arXiv subjects

Margarita Moreno-Betancur

Publications and source records attributed to Margarita Moreno-Betancur.

At least 19 recordsLinked to original sources

arXiv2026

On the use of auxiliary variables in multiple imputation when estimating the average causal effect with missing data

Estimating the average causal effect (ACE) using observational data is a key focus in causal inference for which missing data present an important challenge. Multiple imputation (MI) is a widely used method for handling missing data and can yield unbiased estimates when the imputation is compatible with the substantive analysis. One of the advantages of MI is its scope to include so-called "auxiliary variables", defined as variables associated with incomplete variables that are excluded from the substantive analysis. Although many studies have looked at the use of auxiliary variables in MI for improving precision, the study of auxiliary variables that are necessary for the identifiability (or "recoverability") of the ACE in the presence of missing data has been scant. In this work, we investigate the use of auxiliary variables, both mediators and non-mediators, across a range of typical univariable and multivariable missingness mechanisms depicted by missingness directed acyclic graphs (m-DAGs). For each setting, we derive recoverability results, then evaluate MI-based and complete-case methods for estimating the ACE using correctly specified g-computation, considering different strategies for incorporating auxiliary variables and varying degrees of compatibility for MI models. Based on findings from the simulation studies, we provide practical guidance, highlighting that distinguishing appropriately between mediator and non-mediator auxiliary variables is important to avoid bias as is the use of compatible and flexible (non-parametric) MI methods that incorporate these variables.

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arXiv2026

A roadmap for systematic identification and analysis of multiple biases in causal inference

Observational studies examining causal effects rely on unverifiable assumptions, the violation of which can induce multiple biases. Quantitative bias analysis (QBA) methods examine the sensitivity of findings to such violations, generally, by producing estimates under alternative assumptions, incorporating external information. Although substantial guidance exists for implementing QBA, there is limited guidance on how to systematically determine the assumptions underlying a primary causal analysis and the potential violations that should guide bias analysis. Consequently, many assumptions remain implicit, leading to selective and therefore misleading QBA. To address this gap, we propose a roadmap for systematically identifying and analysing multiple biases. Briefly, this consists of (1) articulating the assumptions underlying the primary analysis through specification and emulation of the ideal trial that defines the causal estimand and depicting these assumptions using a causal diagram; (2) extending the diagram to depict alternative assumptions under which biases may arise; (3) obtaining a single estimate that simultaneously corrects for all potential biases. We illustrate the roadmap using an investigation of the effect of breastfeeding on risk of childhood asthma, and through simulations illustrate the need for analysing multiple biases jointly rather than one at a time.

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arXiv2025

The ideal trial: defining causal estimands that balance relevance and feasibility in target trial emulations and actual randomized trials

Causal inference is the goal of randomized trials and many observational studies. The first step in a formal causal inference framework is to define the causal estimand, and in both types of study this can be intuitively defined as the effect in an ideal trial: a hypothetical perfect randomized experiment (with representative sample, perfect adherence, etc.). The target trial framework is increasingly used for causal inference in observational studies, but clarity is lacking in how a target trial should be specified and how it relates to an ideal trial. In this paper, we review the concept of the ideal trial and highlight the need to balance relevance for decision-making in the real world and feasibility of estimation when specifying it. We then consider the question of how a target trial should be specified, outlining the challenges of a recommended approach, commonly seen in applications, that puts the focus heavily on feasibility of estimation: to specify the target trial such that it is closely aligned with the observational data (e.g. uses the same eligibility criteria). We argue that with this "aligned" approach, biases may remain relative to the estimand of ultimate practical interest, defined by the ideal trial, which mirror the often-overlooked biases of actual trials. We conclude that consideration of the ideal trial and of how the target trial and its emulation or the actual trial differ from it is necessary to identify and manage all bias sources in both settings. An example from respiratory epidemiology is used for illustration.

