Analysis and Reporting of Randomized Trials in Cleft Palate Surgery: Learning from the Timing of Primary Surgery (TOPS) Trial.

The Timing of Primary Surgery (TOPS) trial was published August 2023 in the New England Journal of Medicine and is a milestone achievement for a study focused on cleft palate. Due to the complexity of outcome reporting in cleft and the rarity of such comparative trials, TOPS presents a useful opportunity to critically review the design, analysis and reporting strategies utilised. This perspective article focused on the inclusion of participants, the choice of the primary outcome measure and the analysis of ordinal data within the trial. Considerations for future comparative studies in cleft care are discussed.

Recoverability and estimation of causal effects under typical multivariable missingness mechanisms.

In the context of missing data, the identifiability or "recoverability" of the average causal effect (ACE) depends not only on the usual causal assumptions but also on missingness assumptions that can be depicted by adding variable-specific missingness indicators to causal diagrams, creating missingness directed acyclic graphs (m-DAGs). Previous research described canonical m-DAGs, representing typical multivariable missingness mechanisms in epidemiological studies, and examined mathematically the recoverability of the ACE in each case. However, this work assumed no effect modification and did not investigate methods for estimation across such scenarios. Here, we extend this research by determining the recoverability of the ACE in settings with effect modification and conducting a simulation study to evaluate the performance of widely used missing data methods when estimating the ACE using correctly specified g-computation. Methods assessed were complete case analysis (CCA) and various implementations of multiple imputation (MI) with varying degrees of compatibility with the outcome model used in g-computation. Simulations were based on an example from the Victorian Adolescent Health Cohort Study (VAHCS), where interest was in estimating the ACE of adolescent cannabis use on mental health in young adulthood. We found that the ACE is recoverable when no incomplete variable (exposure, outcome, or confounder) causes its own missingness, and nonrecoverable otherwise, in simplified versions of 10 canonical m-DAGs that excluded unmeasured common causes of missingness indicators. Despite this nonrecoverability, simulations showed that MI approaches that are compatible with the outcome model in g-computation may enable approximately unbiased estimation across all canonical m-DAGs considered, except when the outcome causes its own missingness or causes the missingness of a variable that causes its own missingness. In the latter settings, researchers may need to consider sensitivity analysis methods incorporating external information (e.g., delta-adjustment methods). The VAHCS case study illustrates the practical implications of these findings.

Handling missing data when estimating causal effects with Targeted Maximum Likelihood Estimation.

Targeted Maximum Likelihood Estimation (TMLE) is increasingly used for doubly robust causal inference, but how missing data should be handled when using TMLE with data-adaptive approaches is unclear. Based on the Victorian Adolescent Health Cohort Study, we conducted a simulation study to evaluate eight missing data methods in this context: complete-case analysis, extended TMLE incorporating outcome-missingness model, missing covariate missing indicator method, five multiple imputation (MI) approaches using parametric or machine-learning models. Six scenarios were considered, varying in exposure/outcome generation models (presence of confounder-confounder interactions) and missingness mechanisms (whether outcome influenced missingness in other variables and presence of interaction/non-linear terms in missingness models). Complete-case analysis and extended TMLE had small biases when outcome did not influence missingness in other variables. Parametric MI without interactions had large bias when exposure/outcome generation models included interactions. Parametric MI including interactions performed best in bias and variance reduction across all settings, except when missingness models included a non-linear term. When choosing a method to handle missing data in the context of TMLE, researchers must consider the missingness mechanism and, for MI, compatibility with the analysis method. In many settings, a parametric MI approach that incorporates interactions and non-linearities is expected to perform well.

A comparison of strategies for selecting auxiliary variables for multiple imputation.

