Bootstrap Inference for Multiple Imputation under Uncongeniality and Misspecification

Multiple imputation has become one of the most popular approaches for handling missing data in statistical analyses. Part of this success is due to Rubin's simple combination rules. These give frequentist valid inferences when the imputation and analysis procedures are so called congenial and the complete data analysis is valid, but otherwise may not. Roughly speaking, congeniality corresponds to whether the imputation and analysis models make different assumptions about the data. In practice imputation and analysis procedures are often not congenial, such that tests may not have the correct size and confidence interval coverage deviates from the advertised level. We examine a number of recent proposals which combine bootstrapping with multiple imputation, and determine which are valid under uncongeniality and model misspecification. Imputation followed by bootstrapping generally does not result in valid variance estimates under uncongeniality or misspecification, whereas bootstrapping followed by imputation does. We recommend a particular computationally efficient variant of bootstrapping followed by imputation.Comment: Updated (fixed) reference based simulation results. Now included tables which were previously not included as they were in supplementary information document. Swapped order of the two simulation studies. Added acknowledgement and funding statement

An Interval Estimation Approach to Sample Selection Bias

A widespread and largely unaddressed challenge in statistics is that non-random participation in study samples can bias the estimation of parameters of interest. To address this problem, we propose a computationally efficient interval estimator for a class of population parameters encompassing population means, ordinary least squares and instrumental variables estimands which makes minimal assumptions about the selection mechanism. Using results from stochastic programming, we derive valid confidence intervals and hypothesis tests based on this estimator. In addition, we demonstrate how to tighten the intervals by incorporating additional constraints based on population-level information commonly available to researchers, such as survey response rates and covariate means. We conduct a comprehensive simulation study to evaluate the finite sample performance of our estimator and conclude with a real data study on the causal effect of education on income in the highly-selected UK Biobank cohort. We are able to demonstrate that our method can produce informative bounds under relatively few population-level auxiliary constraints.Comment: 31 pages, 6 figure

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)

Next-Generation Invaders? Hotspots for Naturalised Sleeper Weeds in Australia under Future Climates

Naturalised, but not yet invasive plants, pose a nascent threat to biodiversity. As climate regimes continue to change, it is likely that a new suite of invaders will emerge from the established pool of naturalised plants. Pre-emptive management of locations that may be most suitable for a large number of potentially invasive plants will help to target monitoring, and is vital for effective control. We used species distribution models (SDM) and invasion-hotspot analysis to determine where in Australia suitable habitat may occur for 292 naturalised plants. SDMs were built in MaxEnt using both climate and soil variables for current baseline conditions. Modelled relationships were projected onto two Representative Concentration Pathways for future climates (RCP 4.5 and 8.5), based on seven global climate models, for two time periods (2035, 2065). Model outputs for each of the 292 species were then aggregated into single ‘hotspot’ maps at two scales: continental, and for each of Australia’s 37 ecoregions. Across Australia, areas in the south-east and south-west corners of the continent were identified as potential hotspots for naturalised plants under current and future climates. These regions provided suitable habitat for 288 and 239 species respectively under baseline climates. The areal extent of the continental hotspot was projected to decrease by 8.8% under climates for 2035, and by a further 5.2% by 2065. A similar pattern of hotspot contraction under future climates was seen for the majority of ecoregions examined. However, two ecoregions - Tasmanian temperate forests and Australian Alps montane grasslands - showed increases in the areal extent of hotspots of >45% under climate scenarios for 2065. The alpine ecoregion also had an increase in the number of naturalised plant species with abiotically suitable habitat under future climate scenarios, indicating that this area may be particularly vulnerable to future incursions by naturalised plants.11 page(s

Relative efficiency of joint-model and full-conditional-specification multiple imputation when conditional models are compatible: The general location model.

