In this report, we derive the test on partial parallelism in profile analysis. Parallelism hypothesis, along with the other two hypotheses which are the level and the flatness , has been studied by others. We focus mainly on a specific case of parallelism hypothesis, where the profiles are parallel up to a point and then divert from each other. The test statistics for two different scenarios will be presented.

Background: Advancements in cancer treatments for children, adolescents, and young adults (CAYAs) over recent decades have significantly improved their prognosis. However, these advancements have also led to an increased risk of developing various cardiovascular diseases, such as arrhythmias. Atrial fibrillation (AF) the most common arrhythmia in the general population, becomes more prevalent with age (1). The risk of developing AF is elevated in patients with cancer compared to the general population (2). Nevertheless, symptomatic arrhythmias remain uncommon among long-term survivors of childhood cancer (3).

Purpose: The main objective of this study was to explore the rates and risk of arrhythmias in CAYAs with cancer compared to controls.

Method: The Rebuc study is a retrospective matched cohort study that encompasses all young cancer patients (<25 years) in Sweden from 1958 and 2021, with a total of 65,173 individuals matched with 312,935 controls. All individuals with an arrhythmia diagnosis were identified and compared for the following arrhythmias: AF and atrial flutter (ICD-10: I48), supraventricular tachycardia (I47.1), ventricular tachycardia (I47.2), ventricular fibrillation (I49.0), sick sinus syndrome (I49.5), second- and third-degree atrioventricular block (AV-block) (I44.1 and I44.2).

Results: A total of 12,415 individuals with arrhythmias were identified, 2,352 CAYAs (3.6%) and 10,063 controls (3.2%) (Odds Ratio (OR) 1.13 95% CI 1.08-1.18, p<0.0001).

CAYAs had a higher risk of ventricular fibrillation, (OR 1.66 95% CI 1.22-2.27, p= 0.001) and the risk of third-degree AV-block was also 66% higher (OR, 95% CI 1.32-2.07, p<0.0001) compared to controls. CAYAs had no elevated risk for AF but were 1 year younger when diagnosed (60 vs. 61 years, p= 0.03). No significant difference was found in risk for supraventricular and ventricular tachycardias, second-degree AV-block or sick sinus syndrome.

Conclusion: CAYAs have a higher risk for life-threatening arrhythmias such as ventricular fibrillation and third-degree AV-block.

The risk of AF and other supraventricular arrhythmias was similar, but CAYAs experienced AF at a younger age than controls.

Background: The advances in the management of cancer in children, adolescents, and young adults (CAYAs) over recent decades have greatly improved prognosis. However, the risk of long-term complications persists. (1,2) It is known that cancer treatment such as chemotherapy and radiotherapy as well as conventional cardiovascular risk factors increase the risk of ischemic heart disease (IHD). (3)

Purpose: The aim was to study IHD among CAYAs and investigate mortality and revascularization compared to controls.

Method: This study utilizes the Rebuc study, a retrospective matched cohort study that includes all young (<25 years) cancer patients in Sweden between 1958 and 2021, comprising a total of 65,173 CAYAs and 312,935 controls. From the Rebuc study population, 1,486 CAYAs and 6,258 controls with IHD (ICD-10: I20-I25) were identified and compared regarding all-cause mortality, cardiovascular mortality, and revascularization (percutaneous coronary intervention (PCI) and coronary artery bypass grafting (CABG)).

Results: The median age at index was 22 years (IQR 19–23), and at the end of the study 66 years (IQR 59–72). 801 CAYAs (54%) and 3,265 controls (52%) had myocardial infarction (ICD-10: I21)

The risk for all-cause mortality after diagnosis of IHD was 1.26-fold higher for CAYAs (Hazard ratio (HR) 95% CI 1.14-1.40, p<0.0001) and 1.20-fold higher for cardiovascular mortality (HR 95% CI 1.06-1.37, p<0.001) compared to controls. Revascularisation of any kind was registered among 47% of CAYAs and 48% of controls (p = 0.8). PCI was registered among 39% of CAYAs and 42% of controls (p = 0.045), while CABG was registered among 14% of CAYAs and 11% of controls (p = 0.001).

Conclusion: CAYAs with IHD had a higher risk of all-cause and cardiovascular mortality compared to controls. There was no difference regarding revascularization between the groups, however, CABG was more common among CAYAs while PCI was more common among controls.

