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Table 2 Main bias-control strategies in observational studies of pharmacoepidemiologic databases

From: Bias in pharmacoepidemiologic studies using secondary health care databases: a scoping review

Category Control strategies
 Measured confounding - Multivariate analysis
- Restriction*
- Stratification
- Matching
- New-user design
- Propensity score
- Large-scale, simple randomized trials
- Meta-analysis of clinical trials
* Confounding by indication: Restricting the untreated group to a population with the same indication, or limiting participation to patients without a risk factor for the effect that could have determined the treatment
 Time-dependent confounding - G–estimation
- Marginal structural models
 Unmeasured confounding - Crossover design
- Asymmetric exclusion of patients with extreme propensity-score values
- Instrumental variables
- Proxy measures
- Restriction (active comparison group)
- Sensitivity analysis
- Validation study + external adjustment
Selection bias
 Protopathic bias - Restriction (e.g. restricting the untreated group to a population with the same indication, or restricting the treated group to a population with an indication that is not a subclinical stage of the disease)
- Excluding a specific period of time prior to the date of diagnosis of the disease (lag-time) from the etiologic window
 Losses to follow-up (informative censoring) - Inclusion of variables that affect censoring and event times in the multivariate regression model
- Inverse probability of censoring weighting
- Sensitivity analysis
 Depletion of susceptibles (prevalent user bias) - New-user design
- Meta-analysis of clinical trials
 Missing data - Replacing each absent observation with a mean value based on observed values of the variable or the predicted value based on a regression model
- Imputation methods (e.g. multiple imputation)
- Likelihood-based methods
- Inverse probability weighting
Measurement bias
 Misclassification bias - Validation study (exposure/outcome/confounders) + (sensitivity analysis/misclassification control techniques using multivariate regression)
Time-related bias
 Immortal time bias - Data analysis with procedures that take into account time-dependent exposure in a cohort
- Transferring the start of treatment to the end of the immortal time period in both groups
 Immeasurable time bias - Data analysis accounting for the time-varying exposable period
 Time-window bias - Accounting for duration of treatment in the selection of controls
- Time-dependent analysis
 Time-lag bias - Comparing patients at the same stage of disease