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Table 4 Estimates of lateral cephalometric outcomes from multivariable regression models

From: Comparison of surgical and non-surgical orthodontic treatment approaches on occlusal and cephalometric outcomes in patients with Class II Division I malocclusions

Primary independent variable Outcomes Multivariable regression models
Linear regression model fit with ordinary least squares regression approacha Propensity score regression model fit with GLM methodb Propensity score stratification model fit with GLM methodc
Parameter estimate p value Parameter estimate p value Parameter estimate p value
Surgical treatment Versus non-surgical treatment (reference variable) ABO-COGS deband score −0.854 0.80 −0.562 0.89 −1.06 0.76
Deband ANB angle −2.24 0.002 −2.11 0.01 −2.40 0.001
Deband FMIA angle 0.649 0.75 −0.35 0.89 0.765 0.72
Deband IMPA angle −3.321 0.09 −3.23 0.17 −3.50 0.08
Deband upper incisor to SN plane angle 10.564 0.001 10.03 0.01 11.53 <0.001
Deband overbite −0.606 0.07 −0.570 0.16 −0.610 0.08
Deband overjet 0.188 0.71 0.283 0.65 0.161 0.76
  1. aIn this model, the confounding effects of covariates (age at start of treatment, gender, initial discrepancy index, initial ANB angle, initial FMIA angle, initial IMPA angle, initial U1 to SN angle, initial overbite, and initial overjet) were adjusted. The linear regression models were fit using ordinary least squares regression approach
  2. bA two-staged regression approach was used. In the first stage, propensity scores (predicted probability of a patient having orthognathic surgery) were computed by using covariates (age at start of treatment, gender, initial discrepancy index, initial ANB angle, initial FMIA angle, initial IMPA angle, initial U1 to SN angle, initial overbite, and initial overjet). In the second stage, the effect of surgical versus non-surgical treatment on outcomes was examined by GLM model in which the propensity score was used as continuous variable and was adjusted as a covariate along with all other covariates
  3. cA two-staged regression approach was used. In the first stage, propensity scores (predicted probability of a patient having orthognathic surgery) were computed by using covariates (age at start of treatment, gender, initial discrepancy index, initial ANB angle, initial FMIA angle, initial IMPA angle, initial U1 to SN angle, initial overbite, and initial overjet). In the second stage, the effect of surgical versus non-surgical treatment on outcomes was examined by GLM model in which the propensity score was stratified into five bins (based on distribution of scores) and was adjusted as a covariate along with all other covariates