Analytical approaches to minimizing immeasurable time bias in cohort studies

Validating an approach to overcome the immeasurable time bias in cohort studies: a real-world example and Monte Carlo simulation study

BACKGROUND

Immeasurable time bias arises from the lack of in-hospital medication information. It has been suggested that time-varying adjustment for hospitalization may minimize this potential bias. However, whereas we examined this issue in one case study, there remains a need to assess the validity of this approach in other settings.

METHODS

Using a Monte Carlo simulation, we generated synthetic immeasurable time-varying hospitalization-related factors of duration, frequency and timing. Nine scenarios were created by combining three frequency scenarios and three duration scenarios, where the empirical cohort distribution of hospitalization was used to simulate the 'timing'. We used Korea's healthcare database and a case example of β-blocker use and mortality among patients with heart failure. We estimated the gold-standard hazard ratio (HR) with 95% CI using inpatient and outpatient drug data, and that of the pseudo-outpatient setting using outpatient data only. We assessed the validity of adjusting for time-varying hospitalization in nine different scenarios, using relative bias, confidence limit ratio (CLR) and mean squared error (MSE) compared with the empirical gold-standard estimate across bootstrap resamples.

RESULTS

With the real-world gold standard (HR 0.73; 95% CI 0.67-0.80) as the reference estimate, adjusting for time-varying hospitalization (0.71; 0.63-0.80) effectively reduced the immeasurable time bias and had the following performance metrics across the nine scenarios: relative bias (range: -7.08% to 0.61%), CLR (1.28 to 1.36) and MSE (0.0005 to 0.0031).

CONCLUSIONS

The approach of adjusting for time-varying hospitalization consistently reduced the immeasurable time bias in Monte Carlo simulated data.

Real-world evidence: new opportunities for osteoporosis research. Recommendations from a Working Group from the European Society for Clinical and Economic Aspects of Osteoporosis, Osteoarthritis and Musculoskeletal Diseases (ESCEO)

This narrative review summarises the recommendations of a Working Group of the European Society for Clinical and Economic Aspects of Osteoporosis, Osteoarthritis and Musculoskeletal Diseases (ESCEO) for the conduct and reporting of real-world evidence studies with a focus on osteoporosis research.

PURPOSE

Vast amounts of data are routinely generated at every healthcare contact and activity, and there is increasing recognition that these real-world data can be analysed to generate scientific evidence. Real-world evidence (RWE) is increasingly used to delineate the natural history of disease, assess real-life drug effectiveness, understand adverse events and in health economic analysis. The aim of this work was to understand the benefits and limitations of this type of data and outline approaches to ensure that transparent and high-quality evidence is generated.

METHODS

A ESCEO Working Group was convened in December 2022 to discuss the applicability of RWE to osteoporosis research and approaches to best practice.

RESULTS

This narrative review summarises the agreed recommendations for the conduct and reporting of RWE studies with a focus on osteoporosis research.

CONCLUSIONS

It is imperative that research using real-world data is conducted to the highest standards with close attention to limitations and biases of these data, and with transparency at all stages of study design, data acquisition and curation, analysis and reporting to increase the trustworthiness of RWE study findings.

Bleeding risk and mortality according to antithrombotic agents' exposure in cancer-related stroke patients: nationwide population-based cohort study in South Korea

BACKGROUND

Ischemic stroke with active cancer is thought to have a unique mechanism compared to conventional stroke etiologies. There is no gold standard guideline for secondary prevention in patients with cancer-related stroke, hence, adequate type of antithrombotic agent for treatment is controversial.

METHODS

Subjects who were enrolled in National Health Insurance System Customized Research data during the period between 2010 and 2015 were observed until 2019. Subject diagnosed with ischemic stroke within six months before and 12 months after a cancer diagnosis was defined as cancer-related stroke patient. To solve immeasurable time bias, the drug exposure evaluation was divided into daily units, and each person-day was classified as four groups: antiplatelet, anticoagulant, both types, and unexposed to antithrombotic drugs. To investigate bleeding risk and mortality, Cox proportional hazards regression model with time-dependent covariates were used.

RESULTS

Two thousand two hundred eighty-five subjects with cancer-related stroke were followed and analyzed. A group with anticoagulation showed high estimated hazard ratios (HRs) of all bleeding events compared to a group with antiplatelet (major bleeding HR, 1.35; 95% confidence interval [CI], 1.20-1.52; p < 0.001). And the result was also similar in the combination group (major bleeding HR, 1.54; 95% CI, 1.13-2.09; p = 0.006). The combination group also showed increased mortality HR compared to antiplatelet group (HR, 1.72; 95% CI, 1.47-2.00; p < 0.001).

