An empirical assessment of immeasurable time bias in the setting of nested case‐control studies: Statins and all‐cause mortality among patients with heart failure

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.

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.

Time-related biases in perinatal pharmacoepidemiology: A systematic review of observational studies

BACKGROUND

Time-related biases, such as immortal time and time-window bias, frequently occur in pharmacoepidemiologic research. However, the prevalence of these biases in perinatal pharmacoepidemiology is not well understood.

OBJECTIVE

To describe the frequency of time-related biases in observational studies of medications commonly used during pregnancy (antibiotic, antifungal, and antiemetic drugs) via systematic review.

METHOD

We searched Medline and EMBASE for observational studies published between January 2013 and September 2020 and examining the association between antibiotic, antifungal, or antiemetic drugs and adverse pregnancy outcomes, including spontaneous abortion, stillbirth, preterm delivery, small-for-gestational age, pre-eclampsia, and gestational diabetes. The proportion of studies with time-related biases was estimated overall and by type (immortal time bias, time-window bias).

RESULTS

Our systematic review included 20 studies (16 cohort studies, 3 nested case-control studies, and 1 case-control study), of which 12 examined antibiotic, 6 antiemetic, and 2 anti-fungal drugs. Eleven studies (55%) had immortal time bias due to the misclassification of unexposed, event-free person-time between cohort entry and exposure initiation as exposed. No included study had time-window bias. The direction of effect varied for both studies with and without time-related bias, with many studies reporting very wide confidence intervals around the effect estimates, thus making the direction of effect less interpretable. However, studies with time-related bias were more likely to show protective or null associations compared with studies without time-related bias.

CONCLUSION

Time-related biases occur frequently in observational studies of drug effects during pregnancy. The use of appropriate study design and analytical approaches is needed to prevent time-related biases and ensure study validity.

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.

Analytical approaches to minimizing immeasurable time bias in cohort studies

BACKGROUND

Immeasurable time bias exaggerates drug benefits in pharmacoepidemiological studies due to exposure misclassification arising from the inability to measure in-hospital medications in many health care databases.

METHODS

To compare the ability of different methodological approaches to minimize immeasurable time bias, we conducted a cohort study of β-blocker use and all-cause mortality among patients with heart failure (HF), using a nationwide health care database which contains both in- and outpatient prescriptions. In our gold-standard analysis, we assessed exposure using a time-varying approach involving both in- and outpatient prescriptions. Cox proportional hazard models were used to estimate hazard ratios (HRs) with 95% confidence intervals (CIs) of mortality, with exposure to β-blockers defined as a time-varying variable. To estimate the magnitude of the immeasurable time bias, we repeated the analyses using outpatient prescriptions only and compared 10 approaches to minimize the bias, which are categorized as restriction, adjustment, assumption and weighting.

RESULTS

The HR for β-blocker use versus non-use was 0.76 (95% CI: 0.71 to 0.80) in our gold-standard analysis. When exposure assessment was restricted to outpatient prescriptions only, β-blocker use was substantially more protective (HR 0.43, 95% CI: 0.40 to 0.46). Of the 10 approaches examined, adjusting for hospitalization as a time-varying variable successfully minimized the bias (HR 0.75, 95% CI: 0.68 to 0.82).

CONCLUSIONS

The immeasurable time bias can result in substantial bias in pharmacoepidemiological studies. Time-varying adjustment for hospitalization appears to reduce the immeasurable time bias in the absence of inpatient medication data.

Cardiovascular safety of evogliptin in patients with type 2 diabetes: A nationwide cohort study

AIM

To assess whether the use of evogliptin, a novel dipeptidyl peptidase-4 inhibitor (DPP-4i), was associated with an increased risk of cardiovascular events compared with glimepiride in patients with type 2 diabetes (T2D).

METHODS

We conducted a population-based cohort study using South Korea's nationwide healthcare database from 1 January 2014 to 31 December 2018. We identified a base cohort of patients with T2D who newly initiated metformin monotherapy, from which we identified a study cohort of patients who either added or switched to glimepiride or DPP-4is (including evogliptin). Patients were followed up from initiation of DPP-4is or glimepiride until the earliest of either outcome occurrence or 31 December 2018. Our primary outcome was hospitalization or an emergency visit for cardiovascular events, a composite endpoint comprised of cerebrovascular events, heart failure, myocardial infarction, transient ischaemic attack, angina pectoris and revascularization procedures; secondary outcomes were the individual components of the primary outcome. A multivariable Cox proportional hazards model was used to estimate adjusted hazard ratios (aHRs) with 95% confidence intervals (CIs) for the risk of study outcomes associated with evogliptin compared with glimepiride.

