.59) per 10/d; respectively). Among child-bearing women, long cumulative duration of breastfeeding was inversely associated with risk of PC (HR 0.74, 95% CI 0.61–0.89) for >5.7 months of breastfeeding (median) relative to ≤5.7 months and among HRT users, cumulative duration of HRT use was inversely associated with PC risk (HR 0.71, 95% CI 0.53–0.95, >2.4 versus ≤2.4 years). Further reproductive and hormonal factors, such as age at menarche, number of full-term pregnancies, age at menopause, or use of oral contraceptives, were not significantly associated with PC risk. Conclusions: Pooled analyses of large cohort studies are needed to confirm these results, and detailed data on the type and intensity of HRT are required to better evaluate its effect.

Over the last 20 or more years a lot of clinical applications and methodological development in the area of joint models of longitudinal and time-to-event outcomes have come up. In these studies, patients are followed until an event, such as death, occurs. In most of the work, using subject-specific random-effects as frailty, the dependency of these two processes has been established. In this article, we propose a new joint model that consists of a linear mixed-effects model for longitudinal data and an accelerated failure time model for the time-to-event data. These two sub-models are linked via a latent random process. This model will capture the dependency of the time-to-event on the longitudinal measurements more directly. Using standard priors, a Bayesian method has been developed for estimation. All computations are implemented using OpenBUGS. Our proposed method is evaluated by a simulation study, which compares the conditional model with a joint model with local independence by way of calibration. Data on Duchenne muscular dystrophy (DMD) syndrome and a set of data in AIDS patients have been analysed.

In medical studies, a disease’s progress is often monitored through indicators that signify the improved or worsened condition of the patient. These are known as longitudinal biomarkers, which are observed along with an event of importance. Together, they form the framework of joint modeling, which has a longitudinal process and an inherently associated time-to-event process. In clinical studies, the change in biomarkers is often monitored in the form of a change in the patient’s plasma level after a drug is administered to the patient. Again, in such studies, patients also withdraw from the trials prematurely or at a later phase, thus giving rise to dropouts. In most cases, this dropout is not random (Missing Not at Random). A joint model has been considered to incorporate this informative dropout in longitudinal response. To demonstrate this approach, a one-compartmental pharmacokinetic (PK) nonlinear mixed-effects model consisting of time-dependent parameters has been used in this work. The dropout mechanism has been introduced using a proportional hazard model. A Bayesian model framework is adopted to study the model’s performance through detailed simulation. A PK study on the drug Divalproex subject to an informative dropout model has been discussed. Journal of Statistical Research 2024, Vol. 58, No. 1, pp. 97-110.

Introduction: The evidence synthesis on literature characterizing changes in body fatness and adiposity distribution with sex steroid measurements in different matrices (blood, tissue, urine and other excreted matrices) is lacking. Methods: PubMed and Web of Science were used to identify eligible studies published until June 2026. Random-effects meta-analyses were conducted, and the strength of evidence was determined based on the Continuous Update Project criteria. Results: Of 16,875 screened references, 140 studies deemed relevant: 96 on circulating blood biomarkers from 39 weight loss intervention (surgical and lifestyle) and 60 observational studies reporting direct measure of adiposity distribution (e.g., DEXA, MRI), 20 studies on biomarkers measured in urine or other excreted matrices (i.e., saliva, urine, breast ductal lavage), and 25 tissue-based studies (e.g., adipose, breast, endometrial, skin). Weight loss was associated with a reduction of total and free testosterone, and an increase in sex hormone binding globulin (SHBG). While no significant associations were found between weight loss and premenopausal estradiol, dehydroepiandrosterone sulfate, and androstenedione. Decrease in total and free estradiol and estrone were observed in postmenopausal women. Findings in observational studies generally supported those results, as well as tissue-based studies on estrogen-related markers despite the heterogeneous tissue types. Studies on biomarker measured in excreted matrices were largely inconclusive. Conclusion: Strong evidence of a probable association was found between body fatness and SHBG, androgens, and postmenopausal estrogens. Limited evidence remained for other sex steroids.

Joint modelling of longitudinal observations and event times continues to remain a topic of considerable interest in biomedical research. For example, in HIV studies, the longitudinal bio-marker such as CD4 cell count in a patient's blood over follow up months is jointly modelled with the time to disease progression, death or dropout via a random intercept term mostly assumed to be Gaussian. However, longitudinal observations in these kinds of studies often exhibit non-Gaussian behavior (due to high degree of skewness), and parameter estimation is often compromised under violations of the Gaussian assumptions. In linear mixed-effects model assumptions, the distributional assumption for the subject-specific random-effects is taken as Gaussian which may not be true in many situations. Further, this assumption makes the model extremely sensitive to outlying observations. We address these issues in this work by devising a joint model which uses a robust distribution in a parametric setup along with a conditional distributional assumption that ensures dependency of two processes in case the subject-specific random effects is given.