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In (3), S is the covariance matrix of X, and S − 1 2 is the Cholesky decomposition of S. We follow Diamond and Sekhon (2013) and include in X the variables in our final propensity score model along with the estimated scores.
The final propensity matrix is shown in Supplementary Table S1.
║: number of hd-PS covariates retained in the final propensity score model.
Variables included in the final propensity score model are displayed in Additional file 1: Table E1.
The final propensity model chosen included 17 variables addressing housing, ownership of modern amenities, occupation, habits, education, caste, and prenatal care adequacy.
The final propensity model was then used to generate a patient-specific propensity score that was used to adjust for potential selection bias in the final analyses.
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The final propensity-adjusted Cox models (see online supplementary table S3) also confirmed the significant preventive effects of >3×/week of lower-intensity (HR=0.82, 95% CI 0.71 to 0.96), moderate-intensity (HR=0.57, 95% CI 0.40 to 0.83) and vigorous-intensity PA (HR=0.55, 95% CI 0.38 to 0.79) on fatty liver.
These values are binarized using a cutoff of 0.5; i.e. amino acids with P > 0.5 are assumed to form MoRFs. Finally, in the third step, these propensities are merged with results of alignment of the input protein against the MoRF-annotated proteins in the training dataset to produce the final propensities.
The final obtained TMH propensity is averaged over the results of lengths 13 and 15 for each residue along the sequence.
After the final variables for the propensity score model were chosen, a propensity score was calculated for each subject.
In this approach, the final model (excluding the propensity score) was re-estimated separately within strata based on the propensity score.
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