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arXiv2025

Causal machine learning methods and use of cross-fitting in settings with high-dimensional confounding

Observational epidemiological studies commonly seek to estimate the causal effect of an exposure on an outcome. Adjustment for potential confounding bias in modern studies is challenging due to the presence of high-dimensional confounding, which occurs when there are many confounders relative to sample size or complex relationships between continuous confounders and exposure and outcome. Doubly robust methods such as Augmented Inverse Probability Weighting (AIPW) and Targeted Maximum Likelihood Estimation (TMLE) have the potential to address these challenges, using data-adaptive approaches and cross-fitting, but despite recent advances limited evaluation and guidance are available on their implementation in realistic settings where high-dimensional confounding is present. Motivated by an early-life cohort study, we conducted an extensive simulation study to compare the relative performance of AIPW and TMLE using data-adaptive approaches for estimating the average causal effect (ACE). We evaluated the benefits of using cross-fitting with a varying number of folds, as well as the impact of using a reduced versus full (larger, more diverse) library in the Super Learner ensemble learning approach used for implementation. We found that AIPW and TMLE performed similarly in most cases for estimating the ACE, but TMLE was more stable. Cross-fitting improved the performance of both methods, but was more important for variance estimation and coverage than for point estimates, with the number of folds a less important consideration. Using a full Super Learner library was important to reduce bias and variance in complex scenarios typical of modern health research studies.

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arXiv2025

Handling multivariable missing data in causal mediation analysis estimating interventional effects

The interventional effects approach to causal mediation analysis is increasingly common in epidemiologic research, given its potential to address policy-relevant questions about hypothetical mediator interventions. Multiple imputation (MI) is widely used for handling missing data in epidemiologic studies. However, guidance is lacking on best practices for using MI when estimating interventional mediation effects, specifically regarding the role of the missingness mechanism in the method's performance, how to appropriately specify the MI model when g-computation is used for effect estimation, and suitable approaches to variance estimation. To address this gap, we conducted simulations based on the Victorian Adolescent Health Cohort Study. We considered seven missingness mechanisms involving varying assumptions about the influence of an intermediate confounder, a mediator, and/or the outcome on missingness in key variables. We compared the performance of complete-case analysis, six MI approaches using fully conditional specification (differing in how the imputation model was tailored), and a "substantive model compatible" multiple imputation-fully conditional specification approach. We evaluated MIBoot (MI, then bootstrap) and BootMI (bootstrap, then MI) approaches for variance estimation. All MI approaches, apart from those clearly diverging from best practice, yielded approximately unbiased estimates when none of the intermediate confounder, mediator, and outcome variables influenced missingness in any of these variables, and showed non-negligible bias otherwise. We observed the largest bias for interventional effects when each of the intermediate confounders, mediators, and outcomes influenced their own missingness. BootMI returned variance estimates with smaller bias than MIBoot.

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arXiv2025

Causal machine learning for high-dimensional mediation analysis using interventional effects mapped to a target trial

Causal mediation analysis examines causal pathways linking exposures to disease. The estimation of interventional effects, which are mediation estimands that overcome certain identifiability problems of natural effects, has been advanced through causal machine learning methods, particularly for high-dimensional mediators. Recently, it has been proposed interventional effects can be defined in each study by mapping to a target trial assessing specific hypothetical mediator interventions. This provides an appealing framework to directly address real-world research questions about the extent to which such interventions might mitigate an increased disease risk in the exposed. However, existing estimators for interventional effects mapped to a target trial rely on singly-robust parametric approaches, limiting their applicability in high-dimensional settings. Building upon recent developments in causal machine learning for interventional effects, we address this gap by developing causal machine learning estimators for three interventional effect estimands, defined by target trials assessing hypothetical interventions inducing distinct shifts in joint mediator distributions. These estimands are motivated by a case study within the Longitudinal Study of Australian Children, used for illustration, which assessed how intervening on high inflammatory burden and other non-inflammatory adverse metabolomic markers might mitigate the adverse causal effect of overweight or obesity on high blood pressure in adolescence. We develop one-step and (partial) targeted minimum loss-based estimators based on efficient influence functions of those estimands, demonstrating they are root-n consistent, efficient, and multiply robust under certain conditions.

