Selecting the primary endpoint in a randomized clinical trial: the ARE method

The decision on the primary endpoint in a randomized clinical trial is of paramount importance and the combination of several endpoints might be a reasonable choice. Gómez and Lagakos (2013) have developed a method that quantifies how much more efficient it could be to use a composite instead of an individual relevant endpoint. From the information provided by the frequencies of observing the component endpoints in the control group and by the relative treatment effects on each individual endpoint, the asymptotic relative efficiency (ARE) can be computed. This article presents the applicability of the ARE method as a practical and objective tool to evaluate which components, among the plausible ones, are more efficient in the construction of the primary endpoint. The method is illustrated with two real cardiovascular clinical trials and is extended to allow for different dependence structures between the times to the individual endpoints. The influence of this choice on the recommendation on whether or not to use the composite endpoint as the primary endpoint for the investigation is studied. We conclude that the recommendation between using the composite or the relevant endpoint only depends on the frequencies of the endpoints and the relative effects of the treatment.Peer ReviewedPostprint (author's final draft

Extension of the asymptotic relative efficiency method to select the primary endpoint in a randomized clinical trial

We extend the ARE method proposed in Gómez and Lagakos (2013) devised to decide which primary endpoint to choose when comparing two treatments in a randomized clinical trial. The ARE method is based on the Asymptotic Relative Efficiency (ARE) between two logrank tests to compare two treatments: one is based on a relevant endpoint E1 while the other is based on a composite endpoint E* = E1 ¿ E2, where E2 is an additional endpoint. The ARE depends, besides some intuitive parameters, on the joint law of the times T1 and T2 from randomization to E1 and E2, respectively. Gómez and Lagakos (2013) characterize this joint law by means of Frank’s copula. In our work, several families of copulas can be chosen for the bivariate survival function of (T1, T2) so that different dependence struc- tures between T1 and T2 are feasible. We motivate the problem and show how to apply the method through a real cardiovascular clinical trial. We explore the influence of the copula chosen into the ARE value by means of a simulation study. We conclude that the recommendation on whether or not to use the composite endpoint as the primary endpoint for the investigation is, almost always, independent of the copula chosen.Preprin

Using Gumbel copula to assess the efficiency of the main endpoint in a randomized clinical trial and comparison with Frank copula

In time-to-event randomized clinical trials, it is common to use composite endpoints as the main endpoint when comparing two treatment groups. A new statistical methodology has been recently developed in order to derives guidelines for deciding whether to expand the use a single or composite endpoint. This methodology, developed by Gómez and Lagakos, is based on the asymptotic relative efficiency (ARE) of a logrank test for comparing two treatment groups with respect to a relevant endpoint versus the composite endpoint. In order to compute the ARE, it is necessary to have the joint law of the time to the relevant and additional endpoints and it is obtained using Frank copula. The main aim of this master thesis is to develop this methodology using Gumbel copula and to compare it with the results obtained using Frank copula. This project shows that the results obtained using Gumbel copula are similar to the ones obtained using Frank copula and, therefore, we conclude that the ARE method is robust for the choice of the copula when restricted to Frank and Gumbel copulas.Gómez i Lagakos calculen l'eficiència relativa (ARE) del logrank per comparar dos tractaments A i B usant el temps T1 fins E1 o T* fins E*= E1 U E2. L'ARE s'obté fixant la llei de (T1, T2) i es modela via la còpula de Frank. L'objectiu és modelar la llei amb altres còpulas i estudiar les repercussions que aquest canvi té en el càlcul del ARE. L'estudiant haurà d'estudiar la metodologia de GL; estudiar les propietats de les còpules; escollir una còpula diferent de la de Frank i programar l'ARE; i discutir quan robust és l'ARE en front d'un canvi de còpula

Postpartum psychiatric disorders and subsequent live birth: a population-based cohort study in Denmark

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Lifespan variation among people with a given disease or condition

In addition to fundamental mortality metrics such as mortality rates and mortality rate ratios, life expectancy is also commonly used to investigate excess mortality among a group of individuals diagnosed with specific diseases or conditions. However, as an average measure, life expectancy ignores the heterogeneity in lifespan. Interestingly, the variation in lifespan-a measure commonly used in the field of demography-has not been estimated for people with a specific condition. Based on recent advances in methodology in research within epidemiology and demography, we discuss two metrics, namely, the average life disparity and average lifetable entropy after diagnosis, which estimate the variation in lifespan for time-varying conditions in both absolute and relative aspects. These metrics are further decomposed into early and late components, separated by their threshold ages. We use mortality data for women with mental disorders from Danish registers to design a population-based study and measure such metrics. Compared with women from the general population, women with a mental disorder had a shorter average remaining life expectancy after diagnosis (37.6 years vs. 44.9 years). In addition, women with mental disorders also experienced a larger average lifespan variation, illustrated by larger average life disparity (9.5 years vs 9.1 years) and larger average lifetable entropy (0.33 vs 0.27). More specifically, we found that women with a mental disorder had a larger early average life disparity but a smaller late average life disparity. Unlike the average life disparity, both early and late average lifetable entropy were higher for women with mental disorders compared to the general population. In conclusion, the metric proposed in our study complements the current research focusing merely on life expectancy and further provides a new perspective into the assessment of people's health associated with time-varying conditions

Average lifespan variation among people with mental disorders in Denmark: a nationwide, register-based cohort study

