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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.
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.
Hence discrete-time logistic regression may be considered as an approximation to the Cox model [ 33, 34].
Cue relative weights were calculated from the regression coefficients in the time pressure and no time pressure logistic regression models.
I use discrete time event-history analysis by estimating logistic regression models on a person-year file.
We found similar prediction accuracies for the discrete event simulation and the logistic regression models.
Screen-based activity time was included in the logistic regression models as a continuous variable.
To examine whether longitudinal and time-lag differences were negligible, two logistic regression models were computed.
The odds ratios of variables including SES were estimated by SURVEYLOGISTIC regression analyses, which fit linear logistic regression models for discrete response survey data by using the maximum likelihood methods.
After 6 months and 1 year, both groups were compared using analyses of covariance for continuous data or logistic regression models for discrete data.
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