arXiv subjects

Anders Huitfeldt

Publications and source records attributed to Anders Huitfeldt.

6 recordsLinked to original sources

arXiv2023

Mindel C. Sheps: Counted, dead or alive

In the 1958 paper "Shall we count the living or the dead", Canadian physician and biostatistician Mindel C. Sheps proposed a novel approach to choice of effect measure, which resolves key theoretical shortcomings of relative risk models. Sheps' insights have been independently rediscovered multiple times in different academic disciplines, and her approach is consistent with influential work on mechanism of action from toxicology, as well as work on causal generalizability from the psychology and philosophy literature

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arXiv2022

Shall we count the living or the dead?

In the 1958 paper "Shall we count the living or the dead?", Mindel C. Sheps proposed a principled solution to the familiar problem of asymmetry of the relative risk. We provide causal models to clarify the scope and limitations of Sheps' line of reasoning, and show that her preferred variant of the relative risk will be stable between patient groups under certain biologically interpretable conditions. Such stability is useful when findings from an intervention study must be generalized to support clinical decisions in patients whose risk profile differs from the participants in the study. We show that Sheps' approach is consistent with a substantial body of psychological and philosophical research on how human reasoners carry causal information from one context to another, and that it can be implemented in practice using van der Laan et al's Switch Relative Risk, or equivalently, using Baker and Jackson's Generalized Relative Risk Reduction (GRRR).

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arXiv2019

Effect heterogeneity and variable selection for standardizing causal effects to a target population

The participants in randomized trials and other studies used for causal inference are often not representative of the populations seen by clinical decision-makers. To account for differences between populations, researchers may consider standardizing results to a target population. We discuss several different types of homogeneity conditions that are relevant for standardization: Homogeneity of effect measures, homogeneity of counterfactual outcome state transition parameters, and homogeneity of counterfactual distributions. Each of these conditions can be used to show that a particular standardization procedure will result in unbiased estimates of the effect in the target population, given assumptions about the relevant scientific context. We compare and contrast the homogeneity conditions, in particular their implications for selection of covariates for standardization and their implications for how to compute the standardized causal effect in the target population. While some of the recently developed counterfactual approaches to generalizability rely upon homogeneity conditions that avoid many of the problems associated with traditional approaches, they often require adjustment for a large (and possibly unfeasible) set of covariates.

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arXiv2018

LHC Dark Matter Working Group: Next-generation spin-0 dark matter models

Dark matter (DM) simplified models are by now commonly used by the ATLAS and CMS Collaborations to interpret searches for missing transverse energy ($E_T^\mathrm{miss}$). The coherent use of these models sharpened the LHC DM search program, especially in the presentation of its results and their comparison to DM direct-detection (DD) and indirect-detection (ID) experiments. However, the community has been aware of the limitations of the DM simplified models, in particular the lack of theoretical consistency of some of them and their restricted phenomenology leading to the relevance of only a small subset of $E_T^\mathrm{miss}$ signatures. This document from the LHC Dark Matter Working Group identifies an example of a next-generation DM model, called $\textrm{2HDM+a}$, that provides the simplest theoretically consistent extension of the DM pseudoscalar simplified model. A comprehensive study of the phenomenology of the $\textrm{2HDM+a}$ model is presented, including a discussion of the rich and intricate pattern of mono-$X$ signatures and the relevance of other DM as well as non-DM experiments. Based on our discussions, a set of recommended scans are proposed to explore the parameter space of the $\textrm{2HDM+a}$ model through LHC searches. The exclusion limits obtained from the proposed scans can be consistently compared to the constraints on the $\textrm{2HDM+a}$ model that derive from DD, ID and the DM relic density.

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arXiv2018

The choice of effect measure for binary outcomes: Introducing counterfactual outcome state transition parameters

Standard measures of effect, including the risk ratio, the odds ratio, and the risk difference, are associated with a number of well-described shortcomings, and no consensus exists about the conditions under which investigators should choose one effect measure over another. In this paper, we introduce a new framework for reasoning about choice of effect measure by linking two separate versions of the risk ratio to a counterfactual causal model. In our approach, effects are defined in terms of "counterfactual outcome state transition parameters", that is, the proportion of those individuals who would not have been a case by the end of follow-up if untreated, who would have responded to treatment by becoming a case; and the proportion of those individuals who would have become a case by the end of follow-up if untreated who would have responded to treatment by not becoming a case. Although counterfactual outcome state transition parameters are generally not identified from the data without strong monotonicity assumptions, we show that when they stay constant between populations, there are important implications for model specification, meta-analysis, and research generalization.

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arXiv2017

On the collapsibility of measures of effect in the counterfactual causal framework

A measure of association is said to be collapsible over a set of baseline covariates if the marginal value of the measure of association is equal to a weighted average of the stratum-specific measures of association. In this paper, we consider two subtly different definitions of collapsibility, and show that by considering causal measures of effect based on counterfactual variables it is possible to separate out the component of non-collapsibility which is due to the mathematical properties of the effect measure. We provide weights such that the causal risk difference and the causal risk ratio are collapsible over arbitrary baseline covariates, and demonstrate that such general weights do not exist for the odds ratio.

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