RESULTS

Our base and study cohorts had 317,307 and 128,788 patients, respectively, of which 100,038 were DPP-4i users (2946 were evogliptin users) and 28,750 were glimepiride users within the study cohort. The median follow-up was 195 days for evogliptin and 113 days for glimepiride users. Compared with glimepiride, evogliptin was associated with a reduced risk of the primary outcome (aHR 0.67, 95% CI 0.48-0.95) and cerebrovascular events (aHR 0.41, 95% CI 0.22-0.78) but showed non-significant associations for myocardial infarction (aHR 0.63, 95% CI 0.27-1.46), heart failure (aHR 0.35, 95% CI 0.09-1.47), transient ischaemic attack (aHR 0.23, 95% CI 0.03-1.72) and angina pectoris (aHR 1.35, 95% CI 0.82-2.21).

CONCLUSIONS

Findings from this population-based cohort study provide novel real-world evidence that the use of evogliptin, compared with glimepiride, did not increase the risk of cardiovascular events, including cerebrovascular events, myocardial infarction, heart failure, transient ischaemic attack and angina pectoris.

New methodological approaches were able to effectively reduce immeasurable time bias in case-only designs

OBJECTIVES

The objective of this study was to assess approaches to reduce immeasurable time bias in case-crossover (CCO), case-time-control (CTC), and case-case-time-control (CCTC) designs.

STUDY DESIGN AND SETTING

We used Korea's health care database that has inpatient and outpatient prescriptions and an empirical example of benzodiazepines and mortality among the elderly. We defined our unbiased exposure setting using all prescriptions and a pseudo-outpatient setting using outpatient records only. In the pseudo-outpatient setting, we assessed 10 approaches of restricting, adjusting, stratifying, or weighting on hospitalization-related factors. We conducted conditional logistic regression to estimate odds ratio (OR) with 95% confidence intervals (CI), where an approach was considered effective when its OR was within the unbiased exposure setting OR's 95% CI.

RESULTS

Immeasurable time bias negatively biased the unbiased exposure setting's OR in all three case-only designs, overestimating the protective effect of benzodiazepines on mortality. Of the 10 approaches examined, stratifying the proportion of hospitalized time in 0.01 intervals most effectively repaired the bias in the CCO (OR 1.25, 95% CI 1.10-1.43) and CTC analyses (1.11, 0.95-1.30); no approach was effective in the CCTC analysis.

CONCLUSION

Stratifying the proportion of hospitalized time in 0.01 intervals best approximated the unbiased exposure setting estimate by overcoming the significant impact of immeasurable time bias in CCO and CTC designs.

Risk of Major Adverse Cardiovascular Events Associated with Concomitant Use of Antidepressants and Non-steroidal Anti-inflammatory Drugs: A Retrospective Cohort Study

BACKGROUND

Both antidepressants and non-steroidal anti-inflammatory drugs (NSAIDs) have been reported to affect platelet aggregation, blood pressure and heart rate. Despite the high prevalence of the combined use of antidepressants and NSAIDs, there is limited evidence on the potential risk of major adverse cardiovascular events (MACE) associated with their use.

OBJECTIVE

The objective of this study was to assess the association between concomitant antidepressant and NSAID use and MACE.

METHODS

We conducted a retrospective cohort study using South Korea's nationwide healthcare database. The study cohort was defined as those with new prescriptions for antidepressants and NSAIDs between 2004 and 2015. Exposure was assessed as time varying into four discrete periods: non-use, antidepressant use, NSAID use and concomitant use. Our primary outcome was MACE, a composite of haemorrhagic and thromboembolic events; secondary outcomes were the individual events of MACE. A multivariable Cox proportional hazards model was used to estimate hazards ratios with 95% confidence intervals. We also performed subgroup analyses by class of antidepressant/type of NSAIDs, age and sex.

RESULTS

From 240,982 patients, 235,080, 4393 and 1509 patients were users of NSAIDs, antidepressants or both drugs at cohort entry, respectively. The cohort generated 2.1 million person-years of follow-up with 22,453 events of MACE (incidence rate 1.07 per 100 person-years). Compared with non-use, concomitant use (hazard ratio 1.13, 95% confidence interval 1.01-1.26) and NSAID-only use (1.05, 1.001-1.10) were positively associated with MACE, while antidepressant-only use showed a negative association (0.91, 0.83-0.99). Concomitant use increased the individual risk of haemorrhagic stroke (1.46, 1.06-2.00), ischaemic stroke (1.22, 1.07-1.38) and heart failure (1.19, 1.02-1.38), but showed a protective effect on cardiovascular deaths (0.36, 0.21-0.62). Of the six possible combinations of antidepressants and NSAIDs by their classes, only concomitant use of tricyclic antidepressants and non-selective NSAIDs was positively associated with MACE (1.26, 1.09-1.47). The risk of MACE remained elevated with concomitant use among those aged ≥ 45 years (1.14, 1.01-1.29) and male patients (1.19, 1.01-1.42).

CONCLUSIONS

Concomitant use of antidepressants and NSAIDs moderately elevated the risk of MACE, of which the observed risk appears to be driven by the concomitant use of tricyclic antidepressants and non-selective NSAIDs. Thus, healthcare providers should take precaution when co-prescribing these drugs, weighing the potential benefits and risks associated with their use.