"First-wave" bias when conducting active safety monitoring of newly marketed medications with outcome-indexed self-controlled designs

Thinking Three-Dimensionally: A Self- and Externally-Controlled Approach to Screening for Drug-Drug-Drug Interactions Among High-Risk Populations

The global rise in polypharmacy has increased both the necessity and complexity of drug-drug interaction (DDI) assessments, given the growing potential for interactions involving more than two drugs. Leveraging large-scale healthcare claims data, we piloted a semi-automated, high-throughput case-crossover-based approach for drug-drug-drug interaction (3DI) screening. Cases were direct-acting oral anticoagulant (DOAC) users with either a major bleeding event during ongoing dispensings for potentially interacting, enzyme-inhibiting antihypertensive drugs (AHDs) (Study 1), or a thromboembolic event during ongoing dispensings for potentially interacting, enzyme-inducing antiseizure medications (ASMs) (Study 2). 3DI detection was based on screening for additional drug exposures that served as acute outcome triggers. To mitigate direct effects and confounding by concomitant drugs, self-controlled estimates were adjusted using negative cases (external "control" DOAC users with the same outcomes but co-dispensings for non-interacting AHDs or ASMs). Signal thresholds were set based on P-values and false discovery rate q-values to address multiple comparisons. Study 1: 285 drugs were examined among 3,306 episodes. Self-controlled assessments with q-value thresholds yielded 9 3DI signals (cases) and 40 DDI signals (negative cases). External adjustment generated 10 3DI signals from the P-value threshold and no signals from the q-value threshold. Study 2: 126 drugs were examined among 604 episodes. Assessments with P-value thresholds yielded 3 3DI and 26 DDI signals following self-control, as well as 4 3DI signals following adjustment. No 3DI signals met the q-value threshold. The presented self- and externally-controlled approach aimed to advance paradigms for real-world higher order drug interaction screening among high-susceptibility populations with pre-existent DDI risk.

High-Throughput Computing to Automate Population-Based Studies to Detect the 30-Day Risk of Adverse Outcomes After New Outpatient Medication Use in Older Adults with Chronic Kidney Disease: A Clinical Research Protocol

Background

Safety issues are detected in about one third of prescription drugs in the years following regulatory agency approval. Older adults, especially those with chronic kidney disease, are at particular risk of adverse reactions to prescription drugs. This protocol describes a new approach that may identify credible drug-safety signals more efficiently using administrative health care data.

Objective

To use high-throughput computing and automation to conduct 700+ drug-safety cohort studies in older adults in Ontario, Canada. Each study will compare 74 acute (30-day) outcomes in patients who start a new prescription drug (new users) to a group of nonusers with similar baseline health characteristics. Risks will be assessed within strata of baseline kidney function.

Design and setting

The studies will be population-based, new-user cohort studies conducted using linked administrative health care databases in Ontario, Canada (January 1, 2008, to March 1, 2020). The source population for these studies will be residents of Ontario aged 66 years or older who filled at least one outpatient prescription through the Ontario Drug Benefit (ODB) program during the study period (all residents have universal health care, and those aged 65+ have universal prescription drug coverage through the ODB).

Patients

We identified 3.2 million older adults in the source population during the study period and built 700+ initial medication cohorts, each containing mutually exclusive groups of new users and nonusers. Nonusers were randomly assigned cohort entry dates that followed the same distribution of prescription start dates as new users. Eligibility criteria included a baseline estimated glomerular filtration rate (eGFR) measurement within 12 months before the cohort entry date (median time was 71 days before cohort entry in the new user group), no prior receipt of maintenance dialysis or a kidney transplant, and no prior prescriptions for drugs in the same subclass as the study drug. New users and nonusers will be balanced on ~400 baseline health characteristics using inverse probability of treatment weighting on propensity scores within 3 strata of baseline eGFR: ≥60, 45 to <60, <45 mL/min per 1.73 m2.

Outcomes

We will compare new user and nonuser groups on 74 clinically relevant outcomes (17 composites and 57 individual outcomes) in the 30 days after cohort entry. We used a prespecified approach to identify these 74 outcomes.

