Efficient estimation of the distribution of time to composite endpoint when some endpoints are only partially observed.

Two common features of clinical trials, and other longitudinal studies, are (1) a primary interest in composite endpoints, and (2) the problem of subjects withdrawing prematurely from the study. In some settings, withdrawal may only affect observation of some components of the composite endpoint, for example when another component is death, information on which may be available from a national registry. In this paper, we use the theory of augmented inverse probability weighted estimating equations to show how such partial information on the composite endpoint for subjects who withdraw from the study can be incorporated in a principled way into the estimation of the distribution of time to composite endpoint, typically leading to increased efficiency without relying on additional assumptions above those that would be made by standard approaches. We describe our proposed approach theoretically, and demonstrate its properties in a simulation study

Interventional Effects for Mediation Analysis with Multiple Mediators.

The mediation formula for the identification of natural (in)direct effects has facilitated mediation analyses that better respect the nature of the data, with greater consideration of the need for confounding control. The default assumptions on which it relies are strong, however. In particular, they are known to be violated when confounders of the mediator-outcome association are affected by the exposure. This complicates extensions of counterfactual-based mediation analysis to settings that involve repeatedly measured mediators, or multiple correlated mediators. VanderWeele, Vansteelandt, and Robins introduced so-called interventional (in)direct effects. These can be identified under much weaker conditions than natural (in)direct effects, but have the drawback of not adding up to the total effect. In this article, we adapt their proposal to achieve an exact decomposition of the total effect, and extend it to the multiple mediator setting. Interestingly, the proposed effects capture the path-specific effects of an exposure on an outcome that are mediated by distinct mediators, even when-as often-the structural dependence between the multiple mediators is unknown, for instance, when the direction of the causal effects between the mediators is unknown, or there may be unmeasured common causes of the mediators

Proposer of the vote of thanks and contribution to the Discussion of ‘Assumption‐lean inference for generalised linear model parameters’ by Vansteelandt and Dukes

Vansteelandt and Dukes (henceforth VD) propose a practical resolution to an important tension between two philosophies of statistical inference. I summarise these aspects before discussing how we might revise our understanding of ‘bias–variance trade-off’ in statistical modelling in the light of VD’s work

Monotherapy with major antihypertensive drug classes and risk of hospital admissions for mood disorders

Major depressive and bipolar disorders predispose to atherosclerosis, and there is accruing data from animal model, epidemiological, and genomic studies that commonly used antihypertensive drugs may have a role in the pathogenesis or course of mood disorders. In this study, we propose to determine whether antihypertensive drugs have an impact on mood disorders through the analysis of patients on monotherapy with different classes of antihypertensive drugs from a large hospital database of 525 046 patients with follow-up for 5 years. There were 144 066 eligible patients fulfilling the inclusion criteria: age 40 to 80 years old at time of antihypertensive prescription and medication exposure >90 days. The burden of comorbidity assessed by Charlson and Elixhauser scores showed an independent linear association with mood disorder diagnosis. The median time to hospital admission with mood disorder was 847 days for the 299 admissions (641 685 person-years of follow-up). Patients on angiotensin-converting enzyme inhibitors or angiotensin receptor blockers had the lowest risk for mood disorder admissions, and compared with this group, those on β-blockers (hazard ratio=2.11; [95% confidence interval, 1.12–3.98]; P=0.02) and calcium antagonists (2.28 [95% confidence interval, 1.13–4.58]; P=0.02) showed higher risk, whereas those on no antihypertensives (1.63 [95% confidence interval, 0.94–2.82]; P=0.08) and thiazide diuretics (1.56 [95% confidence interval, 0.65–3.73]; P=0.32) showed no significant difference. Overall, our exploratory findings suggest possible differential effects of antihypertensive medications on mood that merits further study: calcium antagonists and β-blockers may be associated with increased risk, whereas angiotensin-converting enzyme inhibitors and angiotensin receptor blockers may be associated with a decreased risk of mood disorders