Multiple imputation (MI) is a popular method for handling missing data. Auxiliary variables can be added to the imputation model(s) to improve MI estimates. However, the choice of which auxiliary variables to include is not always straightforward. Several data-driven auxiliary variable selection strategies have been proposed, but there has been limited evaluation of their performance. Using a simulation study we evaluated the performance of eight auxiliary variable selection strategies: (1, 2) two versions of selection based on correlations in the observed data; (3) selection using hypothesis tests of the "missing completely at random" assumption; (4) replacing auxiliary variables with their principal components; (5, 6) forward and forward stepwise selection; (7) forward selection based on the estimated fraction of missing information; and (8) selection via the least absolute shrinkage and selection operator (LASSO). A complete case analysis and an MI analysis using all auxiliary variables (the "full model") were included for comparison. We also applied all strategies to a motivating case study. The full model outperformed all auxiliary variable selection strategies in the simulation study, with the LASSO strategy the best performing auxiliary variable selection strategy overall. All MI analysis strategies that we were able to apply to the case study led to similar estimates, although computational time was substantially reduced when variable selection was employed. This study provides further support for adopting an inclusive auxiliary variable strategy where possible. Auxiliary variable selection using the LASSO may be a promising alternative when the full model fails or is too burdensome.

On the use of multiple imputation to address data missing by design as well as unintended missing data in case-cohort studies with a binary endpoint.

BACKGROUND: Case-cohort studies are conducted within cohort studies, with the defining feature that collection of exposure data is limited to a subset of the cohort, leading to a large proportion of missing data by design. Standard analysis uses inverse probability weighting (IPW) to address this intended missing data, but little research has been conducted into how best to perform analysis when there is also unintended missingness. Multiple imputation (MI) has become a default standard for handling unintended missingness and is typically used in combination with IPW to handle the intended missingness due to the case-control sampling. Alternatively, MI could be used to handle both the intended and unintended missingness. While the performance of an MI-only approach has been investigated in the context of a case-cohort study with a time-to-event outcome, it is unclear how this approach performs with a binary outcome. METHODS: We conducted a simulation study to assess and compare the performance of approaches using only MI, only IPW, and a combination of MI and IPW, for handling intended and unintended missingness in the case-cohort setting. We also applied the approaches to a case study. RESULTS: Our results show that the combined approach is approximately unbiased for estimation of the exposure effect when the sample size is large, and was the least biased with small sample sizes, while MI-only and IPW-only exhibited larger biases in both sample size settings. CONCLUSIONS: These findings suggest that a combined MI/IPW approach should be preferred to handle intended and unintended missing data in case-cohort studies with binary outcomes.

The forgotten girls: the state of evidence for health interventions for pregnant adolescents and their newborns in low-income and middle-income countries.

Every year, an estimated 21 million girls aged 15-19 years become pregnant in low-income and middle-income countries (LMICs). Policy responses have focused on reducing the adolescent birth rate whereas efforts to support pregnant adolescents have developed more slowly. We did a systematic review of interventions addressing any health-related outcome for pregnant adolescents and their newborn babies in LMICs and mapped its results to a framework describing high-quality health systems for pregnant adolescents. Although we identified some promising interventions, such as micronutrient supplementation, conditional cash transfers, and well facilitated group care, most studies were at high risk of bias and there were substantial gaps in evidence. These included major gaps in delivery, abortion, and postnatal care, and mental health, violence, and substance misuse-related outcomes. We recommend that the fields of adolescent, maternal, and sexual and reproductive health collaborate to develop more adolescent-inclusive maternal health care and research, and specific interventions for pregnant adolescents. We outline steps to develop high-quality, evidence-based care for the millions of pregnant adolescents and their newborns who currently do not receive this.

Two-Year Outcomes After Minimally Invasive Surfactant Therapy in Preterm Infants: Follow-Up of the OPTIMIST-A Randomized Clinical Trial.