Statistical analysis plan

In each cohort, we will obtain eGFR-stratum-specific weighted risk ratios and risk differences using modified Poisson regression and binomial regression, respectively. Additive and multiplicative interaction by eGFR category will be examined. Drug-outcome associations that meet prespecified criteria (identified signals) will be further examined in additional analyses (including survival, negative-control exposure, and E-value analyses) and visualizations.

Results

The initial medication cohorts had a median of 6120 new users per cohort (interquartile range: 1469-38 839) and a median of 1 088 301 nonusers (interquartile range: 751 697-1 267 009). Medications with the largest number of new users were amoxicillin trihydrate (n = 1 000 032), cephalexin (n = 571 566), prescription acetaminophen (n = 571 563), and ciprofloxacin (n = 504,374); 19% to 29% of new users in these cohorts had an eGFR <60 mL/min per 1.73 m2.

Limitations

Despite our use of robust techniques to balance baseline indicators and to control for confounding by indication, residual confounding will remain a possibility. Only acute (30-day) outcomes will be examined. Our data sources do not include nonprescription (over-the-counter) drugs or drugs prescribed in hospitals and do not include outpatient prescription drug use in children or adults <65 years.

Conclusion

This accelerated approach to conducting postmarket drug-safety studies has the potential to more efficiently detect drug-safety signals in a vulnerable population. The results of this protocol may ultimately help improve medication safety.

Bias Due to Within-Subject Exposure Dependency With or Without Bias Due to Lack of Pairwise Exchangeability When Exposure Is Chronic in Case-Crossover and Case-Time-Control Studies: A Simulation Study

The case-crossover study design has been proposed as a suitable design for use when a brief exposure causes a transient change in risk of an acute-onset disease. In pharmacoepidemiology, the condition of "brief exposure" is rarely satisfied because medication use is often chronic or successive, which may result in bias due to within-subject exposure dependency. Here we describe a simulation of a case-crossover study conducted within a cohort, where patients successively used a drug for 60 or more days and the rate ratio for the outcome occurrence was 4.0. Standard conditional logistic regression for the analysis produced overestimated odds ratios ranging up to 7.8. This bias due to within-subject exposure dependency from chronic use can be removed by the Mantel-Haenszel method or by our recently proposed weighting method. We also show that when some patients are censored after switching to another drug, a lack of pairwise exchangeability causes bias which is similar to bias due to an exposure time trend. This bias can be removed by using the case-time-control study design. We show that bias due to within-subject exposure dependency and lack of pairwise exchangeability occur independently and can occur separately or simultaneously, and we demonstrate how to detect and remove them.

Evaluating the use of methods to mitigate bias from non-transient medications in the case-crossover design: A systematic review

PURPOSE

The case-crossover design is a self-controlled study design used to compare exposure immediately preceding an event occurrence with exposure in earlier control periods. The design is most suitable for transient exposures in order to avoid biases that can be problematic when using the case-crossover design for non-transient (i.e., chronic) exposures. Our goal was to conduct a systematic review of case-crossover studies and its variants (case-time-control and case-case-time-control) in order to compare design and analysis choices by medication type.

METHODS

We conducted a systematic search to identify recent case-crossover, case-time-control, and case-case-time-control studies focused on medication exposures. Articles indexed in MEDLINE and EMBASE using these study designs that were published between January 2015 and December 2021 in the English language were identified. Reviews, methodological studies, commentaries, articles without medications as the exposure of interest, and articles with no available full text were excluded. Study characteristics including study design, outcome, risk window, control window, reporting of discordant pairs, and inclusion of sensitivity analyses were summarized overall and by medication type. We further evaluated the implementation of recommended methods to account for biases introduced by non-transient exposures among articles that used the case-crossover design on a non-transient exposure.

