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Table 3 Analysis plan

From: Integration of smoking cessation into standard treatment for patients receiving opioid agonist therapy who are smoking tobacco: protocol for a randomised controlled trial (ATLAS4LAR)

Variable/outcome

Hypothesis

Outcome measure

Method of analysis

1. Primary

 a. Proportion of patients smoking

Intervention improves smoking cessation rates from baseline to 16 weeks

Carbon monoxide in ppm in exhaled air

Chi-squared test

 b. Proportion achieving at least 50 % reduction in number of cigarettes smoked

Intervention reduces number of cigarettes smoked from baseline to 16 weeks

Self-reported daily number of cigarettes smoked

Chi-squared test

2. Secondary

 Number of cigarettes smoked

Reduction in number of cigarettes

Self-reported daily number of cigarettes smoked

t-test and regression methods with secondary outcomes as dependent variable adjusted for variables defined under additional analysis

 Carbon monoxide in exhaled air

Reduced CO levels

Carbon monoxide in ppm in exhaled air

 C-reactive protein

Reduced levels

CRP in mg/L

 Leucocyte count

Levels within reference limit

Leucocyte count in 109/L

 Psychological well-being

Increased score

Hopkins Symptom Checklist (SCL-10)

 Physical fitness

Increased score

4-min step test, number of steps

 Quality of life

Increased score

EuroQoL EQ-5D-5L-questionaire

 Fatigue

Less Fatigue

Fatigue Symptom Scale (FSS-3)

 Dyspnoea

Less after intervention

Modified Medical Research Council (mMRC)-scale

 Physical activity

Increased

Physical Activity Questionnaire (IPAQ)

3. Additional analysis

 OAT-medication

Choice of OAT-medication impacts primary outcome

 

Regression methods with OAT medication as categorical co-variate.

 OAT-medication doses

Higher doses inhibits smoking cessation

 

Regression methods with OAT-doses as independent variable

 Adjusted for age

Co-variates impact the outcomes of the trial

 

Regression methods with appropriate interaction term

 Adjusted for sex

 

 Adjusted for i.v. drug use

 

 Adjusted for known COPD

 

 Impact of number of cigarettes smoked on secondary outcomes

Fewer cigarettes smoked results in improved secondary outcomes

 

Regression methods with secondary outcome as dependent variable and number of cigarettes smoked as independent variable

  1. Important remarks: In all analyses will be expressed as coefficient, standard errors, corresponding 95%, and associated p-values
  2. Goodness-of-fit will be assessed by examining the residuals for model assumptions and chi-squared test of goodness-of-fit
  3. Bonferroni method will be used to correct for multiple testing