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arXiv2025

Sensitivity analysis for multivariable missing data using multiple imputation: a tutorial

Multiple imputation is a popular method for handling missing data, with fully conditional specification (FCS) being one of the predominant imputation approaches for multivariable missingness. Unbiased estimation with standard implementations of multiple imputation depends on assumptions concerning the missingness mechanism (e.g. that data are "missing at random"). The plausibility of these assumptions can only be assessed using subject-matter knowledge, and not data alone. It is therefore important to perform sensitivity analyses to explore the robustness of results to violations of these assumptions (e.g. if the data are in fact "missing not at random"). In this tutorial, we provide a roadmap for conducting sensitivity analysis using the Not at Random Fully Conditional Specification (NARFCS) procedure for multivariate imputation. Using a case study from the Longitudinal Study of Australian Children, we work through the steps involved, from assessing the need to perform the sensitivity analysis, and specifying the NARFCS models and sensitivity parameters, through to implementing NARFCS using FCS procedures in R and Stata.

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arXiv2024

Sensitivity analysis methods for outcome missingness using substantive-model-compatible multiple imputation and their application in causal inference

When using multiple imputation (MI) for missing data, maintaining compatibility between the imputation model and substantive analysis is important for avoiding bias. For example, some causal inference methods incorporate an outcome model with exposure-confounder interactions that must be reflected in the imputation model. Two approaches for compatible imputation with multivariable missingness have been proposed: Substantive-Model-Compatible Fully Conditional Specification (SMCFCS) and a stacked-imputation-based approach (SMC-stack). If the imputation model is correctly specified, both approaches are guaranteed to be unbiased under the "missing at random" assumption. However, this assumption is violated when the outcome causes its own missingness, which is common in practice. In such settings, sensitivity analyses are needed to assess the impact of alternative assumptions on results. An appealing solution for sensitivity analysis is delta-adjustment using MI, specifically "not-at-random" (NAR)FCS. However, the issue of imputation model compatibility has not been considered in sensitivity analysis, with a naive implementation of NARFCS being susceptible to bias. To address this gap, we propose two approaches for compatible sensitivity analysis when the outcome causes its own missingness. The proposed approaches, NAR-SMCFCS and NAR-SMC-stack, extend SMCFCS and SMC-stack, respectively, with delta-adjustment for the outcome. We evaluate these approaches using a simulation study that is motivated by a case study, to which the methods were also applied. The simulation results confirmed that a naive implementation of NARFCS produced bias in effect estimates, while NAR-SMCFCS and NAR-SMC-stack were approximately unbiased. The proposed compatible approaches provide promising avenues for conducting sensitivity analysis to missingness assumptions in causal inference.

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arXiv2024

On the uses and abuses of regression models: a call for reform of statistical practice and teaching

Regression methods dominate the practice of biostatistical analysis, but biostatistical training emphasises the details of regression models and methods ahead of the purposes for which such modelling might be useful. More broadly, statistics is widely understood to provide a body of techniques for "modelling data", underpinned by what we describe as the "true model myth": that the task of the statistician/data analyst is to build a model that closely approximates the true data generating process. By way of our own historical examples and a brief review of mainstream clinical research journals, we describe how this perspective has led to a range of problems in the application of regression methods, including misguided "adjustment" for covariates, misinterpretation of regression coefficients and the widespread fitting of regression models without a clear purpose. We then outline a new approach to the teaching and application of biostatistical methods, which situates them within a framework that first requires clear definition of the substantive research question at hand within one of three categories: descriptive, predictive, or causal. Within this approach, the development and application of (multivariable) regression models, as well as other advanced biostatistical methods, should proceed differently according to the type of question. Regression methods will no doubt remain central to statistical practice as they provide a powerful tool for representing variation in a response or outcome variable as a function of "input" variables, but their conceptualisation and usage should follow from the purpose at hand.