Importance: The long-term effects of surfactant administration via a thin catheter (minimally invasive surfactant therapy [MIST]) in preterm infants with respiratory distress syndrome remain to be definitively clarified. Objective: To examine the effect of MIST on death or neurodevelopmental disability (NDD) at 2 years' corrected age. Design, Setting, and Participants: Follow-up study of a randomized clinical trial with blinding of clinicians and outcome assessors conducted in 33 tertiary-level neonatal intensive care units in 11 countries. The trial included 486 infants with a gestational age of 25 to 28 weeks supported with continuous positive airway pressure (CPAP). Collection of follow-up data at 2 years' corrected age was completed on December 9, 2022. Interventions: Infants assigned to MIST (n = 242) received exogenous surfactant (200 mg/kg poractant alfa) via a thin catheter; those assigned to the control group (n = 244) received sham treatment. Main Outcomes and Measures: The key secondary outcome of death or moderate to severe NDD was assessed at 2 years' corrected age. Other secondary outcomes included components of this composite outcome, as well as hospitalizations for respiratory illness and parent-reported wheezing or breathing difficulty in the first 2 years. Results: Among the 486 infants randomized, 453 had follow-up data available (median gestation, 27.3 weeks; 228 females [50.3%]); data on the key secondary outcome were available in 434 infants. Death or NDD occurred in 78 infants (36.3%) in the MIST group and 79 (36.1%) in the control group (risk difference, 0% [95% CI, -7.6% to 7.7%]; relative risk [RR], 1.0 [95% CI, 0.81-1.24]); components of this outcome did not differ significantly between groups. Secondary respiratory outcomes favored the MIST group. Hospitalization with respiratory illness occurred in 49 infants (25.1%) in the MIST group vs 78 (38.2%) in the control group (RR, 0.66 [95% CI, 0.54-0.81]) and parent-reported wheezing or breathing difficulty in 73 (40.6%) vs 104 (53.6%), respectively (RR, 0.76 [95% CI, 0.63-0.90]). Conclusions and Relevance: In this follow-up study of a randomized clinical trial of preterm infants with respiratory distress syndrome supported with CPAP, MIST compared with sham treatment did not reduce the incidence of death or NDD by 2 years of age. However, infants who received MIST had lower rates of adverse respiratory outcomes during their first 2 years of life. Trial Registration: anzctr.org.au Identifier: ACTRN12611000916943.

Effectiveness of asthma preventer dispensing for preventing childhood asthma readmissions: a multisite cohort linkage study.

OBJECTIVES: To (1) describe the dispensing of asthma preventers at hospital discharge and estimate its effect on hospital readmissions, and (2) estimate the effect of community asthma preventer dispensing on readmissions for the subgroup of children who were not prescribed an asthma preventer at discharge. DESIGN: Multisite cohort study with linked administrative data. PARTICIPANTS: Children aged 3-18 years admitted with asthma to a tertiary paediatric, mixed paediatric and adult, or regional hospital between 2017 and 2018. MAIN OUTCOME MEASURE: Hospital readmission for asthma within 12 months. RESULTS: Of the 767 participants, 201 (26.2%) were newly prescribed or requested to continue with asthma preventers. Of these, only 91 (45.3%) dispensed their discharge prescription within 3 days or had an active prescription. There was no evidence for a protective effect of discharge asthma preventer dispensing on asthma hospital readmissions within 12 months (OR 1.17, 95% CI 0.69 to 1.97, p=0.57). Of the 566 children who were not prescribed asthma preventers at discharge, 269 (47.5%) had one or more prescriptions dispensed in the community within 12 months. Participants who were in the protected period (asthma preventer dispensed) had reduced risk of an asthma hospital readmission (HR 0.61, 95% CI 0.36 to 1.02, p=0.06), including preschool children (HR 0.48, 95% CI 0.25, 0.93, p=0.03) on subgroup analysis. CONCLUSIONS: There was a low rate for prescribing and dispensing of hospital discharge asthma preventers and no protective effect was found for its impact on readmissions. A protective effect on readmissions was found for community asthma preventer dispensing.

Success of blinding a procedural intervention in a randomised controlled trial in preterm infants receiving respiratory support.