RESULTS

Of the 2036 articles initially identified, 114 articles were included. The case-crossover was the most common study design (88%), followed by the case-time-control (17%), and case-case-time-control (3%). Fifty-three percent of the articles included only transient medications, 35% included only non-transient medications, and 12% included both. Across years, the proportion of case-crossover articles evaluating a non-transient medication ranged from 30% in 2018 to 69% in 2017. We found that 41% of the articles that evaluated a non-transient medication did not apply any of the recommended methods to account for biases and more than half of which were conducted by authors with no previous publication history of case-crossover studies.

CONCLUSION

Using the case-crossover design to evaluate a non-transient medication remains common in pharmacoepidemiology. Researchers should apply appropriate design and analysis choices when opting to use a case-crossover design with non-transient medication exposures.

Risk of parkinsonism and related movement disorders with gabapentinoids or tramadol: A case-crossover study

INTRODUCTION

A safety signal concerning parkinsonism and related movement disorders with gabapentinoids (gabapentin and pregabalin) or tramadol was detected by reviewing individual case reports and data mining in spontaneous report databases. Well-designed pharmacoepidemiological studies are needed to assess the signal.

OBJECTIVE

This study aimed to investigate the association of exposure to gabapentinoids or tramadol with the risk of parkinsonism and related movement disorders.

METHODS

We conducted a case-crossover study using a Japanese electronic medical records database. Patients with newly diagnosed parkinsonism or related movement disorders between January 1, 2007, and April 14, 2019, were identified. The diagnosis date of outcomes was defined as the index date. We assessed the exposure of each patient to gabapentinoids or tramadol during a 90-day hazard period ending 1 day before the index date and in three 90-day reference periods. Multivariable conditional logistic regression models were employed to estimate adjusted odds ratios (aORs) and 95% confidence intervals (CIs). To confirm the robustness of the primary findings, we also performed sensitivity analyses using a case-case-time-control design, a different time window for hazard and reference periods, a different definition of outcome, and different number of reference periods.

RESULTS

A total of 28,972 eligible cases were included in the primary analysis. Exposure to gabapentinoids (aOR, 2.12; 95% CI, 1.73-2.61) and tramadol (aOR, 2.04; 95% CI, 1.57-2.64) was associated with increased risk. Results were consistent across sensitivity analyses.

CONCLUSION

Our findings serve as a caution to physicians who prescribe gabapentinoids or tramadol in routine clinical practice.

Drug-Drug Interaction Surveillance Study: Comparing Self-Controlled Designs in Five Empirical Examples in Real-World Data

Self-controlled designs, specifically the case-crossover (CCO) and the self-controlled case series (SCCS), are increasingly utilized to generate real-world evidence (RWE) on drug-drug interactions (DDIs). Although these designs share the advantages and limitations of within-individual comparison, they also have design-specific assumptions. It is not known to what extent the differences in assumptions lead to different results in RWE DDI analyses. Using a nationwide US commercial healthcare insurance database (2006-2016), we compared the CCO and SCCS designs, as they are implemented in DDI studies, within five DDI-outcome examples: (1) simvastatin + clarithromycin and muscle-related toxicity; (2) atorvastatin + valsartan, and muscle-related toxicity; and (3-5) dabigatran + P-glycoprotein inhibitor (clarithromycin, amiodarone, and verapamil) and bleeding. Analyses were conducted within person-time exposed to the object drug (statins and dabigatran) and adjusted for bias associated with the inhibiting drugs via control groups of individuals unexposed to the object drug. The designs yielded similar estimates in most examples, with SCCS displaying better statistical efficiency. With both designs, results varied across sensitivity analyses, particularly in CCO analyses with small number of exposed individuals. Analyses in controls revealed substantial bias that may be differential across DDI-exposed and control individuals. Thus, both designs showed no association between amiodarone or verapamil and bleeding in dabigatran-exposed but revealed strong positive associations in controls. Overall, bias adjustment via a control group had a larger impact on results than the choice of a design, highlighting the importance and challenges of appropriate control group selection for adequate bias control in self-controlled analyses of DDIs.

