From: Bias in pharmacoepidemiologic studies using secondary health care databases: a scoping review
Category | Control strategies |
---|---|
Confounding | |
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 |