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Missing data are ubiquitous in medical research. Although there is increasing guidance on how to handle missing data, practice is changing slowly and misapprehensions abound, particularly in observational research. Importantly, the lack of transparency around methodological decisions is threatening the validity and reproducibility of modern research. We present a practical framework for handling and reporting the analysis of incomplete data in observational studies, which we illustrate using a case study from the Avon Longitudinal Study of Parents and Children. The framework consists of three steps: 1) Develop an analysis plan specifying the analysis model and how missing data are going to be addressed. An important consideration is whether a complete records’ analysis is likely to be valid, whether multiple imputation or an alternative approach is likely to offer benefits and whether a sensitivity analysis regarding the missingness mechanism is required; 2) Examine the data, checking the methods outlined in the analysis plan are appropriate, and conduct the preplanned analysis; and 3) Report the results, including a description of the missing data, details on how the missing data were addressed, and the results from all analyses, interpreted in light of the missing data and the clinical relevance. This framework seeks to support researchers in thinking systematically about missing data and transparently reporting the potential effect on the study results, therefore increasing the confidence in and reproducibility of research findings. •Missing data are ubiquitous in medical research.•Guidance is available, but missing data are still often not handled appropriately.•We present a framework for handling and reporting analyses of incomplete data.•This framework encourages researchers to think systematically about missing data.•Adoption of this framework will increase the reproducibility of research findings.•This article provides a much needed framework for handling and reporting the analysis of incomplete data in observational studies.•The framework puts a strong emphasis on preplanning the statistical analysis and encourages transparency when reporting the results of a study.•Adoption of this framework will increase the confidence in and reproducibility of research findings.

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. 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). 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. 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: The Avon Longitudinal Study of Parents and Children-Generation 2 (ALSPAC-G2) was set up to provide a unique multi-generational cohort. It builds on the existing ALSPAC resource, which recruited 14,541 pregnancies to women resident in the South West of England who were expected to deliver between 01/04/1991 and 31/12/1992. Those women and their partners (Generation 0; ALSPAC-G0) and their offspring (ALSPAC-G1) have been followed for the last 26 years. This profile describes recruitment and data collection on the next generation (ALSPAC-G2)—the grandchildren of ALSPAC-G0 and children of ALSPAC-G1. Recruitment: Recruitment began on the 6 th of June 2012 and we present details of recruitment and participants up to 30 th June 2018 (~6 years). We knew at the start of recruitment that some ALSPAC-G1 participants had already become parents and ALSPAC-G2 is an open cohort; we recruit at any age. We hope to continue recruiting until all ALSPAC-G1 participants have completed their families. Up to 30 th June 2018 we recruited 810 ALSPAC-G2 participants from 548 families. Of these 810, 389 (48%) were recruited during their mother’s pregnancy, 287 (35%) before age 3 years, 104 (13%) between 3-6 years and 30 (4%) after 6 years. Over 70% of those invited to early pregnancy, late pregnancy, second week of life, 6-, 12- and 24-month assessments (whether for their recruitment, or a follow-up, visit) have attended, with attendance being over 60% for subsequent visits up to 7 years (to few are eligible for the 9- and 11-year assessments to analyse). Data collection: We collect a wide-range of social, lifestyle, clinical, anthropometric and biological data on all family members repeatedly. Biological samples include blood (including cord-blood), urine, meconium and faeces, and placental tissue. In subgroups detailed data collection, such as continuous glucose monitoring and videos of parent-child interactions, are being collected.

The COVID-19 pandemic and mitigation measures are likely to have a marked effect on mental health. It is important to use longitudinal data to improve inferences. To quantify the prevalence of depression, anxiety and mental well-being before and during the COVID-19 pandemic. Also, to identify groups at risk of depression and/or anxiety during the pandemic. Data were from the Avon Longitudinal Study of Parents and Children (ALSPAC) index generation (n = 2850, mean age 28 years) and parent generation (n = 3720, mean age 59 years), and Generation Scotland (n = 4233, mean age 59 years). Depression was measured with the Short Mood and Feelings Questionnaire in ALSPAC and the Patient Health Questionnaire-9 in Generation Scotland. Anxiety and mental well-being were measured with the Generalised Anxiety Disorder Assessment-7 and the Short Warwick Edinburgh Mental Wellbeing Scale. Depression during the pandemic was similar to pre-pandemic levels in the ALSPAC index generation, but those experiencing anxiety had almost doubled, at 24% (95% CI 23-26%) compared with a pre-pandemic level of 13% (95% CI 12-14%). In both studies, anxiety and depression during the pandemic was greater in younger members, women, those with pre-existing mental/physical health conditions and individuals in socioeconomic adversity, even when controlling for pre-pandemic anxiety and depression. These results provide evidence for increased anxiety in young people that is coincident with the pandemic. Specific groups are at elevated risk of depression and anxiety during the COVID-19 pandemic. This is important for planning current mental health provisions and for long-term impact beyond this pandemic.

