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. 2020 Apr 24;10:626. doi: 10.3389/fonc.2020.00626

Table 2.

Effect of surgery on hazard ratios for overall survival.

Models Sample size, No. HR (95% CI)
Surgery Non-surgery Events HR (95% CI) P value
Unadjusted model 618 1,628 1467 0.572 (0.506–0.647) <0.001
Multivariable-adjusted model a 618 1,628 1467 0.642 (0.557–0.740) <0.001
Propensity score-adjusted model b
Within-propensity score quintile
 Quintile 1 (Lowest propensity score) 23 427 324 0.792 (0.457–1.372) 0.405
 Quintile 2 42 407 302 0.495 (0.316–0.776) 0.002
 Quintile 3 86 363 300 0.614 (0.446–0.845) 0.002
 Quintile 4 160 290 293 0.782 (0.606–1.010) 0.060
 Quintile 5 (Highest propensity score) 307 141 248 0.525 (0.395–0.696) <0.001
 Combined c 618 1,628 1,467 0.635 (0.548–0.736) <0.001
Regression adjustment 618 1,628 1,467 0.649 (0.561–0.749) <0.001
Weighting (IPTW) 618 1,628 1,467 0.673 (0.597–0.759) <0.001
Matching 1:1 475 475 598 0.690 (0.585–0.814) <0.001

HR, hazard ratio; CI, confidence interval; IPTW, inverse probability of treatment weighting.

a

Adjusted for age, sex, ethnicity, marriage status, anatomic sites, laterality, tumor grading, year of diagnosis, AJCC stage, tumor size, extent of tumor, and lymph node involvement.

b

The propensity of receiving surgery was estimated using a multivariable logistic regression model that included baseline age, sex, race, marital status, anatomic sites, laterality, tumor grading, year of diagnosis, AJCC stage, tumor size, extent of tumor and lymph node involvement.

c

Results were combined among strata using inverse variance weights under a fixed model, as there was no detectable heterogeneity among strata (all P for heterogeneity = 0.2).