Directed acyclic graphs with edge-specific bounds

VanderWeele, Tyler J.; Tan, Zhiqiang

doi:10.1093/biomet/asr059

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…In a signed DAG, edges are marked with the direction (positive or negative) of the average effect for each pair of directly connected variables, conditional on other relevant variables. The use of signed DAGs for characterizing the directionality of relationships in the diagram and in understanding confounding bias has been described in detail [21, 22]. For example, as depicted in Figure 5, if Y increased the probability of selection conditional on X and Z 1 , then the odds ratio e β SY would be assigned a positive value(> 1).…”

Section: Discussionmentioning

confidence: 99%

Selection bias modeling using observed data augmented with imputed record-level probabilities

Thompson

Arah

2014

Annals of Epidemiology

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PURPOSE Selection bias is a form of systematic error that can be severe in compromised study designs such as case-control studies with inappropriate selection mechanisms or follow-up studies that suffer from extensive attrition. External adjustment for selection bias is commonly undertaken when such bias is suspected, but the methods used can be overly simplistic, if not unrealistic, and fail to allow for simultaneous adjustment of associations of the exposure and covariates with the outcome, when of interest. Internal adjustment for selection bias via inverse-probability-weighting allows bias parameters to vary with levels of covariates but has only been formalized for longitudinal studies with covariate data on patients up until loss-to-follow-up. METHODS We demonstrate the use of inverse-probability-weighting and externally obtained bias parameters to perform internal adjustment of selection bias in studies lacking covariate data on unobserved participants. RESULTS The ‘true’ or selection-adjusted odds ratio for the association between exposure and outcome was successfully obtained by analyzing only data on those in the selected stratum (i.e. responders) weighted by the inverse probability of their being selected as function of their observed covariate data. CONCLUSIONS This internal adjustment technique using user-supplied bias parameters and inverse-probability-weighting for selection bias can be applied to any type of observational study.

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Section: Discussionmentioning

confidence: 99%

Selection bias modeling using observed data augmented with imputed record-level probabilities

Thompson

Arah

2014

Annals of Epidemiology

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“…Their approach requires both a whole DAG over all variables and the signs of all edges on each path between the two variables. VanderWeele and Tan (2012) propose an approach for propagation of bounds within the DAG framework, which requires a known DAG with edgespecific bounds. Rubin (2004) discusses how to combine the results from two randomized trials of anthrax vaccine on human volunteers and macaques, where the outcome of interest, survival when challenged with lethal doses of anthrax, is available only from macaques.…”

Section: Introductionmentioning

confidence: 99%

Qualitative Evaluation of Associations by the Transitivity of the Association Signs

Jiang,

Ding,

Geng

2014

Preprint

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We say that the signs of association measures among three variables {X, Y, Z} are transitive if a positive association measure between the variable X and the intermediate variable Y and further a positive association measure between Y and the endpoint variable Z imply a positive association measure between X and Z. We introduce four association measures with different stringencies, and discuss conditions for the transitivity of the signs of these association measures. When the variables follow exponential family distributions, the conditions become simpler and more interpretable. Applying our results to two data sets from an observational study and a randomized experiment, we demonstrate that the results can help us to draw conclusions about the signs of the association measures between X and Z based only on two separate studies about {X, Y } and {Y, Z}.

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Causal analyses with target trial emulation for real-world evidence removed large self-inflicted biases: systematic bias assessment of ovarian cancer treatment effectiveness

Kuehne¹,

Arvandi²,

Hess³

et al. 2022

Journal of Clinical Epidemiology

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Directed acyclic graphs with edge-specific bounds

Cited by 4 publications

References 16 publications

Selection bias modeling using observed data augmented with imputed record-level probabilities

Selection bias modeling using observed data augmented with imputed record-level probabilities

Qualitative Evaluation of Associations by the Transitivity of the Association Signs

Causal analyses with target trial emulation for real-world evidence removed large self-inflicted biases: systematic bias assessment of ovarian cancer treatment effectiveness

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