BACKGROUND: Blinding of treatment allocation from treating clinicians in neonatal randomised controlled trials can minimise performance bias, but its effectiveness is rarely assessed. METHODS: To examine the effectiveness of blinding a procedural intervention from treating clinicians in a multicentre randomised controlled trial of minimally invasive surfactant therapy versus sham treatment in preterm infants of gestation 25-28 weeks with respiratory distress syndrome. The intervention (minimally invasive surfactant therapy or sham) was performed behind a screen within the first 6 h of life by a 'study team' uninvolved in clinical care including decision-making. Procedure duration and the study team's words and actions during the sham treatment mimicked those of the minimally invasive surfactant therapy procedure. Post-intervention, three clinicians completed a questionnaire regarding perceived group allocation, with the responses matched against actual intervention and categorised as correct, incorrect, or unsure. Success of blinding was calculated using validated blinding indices applied to the data overall (James index, successful blinding defined as > 0.50), or to the two treatment allocation groups (Bang index, successful blinding: -0.30 to 0.30). Blinding success was measured within staff role, and the associations between blinding success and procedural duration and oxygenation improvement post-procedure were estimated. RESULTS: From 1345 questionnaires in relation to a procedural intervention in 485 participants, responses were categorised as correct in 441 (33%), incorrect in 142 (11%), and unsure in 762 (57%), with similar proportions for each of the response categories in the two treatment arms. The James index indicated successful blinding overall 0.67 (95% confidence interval (CI) 0.65-0.70). The Bang index was 0.28 (95% CI 0.23-0.32) in the minimally invasive surfactant therapy group and 0.17 (95% CI 0.12-0.21) in the sham arm. Neonatologists more frequently guessed the correct intervention (47%) than bedside nurses (36%), neonatal trainees (31%), and other nurses (24%). For the minimally invasive surfactant therapy intervention, the Bang index was linearly related to procedural duration and oxygenation improvement post-procedure. No evidence of such relationships was seen in the sham arm. CONCLUSION: Blinding of a procedural intervention from clinicians is both achievable and measurable in neonatal randomised controlled trials.

Assumptions and analysis planning in studies with missing data in multiple variables: moving beyond the MCAR/MAR/MNAR classification.

Researchers faced with incomplete data are encouraged to consider whether their data are 'missing completely at random' (MCAR), 'missing at random' (MAR) or 'missing not at random' (MNAR) when planning their analysis. However, there are two major problems with this classification as originally defined by Rubin in the 1970s. First, when there are missing data in multiple variables, the plausibility of the MAR assumption is difficult to assess using substantive knowledge and is more stringent than is generally appreciated. Second, although MCAR and MAR are sufficient conditions for consistent estimation with specific methods, they are not necessary conditions and therefore this categorization does not directly determine the best approach for handling the missing data in an analysis. How best to handle missing data depends on the assumed causal relationships between variables and their missingness, and what these relationships imply in terms of the 'recoverability' of the target estimand (the population parameter that encodes the answer to the underlying research question). Recoverability is defined as whether the estimand can be consistently estimated from the patterns and associations in the observed data without needing to invoke external information on the extent to which the distribution of missing values might differ from that of observed values. In this manuscript we outline an approach for deciding which method to use to handle multivariable missing data in an analysis, using directed acyclic graphs to depict missingness assumptions and determining the implications in terms of recoverability of the target estimand.

Parental personality and early life ecology: a prospective cohort study from preconception to postpartum.

Personality reliably predicts life outcomes ranging from social and material resources to mental health and interpersonal capacities. However, little is known about the potential intergenerational impact of parent personality prior to offspring conception on family resources and child development across the first thousand days of life. We analysed data from the Victorian Intergenerational Health Cohort Study (665 parents, 1030 infants; est. 1992), a two-generation study with prospective assessment of preconception background factors in parental adolescence, preconception personality traits in young adulthood (agreeableness, conscientiousness, emotional stability, extraversion, and openness), and multiple parental resources and infant characteristics in pregnancy and after the birth of their child. After adjusting for pre-exposure confounders, both maternal and paternal preconception personality traits were associated with numerous parental resources and attributes in pregnancy and postpartum, as well as with infant biobehavioural characteristics. Effect sizes ranged from small to moderate when considering parent personality traits as continuous exposures, and from small to large when considering personality traits as binary exposures. Young adult personality, well before offspring conception, is associated with the perinatal household social and financial context, parental mental health, parenting style and self-efficacy, and temperamental characteristics of offspring. These are pivotal aspects of early life development that ultimately predict a child's long-term health and development.

