Sas statistical software version 9.4 for windows

Mean values of key outcome variables and covariates were calculated by physician gender. Bivariable (unadjusted) regression of annual or daily payments on gender was used to estimate raw gaps in annual and daily payments between male and female physicians without controlling for covariates. Multivariable regression (adjusted) models of daily payments included the following sets of explanatory variables: work inputs, degree of rurality, and practice characteristics, including specialty. Adjusted differences in daily payments among male and female physicians were also estimated separately by specialty, payment model, practice setting, and rurality to illustrate variations in the unexplained portion of the payment gap for physicians in different practice settings or situations.
All analyses were conducted using Stata version 15 (StataCorp) and SAS statistical software version 9.4 for Windows (SAS Institute). P values were 2-sided, and statistical significance was set at α = .05. Data were analyzed from January 2020 to July 2021.

The statistical analyses used in the present study were performed using the SAS statistical software version 9.4 for Windows (SAS Institute, Cary, NC). Vitality, mental health, disability, psychosocial work environment, control and concern about pain was evaluated using a repeated-measures linear mixed model (Proc Mixed) with group, time and group by time as independent variables. Participants nested within department was entered as random effect. The statistical analyses were performed in accordance with the intention-to-treat principle, i.e. using the mixed procedure which accounts for missing values (under the assumption that they are missing at random). All analyses were adjusted for age and the respective baseline value of the outcome measure. Outcomes are reported as between-group differences and 95% confidence intervals at follow-up. An alpha level of 0.05 was accepted as statistically significant.

The continuous data of demographic and clinical characteristics of patients were summarized as mean ± standard deviations (SD) with range (minimum, maximum). The categorical data were expressed as number percentage. The demographic and clinical data of the control group and the study group were compared with independent t-test for continuous data or Chi-square test for categorical data. Multivariate logistic regression analyses were performed to evaluate the association of pregnancy outcomes with early stop of progesterone supplement or not, while controlling for some confounders. Adjusted odds ratio was calculated. All tests were two-tailed, and a value of P < 0.05 was considered to indicate statistical significance with a confidence level of 95%. Data analysis was performed with SAS statistical software version 9.4 for Windows (SAS Institute Inc., Cary, NC, USA).