Control yourself: ISPE-endorsed guidance in the application of self-controlled study designs in pharmacoepidemiology

PURPOSE

Consensus is needed on conceptual foundations, terminology and relationships among the various self-controlled "trigger" study designs that control for time-invariant confounding factors and target the association between transient exposures (potential triggers) and abrupt outcomes. The International Society for Pharmacoepidemiology (ISPE) funded a working group of ISPE members to develop guidance material for the application and reporting of self-controlled study designs, similar to Standards of Reporting Observational Epidemiology (STROBE). This first paper focuses on navigation between the types of self-controlled designs to permit a foundational understanding with guiding principles.

METHODS

We leveraged a systematic review of applications of these designs, that we term Self-controlled Crossover Observational PharmacoEpidemiologic (SCOPE) studies. Starting from first principles and using case examples, we reviewed outcome-anchored (case-crossover [CCO], case-time control [CTC], case-case-time control [CCTC]) and exposure-anchored (self-controlled case-series [SCCS]) study designs.

RESULTS

Key methodological features related to exposure, outcome and time-related concerns were clarified, and a common language and worksheet to facilitate the design of SCOPE studies is introduced.

CONCLUSIONS

Consensus on conceptual foundations, terminology and relationships among SCOPE designs will facilitate understanding and critical appraisal of published studies, as well as help in the design, analysis and review of new SCOPE studies. This manuscript is endorsed by ISPE.

Using the Case-Crossover Design to Assess Short-Term Risks of Bleeding and Arterial Thromboembolism After Switching Between Oral Anticoagulants in a Population-Based Cohort of Patients With Atrial Fibrillation

Using nationwide Danish registries, we conducted a population-based case-crossover study evaluating the association between switching from a vitamin K antagonist (VKA) to a direct oral anticoagulant (DOAC), and vice versa, and 30-day risks of bleeding and arterial thromboembolism in patients with atrial fibrillation (AF). The case-crossover population was identified among oral anticoagulant users during 2011-2018 (n = 123,217) as patients with AF with 1) a case-defining outcome and 2) an anticoagulant switch during the 180 days preceding the outcome. Odds ratios were estimated using conditional logistic regression by comparing the occurrence of switching during the 30-day window immediately preceding the outcome to that in reference windows in the same individual 60-180 days before the outcome. The case-crossover populations for switching from VKA to DOAC and DOAC to VKA comprised 1,382 and 287 case patients, respectively. Switching from VKA to DOAC, but not from DOAC to VKA, was associated with an increased short-term risk of bleeding (odds ratio = 1.42; 95% confidence intervals: 1.13, 1.79, and 1.06; and 0.64, 1.75, respectively) and ischemic stroke (odds ratio = 1.74; 95% confidence intervals: 1.21, 2.51, and 0.92; and 0.46, 1.83, respectively). Our findings suggest that switching from VKA to DOAC is an intermittent risk factor of bleeding and ischemic stroke in patients with AF.

Bias in case-crossover studies of medications due to persistent use: A simulation study

PURPOSE

The case-crossover design is increasingly used to evaluate the effects of chronic medications; however, as traditionally implemented in pharmacoepidemiology, with referent period preceding the outcome, it may lead to bias in the presence of persistent exposures. We aimed to evaluate the extent and magnitude of bias in case-crossover analyses of chronic and persistent exposures, using simulations.

METHODS

We simulated cohorts with either 30-day, 180-day, or 2-year exposure duration; and with varying degrees of persistence (10%, 30%, 50%, 70%, or 90% of patients not stopping exposure). We evaluated all scenarios under the null and the scenario with 30% persistence under varying exposure effects (odds ratios of 0.25 to 4.0). Cohorts were analyzed using conditional logistic regression that compared the odds of exposure on the outcome day to the odds of exposure on a referent day 30 days prior to the outcome. We further implemented the case-time-control design to evaluate its ability to adjust for bias from persistence.

RESULTS

Case-crossover analyses produced unbiased estimates across all scenarios without persistent users, regardless of exposure duration. In scenarios where some patients persisted on treatment, case-crossover analyses resulted in upward bias, which increased with increasing proportion of persistent users, but did not vary substantially in relation to the magnitude of the true effect. Case-time-control analyses removed bias in all scenarios.