Estimating hypothetical estimands with causal inference and missing data estimators in a diabetes trial

The recently published ICH E9 addendum on estimands in clinical trials provides a framework for precisely defining the treatment effect that is to be estimated, but says little about estimation methods. Here we report analyses of a clinical trial in type 2 diabetes, targeting the effects of randomised treatment, handling rescue treatment and discontinuation of randomised treatment using the so-called hypothetical strategy. We show how this can be estimated using mixed models for repeated measures, multiple imputation, inverse probability of treatment weighting, G-formula and G-estimation. We describe their assumptions and practical details of their implementation using packages in R. We report the results of these analyses, broadly finding similar estimates and standard errors across the estimators. We discuss various considerations relevant when choosing an estimation approach, including computational time, how to handle missing data, whether to include post intercurrent event data in the analysis, whether and how to adjust for additional time-varying confounders, and whether and how to model different types of ICE separately

Importance of cholesterol-rich microdomains in the regulation of Nox isoforms and redox signaling in human vascular smooth muscle cells

Vascular smooth muscle cell (VSMC) function is regulated by Nox-derived reactive oxygen species (ROS) and redox-dependent signaling in discrete cellular compartments. Whether cholesterol-rich microdomains (lipid rafts/caveolae) are involved in these processes is unclear. Here we examined the sub-cellular compartmentalization of Nox isoforms in lipid rafts/caveolae and assessed the role of these microdomains in VSMC ROS production and pro-contractile and growth signaling. Intact small arteries and primary VSMCs from humans were studied. Vessels from Cav-1−/− mice were used to test proof of concept. Human VSMCs express Nox1, Nox4, Nox5 and Cav-1. Cell fractionation studies showed that Nox1 and Nox5 but not Nox4, localize in cholesterol-rich fractions in VSMCs. Angiotensin II (Ang II) stimulation induced trafficking into and out of lipid rafts/caveolae for Nox1 and Nox5 respectively. Co-immunoprecipitation studies showed interactions between Cav-1/Nox1 but not Cav-1/Nox5. Lipid raft/caveolae disruptors (methyl-β-cyclodextrin (MCD) and Nystatin) and Ang II stimulation variably increased O2− generation and phosphorylation of MLC20, Ezrin-Radixin-Moesin (ERM) and p53 but not ERK1/2, effects recapitulated in Cav-1 silenced (siRNA) VSMCs. Nox inhibition prevented Ang II-induced phosphorylation of signaling molecules, specifically, ERK1/2 phosphorylation was attenuated by mellitin (Nox5 inhibitor) and Nox5 siRNA, while p53 phosphorylation was inhibited by NoxA1ds (Nox1 inhibitor). Ang II increased oxidation of DJ1, dual anti-oxidant and signaling molecule, through lipid raft/caveolae-dependent processes. Vessels from Cav-1−/− mice exhibited increased O2− generation and phosphorylation of ERM. We identify an important role for lipid rafts/caveolae that act as signaling platforms for Nox1 and Nox5 but not Nox4, in human VSMCs. Disruption of these microdomains promotes oxidative stress and Nox isoform-specific redox signalling important in vascular dysfunction associated with cardiovascular diseases

Network Mendelian randomization: using genetic variants as instrumental variables to investigate mediation in causal pathways.

BACKGROUND: Mendelian randomization uses genetic variants, assumed to be instrumental variables for a particular exposure, to estimate the causal effect of that exposure on an outcome. If the instrumental variable criteria are satisfied, the resulting estimator is consistent even in the presence of unmeasured confounding and reverse causation. METHODS: We extend the Mendelian randomization paradigm to investigate more complex networks of relationships between variables, in particular where some of the effect of an exposure on the outcome may operate through an intermediate variable (a mediator). If instrumental variables for the exposure and mediator are available, direct and indirect effects of the exposure on the outcome can be estimated, for example using either a regression-based method or structural equation models. The direction of effect between the exposure and a possible mediator can also be assessed. Methods are illustrated in an applied example considering causal relationships between body mass index, C-reactive protein and uric acid. RESULTS: These estimators are consistent in the presence of unmeasured confounding if, in addition to the instrumental variable assumptions, the effects of both the exposure on the mediator and the mediator on the outcome are homogeneous across individuals and linear without interactions. Nevertheless, a simulation study demonstrates that even considerable heterogeneity in these effects does not lead to bias in the estimates. CONCLUSIONS: These methods can be used to estimate direct and indirect causal effects in a mediation setting, and have potential for the investigation of more complex networks between multiple interrelated exposures and disease outcomes

