General considerations
Data analysis was prespecified in the DAP and approved by the SSC. Participant population
1. Complete case (CC) population: all randomised pupils with complete follow-up data at T3, including health economic service utilisation data [the intention-to-treat (ITT) population for whom T3 follow-up data were obtainable].
2. ITT population: all subjects who were randomised. Analysis was based on randomisation rather than on receipt of intervention.
Primary and secondary analyses were performed using the CC population. The health economic analysis was also conducted on the CC population. Sensitivity analysis was conducted on the ITT population (employing a range of methods to deal with missing data; see Missing data).
Pupils who joined a trial school after T0 (June 2012) but before phase 1 of the intervention began
(September–December 2012) were first captured at T1 (June 2013). These pupils were included in the
primary and secondary analyses. Pupils who joined trial schools after the beginning of the intervention were excluded from the primary and secondary analyses, unless they had moved from another participating school. Missing data
Missing on scale items: when subjects were missing on individual scale items, the coding instruction for the scale was followed. If no guidance was given, those participants with at least 80% of items completed had the remaining 20% pro-rated.
Missing on primary outcome data: a comparison of the baseline characteristics of cases with primary outcome data and cases was undertaken when these were missing.
Depending on the pattern of missingness, one or more of the following sets of analysis were produced as a sensitivity analyses and compared with analysis on the CC population.
Intention-to-treat analysis employing multiple imputation
Information at T1 and T2 was used to impute credible scores for any missing outcome measures at T3, using multiple imputation with 50 imputed data sets.
Intention-to-treat analysis employing a worst-case analysis
All respondents with missing primary outcome data in the intervention arm were assumed to have‘failed’
(to have consumed≥ 6/≥ 4.5 units in a single session in the previous month). All respondents with missing
primary outcome data in the control arm were assumed to have‘succeeded’ (not consumed ≥ 6/≥ 4.5
units in a single session in the previous month.
Intention-to-treat analysis employing a‘missing = success’ analysis
All missing respondents, regardless of trial arm, were assumed not to have consumed≥ 6/≥ 4.5 units in a
single session in the previous month.
Intention-to-treat analysis employing a‘missing = failure’ analysis
All missing respondents, regardless of trial arm, were assumed to have consumed≥ 6/≥ 4.5 units in a
single session in the previous month.
Missing on baseline covariate data
For time-invariant baseline covariates (sex and SES), missing values at T0 were derived from observed values at follow-up (T1, T2 or T3) when possible. For any remaining missing values in baseline covariates,
mean imputation was employed.76
Outliers
Any unusual measurements were automatically flagged, checked and re-entered when necessary by the trial statistician. Any outlier values that remained after data cleaning and checking were investigated for authenticity. The influence of outlier values on the primary analysis was checked, and any significant influences detected were reported and discussed in Chapter 3.
Analysis time frame
Baseline
The baseline data were collected after randomisation (T0) when pupils were in school year 8 or S1.
Follow-up visits
Adolescent participants were followed up after T1, T2 and T3. Statistical analyses
Descriptive analysis
Summary statistics on school and pupil recruitment, withdrawal and dropout were collated for both trial arms and reported as a participant flow diagram for reporting of CRCTs (Figure 1).
Intraclass correlation coefficient
The ICC for the primary outcomes was calculated and is reported in Chapter 3 [see Heavy episodic drinking T3 (binary outcome)]. This was calculated overall and for each arm separately.
Fidelity test
Appropriate descriptive analysis was used to examine the extent to which the necessary conditions required to permit a valid test of the treatment efficacy were met. This included assessment of achieved statistical power, patterns of attrition, and treatment integrity and discriminability (i.e. that STAMPP was sufficiently distinct from EAN) across the trial sites. This work included analysis of both qualitative and quantitative data.
METHODS
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Randomisation check
Descriptive summaries of baseline participant characteristics from the two trial arms were tabulated to assess between-group equivalence across the trial arms (checked on randomisation). The descriptive data were tabulated to compare attendees at the parental session with those who completed the follow-up questionnaire only. Descriptive summaries were produced for baseline data at the school level. These data were used to check comparability between study arms and generalisability of the study population. Outcome measure scores from the questionnaires were summarised and tabulated for the trial arms. Descriptive statistics, with confidence intervals (CIs) when appropriate, were used for the tabulation of outcomes in the trial arms. CIs presented were adjusted to allow for clustering effects.