Smoking prevalence is higher amongst individuals with schizophrenia and depression compared with the general population. Mendelian randomisation (MR) can examine whether this association is causal using genetic variants identified in genome-wide association studies (GWAS). We conducted two-sample MR to explore the bi-directional effects of smoking on schizophrenia and depression. For smoking behaviour, we used (1) smoking initiation GWAS from the GSCAN consortium and (2) we conducted our own GWAS of lifetime smoking behaviour (which captures smoking duration, heaviness and cessation) in a sample of 462690 individuals from the UK Biobank. We validated this instrument using positive control outcomes (e.g. lung cancer). For schizophrenia and depression we used GWAS from the PGC consortium. There was strong evidence to suggest smoking is a risk factor for both schizophrenia (odds ratio (OR) 2.27, 95% confidence interval (CI) 1.67-3.08, p < 0.001) and depression (OR 1.99, 95% CI 1.71-2.32, p < 0.001). Results were consistent across both lifetime smoking and smoking initiation. We found some evidence that genetic liability to depression increases smoking (β = 0.091, 95% CI 0.027-0.155, p = 0.005) but evidence was mixed for schizophrenia (β = 0.022, 95% CI 0.005-0.038, p = 0.009) with very weak evidence for an effect on smoking initiation. These findings suggest that the association between smoking, schizophrenia and depression is due, at least in part, to a causal effect of smoking, providing further evidence for the detrimental consequences of smoking on mental health.

Increased head circumference is an established finding in autism spectrum disorder (ASD); however, it is unclear when this increase occurs, if it persists and whether it manifests across the whole ASD spectrum. Head circumference is a strong predictor of brain size and can therefore provide key insights into brain development in ASD. We used data from the Avon Longitudinal Study of Parents and Children to compare head circumference trajectories from birth to 15 years in children with an ASD diagnosis ( N  = 78, controls = 6,404) or elevated autistic traits as measured using the Social Communication Disorder Checklist ( N  = 639, controls = 6,230). Exploratory analyses were conducted in those with ASD and co-morbid cognitive learning needs (CLN). Children with an ASD diagnosis had larger head circumference from birth across childhood and adolescence compared to controls in univariable (B = 0.69, 95% confidence interval [CI]: 0.28–1.09, p  = 0.001) and multivariable models (B = 0.38, 95% CI: 0.003–0.75, p  = 0.048). Differences were more marked in those with co-morbid CLN. Children with elevated autistic traits had significantly smaller head circumference compared to controls. There was weak evidence of group differences when height was included as a covariate. Head circumference trajectories in ASD deviate from control children and persist until adolescence. Autistic traits were associated with smaller head circumference, suggesting distinct growth trajectories between clinical cases from those with non-clinical traits.

[This corrects the article DOI: 10.3389/fnhum.2021.669902.].

Depression during adolescence is associated with a number of negative outcomes in later life. Research has examined the longitudinal nature of adolescent depression in order to identify patterns of depressive mood, the early antecedents and later consequences. However, rich longitudinal data is needed to better address these questions. The Avon Longitudinal Study of Parents and Children (ALSPAC) is an intergenerational birth cohort with nine repeated assessments of depressive symptoms throughout late childhood, adolescence and young adulthood. Depressive symptoms are measured using the Short Mood and Feelings Questionnaire (SMFQ). Many studies have used ALSPAC to examine the longitudinal nature of depressive symptoms in combination with the wealth of early life exposure and later outcome data. This data note provides a summary of the SMFQ data, where the data are stored in ALSPAC, the characteristics and distribution of the SMFQ, and highlights some considerations for researchers wanting to use the SMFQ data in ALSPAC.

The Avon Longitudinal Study of Parents and Children (ALSPAC) is a prospective population-based study. Initial recruitment of pregnant women took place in 1990-1992 and the health and development of the index children from these pregnancies and their family members have been followed ever since. The eligible sampling frame was constructed retrospectively using linked recruitment and health service records. Additional offspring that were eligible to enrol in the study have been welcomed through major recruitment drives at the ages of 7 and 18 years; and through opportunistic contacts since the age of 7. This data note provides a status update on the recruitment of the index children since the age of 7 years with a focus on enrolment since the age of 18, which has not been previously described. A total of 913 additional G1 (the cohort of index children) participants have been enrolled in the study since the age of 7 years with 195 of these joining since the age of 18. This additional enrolment provides a baseline sample of 14,901 G1 participants who were alive at 1 year of age.

Individual-level longitudinal data on biological, behavioural, and social dimensions are becoming increasingly available. Typically, these data are analysed using mixed effects models, with the result summarised in terms of an average trajectory plus measures of the individual variations around this average. However, public health investigations would benefit from finer modelling of these individual variations which identify not just one average trajectory, but several typical trajectories. If evidence of heterogeneity in the development of these variables is found, the role played by temporally preceding (explanatory) variables as well as the potential impact of differential trajectories may have on later outcomes is often of interest. A wide choice of methods for uncovering typical trajectories and relating them to precursors and later outcomes exists. However, despite their increasing use, no practical overview of these methods targeted at epidemiological applications exists. Hence we provide: (a) a review of the three most commonly used methods for the identification of latent trajectories (growth mixture models, latent class growth analysis, and longitudinal latent class analysis); and (b) recommendations for the identification and interpretation of these trajectories and of their relationship with other variables. For illustration, we use longitudinal data on childhood body mass index and parental reports of fussy eating, collected in the Avon Longitudinal Study of Parents and Children.