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arXiv2024

Causal inference in multi-cohort studies using the target trial framework to identify and minimize sources of bias

Longitudinal cohort studies, which follow a group of individuals over time, provide the opportunity to examine causal effects of complex exposures on long-term health outcomes. Utilizing data from multiple cohorts has the potential to add further benefit by improving precision of estimates through data pooling and by allowing examination of effect heterogeneity through replication of analyses across cohorts. However, the interpretation of findings can be complicated by biases that may be compounded when pooling data, or, contribute to discrepant findings when analyses are replicated. The "target trial" is a powerful tool for guiding causal inference in single-cohort studies. Here we extend this conceptual framework to address the specific challenges that can arise in the multi-cohort setting. By representing a clear definition of the target estimand, the target trial provides a central point of reference against which biases arising in each cohort and from data pooling can be systematically assessed. Consequently, analyses can be designed to reduce these biases and the resulting findings appropriately interpreted in light of potential remaining biases. We use a case study to demonstrate the framework and its potential to strengthen causal inference in multi-cohort studies through improved analysis design and clarity in the interpretation of findings.

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arXiv2024

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.

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arXiv2024

Multiple imputation for longitudinal data: A tutorial

Longitudinal studies are frequently used in medical research and involve collecting repeated measures on individuals over time. Observations from the same individual are invariably correlated and thus an analytic approach that accounts for this clustering by individual is required. While almost all research suffers from missing data, this can be particularly problematic in longitudinal studies as participation often becomes harder to maintain over time. Multiple imputation (MI) is widely used to handle missing data in such studies. When using MI, it is important that the imputation model is compatible with the proposed analysis model. In a longitudinal analysis, this implies that the clustering considered in the analysis model should be reflected in the imputation process. Several MI approaches have been proposed to impute incomplete longitudinal data, such as treating repeated measurements of the same variable as distinct variables or using generalized linear mixed imputation models. However, the uptake of these methods has been limited, as they require additional data manipulation and use of advanced imputation procedures. In this tutorial, we review the available MI approaches that can be used for handling incomplete longitudinal data, including where individuals are clustered within higher-level clusters. We illustrate implementation with replicable R and Stata code using a case study from the Childhood to Adolescence Transition Study.

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arXiv2023

Confounding-adjustment methods for the causal difference in medians

With continuous outcomes, the average causal effect is typically defined using a contrast of expected potential outcomes. However, in the presence of skewed outcome data, the expectation may no longer be meaningful. In practice the typical approach is to either "ignore or transform" - ignore the skewness altogether or transform the outcome to obtain a more symmetric distribution, although neither approach is entirely satisfactory. Alternatively the causal effect can be redefined as a contrast of median potential outcomes, yet discussion of confounding-adjustment methods to estimate this parameter is limited. In this study we described and compared confounding-adjustment methods to address this gap. The methods considered were multivariable quantile regression, an inverse probability weighted (IPW) estimator, weighted quantile regression and two little-known implementations of g-computation for this problem. Motivated by a cohort investigation in the Longitudinal Study of Australian Children, we conducted a simulation study that found the IPW estimator, weighted quantile regression and g-computation implementations minimised bias when the relevant models were correctly specified, with g-computation additionally minimising the variance. These methods provide appealing alternatives to the common "ignore or transform" approach and multivariable quantile regression, enhancing our capability to obtain meaningful causal effect estimates with skewed outcome data.

