Using Instruments for Selection to Adjust for Selection Bias in Mendelian Randomization

Selection bias is a common concern in epidemiologic studies. In the literature, selection bias is often viewed as a missing data problem. Popular approaches to adjust for bias due to missing data, such as inverse probability weighting, rely on the assumption that data are missing at random and can yield biased results if this assumption is violated. In observational studies with outcome data missing not at random, Heckman's sample selection model can be used to adjust for bias due to missing data. In this paper, we review Heckman's method and a similar approach proposed by Tchetgen Tchetgen and Wirth (2017). We then discuss how to apply these methods to Mendelian randomization analyses using individual-level data, with missing data for either the exposure or outcome or both. We explore whether genetic variants associated with participation can be used as instruments for selection. We then describe how to obtain missingness-adjusted Wald ratio, two-stage least squares and inverse variance weighted estimates. The two methods are evaluated and compared in simulations, with results suggesting that they can both mitigate selection bias but may yield parameter estimates with large standard errors in some settings. In an illustrative real-data application, we investigate the effects of body mass index on smoking using data from the Avon Longitudinal Study of Parents and Children.Comment: Main part: 27 pages, 3 figures, 4 tables. Supplement: 20 pages, 5 figures, 10 tables. Paper currently under revie

Do children with recurrent abdominal pain grow up to become adolescents who control their weight by fasting?:Results from a UK population-based cohort

Objective Gastrointestinal (GI) problems are common in eating disorders, but it is unclear whether these problems predate the onset of disordered eating. Recurrent abdominal pain (RAP) is the most prevalent GI problem of childhood, and this study aimed to explore longitudinal associations between persistent RAP (at ages 7 and 9) and fasting for weight control at 16. Method The Avon Longitudinal Study of Parents and Children (ALSPAC) is a UK population cohort of children. Childhood RAP was reported by mothers and defined as RAP 5+ (5 pain episodes in the past year) in our primary analysis, and RAP 3+ (3 pain episodes) in our sensitivity analysis. Fasting for weight control was reported by adolescents at 16. We used logistic regression models to examine associations, with adjustments for potential confounders. Results After adjustments, we found no association between childhood RAP 5+ and adolescent fasting for weight control at 16 (OR 1.30 (95% Confidence Intervals [CI] 0.87, 1.94) p = .197). However, we did find an association between RAP 3+ and later fasting, in the fully adjusted model (OR 1.50 [95% CI 1.16, 1.94] p = .002), and after excluding those with pre‐existing anxiety (OR 1.52 [95% CI 1.17, 1.97] p = .002). Discussion Our findings suggest a possible independent contribution of RAP to later risk of fasting for weight control, and RAP should be enquired about in the assessment of eating disorders. However, frequency of childhood abdominal pain (as captured by ALSPAC) may be less important to long‐term outcomes than functional impairment

Adverse childhood experiences in the children of the Avon Longitudinal Study of Parents and Children (ALSPAC)

Background: Exposure to adverse childhood experiences (ACEs) is a risk factor for poor later life health. Here, we describe the ACE variables measured in the children of the Avon Longitudinal Study of Parents and Children (ALSPAC) study, and a method used to derive summary measures and deal with missing data in them.    Methods: The ALSPAC data catalogue (59 608 variables) was searched in September 2017 for measures on adversity exposure between birth and 18 years. 6140 adversity questions were then screened for conforming to our ACE definitions and suitability for dichotomisation. This screening identified 541 questions on ten ‘classic’ ACEs (sexual, physical or emotional abuse, emotional neglect, substance abuse by the parents, parental mental illness or suicide attempt, violence between parents, parental separation, bullying and parental criminal conviction) and nine additional ACEs (bond between parent and child, satisfaction with neighbourhood, social support for the parent, social support for the child, physical illness of a parent, physical illness of the child, financial difficulties, low social class and violence between child and partner). These were used to derive a binary construct for exposure to each ACE. Finally, as cumulative measures of childhood adversity, different combinations of the 19 ACE constructs were summed to give total adversity scores. An appropriate strategy for multiple imputation was developed to deal with the complex patterns of missing data. Results: The ACE constructs and ACE-scores for exposure between birth and 16 years had prevalence estimates that were comparable to previous reports (for instance 4% sexual abuse, 18% physical abuse, 25% bullied, 32% parental separation). Conclusions: ACE constructs, derived using a pragmatic approach to handle the high dimensional ALSPAC data, can be used in future analyses on childhood adversity in ALSPAC children