Efficient design and modeling strategies for follow-up studies with time-varying covariates
Epidemiological studies can often be designed in several ways, some of which may be more optimal than others. Possible designs may diﬀer in the required resources or the ability to provide reliable answers to the questions under study. In addition, once the data are collected, the selected modeling approach may aﬀect how eﬃciently the data are utilized. The purpose of this dissertation is to investigate eﬃcient designs and analysis meth ods in follow-up studies with longitudinal measurements. A key question is how to select optimally a subcohort for a new longitudinal covariate measurement if we cannot aﬀord to measure the entire cohort. Another key question we consider is how to determine the reasonable number of longitudinal measurements. Diﬀerent ways to utilize longitudinal covariate measurements in modeling cardiovascular disease (CVD) mortality are also studied. Follow-up data are modeled using parametric or semiparametric proportional haz ards models. Subcohort selections are carried out using optimality criteria initially developed for optimal experimental design. Measures of model discrimination are ap plied to plan the number of longitudinal measurements. The topics are studied using simulations and the East–West data, which are Finnish part of an international follow- up study in the ﬁeld of cardiovascular epidemiology, the Seven Countries Study. This work demonstrates that the cost-eﬃciency of follow-up designs can be improved by careful planning. The proposed method for selecting optimal subcohorts is shown to outperform simple random sampling and it is demonstrated how the number of longi tudinal measurements can be determined using simulated data and data from previous similar studies. The results also indicate that individual-level changes and cumulative averages of classical risk factors are good predictors of CVD mortality. ...
PublisherUniversity of Jyväskylä
aikariippuvat kovariaatit follow-up study time-varying covariates longitudinal measurements optimal design data collection risk prediction cardiovascular disease mortality Seurantatutkimus epidemiologia tutkimusmenetelmät kustannustehokkuus seurantatutkimus pitkittäistutkimus kohorttitutkimus tutkimusaineisto data analyysimenetelmät optimaalisuus simulointi terveysriskit ennusteet sydän- ja verisuonitaudit kuolleisuus
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Reinikainen, Jaakko; Karvanen, Juha (Wiley-Blackwell, 2022)We propose an approach for the planning of longitudinal covariate measurements in follow-up studies where covariates are time-varying. We assume that the entire cohort cannot be selected for longitudinal measurements due ...
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Nauman, Javaid; Sui, Xuemei; Lavie, Carl J.; Wen, Chi Pang; Laukkanen, Jari A.; Blair, Steven N.; Dunn, Patrick; Arena, Ross; Wisløff, Ulrik (Elsevier, 2021)Importance Personal activity intelligence (PAI) is a novel activity metric that can be integrated into self-assessment heart rate devices, and translates heart rate variations during exercise into a weekly score. Previous ...
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Sauna bathing is associated with reduced cardiovascular mortality and improves risk prediction in men and women: a prospective cohort study Laukkanen, Tanjaniina; Kunutsor, Setor K.; Khan, Hassan; Willeit, Peter; Zaccardi, Francesco; Laukkanen, Jari (BioMed Central, 2018)Background: Previous evidence indicates that sauna bathing is related to a reduced risk of fatal cardiovascular disease (CVD) events in men. The aim of this study was to investigate the relationship between sauna habits ...