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In addition, we modeled the odds of developing related GU conditions (cryptorchidism/hypospadias and hernia/hydrocele).
Table 3 shows the results when we modeled the odds of a male birth by estimated PBB at the conception date of the offspring.
We modeled the odds of a male birth with generalized estimating equations accounting for the non-independence of siblings born to the same parents.
We modeled the odds of developing any GU condition among all sons and compared these estimates with models excluding the 71 sons who were born after the exposure period but before their mothers' PBB levels were measured.
We modeled the odds of sleep disturbance for the following categories: sleep efficiency <70% compared with ≥ 70%; sleep latency ≥ 60 minutes compared with <60 minutes and; WASO ≥ 90 minutes compared with <90 minutes; number of long-wake episodes ≥ 8 compared with <8; and napping ≥ 2 hours compared with <2 hours.
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A propensity-adjusted multivariate logistic regression analysis was then performed modeling the odds of a composite complication [32].
Logistic regression was used to model the odds of the presence of each particular subtype (separate model for each subtype) with the same covariates.
A logistic regression model is used to model the odds ratio (OR) for abstention.
Logistic regression was used to model the odds ratio of being employed as an electrician.
Logistic regression analysis was constructed to model the odds of amputation versus nonamputation.
Table 3 shows the result of the logistic regression modeling the odds that a patient is recommended for inpatient services.
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