Statistical model building: Background "knowledge" based on inappropriate preselection causes misspecification
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Statistical model building: Background "knowledge" based on inappropriate preselection causes misspecification. / Hafermann, Lorena; Becher, Heiko; Herrmann, Carolin; Klein, Nadja; Heinze, Georg; Rauch, Geraldine.
In: BMC MED RES METHODOL, Vol. 21, No. 1, 29.09.2021, p. 196.Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review
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TY - JOUR
T1 - Statistical model building: Background "knowledge" based on inappropriate preselection causes misspecification
AU - Hafermann, Lorena
AU - Becher, Heiko
AU - Herrmann, Carolin
AU - Klein, Nadja
AU - Heinze, Georg
AU - Rauch, Geraldine
N1 - © 2021. The Author(s).
PY - 2021/9/29
Y1 - 2021/9/29
N2 - BACKGROUND: Statistical model building requires selection of variables for a model depending on the model's aim. In descriptive and explanatory models, a common recommendation often met in the literature is to include all variables in the model which are assumed or known to be associated with the outcome independent of their identification with data driven selection procedures. An open question is, how reliable this assumed "background knowledge" truly is. In fact, "known" predictors might be findings from preceding studies which may also have employed inappropriate model building strategies.METHODS: We conducted a simulation study assessing the influence of treating variables as "known predictors" in model building when in fact this knowledge resulting from preceding studies might be insufficient. Within randomly generated preceding study data sets, model building with variable selection was conducted. A variable was subsequently considered as a "known" predictor if a predefined number of preceding studies identified it as relevant.RESULTS: Even if several preceding studies identified a variable as a "true" predictor, this classification is often false positive. Moreover, variables not identified might still be truly predictive. This especially holds true if the preceding studies employed inappropriate selection methods such as univariable selection.CONCLUSIONS: The source of "background knowledge" should be evaluated with care. Knowledge generated on preceding studies can cause misspecification.
AB - BACKGROUND: Statistical model building requires selection of variables for a model depending on the model's aim. In descriptive and explanatory models, a common recommendation often met in the literature is to include all variables in the model which are assumed or known to be associated with the outcome independent of their identification with data driven selection procedures. An open question is, how reliable this assumed "background knowledge" truly is. In fact, "known" predictors might be findings from preceding studies which may also have employed inappropriate model building strategies.METHODS: We conducted a simulation study assessing the influence of treating variables as "known predictors" in model building when in fact this knowledge resulting from preceding studies might be insufficient. Within randomly generated preceding study data sets, model building with variable selection was conducted. A variable was subsequently considered as a "known" predictor if a predefined number of preceding studies identified it as relevant.RESULTS: Even if several preceding studies identified a variable as a "true" predictor, this classification is often false positive. Moreover, variables not identified might still be truly predictive. This especially holds true if the preceding studies employed inappropriate selection methods such as univariable selection.CONCLUSIONS: The source of "background knowledge" should be evaluated with care. Knowledge generated on preceding studies can cause misspecification.
U2 - 10.1186/s12874-021-01373-z
DO - 10.1186/s12874-021-01373-z
M3 - SCORING: Journal article
C2 - 34587892
VL - 21
SP - 196
JO - BMC MED RES METHODOL
JF - BMC MED RES METHODOL
SN - 1471-2288
IS - 1
ER -