Aims: Mortality associated with mental disorders has been estimated using metrics such as mortality rate ratios and life expectancy. However, the variation around the average life expectancy has never been quantified. The main aim of this study was to measure life disparity for people with mental disorders as a measure of inequality at the time of death. Methods: Using data from Danish registries, average life disparity was introduced and calculated to measure the lifespan variation associated with major types of mental disorders. Average life expectancy is also reported for completeness. Results: Compared with the general population, people with mental disorders not only had shorter average life expectancy, but experienced larger average life disparity. For those diagnosed with a mental disorder, average life expectancy increased between 1995 and 2021; however, average life disparity declined in women only, and did not change for men. In addition, the differences in both metrics between those with mental disorders and the general population were largest for substance use disorders and schizophrenia spectrum disorders. For these disorders, the differences even increased during the study period. Conclusions: Mortality rates for individuals with mental disorders have been declining in recent decades in Denmark; however, the increase in the average life disparity emphasizes the increasing heterogeneity and inequality in lifespans within this group, which requires measures to promote a longer and more equal life for those with mental disorders

Representativeness of survey participants in relation to mental disorders: a linkage between national registers and a population-representative survey

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Exploring the association between metabolic syndrome, its components and subsequent cancer incidence : A cohort study in Catalonia

Altres ajuts: World Cancer Research Fund International (2017/1630)Background: Metabolic syndrome (MS) has emerged as a significant global health concern. The relationship between MS and the risk of cancer doesn't seem clear, whether examining by components or in combination. The objective of this study is to examine the relationship between MS, its components, and the overall risk of cancer, including the risk of 13 specific cancer types. Methods: We included 3,918,781 individuals aged 40 years or older sourced from the SIDIAP database between 2008 and 2017. Cox models were employed with MS components and their combinations. A subsample was created using a matched cohort (by age and sex). Incidence curves were computed to determine the time elapsed between the date of having 1-5 MS components and cancer incidence, compared to matched participants with no MS components, which showed that individuals who had one MS component experienced a greater incidence of cancer over 5 and 10 years than individuals with no MS, and the incidence rose with an increase in the number of MS components. Results: Individuals exposed to MS components were diagnosed with cancer earlier than those who were not exposed to them. In the Cox model, HDL (HR 1.46, 95% CI: 1.41-1.52) and Glycemia (HR 1.40, 95% CI: 1.37-1.44) were the individual combinations with the highest risk of overall cancer. In combinations with two components, the highest HR was HDL+Glycemia (HR 1.52, 95% CI: 1.45-1.59) and Glycemia+HBP (HR 1.48, 95% CI: 1.45-1.50). In combinations with three components, the highest HR was HDL+Glycemia+HBP (HR 1.58, 95% CI: 1.55-1.62). Conclusion: In summary, having one or more MS components raises the risk of developing at least 11 cancer types and these risk differ according to type of component included. Some sex differences are also observed. Our findings suggest that implementing prevention measures aimed at specific MS components may lower the risk of various cancer types

Comorbidity between types of eating disorder and general medical conditions.

BACKGROUND: Comorbidity with general medical conditions is common in individuals with eating disorders. Many previous studies do not evaluate types of eating disorder. AIMS: To provide relative and absolute risks of bidirectional associations between (a) anorexia nervosa, bulimia nervosa and eating disorders not otherwise specified and (b) 12 general medical conditions. METHOD: We included all people born in Denmark between 1977 and 2010. We collected information on eating disorders and considered the risk of subsequent medical conditions, using Cox proportional hazards regression. Absolute risks were calculated using competing risks survival analyses. We also considered risks for prior medical conditions and subsequent eating disorders. RESULTS: An increased risk was seen for almost all disorder pairs (69 of 70). Hazard ratios for those with a prior eating disorder receiving a subsequent diagnosis of a medical condition ranged from 0.94 (95% CI 0.57-1.55) to 2.05 (95% CI 1.86-2.27). For those with a prior medical condition, hazard ratios for later eating disorders ranged from 1.35 (95% CI 1.26-1.45) to 1.98 (95% CI 1.71-2.28). Absolute risks for most later disorders were increased for persons with prior disorders, compared with reference groups. CONCLUSIONS: This is the largest and most detailed examination of eating disorder-medical condition comorbidity. The findings indicate that medical condition comorbidity is increased among those with eating disorders and vice versa. Although there was some variation in comorbidity observed across eating disorder types, magnitudes of relative risks did not differ greatly

Interaction between mental disorders and social disconnectedness on mortality:A population-based cohort study

Background: Despite the recognised importance of mental disorders and social disconnectedness for mortality, few studies have examined their co-occurrence. Aims: To examine the interaction between mental disorders and three distinct aspects of social disconnectedness on mortality, while taking into account sex, age and characteristics of the mental disorder. Method: This cohort study included participants from the Danish National Health Survey in 2013 and 2017 who were followed until 2021. Survey data on social disconnectedness (loneliness, social isolation and low social support) were linked with register data on hospital-diagnosed mental disorders and mortality. Poisson regression was applied to estimate independent and joint associations with mortality, interaction contrasts and attributable proportions. Results: A total of 162 497 individuals were followed for 886 614 person-years, and 9047 individuals (5.6%) died during follow-up. Among men, interaction between mental disorders and loneliness, social isolation and low social support, respectively, accounted for 47% (95% CI: 21-74%), 24% (95% CI: -15 to 63%) and 61% (95% CI: 35-86%) of the excess mortality after adjustment for demographics, country of birth, somatic morbidity, educational level, income and wealth. In contrast, among women, no excess mortality could be attributed to interaction. No clear trends were identified according to age or characteristics of the mental disorder. Conclusions: Mortality among men, but not women, with a co-occurring mental disorder and social disconnectedness was substantially elevated compared with what was expected. Awareness of elevated mortality rates among socially disconnected men with mental disorders could be of importance to qualify and guide prevention efforts in psychiatric services.</p