CONCLUSIONS

Investigators should be aware of bias due to treatment persistence in unidirectional case-crossover analyses of chronic medications, which can be remedied with a control group of similarly persistent noncases.

Comparison of Different Case-Crossover Variants in Handling Exposure-Time Trend or Persistent-User Bias: Using Dipeptidyl Peptidase-4 Inhibitors and the Risk of Heart Failure as an Example

OBJECTIVES

Inappropriate use of the case-crossover design, which is efficient for examining associations between brief exposure and abrupt outcomes, in evaluating the effects of medications in the presence of exposure-time trends or persistent drug use may generate spurious associations. We compared different approaches to adjusting for these sources of bias by examining the risk of heart failure hospitalization (HFH) associated with dipeptidyl peptidase-4 (DPP-4) inhibitors. Overall, existing evidence does not suggest a higher risk of HFH associated with DPP-4 inhibitors; however, case-crossover analyses of these medications may be susceptible to bias.

METHODS

We conducted case-crossover; age, sex, risk-set (ASR) matched case-time-control; disease risk score (DRS)-matched case-time-control; and case-case-time-control analyses to assess the association between DPP-4 inhibitors and HFH among patients with diabetes mellitus (DM) in a population-based Taiwanese database. We also examined metformin and sulfonylureas, both with assumed null associations.

RESULTS

Among 362 022 DM patients, 4105 (case-crossover), 4103 (ASR-matched case-time-control), 3957 (DRS-matched case-time-control), and 2812 (case-case-time-control) HFH cases were identified. The OR for DPP-4 inhibitors and HFH was elevated in the case-crossover analysis (1.52; 95% confidence interval [95% CI] 0.95-2.42). The ASR-matched case-time control, DRS-matched case-time-control, and case-case-time control analyses yielded near-null associations (0.90 [95% CI 0.45-1.83], 0.96 [95% CI 0.46-2.02], and 0.92 [95% CI 0.39-2.21], respectively). Null effects were observed for metformin across designs and for sulfonylureas in the case-case-time control analysis.

CONCLUSIONS

Our case-crossover analysis suggested DPP-4 inhibitors may be associated with HFH; however, each method for adjusting for exposure-time and persistent user bias attenuated the findings. The case-case-time-control analysis had the least precision.

Antidepressant use during pregnancy and risk of congenital heart defects: A case-time-control study

PURPOSE

We estimated the association between maternal antidepressant (AD) use in early pregnancy and risk of congenital heart defects.

METHODS

We applied a case-time-control design with the aim of controlling for confounding from time-invariant factors and compared the results of the design to results from a cohort design in a population of 792 685 singletons born alive in Denmark during 1995-2008. In the case-time-control design, we identified children diagnosed with a congenital heart defect in the first 5 years of life (cases) and compared maternal AD use in the risk period (the first 3 months of pregnancy) and the reference period (gestational months 5-7). A nondiseased control group was included to adjust for time trends of exposure. In the cohort design, we identified children whose mothers redeemed at least one AD prescription in the first 3 months of pregnancy (the exposed) and two other groups including the unexposed children with maternal AD prescriptions in the 12 months before pregnancy. We applied conditional logistic regression and logistic regression to compute odds ratios (ORs) and 95% confidence intervals (CIs).

RESULTS

The case-time-control OR for any congenital heart defect was 1.03 (95% CI, 0.61-1.73), which was similar to the OR (1.09, 95% CI, 0.88-1.35) from the cohort design when we compared the exposed children with the unexposed children with maternal AD use before pregnancy.

CONCLUSIONS

The case-time-control design provided results similar to the cohort design when the cohort design had a better confounder control strategy. We discussed the strengths and drawbacks of case-time-control design.

