Methodological Challenges of Emulating a Target Trial to Assess Effectiveness of Timing of PCSK9 Inhibitor Treatment Initiation Post Myocardial Infarction

TL;DR

The study explores challenges in comparing early versus late initiation of PCSK9 inhibitor treatment after heart attacks using a method called clone-censor-weighting.

Contribution

The paper evaluates the clone-censor-weight method's effectiveness in reducing bias when comparing treatment timing in real-world data.

Findings

Very few patients started PCSK9i within 12 months post-MI, and these patients differed in age, LDL-C levels, and use of ezetimibe.

Clone-censor-weighting reduced immortal time bias but struggled with covariate balance due to small and imbalanced groups.

Truncation of weights improved stability but introduced some covariate imbalances.

Abstract

Studies comparing treatment strategies based on initiation timing—such as starting PCSK9 inhibitor (PCSK9i) therapy sooner versus later after a myocardial infarction (MI)—are prone to immortal time bias. Clone‐censor‐weight methods can address these issues and allow the researcher to emulate a trial in which patients are assigned to protocols dictating when PCSK9i is initiated. This study aimed to evaluate the comparability of patients in a clone‐censor‐weight setup who initiated a PCSK9i within 12 months post‐MI versus non‐initiators.

We included adult patients hospitalized for MI in Sweden (2015–2021) and followed them for 3 years. We considered two treatment strategies: initiating PCSK9i within 12 months versus not initiating PCSK9i during the same period. We applied the clone‐censor‐weight method to address immortal time bias and assessed remaining bias using covariate balance metrics and negative control outcomes.

The primary study sample included 38  627 episodes of MI, with 561 (1.5%) initiating PCSK9i treatment within 12 months. These patients were younger, had higher baseline LDL‐C levels, and were more frequently treated with ezetimibe during their post‐MI follow‐up compared to non‐initiators. Although clone‐censor‐weight estimation was free of immortal time bias, it faced challenges in achieving adequate balance of covariates due to the high rates of censoring (relatively small number of people initiating a PCSK9i in the first year) and strong association between covariates and censoring. Truncation of weights provided more stable estimates but at the expense of some covariate imbalances.

The clone‐censor‐weight method is a promising approach that allows researchers to answer questions about the effect of treatment policies. But practical guidance is needed to address problems that arise from small, highly imbalanced groups, which is common with most newly introduced treatments.

Studies comparing patients who start a medicine at different times after a clinical event can be complicated. Timing often reflects disease duration or severity. For example, after a heart attack, some patients may start cholesterol‐lowering PCSK9 inhibitor treatment early, while others delay or never start. Comparing these groups is difficult because patients must survive long enough to receive the treatment, introducing bias. Early treatment may also only be offered to select patients. Using Swedish healthcare data covering over 38 000 cases of heart attacks between 2015 and 2021, we compared patients who started PCSK9 inhibitors within 12 months to those who did not. We found that very few received early treatment, and they differed in terms of age, low‐density lipoprotein cholesterol levels, and use of another cholesterol‐lowering drug, ezetimibe. We then used a method called clone‐censor‐weighting to help reduce the impact of these differences on health outcomes between treatment groups. We used several methods to check for remaining bias, including looking at unrelated health outcomes that shouldn't be affected by the treatment. Our analysis showed that while the clone‐censor‐weighting method helped reduce bias, it still had challenges when the groups being compared were very different in terms of number of cases and background of patients.

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