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arXiv2023

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 on causal and missingness assumptions. The latter can be depicted by adding variable-specific missingness indicators to causal diagrams, creating "missingness-directed acyclic graphs" (m-DAGs). Previous research described ten canonical m-DAGs, representing typical multivariable missingness mechanisms in epidemiological studies, and determined the recoverability of the ACE in the absence of effect modification. We extend the research by determining the recoverability of the ACE in settings with effect modification and conducting a simulation study evaluating the performance of widely used missing data methods when estimating the ACE using correctly specified g-computation, which has not been previously studied. Methods assessed were complete case analysis (CCA) and various multiple imputation (MI) implementations regarding the degree 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. In the canonical m-DAGs that excluded unmeasured common causes of missingness indicators, we derived the recoverable ACE if no incomplete variable causes its missingness, and non-recoverable otherwise. Besides, the simulation showed that compatible MI approaches may enable approximately unbiased ACE estimation, unless the outcome causes its missingness or it causes the missingness of a variable that causes its missingness. Researchers must consider sensitivity analysis methods incorporating external information in the latter setting. The VAHCS case study illustrates the practical implications of these findings.

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arXiv2022

Evaluation of multiple imputation to address intended and unintended missing data in case-cohort studies with a binary endpoint

Case-cohort studies are conducted within cohort studies, wherein 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, but when used in combination with IPW, the imputation model needs to take account of the weighting to ensure compatibility with the analysis model. Alternatively, MI could be used to handle both the intended and unintended missingness. While the performance of a solely MI 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 binary outcomes. 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 this setting. We also applied the approaches to a case study. 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 or IPW-only exhibited larger biases in both sample size settings. These findings suggest that MI is the preferred approach to handle intended and unintended missing data in case-cohort studies with binary outcomes.

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arXiv2022

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 in the imputation model is not always straightforward. Including too few may lead to important information being discarded, but including too many can cause problems with convergence of the estimation procedures for imputation models. Several data-driven auxiliary variable selection strategies have been proposed. This paper uses a simulation study and a case study to provide a comprehensive comparison of the performance of eight auxiliary variable selection strategies, with the aim of providing practical advice to users of MI. A complete case analysis and an MI analysis with all auxiliary variables included in the imputation model (the full model) were also performed for comparison. Our simulation study results suggest that the full model outperforms all auxiliary variable selection strategies, providing further support for adopting an inclusive auxiliary variable strategy where possible. Auxiliary variable selection using the Least Absolute Selection and Shrinkage Operator (LASSO) was the best performing auxiliary variable selection strategy overall and is a promising alternative when the full model fails. All MI analysis strategies that we were able to apply to the case study led to similar estimates.

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arXiv2020

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. While 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 simulation in the context of a 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.

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arXiv2020

Mediation effects that emulate a target randomised trial: Simulation-based evaluation of ill-defined interventions on multiple mediators

Many epidemiological questions concern potential interventions to alter the pathways presumed to mediate an association. For example, we consider a study that investigates the benefit of interventions in young adulthood for ameliorating the poorer mid-life psychosocial outcomes of adolescent self-harmers relative to their healthy peers. Two methodological challenges arise. Firstly, mediation methods have hitherto mostly focused on the elusive task of discovering pathways, rather than on the evaluation of mediator interventions. Secondly, the complexity of such questions is invariably such that there are no existing data on well-defined interventions (i.e. actual treatments, programs, etc.) capturing the populations, outcomes and time-spans of interest. Instead, researchers must rely on exposure (non-intervention) data to address these questions, such as self-reported substance use and employment. We address the resulting challenges by specifying a target trial addressing three policy-relevant questions, regarding the impacts of hypothetical (rather than actual) interventions that would shift the mediators' distributions (separately, jointly or sequentially) to user-specified distributions that can be emulated with the observed data. We then define novel interventional effects that map to this trial, emulating shifts by setting mediators to random draws from those distributions. We show that estimation using a g-computation method is possible under an expanded set of causal assumptions relative to inference with well-defined interventions. These expanded assumptions reflect the lower level of evidence that is inevitable with ill-defined interventions. Application to the self-harm example using data from the Victorian Adolescent Health Cohort Study illustrates the value of our proposal for informing the design and evaluation of actual interventions in the future.

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