Should multiple imputation be stratified by exposure group when estimating causal effects via outcome regression in observational studies?

BACKGROUND: Despite recent advances in causal inference methods, outcome regression remains the most widely used approach for estimating causal effects in epidemiological studies with a single-point exposure and outcome. Missing data are common in these studies, and complete-case analysis (CCA) and multiple imputation (MI) are two frequently used methods for handling them. In randomised controlled trials (RCTs), it has been shown that MI should be conducted separately by treatment group. In observational studies, causal inference is now understood as the task of emulating an RCT, which raises the question of whether MI should be conducted by exposure group in such studies. METHODS: We addressed this question by evaluating the performance of seven methods for handling missing data when estimating causal effects with outcome regression. We conducted an extensive simulation study based on an illustrative case study from the Victorian Adolescent Health Cohort Study, assessing a range of scenarios, including seven outcome generation models with exposure-confounder interactions of differing strength. RESULTS: The simulation results showed that MI by exposure group led to the least bias when the size of the smallest exposure group was relatively large, followed by MI approaches that included the exposure-confounder interactions. CONCLUSIONS: The findings from our simulation study, which was designed based on a real case study, suggest that current practice for the conduct of MI in causal inference may need to shift to stratifying by exposure group where feasible, or otherwise including exposure-confounder interactions in the imputation model.

Modelling timing and tempo of adrenarche in a prospective cohort study.

To better understand how health risk processes are linked to adrenarche, measures of adrenarcheal timing and tempo are needed. Our objective was to describe and classify adrenal trajectories, in terms of timing and tempo, in a population of children transitioning to adolescence with repeated measurements of salivary dehydroepiandrosterone (DHEA), DHEA-sulphate, and testosterone. We analysed data from the Childhood to Adolescence Transition Study (CATS), a longitudinal study of 1239 participants, recruited at 8-9 years old and followed up annually. Saliva samples were assayed for adrenal hormones. Linear mixed-effect models with subject-specific random intercepts and slopes were used to model longitudinal hormone trajectories by sex and derive measures of adrenarcheal timing and tempo. The median values for all hormones were higher at each consecutive study wave for both sexes, and higher for females than males. For all hormones, between-individual variation in hormone levels at age 9 (timing) was moderately large and similar for females and males. Between-individual variation in hormone progression over time (tempo) was of moderate magnitude compared with the population average age-slope, which itself was small compared with overall hormone level at each age. This suggests that between-individual variation in tempo was less important for modelling hormone trajectories. Between-individual variation in timing was more important for determining relative adrenal hormonal level in childhood than tempo. This finding suggests that adrenal hormonal levels at age 8-9 years can be used to predict relative levels in early adolescence (up to 13 years).

Serological testing of blood donors to characterise the impact of COVID-19 in Melbourne, Australia, 2020.

