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. Author manuscript; available in PMC: 2017 Oct 16.
Published in final edited form as: Cancer. 2014 Sep 22;120(22):3527–3535. doi: 10.1002/cncr.28811

Table 2.

Results of two logistic regression models predicting smoking cessation outcomes

Complete Case Analysis (n=414*)1 Intent to Treat Analysis (n=699*)2

Odds Ratio 95%
Confidence
Interval
p-valuesa Odds
Ratio
95%
Confidence
Interval
p-valuesa

E-cigarette
  E-cigarette use .95 0.53, 1.72 - 2.00 1.23, 3.26 p = 0.005
  No e-cigarette use^ (1.00) - (1.00) -
Nicotine dependence
  High nicotine dependence 1.22 0.78, 1.91 - 1.14 0.77, 1.69 -
  Low nicotine dependence^ (1.00) - (1.00) -
Number of past quit attempts
  Tried quitting at least twice 1.77 1.14, 2.74 p = 0.010 1.83 1.25, 2.67 p = 0.002
  Tried quitting one time or less^ (1.00) - (1.00) -
Cancer diagnosis
  Thoracic or head and neck .56 0.36, 0.89 p = 0.014 0.71 0.48, 1.04 -
  Other^ (1.00) - (1.00) -
*

Data are given as sample size except where noted. Counts do not always add up to the total sample size due to missing responses and deceased patients.

1

Complete Case Analysis: Self-reported seven day point prevalence abstinence at six months, excluding lost to follow-up patients.

2

Intent to Treat analysis: Self-reported seven day point prevalence abstinence at six months, lost to follow-up patients assumed to be smoking.

a

p-values were calculated using chi-square statistics for categorical variables and independent-sample t-test for continuous variables; p-values greater than 0.05 were omitted.

^

Reference categories