Hypothetical Estimands in Clinical Trials: A Unification of Causal Inference and Missing Data Methods.

The ICH E9 addendum introduces the term intercurrent event to refer to events that happen after treatment initiation and that can either preclude observation of the outcome of interest or affect its interpretation. It proposes five strategies for handling intercurrent events to form an estimand but does not suggest statistical methods for estimation. In this article we focus on the hypothetical strategy, where the treatment effect is defined under the hypothetical scenario in which the intercurrent event is prevented.

Non-thermal disruption of β-adrenergic receptor-activated Ca signalling and apoptosis in human ES-derived cardiomyocytes by microwave electric fields at 2.4 GHz.

The ubiquity of wireless electronic-device connectivity has seen microwaves emerge as one of the fastest growing forms of electromagnetic exposure. A growing evidence-base refutes the claim that wireless technologies pose no risk to human health at current safety levels designed to limit thermal (heating) effects. The potential impact of non-thermal effects of microwave exposure, especially in electrically-excitable tissues (e.

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.

Using Negative Control Outcomes and Difference-in-Differences Analysis to Estimate Treatment Effects in an Entirely Treated Cohort: The Effect of Ivacaftor in Cystic Fibrosis.

When an entire cohort of patients receives a treatment, it is difficult to estimate the treatment effect in the treated because there are no directly comparable untreated patients. Attempts can be made to find a suitable control group (e.g.

CATS II Long-term Anthropometric and Metabolic Effects of Maternal Sub-optimal Thyroid Function in Offspring and Mothers.

Context And Objectives: The Controlled Antenatal Thyroid Screening Study I (CATS-I) was a randomized controlled trial investigating the effects of levothyroxine therapy for suboptimal gestational thyroid function (SGTF), comparing outcomes in children of treated (SGTF-T) with untreated (SGTF-U) women during pregnancy. This follow-up study, CATS-II, reports the long-term effects on anthropometric, bone, and cardiometabolic outcomes in mothers and offspring and includes a group with normal gestational thyroid function (NGTF).

Design & Participants: 332 mothers (197 NGTF, 56 SGTF-U, 79 SGTF-T) aged 41.

Maternal Prepregnancy Weight Status and Adolescent Eating Disorder Behaviors: A Longitudinal Study of Risk Pathways.

Background: Maternal characteristics and childhood growth have been identified as risk factors for eating disorders. Most studies to date have been unable to investigate these factors prospectively while accounting for their interdependencies. We address this by investigating whether the association of maternal prepregnancy body mass index (ppBMI) with adolescent eating disorder behaviors can be explained by childhood growth and/or a concurrent environmental pathway captured by maternal eating habits.

Estimating long-term treatment effects in observational data: A comparison of the performance of different methods under real-world uncertainty.

In the presence of time-dependent confounding, there are several methods available to estimate treatment effects. With correctly specified models and appropriate structural assumptions, any of these methods could provide consistent effect estimates, but with real-world data, all models will be misspecified and it is difficult to know if assumptions are violated. In this paper, we investigate five methods: inverse probability weighting of marginal structural models, history-adjusted marginal structural models, sequential conditional mean models, g-computation formula, and g-estimation of structural nested models.

Analysis of Longitudinal Studies With Repeated Outcome Measures: Adjusting for Time-Dependent Confounding Using Conventional Methods.

Estimation of causal effects of time-varying exposures using longitudinal data is a common problem in epidemiology. When there are time-varying confounders, which may include past outcomes, affected by prior exposure, standard regression methods can lead to bias. Methods such as inverse probability weighted estimation of marginal structural models have been developed to address this problem.

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.

An Assessment and Extension of the Mechanism-Based Approach to the Identification of Age-Period-Cohort Models.

Many methods have been proposed to solve the age-period-cohort (APC) linear identification problem, but most are not theoretically informed and may lead to biased estimators of APC effects. One exception is the mechanism-based approach recently proposed and based on Pearl's front-door criterion; this approach ensures consistent APC effect estimators in the presence of a complete set of intermediate variables between one of age, period, cohort, and the outcome of interest, as long as the assumed parametric models for all the relevant causal pathways are correct. Through a simulation study mimicking APC data on cardiovascular mortality, we demonstrate possible pitfalls that users of the mechanism-based approach may encounter under realistic conditions: namely, when (1) the set of available intermediate variables is incomplete, (2) intermediate variables are affected by two or more of the APC variables (while this feature is not acknowledged in the analysis), and (3) unaccounted confounding is present between intermediate variables and the outcome.

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.

A comparison of methods to adjust for continuous covariates in the analysis of randomised trials.

Background: Although covariate adjustment in the analysis of randomised trials can be beneficial, adjustment for continuous covariates is complicated by the fact that the association between covariate and outcome must be specified. Misspecification of this association can lead to reduced power, and potentially incorrect conclusions regarding treatment efficacy.

Methods: We compared several methods of adjustment to determine which is best when the association between covariate and outcome is unknown.

Medication Use in Early-HD Participants in Track-HD: an Investigation of its Effects on Clinical Performance.

 Insufficient evidence exists to guide the long-term pharmacological management of Huntington's disease (HD) although most current interventions rely on symptomatic management. The effect of many frontline treatments on potential endpoints for HD clinical trials remains unknown. Our objective was to investigate how therapies widely used to manage HD affect the symptom for which they are prescribed and other endpoints using data from TRACK-HD.

Mediation analysis with intermediate confounding: structural equation modeling viewed through the causal inference lens.

The study of mediation has a long tradition in the social sciences and a relatively more recent one in epidemiology. The first school is linked to path analysis and structural equation models (SEMs), while the second is related mostly to methods developed within the potential outcomes approach to causal inference. By giving model-free definitions of direct and indirect effects and clear assumptions for their identification, the latter school has formalized notions intuitively developed in the former and has greatly increased the flexibility of the models involved.

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).

Incidence of community-acquired lower respiratory tract infections and pneumonia among older adults in the United Kingdom: a population-based study.

Community-acquired lower respiratory tract infections (LRTI) and pneumonia (CAP) are common causes of morbidity and mortality among those aged ≥65 years; a growing population in many countries. Detailed incidence estimates for these infections among older adults in the United Kingdom (UK) are lacking. We used electronic general practice records from the Clinical Practice Research Data link, linked to Hospital Episode Statistics inpatient data, to estimate incidence of community-acquired LRTI and CAP among UK older adults between April 1997-March 2011, by age, sex, region and deprivation quintile.

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.

Using causal diagrams to guide analysis in missing data problems.

Estimating causal effects from incomplete data requires additional and inherently untestable assumptions regarding the mechanism giving rise to the missing data. We show that using causal diagrams to represent these additional assumptions both complements and clarifies some of the central issues in missing data theory, such as Rubin's classification of missingness mechanisms (as missing completely at random (MCAR), missing at random (MAR) or missing not at random (MNAR)) and the circumstances in which causal effects can be estimated without bias by analysing only the subjects with complete data. In doing so, we formally extend the back-door criterion of Pearl and others for use in incomplete data examples.