Rapidly identifying and isolating people with acute SARS-CoV-2 infection has been a core strategy to contain COVID-19 in Australia, but a proportion of infections go undetected. We estimated SARS-CoV-2 specific antibody prevalence (seroprevalence) among blood donors in metropolitan Melbourne following a COVID-19 outbreak in the city between June and September 2020. The aim was to determine the extent of infection spread and whether seroprevalence varied demographically in proportion to reported cases of infection. The design involved stratified sampling of residual specimens from blood donors (aged 20-69 years) in three postcode groups defined by low (<3 cases/1,000 population), medium (3-7 cases/1,000 population) and high (>7 cases/1,000 population) COVID-19 incidence based on case notification data. All specimens were tested using the Wantai SARS-CoV-2 total antibody assay. Seroprevalence was estimated with adjustment for test sensitivity and specificity for the Melbourne metropolitan blood donor and residential populations, using multilevel regression and poststratification. Overall, 4,799 specimens were collected between 23 November and 17 December 2020. Seroprevalence for blood donors was 0.87% (90% credible interval: 0.25-1.49%). The highest estimates, of 1.13% (0.25-2.15%) and 1.11% (0.28-1.95%), respectively, were observed among donors living in the lowest socioeconomic areas (Quintiles 1 and 2) and lowest at 0.69% (0.14-1.39%) among donors living in the highest socioeconomic areas (Quintile 5). When extrapolated to the Melbourne residential population, overall seroprevalence was 0.90% (0.26-1.51%), with estimates by demography groups similar to those for the blood donors. The results suggest a lack of extensive community transmission and good COVID-19 case ascertainment based on routine testing during Victoria's second epidemic wave. Residual blood donor samples provide a practical epidemiological tool for estimating seroprevalence and information on population patterns of infection, against which the effectiveness of ongoing responses to the pandemic can be assessed.

Evaluation of multiple imputation approaches for handling missing covariate information in a case-cohort study with a binary outcome.

BACKGROUND: In case-cohort studies a random subcohort is selected from the inception cohort and acts as the sample of controls for several outcome investigations. Analysis is conducted using only the cases and the subcohort, with inverse probability weighting (IPW) used to account for the unequal sampling probabilities resulting from the study design. Like all epidemiological studies, case-cohort studies are susceptible to missing data. Multiple imputation (MI) has become increasingly popular for addressing missing data in epidemiological studies. It is currently unclear how best to incorporate the weights from a case-cohort analysis in MI procedures used to address missing covariate data. METHOD: A simulation study was conducted with missingness in two covariates, motivated by a case study within the Barwon Infant Study. MI methods considered were: using the outcome, a proxy for weights in the simple case-cohort design considered, as a predictor in the imputation model, with and without exposure and covariate interactions; imputing separately within each weight category; and using a weighted imputation model. These methods were compared to a complete case analysis (CCA) within the context of a standard IPW analysis model estimating either the risk or odds ratio. The strength of associations, missing data mechanism, proportion of observations with incomplete covariate data, and subcohort selection probability varied across the simulation scenarios. Methods were also applied to the case study. RESULTS: There was similar performance in terms of relative bias and precision with all MI methods across the scenarios considered, with expected improvements compared with the CCA. Slight underestimation of the standard error was seen throughout but the nominal level of coverage (95%) was generally achieved. All MI methods showed a similar increase in precision as the subcohort selection probability increased, irrespective of the scenario. A similar pattern of results was seen in the case study. CONCLUSIONS: How weights were incorporated into the imputation model had minimal effect on the performance of MI; this may be due to case-cohort studies only having two weight categories. In this context, inclusion of the outcome in the imputation model was sufficient to account for the unequal sampling probabilities in the analysis model.

Evaluating the performance of Bayesian and restricted maximum likelihood estimation for stepped wedge cluster randomized trials with a small number of clusters.