Background: In recent decades, numerous cohorts have been established to investigate cardiovascular disease (CVD) and mortality among young cancer patients.(1,2) However, these cohorts rarely include young females with cervical cancer, a disease often treated surgically and associated with human papillomavirus (HPV) infection. HPV infections, which can be prevented through vaccination, has also been associated to an elevated risk of CVD. (3)

Purpose: The aim was to study CVD and mortality in young cervical cancer patients.

Method: Using the Rebuc study, which includes all young (<25 years) cancer patients in Sweden from 1958 to 2021. 30,507 individuals aged 15-24 years with cervical cancer (ICD-10 C53) were identified and compared to 152,276 matched controls with regard to CVD (ICD-10 I00–I99 and G45), all-cause mortality, and CV mortality.

Results: The median age at cervical cancer diagnosis was 23 years (IQR 22-24) and the median age at the study’s end was 41 years in both groups (IQR 29-59 vs. 29-60).

CVDs were identified in 5,460 cervical cancer patients (17.9%) and 24,113 controls (15.8%), (Odds ratio (OR) 1.16 95% CI 1.12-1.20 p<0.0001). The risk for ischemic heart disease was 42% higher in cervical cancer patients (OR 95%CI 1.31-1.54, p<0.0001), 54% higher for myocardial infarction (OR 95%CI 1.38-1.72 p<0.0001), 35% higher for heart failure (OR 95% CI1.38-1.72 p<0.0001), and 38% higher risk for cerebrovascular diseases (OR 95%CI 1.26-1.50 p<0.0001) compared to controls.

The risk for all-cause mortality was 64% higher, and the risk for cardiovascular death was 49% higher among females with cervical cancer compared to controls. (Hazard Ratio 95%CI 1.52-1.76; and 1.29-1.70 p <0.0001). Myocardial infarction, sudden cardiac death, and heart failure was the most common cardiovascular causes of death in these young females.

Conclusion: Young females with cervical cancer are at a higher risk of severe CVD and mortality compared to controls. These findings highlight the importance of cardiovascular risk evaluation in cancer patients and contribute to the growing evidence supporting a broad implementation of HPV vaccination programs to reduce the burden of HPV-related diseases.

Quadratic discriminant analysis is a well-established supervised classification method, which extends the linear the linear discriminant analysis by relaxing the assumption of equal variances across classes. In this study, quadratic discriminant analysis is used to develop a quadratic classification rule based on repeated measurements. We employ a bilinear regression model to assign new observations to predefined populations and approximate the misclassification probability. Through weighted estimators, we estimate unknown mean parameters and derive moments of the quadratic classifier. We then conduct numerical simulations to compare misclassification probabilities using true and estimated mean parameters, as well as probabilities computed through simulation. Our findings suggest that as the distance between groups widens, the misclassification probability curve decreases, indicating that classifying observations is easier in widely separated groups compared to closely clustered ones.

In this paper least squares and maximum likelihood estimates under univariate and multivariate linear models with a priori information related to maximum effects in the models are determined. Both loss functions (the least squares and negative log-likelihood) and the constraints are convex, so the convex optimization theory can be utilized to obtain estimates, which in this paper are called Safety belt estimates. In particular, the complementary slackness condition, common in convex optimization, implies two alternative types of solutions, strongly dependent on the data and the restriction.

It is experimentally shown that, despite of the similarity to the ridge regression estimation under the univariate linear model, the Safety belt estimates behave usually better than estimates obtained via ridge regression. Moreover, concerning the multivariate model, the proposed technique represents a completely novel approach.

The likelihood approach used in this paper leads to quadratic discriminant functions. Classification into one of two known multivariate normal populations with a known and unknown covariance matrix are separately considered, where the two cases depend on the sample size and an unknown squared Mahalanobis distance. Their exact distributions are complicated to obtain. Therefore, moments for the likelihood based discriminant functions are established to express the basic characteristics of respective distribution.

The exact distribution of a classification function is often complicated to allow for easy numerical calculations of misclassification errors. The use of expansions is one way of dealing with this difficulty. In this paper, approximate probabilities of misclassification of the maximum likelihood-based discriminant function are established via an Edgeworth-type expansion based on the standard normal distribution for discriminating between two multivariate normal populations.

In this paper, probabilities of misclassification of a two-step likelihood-based discriminant rule are established for the classification of growth curves. The defined two-step classifier considers the fact that the growth curves might not belong to any of the two predetermined populations. The distribution for the classifier is approximated via an Edgeworth-type expansion.

The possibility that a new observation can be allocated to an unknown population is considered. von Rosen and Singull (2022) derived a classi cation rule taking into account this perspective. The classi cation rule consists of two criteria. In this paper, the mean and variance of these criteria needed to discriminate between two growth curves are established.