Use of negative control outcomes to assess the comparability of patients initiating lipid-lowering therapies

Staging and clean room: Constructs designed to facilitate transparency and reduce bias in comparative analyses of real-world data

PURPOSE

We describe constructs designed to protect the integrity of the results from comparative analyses using real-world data (RWD): staging and clean room.

METHODS

Staging involves performing sequential preliminary analyses and evaluating the population size available and potential bias before conducting comparative analyses. A clean room involves restricted access to data and preliminary results, policies governing exploratory analyses and protocol deviations, and audit trail. These constructs are intended to allow decisions about protocol deviations, such as changes to design or model specification, to be made without knowledge of how they might affect subsequent analyses. We describe an example for implementing staging with a clean room.

RESULTS

Stage 1 may involve selecting a data source, developing and registering a protocol, establishing a clean room, and applying inclusion/exclusion criteria. Stage 2 may involve attempting to achieve covariate balance, often through propensity score models. Stage 3 may involve evaluating the presence of residual confounding using negative control outcomes. After each stage, check points may be implemented when a team of statisticians, epidemiologists and clinicians masked to how their decisions may affect study outcomes, reviews the results. This review team may be tasked with making recommendations for protocol deviations to address study precision or bias. They may recommend proceeding to the next stage, conducting additional analyses to address bias, or terminating the study. Stage 4 may involve conducting the comparative analyses.

CONCLUSIONS

The staging and clean room constructs are intended to protect the integrity and enhance confidence in the results of analyses of RWD.

Statin use and risk of colorectal cancer in patients with inflammatory bowel disease

Background

Statin use has been linked to a reduced risk of advanced colorectal adenomas, but its association with colorectal cancer (CRC) in patients with inflammatory bowel disease (IBD) - a high risk population for CRC - remains inconclusive.

Methods

From a nationwide IBD cohort in Sweden, we identified 5273 statin users and 5273 non-statin users (1:1 propensity score matching) from July 2006 to December 2018. Statin use was defined as the first filled prescription for ≥30 cumulative defined daily doses and followed until December 2019. Primary outcome was incident CRC. Secondary outcomes were CRC-related mortality and all-cause mortality. Cox regression estimated adjusted hazard ratios (aHRs) and 95% confidence intervals (CIs).

Findings

During a median follow-up of 5.6 years, 70 statin users (incidence rate (IR): 21.2 per 10,000 person-years) versus 90 non-statin users (IR: 29.2) were diagnosed with incident CRC (rate difference (RD), -8.0 (95% CIs: -15.8 to -0.2 per 10,000 person-years); aHR = 0.76 (95% CIs: 0.61 to 0.96)). The benefit for incident CRC was duration-dependent in a nested case-control design: as compared to short-term use (30 days to <1 year), the adjusted odd ratios were 0.59 (0.25 to 1.43) for 1 to <2 years of use, 0.46 (0.21 to 0.98) for 2 to <5 years of use, and 0.38 (0.16 to 0.86) for ≥5 years of use (Pfor tread = 0.016). Compared with non-statin users, statin users also had a decreased risk for CRC-related mortality (IR: 6.0 vs. 11.9; RD, -5.9 (-10.5 to -1.2); aHR, 0.56 (0.37 to 0.83)) and all-cause mortality (IR: 156.4 vs. 231.4; RD, -75.0 (-96.6 to -53.4); aHR, 0.63 (0.57 to 0.69)).

Interpretation

Statin use was associated with a lower risk of incident CRC, CRC-related mortality, and all-cause mortality. The benefit for incident CRC was duration-dependent, with a significantly lower risk after ≥2 years of statin use.

Funding

This research was supported by Forte (i.e., the Swedish Research Council for Health, Working Life and Welfare).

Visualizations throughout pharmacoepidemiology study planning, implementation, and reporting

Transparency is increasingly promoted to instill trust in nonrandomized studies using real-world data. Graphics and data visualizations support transparency by aiding communication and understanding, and can inform study design and analysis decisions. However, other than graphical representation of a study design and flow diagrams (e.g., a Consolidated Standards of Reporting Trials [CONSORT] like diagram), specific standards on how to maximize validity and transparency with visualization are needed. This paper provides guidance on how to use visualizations throughout the life cycle of a pharmacoepidemiology study-from initial study design to final report-to facilitate rationalized and transparent decision-making about study design and implementation, and clear communication of study findings. Our intent is to help researchers align their practices with current consensus statements on transparency.