Examining the Effect of Missing Data and Unmeasured Confounding ...

This exploratory study confirmed external comparator strengths and limitations by quantifying the impact of missing data and unmeasured ...

This work evaluates bias and other performance characteristics depending on missingness and unmeasured confounding by means of two case studies and simulations.

Examining the Effect of Missing Data and Unmeasured Confounding ...

Examining the Effect of Missing Data and Unmeasured Confounding on External Comparator Studies: Case Studies and Simulations ... Citation: Rippin G, Sanz H, ...

Adjustments for Unmeasured Confounders in ...

The advantages of health care utilization data for the assessment of intended and unintended drug effects have a price: the relatively narrow set of variables ...

simulation-based bias analysis to assess the impact of unmeasured ...

When relevant confounders of an exposure-outcome relationship are inadequately measured, missing, or unknown, the resulting residual confounding leads to biased ...

Missing Data and Multiple Imputation

2. Missing values can be treated as a separate category. Using this approach for confounders may allow for residual confounding if the missing category is not ...

Sensitivity Analysis for Selection bias and unmeasured Confounding ...

Sensitivity Analysis for Selection bias and unmeasured Confounding in missing Data and Causal inference models ... effect in the context of non ...

Sensitivity Analyses for Unmeasured Confounders

Additionally, single number summaries, such as the E-value or robustness value, have been proposed to allow for ease of computation when less is ...

Evaluating the Impact of Unmeasured Confounding with Internal ...

The quantitative assessment of the potential influence of unmeasured confounders in the analysis of observational data is rare, despite reliance on the “no ...

How unmeasured confounding in a competing risks setting can ...

We designed simulations to examine bias in the estimated treatment effect under Cox and Fine & Gray models with unmeasured confounding present.

Adjusting Effect Estimates for Unmeasured Confounding with ...

Often, data on important confounders are not available in cohort studies. Sensitivity analyses based on the relation of single, but not multiple, unmeasured ...

Accounting for missing data caused by drug cessation in ...

Omitting to consider confounding, treatment cessation or dropouts leads to biased estimation of effectiveness. The present article provides advices, methods and ...

Magnitude and direction of missing confounders had different ...

We have shown that omitting confounders from a PS analysis can lead to biased treatment effects. However, this bias is only clinically meaningful if the ...

Multiple imputation with missing indicators as proxies for ...

Alongside missing data, confounding is a threat to causal effect estimation in observational studies, especially where this is caused by ...

Instrumental variable analysis in the presence of unmeasured ...

While randomized controlled trials (RCTs) are able to balance both measured and unmeasured confounders between comparison groups by the mechanism of ...

A comparison of different methods to handle missing data in the ...

In some situations, complete case analysis was also able to partially correct for unmeasured confounding. Multiple imputation worked well if the data were ...

Chapter 6: Methods to address bias and confounding - ENCePP

Another important finding of this study was that when confounding factors (measured or unmeasured) are interrelated (e.g., in situations of confounding by ...

Unpredictable bias when using the missing indicator method or ...

... analysis for missing confounder values: an empirical example." by M. Knol et al ... The impact of missing data on analyses of a time-dependent exposure in ...

Technical Methods Report: What to Do When Data Are Missing in ...

A second category—Missing at Random (MAR)—is one in which missingness is random conditional on the observed characteristics of the study sample. For example, ...

Sensitivity Analysis for Causal Inference Under Unmeasured ...

... consequences, as well as the simplicity ... Sensitivity analysis for selection bias and unmeasured confounding in missing data and causal inference models.