Objectives Researchers are concerned whether multiple imputation (MI) or complete case analysis should be used when a large proportion of data are missing. We aimed to provide guidance for drawing conclusions from data with a large proportion of missingness. Study Design and Setting Via simulations, we investigated how the proportion of missing data, the fraction of missing information (FMI), and availability of auxiliary variables affected MI performance. Outcome data were missing completely at random or missing at random (MAR). Results Provided sufficient auxiliary information was available; MI was beneficial in terms of bias and never detrimental in terms of efficiency. Models with similar FMI values, but differing proportions of missing data, also had similar precision for effect estimates. In the absence of bias, the FMI was a better guide to the efficiency gains using MI than the proportion of missing data. Conclusion We provide evidence that for MAR data, valid MI reduces bias even when the proportion of missingness is large. We advise researchers to use FMI to guide choice of auxiliary variables for efficiency gain in imputation analyses, and that sensitivity analyses including different imputation models may be needed if the number of complete cases is small.

Background Missing data are unavoidable in epidemiological research, potentially leading to bias and loss of precision. Multiple imputation (MI) is widely advocated as an improvement over complete case analysis (CCA). However, contrary to widespread belief, CCA is preferable to MI in some situations. Methods We provide guidance on choice of analysis when data are incomplete. Using causal diagrams to depict missingness mechanisms, we describe when CCA will not be biased by missing data and compare MI and CCA, with respect to bias and efficiency, in a range of missing data situations. We illustrate selection of an appropriate method in practice. Results For most regression models, CCA gives unbiased results when the chance of being a complete case does not depend on the outcome after taking the covariates into consideration, which includes situations where data are missing not at random. Consequently, there are situations in which CCA analyses are unbiased while MI analyses, assuming missing at random (MAR), are biased. By contrast MI, unlike CCA, is valid for all MAR situations and has the potential to use information contained in the incomplete cases and auxiliary variables to reduce bias and/or improve precision. For this reason, MI was preferred over CCA in our real data example. Conclusions Choice of method for dealing with missing data is crucial for validity of conclusions, and should be based on careful consideration of the reasons for the missing data, missing data patterns and the availability of auxiliary information.

SummaryBackgroundSchools in many countries undertake programmes for smoking prevention, but systematic reviews have shown mixed evidence of their effectiveness. Most peer-led approaches have been classroom-based, and rigorous assessments are scarce. We assessed the effectiveness of a peer-led intervention that aimed to prevent smoking uptake in secondary schools.MethodsWe undertook a cluster randomised controlled trial of 10 730 students aged 12–13 years in 59 schools in England and Wales. 29 schools (5372 students) were randomly assigned by stratified block randomisation to the control group to continue their usual smoking education and 30 (5358 students) to the intervention group. The intervention (ASSIST [A Stop Smoking In Schools Trial] programme) consisted of training influential students to act as peer supporters during informal interactions outside the classroom to encourage their peers not to smoke. Follow-up was immediately after the intervention and at 1 and 2 years. Primary outcomes were smoking in the past week in both the school year group and in a group at high risk of regular smoking uptake, which was identified at baseline as occasional, experimental, or ex-smokers. Analysis was by intention to treat. This study is registered, number ISRCTN55572965.FindingsThe odds ratio of being a smoker in intervention compared with control schools was 0·75 (95% CI 0·55–1·01) immediately after the intervention (n=9349 students), 0·77 (0·59–0·99) at 1-year follow-up (n=9147), and 0·85 (0·72–1·01) at 2-year follow-up (n=8756). The corresponding odds ratios for the high-risk group were 0·79 (0·55–1·13 [n=3561]), 0·75 (0·56–0·99 [n=3483]), and 0·85 (0·70–1·02 [n=3294]), respectively. In a three-tier multilevel model with data from all three follow-ups, the odds of being a smoker in intervention compared with control schools was 0·78 (0·64–0·96).InterpretationThe results suggest that, if implemented on a population basis, the ASSIST intervention could lead to a reduction in adolescent smoking prevalence of public-health importance.FundingMRC (UK).

Background: Little is known about associations of gestational weight gain (GWG) with long-term maternal health.Objective: We aimed to examine associations of prepregnancy weight and GWG with maternal body mass index (BMI; in kg/m2), waist circumference (WC), systolic blood pressure (SBP), and diastolic blood pressure (DBP) 16 y after pregnancy.Design: This is a prospective study in 2356 mothers from the Avon Longitudinal Study of Parents and Children (ALSPAC)—a population-based pregnancy cohort.Results: Women with low GWG by Institute of Medicine recommendations had a lower mean BMI (−1.56; 95% CI: −2.12, −1.00) and WC (−3.37 cm; −4.91, −1.83 cm) than did women who gained weight as recommended. Women with a high GWG had a greater mean BMI (2.90; 2.27, 3.52), WC (5.84 cm; 4.15, 7.54 cm), SBP (2.87 mm Hg; 1.22, 4.52 mm Hg), and DBP (1.00 mm Hg; −0.02, 2.01 mm Hg). Analyses were adjusted for age, offspring sex, social class, parity, smoking, physical activity and diet in pregnancy, mode of delivery, and breastfeeding. Women with a high GWG had 3-fold increased odds of overweight and central adiposity. On the basis of estimates from random-effects multilevel models, prepregnancy weight was positively associated with all outcomes. GWG in all stages of pregnancy was positively associated with later BMI, WC, increased odds of overweight or obesity, and central adiposity. GWG in midpregnancy (19–28 wk) was associated with later greater SBP, DBP, and central adiposity but only in women with a normal prepregnancy BMI.Conclusions: Results support initiatives aimed at optimizing prepregnancy weight. Recommendations on optimal GWG need to balance contrasting associations with different outcomes in both mothers and offspring.