Estimation of the linear mixed integrated Ornstein-Uhlenbeck model.

The linear mixed model with an added integrated Ornstein-Uhlenbeck (IOU) process (linear mixed IOU model) allows for serial correlation and estimation of the degree of derivative tracking. It is rarely used, partly due to the lack of available software. We implemented the linear mixed IOU model in Stata and using simulations we assessed the feasibility of fitting the model by restricted maximum likelihood when applied to balanced and unbalanced data. We compared different (1) optimization algorithms, (2) parameterizations of the IOU process, (3) data structures and (4) random-effects structures. Fitting the model was practical and feasible when applied to large and moderately sized balanced datasets (20,000 and 500 observations), and large unbalanced datasets with (non-informative) dropout and intermittent missingness. Analysis of a real dataset showed that the linear mixed IOU model was a better fit to the data than the standard linear mixed model (i.e. independent within-subject errors with constant variance)

Associations of wheezing phenotypes with late asthma outcomes in the Avon Longitudinal Study of Parents and Children:A population-based birth cohort

BackgroundVariable patterns of childhood wheezing might indicate differences in the cause and prognosis of respiratory illnesses. Better understanding of these patterns could facilitate identification of modifiable factors related to development of asthma.ObjectivesWe characterized childhood wheezing phenotypes from infancy to adolescence and their associations with asthma outcomes.MethodsLatent class analysis was used to derive phenotypes based on patterns of wheezing recorded at up to 14 time points from birth to 16½ years among 12,303 participants from the Avon Longitudinal Study of Parents and Children. Measures of lung function (FEV1, forced vital capacity [FVC], and forced expiratory flow between 25% and 75% [FEF25-75]) and fraction of exhaled nitric oxide (Feno) were made at 14 to 15 years of age.ResultsSix wheezing phenotypes were identified: never/infrequent, preschool-onset remitting, midchildhood-onset remitting, school age–onset persisting, late childhood–onset persisting, and continuous wheeze. The 3 persistent phenotypes were associated with bronchodilator reversibility of 12% or greater (BDR) from baseline (odds ratio [OR] range, 2.14-3.34), a Feno value of 35 ppb or greater (OR range, 3.82-6.24), and lung function decrements (mean range of differences: −0.22 to −0.27 SD units (SDU) for FEV1/FVC ratio and −0.21 to −0.33 SDU for FEF25-75) compared with never/infrequent wheeze. Midchildhood-onset (4½ years) remitting wheeze was associated with BDR (OR, 1.77; 95% CI, 1.11-2.82), a Feno value of 35 ppb or greater (OR, 1.72; 95% CI, 1.14-2.59), FEV1/FVC ratio decrements (OR, −0.22 SDU; 95% CI, −0.36 to −0.08 SDU), and FEF25-75 decrements (OR, −0.16 SDU; 95% CI, −0.30 to −0.01 SDU). Preschool-onset (18 months) remitting wheeze was only associated with FEV1/FVC ratio decrements (OR, −0.15 SDU; 95% CI, −0.25 to −0.05 SDU) and FEF25-75 decrements (OR, −0.14 SDU; 95% CI, −0.24 to −0.04 SDU). The persisting phenotypes showed evidence of sex stratification during adolescence.ConclusionsEarly childhood–onset wheezing that persists into adolescence represents the clearest target group for interventions to maximize lung function outcomes

Appropriate inclusion of interactions was needed to avoid bias in multiple imputation

OBJECTIVE: Missing data are a pervasive problem, often leading to bias in complete records analysis (CRA). Multiple imputation (MI) via chained equations is one solution, but its use in the presence of interactions is not straightforward. STUDY DESIGN AND SETTING: We simulated data with outcome Y dependent on binary explanatory variables X and Z and their interaction XZ. Six scenarios were simulated (Y continuous and binary, each with no interaction, a weak and a strong interaction), under five missing data mechanisms. We use directed acyclic graphs to identify when CRA and MI would each be unbiased. We evaluate the performance of CRA, MI without interactions, MI including all interactions, and stratified imputation. We also illustrated these methods using a simple example from the National Child Development Study (NCDS). RESULTS: MI excluding interactions is invalid and resulted in biased estimates and low coverage. When XZ was zero, MI excluding interactions gave unbiased estimates but overcoverage. MI including interactions and stratified MI gave equivalent, valid inference in all cases. In the NCDS example, MI excluding interactions incorrectly concluded there was no evidence for an important interaction. CONCLUSIONS: Epidemiologists carrying out MI should ensure that their imputation model(s) are compatible with their analysis model

