Table 2 Data extraction items
From: A scoping review of studies using observational data to optimise dynamic treatment regimens
Data | Definitiona |
|---|---|
Complete reference | Title, publication source, authorship, year published |
Clinical area | Disease or medical condition studied, e.g., HIV/AIDS, cancer. |
Outcome type | Type of primary outcome, e.g., binary, continuous, time-to-event. |
Participants | Number of study participants included in the model (largest number if multiple analyses were performed). |
Funding source/s | What direct funding sources were acknowledged? E.g., public, non-profit, industry-sponsored, not funded, not reported. |
Statistical method/s | The statistical method/s used to estimate the value of the dynamic treatment regimen/s decision rules, e.g., inverse probability weighting, parametric G-formula, Q-learning. |
Clinical focus | Was the main discussion and methodology of the study focused on directly informing clinical practice, or developing and evaluating a statistical method to answer a medical question? |
Missing data | Were methods used to account for missing data included, e.g., multiple imputation, last observation carried forward, complete case analysis. Note: applies only to original data, not augmented data? |
Model evaluation | Were methods used to evaluate the estimated model included, e.g., cross-validation, Bayesian information criterion, residual analysis? |
Covariate selection | Was the approach for selecting the covariates stated, e.g., stepwise selection, convenience, subject matter expertise, causal directed acyclic graph, or analogous method? |
Sensitivity analysis | Was model sensitivity assessed and how this was performed included, e.g., alternative model specification, truncated inverse probability weights? |
Software included | If any analysis software code was included, what language was it written in, e.g., R, SAS, Python, Stata? |