Skip to main content

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?
  1. aIf multiple models evaluated, definitions relate to dynamic treatment regimen models only
\