A Case-Crossover-Based Screening Approach to Identifying Clinically Relevant Drug-Drug Interactions in Electronic Healthcare Data

We sought to develop a semiautomated screening approach using electronic healthcare data to identify drug-drug interactions (DDIs) that result in clinical outcomes. Using a case-crossover design with 30-day hazard and referent windows, we evaluated codispensed drugs (potential precipitants) in 7,801 patients who experienced rhabdomyolysis while on cytochrome P450 (CYP)3A4-metabolized statins and in 15,147 who experienced bleeding while on dabigatran. Estimates of direct associations between precipitant drugs and outcomes were used to adjust for bias and precipitants' direct effects. The P values were adjusted for multiple testing using the false discovery rate (FDR). From among 460 drugs codispensed with statins, 1 drug (clarithromycin) generated an alert (adjusted odds ratio (OR) 5.83, FDR < 0.05). From among 485 drugs codispensed with dabigatran, 2 drugs (naproxen and enoxaparin, ORs 2.50 and 2.75; FDR < 0.05) generated an alert. All three signals reflected known pharmacologic interactions, confirming the potential of case-crossover-based approaches for DDI screening in electronic healthcare data.

Data Mining for Adverse Drug Events With a Propensity Score-matched Tree-based Scan Statistic

The tree-based scan statistic is a statistical data mining tool that has been used for signal detection with a self-controlled design in vaccine safety studies. This disproportionality statistic adjusts for multiple testing in evaluation of thousands of potential adverse events. However, many drug safety questions are not well suited for self-controlled analysis. We propose a method that combines tree-based scan statistics with propensity score-matched analysis of new initiator cohorts, a robust design for investigations of drug safety. We conducted plasmode simulations to evaluate performance. In multiple realistic scenarios, tree-based scan statistics in cohorts that were propensity score matched to adjust for confounding outperformed tree-based scan statistics in unmatched cohorts. In scenarios where confounding moved point estimates away from the null, adjusted analyses recovered the prespecified type 1 error while unadjusted analyses inflated type 1 error. In scenarios where confounding moved point estimates toward the null, adjusted analyses preserved power, whereas unadjusted analyses greatly reduced power. Although complete adjustment of true confounders had the best performance, matching on a moderately mis-specified propensity score substantially improved type 1 error and power compared with no adjustment. When there was true elevation in risk of an adverse event, there were often co-occurring signals for clinically related concepts. TreeScan with propensity score matching shows promise as a method for screening and prioritization of potential adverse events. It should be followed by clinical review and safety studies specifically designed to quantify the magnitude of effect, with confounding control targeted to the outcome of interest.

Self-controlled designs in pharmacoepidemiology involving electronic healthcare databases: a systematic review

Background

Observational studies are widely used in pharmacoepidemiology. Several designs can be used, in particular self-controlled designs (case-crossover and self-controlled case series). These designs offer the advantage of controlling for time-invariant confounders, which may not be collected in electronic healthcare databases. They are particularly useful in pharmacoepidemiology involving healthcare database. To be valid, they require the presence of some characteristics (key validity assumptions), and in such situations, these designs should be preferred. We aimed at describing the appropriate use and reporting of the key validity assumptions in self-controlled design studies.

Methods

Articles published between January 2011 and December 2014, and describing a self-controlled study design involving electronic healthcare databases were retrieved. The appropriate use (fulfilment of key assumptions) was studied in terms of major (abrupt onset event, rare or recurrent event, and intermittent exposure) and minor assumptions (those for which the design can be adapted).

Results

Among the 107 articles describing a self-controlled design, 35/53 (66%) case-crossover studies, and 48/55 (87%) self-controlled case series fulfilled the major validity assumptions for use of the design; 4/35 and 14/48 respectively did not fulfill the minor assumptions. Overall, 31/53 (58%) case-crossover studies and 34/55 (62%) self-controlled case series fulfilled both major and minor assumptions. The reporting of the methodology or the results was appropriate, except for power calculation.

Conclusions

Self-controlled designs were not appropriately used in34% and 13% of the articles we reviewed that described a case-crossover or a self-controlled case series design, respectively. We encourage better use of these designs in situations in which major validity assumptions are fulfilled (i.e., for which they are recommended), accounting for situations for which the design can be adapted.

Electronic supplementary material

The online version of this article (doi:10.1186/s12874-016-0278-0) contains supplementary material, which is available to authorized users.