Estimating the parameters of a regression model of interest is complicated by missing data on the variables in that model. Multiple imputation is commonly used to handle these missing data. Joint model multiple imputation and full-conditional specification multiple imputation are known to yield imputed data with the same asymptotic distribution when the conditional models of full-conditional specification are compatible with that joint model. We show that this asymptotic equivalence of imputation distributions does not imply that joint model multiple imputation and full-conditional specification multiple imputation will also yield asymptotically equally efficient inference about the parameters of the model of interest, nor that they will be equally robust to misspecification of the joint model. When the conditional models used by full-conditional specification multiple imputation are linear, logistic and multinomial regressions, these are compatible with a restricted general location joint model. We show that multiple imputation using the restricted general location joint model can be substantially more asymptotically efficient than full-conditional specification multiple imputation, but this typically requires very strong associations between variables. When associations are weaker, the efficiency gain is small. Moreover, full-conditional specification multiple imputation is shown to be potentially much more robust than joint model multiple imputation using the restricted general location model to mispecification of that model when there is substantial missingness in the outcome variable

Calcium channel blockers and breast cancer incidence: An updated systematic review and meta-analysis of the evidence

Controversy exists regarding the potential association between taking calcium channel blockers (CCBs) and the development of breast cancer. As a positive association would have important public health implications due to the widespread use of CCBs, this study aimed to incorporate new evidence to determine whether an association is likely to exist. We searched MEDLINE, EMBASE and the Cochrane Library to 28 June 2016 for relevant literature. References and citing articles were checked and authors contacted as necessary. Two authors independently selected articles and extracted data. Twenty-nine studies were reviewed; 26 were non-randomised studies (NRS). Meta-analysis of study data where adjustment for ‘confounding by indication’ was judged to be present suggests that an association, if any, is likely to be modest in magnitude (pooled odds/risk ratio 1.09 (95% confidence interval (CI) 1.03–1.15, I 2 = 0%, 8 sub-studies; pooled hazard ratio 0.99 (95% CI 0.94–1.03, I 2 = 35%, 9 sub-studies)). There are credible study data showing an increased relative risk with long-term use of CCBs, but the results of our meta-analysis and of meta-regression of log relative risk against minimum follow-up time are mixed. The current summative evidence does not support a clear association between taking CCBs and developing breast cancer. However, uncertainty remains, especially for long-term use and any association might not be uniform between different populations and/or breast cancer sub-types. We t hus recommend further NRS in settings where CCB use is highly prevalent and population-based cancer, prescription and health-registries exist, to resolve this continuing uncertainty. PROSPERO, CRD42015026712

A mixed methods systematic review of digital interventions to support the psychological health and well-being of people living with dermatological conditions

Background: Dermatological conditions can have a substantial impact on psychological as well as physical health yet dedicated face-to-face psychological support for patients is lacking. Thus, individuals may require additional support to self-manage dermatological conditions effectively. Digital technology can contribute to long-term condition management, but knowledge of the effectiveness of digital interventions addressing psychological (cognitive, emotional, and behavioural) aspects of dermatological conditions is limited. Objectives: To identify, determine the effectiveness, and explore people’s views and experiences of digital interventions supporting the psychological health of people with dermatological conditions. Methods: A mixed methods systematic review informed by JBI methodology. The protocol was registered on PROSPERO. Eight electronic databases were searched for papers written between January 2002 and October 2021. Data screening and extraction were conducted in Covidence. The methodological quality of studies were scrutinised against JBI critical appraisal tools. Intervention characteristics were captured using the Template for Intervention Description and Replication checklist and guide. Data were synthesised using a convergent segregated approach. The results were reported in a narrative summary. Results: Twenty-three papers were identified from 4,883 references, including 15 randomised controlled trials. Nineteen interventions were condition-specific, 13 were delivered online, 16 involved an educational component, and 7 endorsed established, evidence-based therapeutic approaches. Improvements in knowledge, mood, quality of life, the therapeutic relationship, and reduced disease severity in the short to medium term, were reported, although there was substantial heterogeneity within the literature. Thirteen studies captured feedback from users, who considered various digital interventions as convenient and helpful for improving knowledge, emotion regulation, and personal control, but technical and individual barriers to use were reported. Use of established qualitative methodologies was limited and, in some cases, poorly reported. Conclusion: Some web-based digital psychological interventions seem to be acceptable to people living with mainly psoriasis and eczema. Whilst some digital interventions benefitted cognitive and emotional factors, heterogeneity and inconsistencies in the literature meant definitive statements about their effectiveness could not be drawn. Interdisciplinary and patient-centred approaches to research are needed to develop and test quality digital interventions supporting the psychological health of adults living with common and rare dermatological conditions