Browsing by Author "Scheckenbach, Frank"
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Item Open Access Automatic identification of variables in epidemiological datasets using logic regression.(BMC medical informatics and decision making, 2017-04) Lorenz, Matthias W; Abdi, Negin Ashtiani; Scheckenbach, Frank; Pflug, Anja; Bülbül, Alpaslan; Catapano, Alberico L; Agewall, Stefan; Ezhov, Marat; Bots, Michiel L; Kiechl, Stefan; Orth, Andreas; PROG-IMT study groupBackground
For an individual participant data (IPD) meta-analysis, multiple datasets must be transformed in a consistent format, e.g. using uniform variable names. When large numbers of datasets have to be processed, this can be a time-consuming and error-prone task. Automated or semi-automated identification of variables can help to reduce the workload and improve the data quality. For semi-automation high sensitivity in the recognition of matching variables is particularly important, because it allows creating software which for a target variable presents a choice of source variables, from which a user can choose the matching one, with only low risk of having missed a correct source variable.Methods
For each variable in a set of target variables, a number of simple rules were manually created. With logic regression, an optimal Boolean combination of these rules was searched for every target variable, using a random subset of a large database of epidemiological and clinical cohort data (construction subset). In a second subset of this database (validation subset), this optimal combination rules were validated.Results
In the construction sample, 41 target variables were allocated on average with a positive predictive value (PPV) of 34%, and a negative predictive value (NPV) of 95%. In the validation sample, PPV was 33%, whereas NPV remained at 94%. In the construction sample, PPV was 50% or less in 63% of all variables, in the validation sample in 71% of all variables.Conclusions
We demonstrated that the application of logic regression in a complex data management task in large epidemiological IPD meta-analyses is feasible. However, the performance of the algorithm is poor, which may require backup strategies.Item Open Access Carotid intima-media thickness progression and risk of vascular events in people with diabetes: results from the PROG-IMT collaboration.(Diabetes care, 2015-10) Lorenz, Matthias W; Price, Jackie F; Robertson, Christine; Bots, Michiel L; Polak, Joseph F; Poppert, Holger; Kavousi, Maryam; Dörr, Marcus; Stensland, Eva; Ducimetiere, Pierre; Ronkainen, Kimmo; Kiechl, Stefan; Sitzer, Matthias; Rundek, Tatjana; Lind, Lars; Liu, Jing; Bergström, Göran; Grigore, Liliana; Bokemark, Lena; Friera, Alfonsa; Yanez, David; Bickel, Horst; Ikram, M Arfan; Völzke, Henry; Johnsen, Stein Harald; Empana, Jean Philippe; Tuomainen, Tomi-Pekka; Willeit, Peter; Steinmetz, Helmuth; Desvarieux, Moise; Xie, Wuxiang; Schmidt, Caroline; Norata, Giuseppe D; Suarez, Carmen; Sander, Dirk; Hofman, Albert; Schminke, Ulf; Mathiesen, Ellisiv; Plichart, Matthieu; Kauhanen, Jussi; Willeit, Johann; Sacco, Ralph L; McLachlan, Stela; Zhao, Dong; Fagerberg, Björn; Catapano, Alberico L; Gabriel, Rafael; Franco, Oscar H; Bülbül, Alpaslan; Scheckenbach, Frank; Pflug, Anja; Gao, Lu; Thompson, Simon GObjective
Carotid intima-media thickness (CIMT) is a marker of subclinical organ damage and predicts cardiovascular disease (CVD) events in the general population. It has also been associated with vascular risk in people with diabetes. However, the association of CIMT change in repeated examinations with subsequent CVD events is uncertain, and its use as a surrogate end point in clinical trials is controversial. We aimed at determining the relation of CIMT change to CVD events in people with diabetes.Research design and methods
In a comprehensive meta-analysis of individual participant data, we collated data from 3,902 adults (age 33-92 years) with type 2 diabetes from 21 population-based cohorts. We calculated the hazard ratio (HR) per standard deviation (SD) difference in mean common carotid artery intima-media thickness (CCA-IMT) or in CCA-IMT progression, both calculated from two examinations on average 3.6 years apart, for each cohort, and combined the estimates with random-effects meta-analysis.Results
Average mean CCA-IMT ranged from 0.72 to 0.97 mm across cohorts in people with diabetes. The HR of CVD events was 1.22 (95% CI 1.12-1.33) per SD difference in mean CCA-IMT, after adjustment for age, sex, and cardiometabolic risk factors. Average mean CCA-IMT progression in people with diabetes ranged between -0.09 and 0.04 mm/year. The HR per SD difference in mean CCA-IMT progression was 0.99 (0.91-1.08).Conclusions
Despite reproducing the association between CIMT level and vascular risk in subjects with diabetes, we did not find an association between CIMT change and vascular risk. These results do not support the use of CIMT progression as a surrogate end point in clinical trials in people with diabetes.