Risk of selection bias due to non-participation in a cohort study on pubertal timing.

BackgroundNon-participation in aetiologic studies of pubertal timing is frequent. However, little effort has been given to explore the risk and potential impact of selection bias in studies of pubertal timing.ObjectiveWe aimed to explore the risk of selection bias due to non-participation in a newly established puberty cohort.MethodsWe evaluated whether three maternal exposures chosen a priori (pre-pregnancy obesity, smoking, and alcohol drinking during pregnancy) were associated with participation, whether pubertal timing was associated with participation, and whether selection bias influenced the associations between these exposures and pubertal timing. In total, 22 439 children from the Danish National Birth Cohort born 2000-2003 were invited to the Puberty Cohort and 15 819 (70%) participated. Exposures were self-reported during pregnancy. Pubertal timing was measured using a previously validated marker, "the height difference in standard deviations" (HD:SDS), which is the difference between pubertal height and adult height, both in standard deviations. For this study, pubertal height at around 13 years in sons and around 11 years in daughters was obtained from an external database, and adult height was predicted based on parental height reported by mothers.ResultsParticipation was associated with most exposures but not with pubertal timing, measured by HD:SDS. The associations between exposures and HD:SDS were comparable for participants only and all invited for participation.ConclusionIn conclusion, the risk of selection bias in aetiologic studies on pubertal timing in the Puberty Cohort appears minimal

Shared Frailty Models for Recurrent Events and a Terminal Event

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/89578/1/j.0006-341X.2004.00225.x.pd

Multivariate frailty models for multi-type recurrent event data and its application to cancer prevention trial

Multi-type recurrent event data arise in many situations when two or more different event types may occur repeatedly over an observation period. For example, in a randomized controlled clinical trial to study the efficacy of nutritional supplements for skin cancer prevention, there can be two types of skin cancer events occur repeatedly over time. The research objectives of analyzing such data often include characterizing the event rate of different event types, estimating the treatment effects on each event process, and understanding the correlation structure among different event types. In this paper, we propose the use of a proportional intensity model with multivariate random effects to model such data. The proposed model can take into account the dependence among different event types within a subject as well as the treatment effects. Maximum likelihood estimates of the regression coefficients, variance-covariance components, and the nonparametric baseline intensity function are obtained via a Monte Carlo Expectation-Maximization (MCEM) algorithm. The expectation step of the algorithm involves the calculation of the conditional expectations of the random effects by using the Metropolis-Hastings sampling. Our proposed method can easily handle recurrent event data that have more than two types of events. Simulation studies were used to validate the performance of the proposed method, followed by an application to the skin cancer prevention data.</p

Estimating a population cumulative incidence under calendar time trends

Abstract Background The risk of a disease or psychiatric disorder is frequently measured by the age-specific cumulative incidence. Cumulative incidence estimates are often derived in cohort studies with individuals recruited over calendar time and with the end of follow-up governed by a specific date. It is common practice to apply the Kaplan\u2013Meier or Aalen\u2013Johansen estimator to the total sample and report either the estimated cumulative incidence curve or just a single point on the curve as a description of the disease risk. Methods We argue that, whenever the disease or disorder of interest is influenced by calendar time trends, the total sample Kaplan\u2013Meier and Aalen\u2013Johansen estimators do not provide useful estimates of the general risk in the target population. We present some alternatives to this type of analysis. Results We show how a proportional hazards model may be used to extrapolate disease risk estimates if proportionality is a reasonable assumption. If not reasonable, we instead advocate that a more useful description of the disease risk lies in the age-specific cumulative incidence curves across strata given by time of entry or perhaps just the end of follow-up estimates across all strata. Finally, we argue that a weighted average of these end of follow-up estimates may be a useful summary measure of the disease risk within the study period. Conclusions Time trends in a disease risk will render total sample estimators less useful in observational studies with staggered entry and administrative censoring. An analysis based on proportional hazards or a stratified analysis may be better alternatives

Run Clever - No difference in risk of injury when comparing progression in running volume and running intensity in recreational runners:A randomised trial

Background/aimThe Run Clever trial investigated if there was a difference in injury occurrence across two running schedules, focusing on progression in volume of running intensity (Sch-I) or in total running volume (Sch-V). It was hypothesised that 15% more runners with a focus on progression in volume of running intensity would sustain an injury compared with runners with a focus on progression in total running volume.MethodsHealthy recreational runners were included and randomly allocated to Sch-I or Sch-V. In the first eight weeks of the 24-week follow-up, all participants (n=839) followed the same running schedule (preconditioning). Participants (n=447) not censored during the first eight weeks entered the 16-week training period with a focus on either progression in intensity (Sch-I) or volume (Sch-V). A global positioning system collected all data on running. During running, all participants received real-time, individualised feedback on running intensity and running volume. The primary outcome was running-related injury (RRI).ResultsAfter preconditioning a total of 80 runners sustained an RRI (Sch-I n=36/Sch-V n=44). The cumulative incidence proportion (CIP) in Sch-V (reference group) were CIP2 weeks4.6%; CIP4 weeks8.2%; CIP8 weeks13.2%; CIP16 weeks28.0%. The risk differences (RD) and 95% CI between the two schedules were RD2 weeks=2.9%(−5.7% to 11.6%); RD4 weeks=1.8%(−9.1% to 12.8%); RD8 weeks=−4.7%(−17.5% to 8.1%); RD16 weeks=−14.0% (−36.9% to 8.9%).ConclusionA similar proportion of runners sustained injuries in the two running schedules.</jats:sec

Familial risk of sinus node dysfunction indicating pacemaker implantation:a nationwide cohort study

AIMS: We aimed to investigate the risk of sinus node dysfunction (SND) indicating cardiac pacing and mortality in first-degree relatives to patients with a pacemaker implanted on this indication and assess the effect of onset-age on disease risk. METHODS AND RESULTS: In this nationwide register-based study, we used the Danish Civil Registration Registry to establish family structures and merged data with the Danish National Patient Registry and the Danish Pacemaker and ICD Registry containing information on all pacemakers implanted due to SND in Denmark. We followed 6 027 090 individuals born after 1954 in the period between 1982 and 2022 (180 775 041 person-years) among whom 2.477 pacemakers were implanted due to SND. The adjusted rate ratio (RR) of pacemaker-treated SND was 2.9 (2.4-3.6) for individuals having any father, mother, or sibling with a pacemaker implanted on this indication compared with the general population (derived cumulative incidence at the age of 68 years: 0.79 and 0.27%, respectively). This risk was inversely proportional to implantation age in the index person [≤60 years: RR = 5.5 (3.4-9.0)]. Overall, mortality was similar between individuals having a father, mother, or sibling with SND and the general population, but higher for relatives to index persons with an early onset [≤60 years: RR = 1.22 (1.05-1.41)]. CONCLUSION: First-degree relatives to SND patients are at increased risk of SND with risk being inversely associated with pacemaker implantation age in the index person. Mortality in first-degree relatives was comparable with the general population, although subgroup findings suggest an increased mortality among individuals with a family history of early-onset SND.</p