6.
Wynants L, Van Calster B, Collins G, Riley R, Heinze G, Schuit E
. Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal. BMJ. 2020; 369:m1328.
PMC: 7222643.
DOI: 10.1136/bmj.m1328.
View
7.
Hemingway H, Croft P, Perel P, Hayden J, Abrams K, Timmis A
. Prognosis research strategy (PROGRESS) 1: a framework for researching clinical outcomes. BMJ. 2013; 346:e5595.
PMC: 3565687.
DOI: 10.1136/bmj.e5595.
View
8.
Van Calster B, McLernon D, van Smeden M, Wynants L, Steyerberg E
. Calibration: the Achilles heel of predictive analytics. BMC Med. 2019; 17(1):230.
PMC: 6912996.
DOI: 10.1186/s12916-019-1466-7.
View
9.
Van Calster B, van Smeden M, De Cock B, Steyerberg E
. Regression shrinkage methods for clinical prediction models do not guarantee improved performance: Simulation study. Stat Methods Med Res. 2020; 29(11):3166-3178.
DOI: 10.1177/0962280220921415.
View
10.
van der Ploeg T, Austin P, Steyerberg E
. Modern modelling techniques are data hungry: a simulation study for predicting dichotomous endpoints. BMC Med Res Methodol. 2014; 14:137.
PMC: 4289553.
DOI: 10.1186/1471-2288-14-137.
View
11.
Heinze G, Dunkler D
. Five myths about variable selection. Transpl Int. 2016; 30(1):6-10.
DOI: 10.1111/tri.12895.
View
12.
Hafermann L, Becher H, Herrmann C, Klein N, Heinze G, Rauch G
. Statistical model building: Background "knowledge" based on inappropriate preselection causes misspecification. BMC Med Res Methodol. 2021; 21(1):196.
PMC: 8480029.
DOI: 10.1186/s12874-021-01373-z.
View
13.
Bergersen L, Glad I, Lyng H
. Weighted lasso with data integration. Stat Appl Genet Mol Biol. 2012; 10(1).
DOI: 10.2202/1544-6115.1703.
View
14.
Collins G, Reitsma J, Altman D, Moons K
. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ. 2015; 350:g7594.
DOI: 10.1136/bmj.g7594.
View
15.
Heinze G, Hronsky M, Reichardt B, Baumgartel C, Mullner M, Bucsics A
. Potential savings in prescription drug costs for hypertension, hyperlipidemia, and diabetes mellitus by equivalent drug substitution in Austria: a nationwide cohort study. Appl Health Econ Health Policy. 2014; 13(2):193-205.
DOI: 10.1007/s40258-014-0143-4.
View
16.
Heinze G, Jandeck L, Hronsky M, Reichardt B, Baumgartel C, Bucsics A
. Prevalence and determinants of unintended double medication of antihypertensive, lipid-lowering, and hypoglycemic drugs in Austria: a nationwide cohort study. Pharmacoepidemiol Drug Saf. 2015; 25(1):90-9.
DOI: 10.1002/pds.3898.
View
17.
Strobl C, Boulesteix A, Zeileis A, Hothorn T
. Bias in random forest variable importance measures: illustrations, sources and a solution. BMC Bioinformatics. 2007; 8:25.
PMC: 1796903.
DOI: 10.1186/1471-2105-8-25.
View
18.
Tian Y, Reichardt B, Dunkler D, Hronsky M, Winkelmayer W, Bucsics A
. Comparative effectiveness of branded vs. generic versions of antihypertensive, lipid-lowering and hypoglycemic substances: a population-wide cohort study. Sci Rep. 2020; 10(1):5964.
PMC: 7136234.
DOI: 10.1038/s41598-020-62318-y.
View
19.
Morris T, White I, Crowther M
. Using simulation studies to evaluate statistical methods. Stat Med. 2019; 38(11):2074-2102.
PMC: 6492164.
DOI: 10.1002/sim.8086.
View
20.
Haller M, Aschauer C, Wallisch C, Leffondre K, van Smeden M, Oberbauer R
. Prediction models for living organ transplantation are poorly developed, reported, and validated: a systematic review. J Clin Epidemiol. 2022; 145:126-135.
DOI: 10.1016/j.jclinepi.2022.01.025.
View