Enrolment
Direct recruitment of schools by STAMPP trial manager (November 2011–January 2012)
Assessed for eligibility (N = 105) Randomised (N = 105; n = 12,738) Excluded (N = 0) Allocated to EAN (N = 53; n = 6359) Allocated to EAN (N = 53; n = 5567) Lost to follow-up (N = 0; n = 1199)
(1) Heavy episodic drinking analysed (N = 53; n = 5073)
Excluded from analysis (owing to item missing or providing multiple responses + 3) n = 87
(2) Own harms analysed (N = 53; n = 5146) Excluded from analysis (owing to item missing or providing multiple responses + 3 on all items), n = 14
Allocated to intervention (N = 52; n = 6379) Intervention (N = 52; n = 5749) Lost to follow-up (N = 0; n = 1134)
(1) Heavy episodic drinking analysed (N = 52; n = 5160)
Excluded from analysis (owing to item missing or providing multiple responses + 3) n = 85
(2) Own harms analysed (N = 52; n = 5234) Excluded from analysis (owing to item missing or providing multiple responses + 3 on all items), n = 11
Allocation Follow-up (T3) + 33 months (February 2015) Surveyed at baseline (T0) (June 2012) Analysis
FIGURE 1 School and participant flow diagram: STAMPP. Analysis was conducted at T3 on pupils who had completed each of the primary outcome measures. N, number of schools; n, number of pupils.
Analysis of primary outcomes
The initial outcome analysis was an ITT using the CC population such that all cases were assessed regardless of intervention and intervention dosage. However, as the study design was clustered (i.e. randomisation occurred at the school level) the lack of independency between individual cluster members was taken into account to avoid underestimated standard errors (SEs) (which would otherwise inflate statistical significance). For each primary outcome, a two-level regression model was fitted, with pupils nested within schools, to assess the
impact of STAMPP on the outcome measures. For self-reported consumption of≥ 6/≥ 4.5 units, the model
used was logistic regression. For the number of self-reported harms, a negative binomial model was used. The primary outcome model was adjusted for the impact of covariates on intervention outcome. Covariates included in the models were those used within the randomisation process (sex and SES),
baseline outcome measures (consumption of≥ 6/≥ 4.5 units and number of self-reported harms
depending on outcome) and location (NI or Glasgow/Inverclyde). For each primary outcome, a statistically
significant result was concluded if the p-value for the trial arm explanatory variable was< 0.025.
Analysis of secondary outcomes
Differences in self-reported alcohol use (HED, defined as self-reported consumption of≥ 6 units in a single
episode in the previous 30 days for males and≥ 4.5 units for females, which was dichotomised at never/
one or more occasions) at T1 and T2 were assessed using two-level logistic regression models with covariates (baseline alcohol use, sex, SES and location). Similar models were constructed for self-reported alcohol use in lifetime, previous year and previous month (all dichotomised) and for unsupervised alcohol use (drinking without the supervision of parents/carers, which was dichotomised) at T1, T2 and T3. A negative binomial model with covariates (baseline harms, sex, SES and location) was estimated for the number of self-reported harms (harms caused by own drinking) at T1 and T2. Similar models were estimated for the number of self-reported harms caused by the drinking of others and the number of
drinks consumed in‘typical’ and last-use episodes at T1, T2 and T3.
Time to alcohol initiation (age at which a whole alcoholic drink was first consumed, and not just a sip or a shared drink) at T1, T2 and T3 was compared between trial arms by estimating a two-level Cox proportional hazards model in those who had not already initiated alcohol consumption at baseline (T0). The model controlled for sex, SES and location.
Subgroup analyses
To explore differential treatment effects on the primary and secondary outcome measures, prespecified interaction terms were fitted between trial arm and baseline measures thought to predict the effect of treatment.
These were:
l age, in months, of the pupil at baseline
l gender
l SES (using the proportion of FSM provision)
l alcohol use behaviour at baseline: age of initiation, use of alcohol in the year prior to baseline,
context of use (abstainer/supervised/unsupervised)
l grammar/secondary school analysis (only in NI).
Sensitivity analyses or model testing
Analyses were undertaken to assess the robustness of the outcome analysis. These included the repetition of the analysis on alternative specification of outcomes measures, using the ITT population and with different missing data models.
METHODS
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