Visualizing the target estimand in comparative effectiveness studies with multiple treatments.

Aim: Comparative effectiveness research using real-world data often involves pairwise propensity score matching to adjust for confounding bias. We show that corresponding treatment effect estimates may have limited external validity, and propose two visualization tools to clarify the target estimand. Materials & methods: We conduct a simulation study to demonstrate, with bivariate ellipses and joy plots, that differences in covariate distributions across treatment groups may affect the external validity of treatment effect estimates. We showcase how these visualization tools can facilitate the interpretation of target estimands in a case study comparing the effectiveness of teriflunomide (TERI), dimethyl fumarate (DMF) and natalizumab (NAT) on manual dexterity in patients with multiple sclerosis. Results: In the simulation study, estimates of the treatment effect greatly differed depending on the target population. For example, when comparing treatment B with C, the estimated treatment effect (and respective standard error) varied from -0.27 (0.03) to -0.37 (0.04) in the type of patients initially receiving treatment B and C, respectively. Visualization of the matched samples revealed that covariate distributions vary for each comparison and cannot be used to target one common treatment effect for the three treatment comparisons. In the case study, the bivariate distribution of age and disease duration varied across the population of patients receiving TERI, DMF or NAT. Although results suggest that DMF and NAT improve manual dexterity at 1 year compared with TERI, the effectiveness of DMF versus NAT differs depending on which target estimand is used. Conclusion: Visualization tools may help to clarify the target population in comparative effectiveness studies and resolve ambiguity about the interpretation of estimated treatment effects.

Development of a decision analytical framework to prioritise operating room capacity: lessons learnt from an empirical example on delayed elective surgeries during the COVID-19 pandemic in a hospital in the Netherlands.

OBJECTIVE: To develop a prioritisation framework to support priority setting for elective surgeries after COVID-19 based on the impact on patient well-being and cost. DESIGN: We developed decision analytical models to estimate the consequences of delayed elective surgical procedures (eg, total hip replacement, bariatric surgery or septoplasty). SETTING: The framework was applied to a large hospital in the Netherlands. OUTCOME MEASURES: Quality measures impacts on quality of life and costs were taken into account and combined to calculate net monetary losses per week delay, which quantifies the total loss for society expressed in monetary terms. Net monetary losses were weighted by operating times. RESULTS: We studied 13 common elective procedures from four specialties. Highest loss in quality of life due to delayed surgery was found for total hip replacement (utility loss of 0.27, ie, 99 days lost in perfect health); the lowest for arthroscopic partial meniscectomy (utility loss of 0.05, ie, 18 days lost in perfect health). Costs of surgical delay per patient were highest for bariatric surgery (€31/pp per week) and lowest for arthroscopic partial meniscectomy (-€2/pp per week). Weighted by operating room (OR) time bariatric surgery provides most value (€1.19/pp per OR minute) and arthroscopic partial meniscectomy provides the least value (€0.34/pp per OR minute). In a large hospital the net monetary loss due to prolonged waiting times was €700 840 after the first COVID-19 wave, an increase of 506% compared with the year before. CONCLUSIONS: This surgical prioritisation framework can be tailored to specific centres and countries to support priority setting for delayed elective operations during and after the COVID-19 pandemic, both in and between surgical disciplines. In the long-term, the framework can contribute to the efficient distribution of OR time and will therefore add to the discussion on appropriate use of healthcare budgets. The online framework can be accessed via: https://stanwijn.shinyapps.io/priORitize/.