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Using a backward reasoning technique we derive the minimal context (what must have happened) that explains the observed events.
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Model simplification was performed using a backward stepwise algorithm.
These covariates were selected using a backward stepwise procedure.
The model was optimized using a backward stepwise regression.
Prognostic factors were identified using a backward selection procedure.
Variables were selected using a backward stepwise process.
Non-significant predictors were eliminated using a backward elimination method.
A multiple logistic regression model using a backward procedure was used.
Generalized estimating equations were used to develop the risk models using a backward elimination process.
Confounding was evaluated starting with a full multivariate model and using a backward elimination approach (36).
Final models were chosen using a backward selection process with an exit criterion of p > 0.001.
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