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Second, a discrete-time logistic regression model was used to assess the importance of individual-level risk factors for the risk of a new primary cancer after skin cancer versus a first primary malignancy.

Table  2 shows estimates from a fully saturated discrete-time logistic regression model, comparing the risk of a first cancer in the general population with that of a second cancer in skin cancer survivors.

Discrete-time logistic regression models were applied to individual-level data to estimate cancer risk among those with and without a prior skin cancer, accounting for residential region, education, income, parenthood, marital status and parental cancer status, using a 20% random sample of the entire Norwegian population as reference.

Hence discrete-time logistic regression may be considered as an approximation to the Cox model [ 33, 34].

A discrete survival model is a logistic regression model of the discrete-time hazards, and is easily implemented in most standard statistical softwares.

Using the current location and time features, the logistic regression model outperforms the Markov chain based model.

Odds ratios (ORs) with 95%% confidence intervals (95 % CIs) were calculated for the discrete variables in the logistic regression model.

Cue relative weights were calculated from the regression coefficients in the time pressure and no time pressure logistic regression models.

Discrete-time multilevel survival models are thus equivalent to fitting a logistic regression model to the expanded dataset [ 14, 33].

Delivery timing determined based on the logistic regression model.

Exp.2 Delivery timing determined based on the logistic regression model.

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