Disease risk score as a confounder summary method: systematic review and recommendations

Abstract

Purpose To systematically examine trends and applications of the disease risk score (DRS) as a confounder summary method. Methods We completed a systematic search of MEDLINE and Web of Science® to identify all English language articles that applied DRS methods. We tabulated the number of publications by year and type (empirical application, methodological contribution, or review paper) and summarized methods used in empirical applications overall and by publication year (<2000, ≥2000). Results Of 714 unique articles identified, 97 examined DRS methods and 86 were empirical applications. We observed a bimodal distribution in the number of publications over time, with a peak 1979-1980, and resurgence since 2000. The majority of applications with methodological detail derived DRS using logistic regression (47%), used DRS as a categorical variable in regression (93%), and applied DRS in a non-experimental cohort (47%) or case-control (42%) study. Few studies examined effect modification by outcome risk (23%). Conclusion Use of DRS methods has increased yet remains low. Comparative effectiveness research may benefit from more DRS applications, particularly to examine effect modification by outcome risk. Standardized terminology may facilitate identification, application, and comprehension of DRS methods. More research is needed to support the application of DRS methods, particularly in case-control studies.

Association of Oral Anticoagulants and Proton Pump Inhibitor Cotherapy With Hospitalization for Upper Gastrointestinal Tract Bleeding

“…Disease risk scores are a standard technique for risk stratification within a specific population because the covariate definitions and their weights are internally derived. 18,19 Consequently, they incorporate information from all measured patient factors and are specifically calibrated for the study endpoint. The score was expressed as a risk quantile from 0 to 19; thus, 0 indicates the lowest-risk 5% and 19 the highest-risk 5% of the cohort.…”

Case‐crossover study design in pharmacoepidemiology: systematic review and recommendations

“…We then used Web of Science® to perform a citation search to identify papers that referenced the case‐crossover study design seminal paper . A citation search can be important to identify early methodological applications because researchers may be slow to adopt the new terminology, particularly when there is no medical subject heading term . Case‐crossover review papers, methodological contributions, and empirical applications in the area of pharmacoepidemiology were eligible.…”

Adjusting for Confounding in Early Postlaunch Settings

“…ideally, the training dataset consists exclusively of untreated patients. 19 Otherwise disease risk score models might be biased by possible treatment by confounder interaction. to explore how sensitive this method is to unobserved interaction, we compared four disease risk score models.…”

“…Vittinghoff and Mcculloch 12 showed that in this case, logistic regression models with events per coefficient as small as six can adequately adjust for confounding. in settings where logistic regression models are expected to perform poorly (i.e., events per coefficient smaller than six), propensity score, 13,14 disease risk score, [15][16][17][18][19] or inverse probability weighting, [20][21][22] methods can be applied to summarize information of multiple confounders into a single variable. it remains unclear how many events/exposures per variable are needed to sufficiently control for confounding using propensity score, disease risk score, and inverse probabilityweighting methods.…”