Empirical evidence of study design biases in randomized trials:Systematic review of meta-epidemiological studies

To synthesise evidence on the average bias and heterogeneity associated with reported methodological features of randomized trials.Systematic review of meta-epidemiological studies.We retrieved eligible studies included in a recent AHRQ-EPC review on this topic (latest search September 2012), and searched Ovid MEDLINE and Ovid EMBASE for studies indexed from Jan 2012-May 2015. Data were extracted by one author and verified by another. We combined estimates of average bias (e.g. ratio of odds ratios (ROR) or difference in standardised mean differences (dSMD)) in meta-analyses using the random-effects model. Analyses were stratified by type of outcome ("mortality" versus "other objective" versus "subjective"). Direction of effect was standardised so that ROR < 1 and dSMD < 0 denotes a larger intervention effect estimate in trials with an inadequate or unclear (versus adequate) characteristic.We included 24 studies. The available evidence suggests that intervention effect estimates may be exaggerated in trials with inadequate/unclear (versus adequate) sequence generation (ROR 0.93, 95% CI 0.86 to 0.99; 7 studies) and allocation concealment (ROR 0.90, 95% CI 0.84 to 0.97; 7 studies). For these characteristics, the average bias appeared to be larger in trials of subjective outcomes compared with other objective outcomes. Also, intervention effects for subjective outcomes appear to be exaggerated in trials with lack of/unclear blinding of participants (versus blinding) (dSMD -0.37, 95% CI -0.77 to 0.04; 2 studies), lack of/unclear blinding of outcome assessors (ROR 0.64, 95% CI 0.43 to 0.96; 1 study) and lack of/unclear double blinding (ROR 0.77, 95% CI 0.61 to 0.93; 1 study). The influence of other characteristics (e.g. unblinded trial personnel, attrition) is unclear.Certain characteristics of randomized trials may exaggerate intervention effect estimates. The average bias appears to be greatest in trials of subjective outcomes. More research on several characteristics, particularly attrition and selective reporting, is needed

The association between BMI and mortality using offspring BMI as an indicator of own BMI: large intergenerational mortality study

Objectives To obtain valid estimates of the association between body mass index (BMI) and mortality by using offspring BMI as an instrumental variable for own BMI

An evaluation of a multi-component adult weight management on referral intervention in a community setting

BACKGROUND: National Institute for Health and Care Excellence (NICE) guidance on adult weight management recommends interventions are multi-component. We aimed to assess the implementation and health benefits of a primary care referral to an adult multi-component weight management intervention in a community setting. The intervention was offered through Primary care in National Health Service (NHS) South Gloucestershire, UK, from Oct 2008 to Nov 2010, in partnership with statutory, community and commercial providers. The scheme offered 12 weeks’ community based concurrent support of dietary (Weight Watchers, WW), physical activity (Exercise on Prescription, EOP) and behavioural change (motivational interviewing) components to obese adults. Funding was available for 600 places. RESULTS: Five hundred and fifty nine participants engaged with the intervention, mean age 48 years, 88 % female. Mean weight loss for all engagers was 3.7 kg (95 % confidence interval 3.4, 4.1). Participants completing the intervention achieved the largest weight reduction (mean loss 5.9 kg; 5.3, 6.6). Achievement of 5 % weight loss was higher in completers (58 %; 50, 65) compared to non-completers (19 %; 12, 26) and people who only participated in one commercial component of the intervention (either WW or EOP; 19 %; 13, 24). CONCLUSION: A multi-component weight management programme may be beneficial for weight loss, but a randomized controlled trial is needed to establish effectiveness and to evaluate cost