Sensitivity analysis using bias functions for studies extending inferences from a randomized trial to a target population

Dahabreh, Issa J; Robins, James M.; Haneuse, Sebastien; Saeed, Iman; Robertson, Sarah E.; Stuart, Elisabeth; Hernán, Miguel A.

doi:10.1002/sim.9550

Cited by 14 publications

(7 citation statements)

References 45 publications

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“…Conditional independence of the outcome and data source is an untestable assumption in our setting because outcome information is unavailable from the target population sample. Instead, the plausibility of the assumption should be evaluated using subject matter knowledge; future work could develop sensitivity analysis methods for exploring how violations of the assumption affect conclusions (Dahabreh et al., 2022; Robins et al., 2000).…”

Section: Discussionmentioning

confidence: 99%

Estimating the Area under the ROC Curve When Transporting a Prediction Model to a Target Population

Gatsonis

Dahabreh

et al. 2022

Biometrics

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We propose methods for estimating the area under the receiver operating characteristic (ROC) curve (AUC) of a prediction model in a target population that differs from the source population that provided the data used for original model development. If covariates that are associated with model performance, as measured by the AUC, have a different distribution in the source and target populations, then AUC estimators that only use data from the source population will not reflect model performance in the target population. Here, we provide identification results for the AUC in the target population when outcome and covariate data are available from the sample of the source population, but only covariate data are available from the sample of the target population. In this setting, we propose three estimators for the AUC in the target population and show that they are consistent and asymptotically normal. We evaluate the finite‐sample performance of the estimators using simulations and use them to estimate the AUC in a nationally representative target population from the National Health and Nutrition Examination Survey for a lung cancer risk prediction model developed using source population data from the National Lung Screening Trial.

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Section: Discussionmentioning

confidence: 99%

Estimating the Area under the ROC Curve When Transporting a Prediction Model to a Target Population

Gatsonis

Dahabreh

et al. 2022

Biometrics

Self Cite

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“…Defining a sensitivity parameter in terms of the deviation from a counterfactual independence assumption is standard in the causal inference literature, and used, for instance, in sensitivity analysis for truncation by death, 30 marginal structural models 31 and transportability in randomized trials. 32 Since |b(L, Z; 𝛼)| expresses the magnitude of the assumptions' violation, we can expect that for given values of L and Z, |b(L, Z; 𝛼)| will be a monotonically non-decreasing function of |𝛼|.…”

Section: Sensitivity Analysis With a Single Parameter 𝜶mentioning

confidence: 99%

“…Consequently,

b\left(L,Z;\alpha \right)

is non‐zero for

\alpha \ne 0

. Defining a sensitivity parameter in terms of the deviation from a counterfactual independence assumption is standard in the causal inference literature, and used, for instance, in sensitivity analysis for truncation by death, 30 marginal structural models 31 and transportability in randomized trials 32 . Since

\mid b\left(L,Z;\alpha \right)\mid

expresses the magnitude of the assumptions' violation, we can expect that for given values of

L

and

Z

\mid b\left(L,Z;\alpha \right)\mid

will be a monotonically non‐decreasing function of

\mid \alpha \mid

.…”

Section: Notations Definitions and Assumptionsmentioning

confidence: 99%

Sensitivity analysis of G‐estimators to invalid instrumental variables

Vancak

Sjölander

2023

Statistics in Medicine

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Instrumental variables regression is a tool that is commonly used in the analysis of observational data. The instrumental variables are used to make causal inference about the effect of a certain exposure in the presence of unmeasured confounders. A valid instrumental variable is a variable that is associated with the exposure, affects the outcome only through the exposure (exclusion), and is not confounded with the outcome (exogeneity). Unlike the first assumption, the other two are generally untestable and rely on subject‐matter knowledge. Therefore, a sensitivity analysis is desirable to assess the impact of assumptions' violation on the estimated parameters. In this paper, we propose and demonstrate a new method of sensitivity analysis for G‐estimators in causal linear and non‐linear models. We introduce two novel aspects of sensitivity analysis in instrumental variables studies. The first is a single sensitivity parameter that captures violations of exclusion and exogeneity assumptions. The second is an application of the method to non‐linear models. The introduced framework is theoretically justified and is illustrated via a simulation study. Finally, we illustrate the method by application to real‐world data and provide guidelines on conducting sensitivity analysis.

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“…In addition, explicitly describing the assumptions allows us one to construct sensitivity analyses of violations of these assumptions. 52 We recommend that practitioners conduct such sensitivity analyses when the validity of the causal assumptions is in question.…”

Section: Limitations and Future Directionsmentioning

confidence: 99%

Causally interpretable meta‐analysis: Clearly defined causal effects and two case studies

Rott,

Bronfort,

Chu

et al. 2023

Research Synthesis Methods

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Meta‐analysis is commonly used to combine results from multiple clinical trials, but traditional meta‐analysis methods do not refer explicitly to a population of individuals to whom the results apply and it is not clear how to use their results to assess a treatment's effect for a population of interest. We describe recently‐introduced causally interpretable meta‐analysis methods and apply their treatment effect estimators to two individual‐participant data sets. These estimators transport estimated treatment effects from studies in the meta‐analysis to a specified target population using the individuals' potentially effect‐modifying covariates. We consider different regression and weighting methods within this approach and compare the results to traditional aggregated‐data meta‐analysis methods. In our applications, certain versions of the causally interpretable methods performed somewhat better than the traditional methods, but the latter generally did well. The causally interpretable methods offer the most promise when covariates modify treatment effects and our results suggest that traditional methods work well when there is little effect heterogeneity. The causally interpretable approach gives meta‐analysis an appealing theoretical framework by relating an estimator directly to a specific population and lays a solid foundation for future developments.

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Sensitivity analysis using bias functions for studies extending inferences from a randomized trial to a target population

Cited by 14 publications

References 45 publications

Estimating the Area under the ROC Curve When Transporting a Prediction Model to a Target Population

Estimating the Area under the ROC Curve When Transporting a Prediction Model to a Target Population

Sensitivity analysis of G‐estimators to invalid instrumental variables

Causally interpretable meta‐analysis: Clearly defined causal effects and two case studies

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