The SAP for the primary analysis was finalised before any analyses started. For analyses to be conducted on follow-up data, the analysis plan was added to and finalised during the follow-up period, before any follow-up analyses commenced. (The SAP containing both primary and follow-up analyses can be found at http://eprints.nottingham.ac.uk/3283/). Data cleaning and preparatory work were performed blind to study arm allocation and all analyses of outcomes recorded at delivery were performed blind to study arm allocation, with treatment codes revealed after these were completed. However, it was not possible to perform all follow-up analyses blind. Statistical analyses were performed using SAS software version 9.1.3 (SAS Institute Inc., Cary, NC, USA) and Stata/SE version 11.2 (StataCorp LP, College Station, TX, USA).
Analysis to primary outcome point (delivery)
Analysis was on an intention-to-treat (ITT) basis and participants who, for any reason, had missing outcome data were assumed to be smoking. The proportion of women who reported prolonged abstinence from smoking immediately before childbirth was compared between treatment groups by logistic regression and adjusted for recruitment centre as a stratification variable. Statistical significance was assessed using the likelihood ratio test. The primary analysis adjusted for no further variables as multivariate analysis results, and therefore overall conclusions, can be sensitive to decisions concerning which variables to adjust for and how these are specified. Nevertheless, we planned a secondary analysis adjusting for baseline COT (continuous variable), maternal education (in years) and partners’smoking status (binary variable), as adjusting for potentially important prognostic factors can improve the precision of treatment effect estimates.51Other smoking cessation outcomes were analysed similarly.
Fetal and maternal birth outcomes were compared on an ITT basis. For binary outcomes, ORs were obtained using logistic regression adjusted for recruitment centre and also using the likelihood ratio test (with Fisher’s exact test used and stratification by centre ignored when numbers of events were small). For continuous outcomes, we compared means between groups with adjustment for recruitment centre using multiple linear regression.
For fetal outcomes, primary analysis was of singleton births only to allow for the fact that observations will be non-independent and that non-singleton births are likely to have different birth outcomes. However, we undertook a sensitivity analysis including multiple births, with clustering of outcomes accounted for using an approach previously published. This adapts the methodology previously created for use with cluster randomised controlled trials (RCTs), assuming that each woman is regarded as the‘cluster’and her number of offspring the cluster size.52
In all analyses, ap-value of<0.05 was taken to indicate statistical significance and 95% CIs were calculated.
Analysis at the 2-year follow-up point
The ASQ-3 does not require adjustment of an infant’s age to allow for prematurity once he or she reaches 24 months of age; therefore, as the questionnaire was sent shortly before the child’s second birthday, no adjustment to infant ages was made in analyses.
Maternal characteristics at baseline and delivery and infant birth outcomes at delivery were compared between those participants and infants who did and did not have outcomes ascertained at 2 years after delivery. We also compared maternal and infant characteristics according to whether follow-up at 2 years was by PQ2, HPQ or neither.
Analysis of early childhood outcomes was on an ITT basis with participants analysed in the treatment groups to which they were randomised. Participants with no live birth (i.e. miscarriage, stillbirth or elective
termination) or those where the pregnancy outcome was unknown were excluded from the ITT analysis, but postnatal infant deaths were included in the denominator for developmental outcomes. The primary analysis was restricted to singleton births to allow for the fact that observations will be non-independent and that multiple births may have very different outcomes. For the primary outcome, survival with no impairment, a complete case analysis was compared with an analysis using multiple imputation to deal with missing values. Multiple imputation was carried out using the‘mi’commands in Stata and in our multiple imputation we included all of the complete baseline and the treatment code, and used 20 imputations. Using this approach, multiple imputation was also used for the other developmental outcomes: suspected and definite
developmental impairment. The infant impairment and respiratory outcomes at 2 years were analysed as binary indicators of presence or absence of the outcome. The ORs for the effect of treatment group were obtained by logistic regression adjusting for centre as the stratification factor. In a subsidiary analysis, multiple births were included and clustering accounted for by the same method as in analysis at delivery. We also conducted sensitivity analyses comparing the results of analyses based on parental responses only and those based on a combination of parental and health professional responses.
Smoking outcomes were also analysed on an ITT basis, with all women analysed in the treatment groups to which they were randomised and all non-respondents assumed to be smoking. ORs for the effect of treatment group on smoking cessation outcomes were also obtained by logistic regression. As at delivery, the primary analysis adjusted only for centre, but we carried out sensitivity analysis that also adjusted for baseline COT, partner’s smoking status and age at finishing education.
We tested the assumption that those missing at follow-up were smokers by exploring alternative associations for the relationship between smoking status and‘missingness’.53In this analysis, we defined the OR for the
association between quitting and being missing as the informatively missing odds ratio (IMOR) and we looked at the effect on the size of the treatment effect on smoking abstinence outcome by varying the size of this OR between 0 and 1. In the main analysis, the assumption that those missing at follow-up are smokers is equivalent to assuming that IMOR equals 0 (i.e. that all those who are missing are smokers). We altered this OR up to IMOR equals 1, which is equivalent to assuming that there is no association between being missing data and smoking status. We carried out this analysis using the mean score method to estimate the treatment effect under the pattern mixture model, logit[E(y|r,my)]=α1+β1r+myδ, where myis an indicator of whether
the outcome is missing or otherwise, r is the treatment effect, and exponential (δ) [the OR between outcome y and my(IMOR)] is varied in the range 0–1.54α1andβ1are estimated using the subgroup with outcome data,
missing values of y (the outcome) are replaced by invlogit(α1+β1r+δ), the mean of this new y variable is
calculated for the intervention and control arms (say a1 and a0), and ORs calculated from these mean values accordingly [a1/(1–a1)]/[a0/1–a0)].
Secondary analysis
A priori, we planned to investigate whether or not there was any relationship between self-reported nicotine patch use in pregnancy and the presence or absence of developmental impairment in infants at 2 years. For this, we conducted an exploratory regression analysis with absence of impairment at 2 years as the dependent variable and the number of nicotine patches women reported having used when asked at delivery as an explanatory variable.‘Suspected’and‘definite’impairment categories were combined into one group representing infants who did not have impairment-free survival at 2 years. We investigated the possibility that baseline maternal characteristics may have a confounding effect on any relationship and adjusted for
confounders as appropriate. For those in the placebo group, we set adherence with nicotine patches as zero. Additionally, if data on adherence were not reported at delivery, we imputed 0 days use of nicotine patches.