Methods of analysis for survival outcomes with time-updated mediators, with application to longitudinal disease registry data.

Mediation analysis is a useful tool to illuminate the mechanisms through which an exposure affects an outcome but statistical challenges exist with time-to-event outcomes and longitudinal observational data. Natural direct and indirect effects cannot be identified when there are exposure-induced confounders of the mediator-outcome relationship. Previous measurements of a repeatedly-measured mediator may themselves confound the relationship between the mediator and the outcome. To overcome these obstacles, two recent methods have been proposed, one based on path-specific effects and one based on an additive hazards model and the concept of exposure splitting. We investigate these techniques, focusing on their application to observational datasets. We apply both methods to an analysis of the UK Cystic Fibrosis Registry dataset to identify how much of the relationship between onset of cystic fibrosis-related diabetes and subsequent survival acts through pulmonary function. Statistical properties of the methods are investigated using simulation. Both methods produce unbiased estimates of indirect and direct effects in scenarios consistent with their stated assumptions but, if the data are measured infrequently, estimates may be biased. Findings are used to highlight considerations in the interpretation of the observational data analysis

Dynamic survival prediction combining landmarking with a machine learning ensemble: Methodology and empirical comparison

Dynamic prediction models provide predicted survival probabilities that can be updated over time for an individual as new measurements become available. Two techniques for dynamic survival prediction with longitudinal data dominate the statistical literature: joint modelling and landmarking. There is substantial interest in the use of machine learning methods for prediction; however, their use in the context of dynamic survival prediction has been limited. We show how landmarking can be combined with a machine learning ensemble—the Super Learner. The ensemble combines predictions from different machine learning and statistical algorithms with the goal of achieving improved performance. The proposed approach exploits discrete time survival analysis techniques to enable the use of machine learning algorithms for binary outcomes. We discuss practical and statistical considerations involved in implementing the ensemble. The methods are illustrated and compared using longitudinal data from the UK Cystic Fibrosis Registry. Standard landmarking and the landmark Super Learner approach resulted in similar cross-validated predictive performance, in this case, outperforming joint modelling

Socioeconomic determinants of growth in a longitudinal study in Nepal.

Socioeconomic status (SES) is associated with childhood anthropometry, but little is known about how it is associated with tissue growth and body composition. To investigate this, we looked at components of SES at birth with growth in early and mid-childhood, and body composition in a longitudinal study in Nepal. The exposure variables (material assets, land ownership, and maternal education) were quantified from questionnaire data before birth. Anthropometry data at birth, 2.5 and 8.5 years, were normalized using WHO reference ranges and conditional growth calculated. Associations with child growth and body composition were explored using multiple regression analysis. Complete anthropometry data were available for 793 children. There was a positive association between SES and height-for-age and weight-for-age, and a reduction in odds of stunting and underweight for each increase in rank of SES variable. Associations tended to be significant when moving from the lower to the upper asset score, from none to secondary education, and no land to >30 dhur (~500 m2 ). The strongest associations were for maternal secondary education, showing an increase of 0.6-0.7 z scores in height-for-age and weight-for-age at 2.5 and 8.5 years and 0.3 kg/m2 in fat and lean mass compared to no education. There was a positive association with conditional growth in the highest asset score group and secondary maternal education, and generally no association with land ownership. Our results show that SES at birth is important for the growth of children, with a greater association with fat mass. The greatest influence was maternal secondary education