CONCLUSIONS

Bleeding risk increased in the anticoagulant-exposed group compared to antiplatelet-exposed group in cancer-related stroke patients. Thus, this result should be considered when selecting a secondary prevention drug.

Immeasurable Time Bias in Self-controlled Designs: Case-crossover, Case-time-control, and Case-case-time-control Analyses

BACKGROUND

Impact of immeasurable time bias (IMTB) is yet to be examined in self-controlled designs.

METHODS

We conducted case-crossover, case-time-control, and case-case-time-control analyses using Korea's healthcare database. Two empirical examples among elderly patients were used: 1) benzodiazepines-hip fracture; 2) benzodiazepines-mortality. For cases, the date of hip fracture diagnosis or death was defined as the index date, and the inherited date of their matched cases for controls or future cases. Exposure was assessed in the 1-30 day (hazard) and 61-90 day (control) windows preceding the index date. A non-missing exposure setting included in- and outpatient prescriptions and the pseudo-outpatient setting included only the outpatients. Conditional logistic regression was done to estimate odds ratios (ORs) with 95% confidence intervals (CIs), where the relative difference in OR among the two settings was calculated to quantify the IMTB.

RESULTS

The IMTB had negligible impacts in the hip fracture example in the case-crossover (non-missing exposure setting OR 1.27; 95% CI, 1.12-1.44; pseudo-outpatient setting OR 1.21; 95% CI, 1.06-1.39; magnitude 0.05), case-time-control (OR 1.18; 95% CI, 0.98-1.44; OR 1.13; 95% CI, 0.92-1.38; 0.04, respectively), and case-case-time-control analyses (OR 0.99; 95% CI, 0.80-1.23; OR 0.94; 95% CI, 0.75-1.18; 0.05, respectively). In the mortality example, IMTB had significant impacts in the case-crossover (non-missing exposure setting OR 1.44; 95% CI, 1.36-1.52; pseudo-outpatient setting OR 0.72; 95% CI, 0.67-0.78; magnitude 1.00), case-time-control (OR 1.38; 95% CI, 1.26-1.51; OR 0.68; 95% CI, 0.61-0.76; 1.03, respectively), and case-case-time-control analyses (OR 1.27; 95% CI, 1.15-1.40; OR 0.62; 95% CI, 0.55-0.69; 1.05, respectively).

CONCLUSION

Although IMTB had negligible impacts on the drug's effect on acute events, as these are unlikely to be accompanied with hospitalizations, it negatively biased the drug's effect on mortality, an outcome with prodromal phases, in the three self-controlled designs.

Analytical Approaches to Reduce Selection Bias in As-Treated Analyses with Missing In-Hospital Drug Information

INTRODUCTION

While much attention has focused on immeasurable time bias as a potential exposure misclassification bias, it may also result in potential selection bias in cohort studies using an as-treated (or per protocol) exposure definition in which patients are censored upon treatment discontinuation.

METHODS

We examined analytical approaches to minimise informative censoring due to the absence of in-hospital drug data using a case study of β-blocker use and mortality in heart failure. We conducted a cohort study using Korea's healthcare database, including inpatient and outpatient drug data. Using an as-treated exposure definition, patients were followed up until death, β-blocker discontinuation (in the exposed), β-blocker initiation (in the unexposed), or end of study period. In 'complete prescription' analysis using inpatient and outpatient drug data, we estimated hazard ratios (HR) and 95% confidence intervals (CI) using a Cox proportional hazard model. In outpatient drug-based analyses, we attempted to reduce the bias using stabilised inverse probability weighting (IPW) for treatment crossovers, hospitalisation, and all artificial censorings.

RESULTS

An HR of 0.89 (95% CI 0.74-1.07) for β-blocker use versus non-use for all-cause mortality was found in 'complete prescription' analysis. Benefits were exaggerated when follow-up was assessed using outpatient drug data only (HR 0.71; 95% CI 0.57-0.89). Weighting by stabilised IPW for treatment crossovers and hospitalisation reduced the bias.

CONCLUSIONS

When using an as-treated exposure definition, missing in-hospital drug data induced selection bias in our case study. Using IPW for censoring mitigated bias from the hospitalisation-induced censorings.