BACKGROUND: Stepped wedge trials are an appealing and potentially powerful cluster randomized trial design. However, they are frequently implemented with a small number of clusters. Standard analysis methods for these trials such as a linear mixed model with estimation via maximum likelihood or restricted maximum likelihood (REML) rely on asymptotic properties and have been shown to yield inflated type I error when applied to studies with a small number of clusters. Small-sample methods such as the Kenward-Roger approximation in combination with REML can potentially improve estimation of the fixed effects such as the treatment effect. A Bayesian approach may also be promising for such multilevel models but has not yet seen much application in cluster randomized trials. METHODS: We conducted a simulation study comparing the performance of REML with and without a Kenward-Roger approximation to a Bayesian approach using weakly informative prior distributions on the intracluster correlation parameters. We considered a continuous outcome and a range of stepped wedge trial configurations with between 4 and 40 clusters. To assess method performance we calculated bias and mean squared error for the treatment effect and correlation parameters and the coverage of 95% confidence/credible intervals and relative percent error in model-based standard error for the treatment effect. RESULTS: Both REML with a Kenward-Roger standard error and degrees of freedom correction and the Bayesian method performed similarly well for the estimation of the treatment effect, while intracluster correlation parameter estimates obtained via the Bayesian method were less variable than REML estimates with different relative levels of bias. CONCLUSIONS: The use of REML with a Kenward-Roger approximation may be sufficient for the analysis of stepped wedge cluster randomized trials with a small number of clusters. However, a Bayesian approach with weakly informative prior distributions on the intracluster correlation parameters offers a viable alternative, particularly when there is interest in the probability-based inferences permitted within this paradigm.

Seroprevalence of Severe Acute Respiratory Syndrome Coronavirus 2-Specific Antibodies in Australia After the First Epidemic Wave in 2020: A National Survey.

BACKGROUND: As of mid-2021, Australia's only nationwide coronavirus disease 2019 (COVID-19) epidemic occurred in the first 6 months of the pandemic. Subsequently, there has been limited transmission in most states and territories. Understanding community spread during the first wave was hampered by initial limitations on testing and surveillance. To characterize the prevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-specific antibody seroprevalence generated during this time, we undertook Australia's largest national SARS-CoV-2 serosurvey. METHODS: Between June 19 and August 6, 2020, residual specimens were sampled from people undergoing general pathology testing (all ages), women attending antenatal screening (20-39 years), and blood donors (20-69 years) based on the Australian population's age and geographic distributions. Specimens were tested by Wantai total SARS-CoV-2-antibody assay. Seroprevalence estimates adjusted for test performance were produced. The SARS-CoV-2 antibody-positive specimens were characterized with microneutralization assays. RESULTS: Of 11 317 specimens (5132 general pathology; 2972 antenatal; 3213 blood-donors), 71 were positive for SARS-CoV-2-specific antibodies. Seroprevalence estimates were 0.47% (95% credible interval [CrI], 0.04%-0.89%), 0.25% (CrI, 0.03%-0.54%), and 0.23% (CrI, 0.04%-0.54%), respectively. No seropositive specimens had neutralizing antibodies. CONCLUSIONS: Australia's seroprevalence was extremely low (<0.5%) after the only national COVID-19 wave thus far. These data and the subsequent limited community transmission highlight the population's naivety to SARS-CoV-2 and the urgency of increasing vaccine-derived protection.

Evaluation of approaches for accommodating interactions and non-linear terms in multiple imputation of incomplete three-level data.

Three-level data structures arising from repeated measures on individuals clustered within larger units are common in health research studies. Missing data are prominent in such studies and are often handled via multiple imputation (MI). Although several MI approaches can be used to account for the three-level structure, including adaptations to single- and two-level approaches, when the substantive analysis model includes interactions or quadratic effects, these too need to be accommodated in the imputation model. In such analyses, substantive model compatible (SMC) MI has shown great promise in the context of single-level data. Although there have been recent developments in multilevel SMC MI, to date only one approach that explicitly handles incomplete three-level data is available. Alternatively, researchers can use pragmatic adaptations to single- and two-level MI approaches, or two-level SMC-MI approaches. We describe the available approaches and evaluate them via simulations in the context of three three-level random effects analysis models involving an interaction between the incomplete time-varying exposure and time, an interaction between the time-varying exposure and an incomplete time-fixed confounder, or a quadratic effect of the exposure. Results showed that all approaches considered performed well in terms of bias and precision when the target analysis involved an interaction with time, but the three-level SMC MI approach performed best when the target analysis involved an interaction between the time-varying exposure and an incomplete time-fixed confounder, or a quadratic effect of the exposure. We illustrate the methods using data from the Childhood to Adolescence Transition Study.