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If a union consists of both risk-increasing and risk-decreasing predictors, effects will cancel and power will be lost.
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Power calculations using G*Power (Buchner et al., 2007) using regression effect size and 18 potential predictors (effect size f=0.15, α = 0.05, power =0.95) indicated that a minimum sample of 213 was required in each mode of conception group.
Hierarchical quantile Poisson log-normal regression is used to assess the most important predictors, variation in predictor effects by quantile, and varying impacts of predictors by region.
Table 2 expresses predictor effects in terms of odds ratios (exponentials of logit coefficients), interpreted as multiplicative effects on the odds favoring renewable energy development, or anthropogenic climate change, per 1-unit increase in each predictor.
Adjusted predictor effects were summarized using hazard ratios (HR) from the multivariable models.
After fitting the predictor effects of sleeping proximity, age and BCG scar status, the best fit was determined and the predicted probabilities for each ELISPOT combination were calculated.
The overall predictor effects in the validation data were re-estimated and compared with the reference overall predictor effects in the prediction model (reference is set to zero).
Conditional models estimate predictor effects for patients in the specific clusters.
A similar recommendation is for researchers to examine between-study heterogeneity in predictor effects.
In prediction studies, the focus is on all estimated predictor effects.
The results indicated that predictor effects were similar across datasets (web appendix table 6).
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