Xabier García-Albéniz

Background: Since real‐world data lack information on treatment assignment (usually the only information available is treatment prescription/administration) and early outcomes can happen in advanced cancer, some design choices may introduce immortal time due to selection or due to treatment strategy misclassification. Objective: To illustrate design choices that would biassedly introduce immortal time and propose unbiased alternatives, using as a case study the estimation of the effects of different monoclonal antibody (mAb) sequencing strategies on overall survival in colorectal cancer patients. Methods: We specified a target trial to estimate the effect on overall survival of initiating mAb within 8 weeks of starting first line chemotherapy versus initiating mAb with second line chemotherapy. The first biased design choice would be requiring the initiation of second line chemotherapy for eligibility. This was avoided by aligning eligibility with the initiation of first line chemotherapy. The second biased design choice would be excluding from the first line mAb strategy those who die during the grace period without starting mAb. This was avoided separately via cloning, censoring and weighting and by sequential emulation. Results: There were 1014 eligible patients in GEMCAD 1401. The CCW approach estimated a 4‐year survival difference (second—first line mAb) of 4.3% (95% CI) (−3.3%; 10.0%) and a risk ratio (RR) of 0.95 (0.88; 1.04). Sequential trial emulation estimated a survival difference of −2.8% (−7.5%; 1.2%) and a RR of 1.04 (0.98; 1.10). Biased analyses that introduced immortal time estimated an implausible early survival benefit for mAb initiation with second‐line (6‐month RR of 0.13 (0.03; 0.28)). Conclusions: Our results are compatible with no effect on survival by mAb sequencing. Misalignment of time zero, treatment assignment, and eligibility introduced immortal time.

Randomized trials provide evidence for regulatory and clinical decision making in oncology. However, trials cannot answer every important clinical question. Evidence gaps often persist in under-represented or small patient populations and for questions about head-to-head comparisons of active treatments, complex treatment strategies (including treatment sequencing and other dynamic treatment strategies), and long-term or rare outcomes. To address these questions, clinicians and researchers increasingly turn to observational (real-world) data. The target trial framework provides a systematic approach for designing observational analyses that attempt to emulate a hypothetical pragmatic trial. This process requires that investigators specify the key components of the causal question as elements of the target trial protocol: eligibility criteria, treatment strategies, assignment procedures, follow-up, outcomes, and causal contrasts. Explicitly emulating the target trial helps investigators avoid common study design flaws that can lead to immortal time and selection bias. Although the target trial framework helps to avoid such design-related biases, it cannot eliminate biases due to inherent data limitations, such as confounding and measurement error. Here we review target trial emulation to supplement evidence from randomized trials and inform regulatory and clinical decision making in oncology. Understanding the strengths and limitations of the target trial framework improves the integration of real-world evidence into modern cancer care.

Objective: To assess the effectiveness of indapamide prolonged release and perindopril in combination using blood pressure (BP) records collected in routine practice. Methods: Using a target trial emulation framework, an observational retrospective cohort study was conducted. The data source was the United Kingdom's CPRD Aurum general practice database. Adults with systolic BP (SBP) ≥ 145 mmHg treated with perindopril 4/5 mg for ≥ 4 weeks at a stable dose who either added indapamide 1.5 mg to perindopril ( n = 193) or continued on perindopril monotherapy ( n = 14 571) were included. Balance between treatment arms was achieved with propensity score matching; results were explored in additional analyses using different eligibility criteria and alternative statistical methodologies. The primary outcome was change in SBP from baseline to Week 8 between indapamide added to perindopril versus perindopril monotherapy. Results: In the primary analysis, indapamide added to perindopril yielded an additional SBP reduction of −6.3 mmHg (95% confidence interval [CI] −8.7 to −3.9) over perindopril monotherapy at Week 8. Results of additional analyses were consistent with the main analysis, but effect estimates varied due to diverse underlying assumptions. Conclusions: Target trial emulation allowed assessment of antihypertensive treatment effectiveness, and indapamide plus perindopril yielded clinically meaningful decreases in SBP over perindopril monotherapy. Secondary and sensitivity analyses support that these findings were robust.

Importance: When randomized trials are unavailable or not feasible, observational studies can be used to answer causal questions about the comparative effects of interventions by attempting to emulate a hypothetical pragmatic randomized trial (target trial). Published guidance to aid reporting of these studies is not available.Objective: To develop consensus-based guidance for reporting observational studies performed to estimate causal effects by explicitly emulating a target trial.Design, Setting, and Participants: The Transparent Reporting of Observational Studies Emulating a Target Trial (TARGET) guideline was developed using the Enhancing the Quality and Transparency of Health Research (EQUATOR) framework. The development included (1) a systematic review of reporting practices in published studies that had explicitly aimed to emulate a target trial; (2) a 2-round online survey (August 2023 to March 2024; 18 expert participants from 6 countries) to assess the importance of candidate items selected from previous research and to identify additional items; (3) a 3-day expert consensus meeting (June 2024; 18 panelists) to refine the scope of the guideline and draft the checklist; and (4) pilot of the draft checklist with stakeholders (n = 108; September 2024 to February 2025). The checklist was further refined based on feedback on successive drafts.Findings: The 21-item TARGET checklist is organized into 6 sections (abstract, introduction, methods, results, discussion, other information). TARGET provides guidance for reporting observational studies of interventions explicitly emulating a parallel group, individually randomized target trial, with adjustment for baseline confounders. Key recommendations are to (1) identify the study as an observational emulation of a target trial; (2) summarize the causal question and reason for emulating a target trial, (3) clearly specify the target trial protocol (ie, the causal estimand, identifying assumptions, data analysis plan) and how it was mapped to the observational data, and (4) report the estimate obtained for each causal estimand, its precision, and findings from additional analyses to assess the sensitivity of the estimates to assumptions, and design and analysis choices.Conclusions and Relevance: Application of the TARGET guideline recommendations aims to improve reporting transparency and peer review and help researchers, clinicians, and other readers interpret and apply the results.

Background: Benchmarking an observational analysis against a randomized trial can increase confidence in the use of observational data to complement inferences made in trials. Until now, few examples of benchmarking have been within oncology. However, benchmarking trials of a cancer treatment poses a unique set of challenges, such as defining composite outcomes like disease-free survival. Methods and findings: We designed a target trial with a protocol as similar as possible to the B-31 and N9831 randomized trials, which estimated the effect of adjuvant trastuzumab plus chemotherapy compared with chemotherapy alone in individuals with early human epidermal growth factor receptor 2-positive breast cancer. We then carried out an observational analysis by emulating the target trial using routinely collected data from Swedish registries to understand if we can estimate a similar effect of trastuzumab as the trial. The primary endpoint was the composite of disease-free survival consisting of the earliest of (1) local or regional recurrences, (2) distant recurrences, (3) contralateral breast cancer, (4) other second primary cancer, or (5) death from any cause. Individuals who had data compatible with both treatment strategies at baseline were cloned and one copy was assigned to each arm. We applied inverse probability weights to adjust for baseline and time-varying confounding (e.g., age and hematological events like neutropenia). Our observational analysis included 1,578 women, with a median age of 59 years, and who were diagnosed between 2008 and 2015. We estimated a similar effect after five years of follow-up (RR: 0.54, 95% CI [0.44, 0.67]) for the composite endpoint of disease-free survival as the two jointly analyzed B-31 and N9831 trials (HR: 0.48, 95% CI [0.39, 0.59]). While the comparability of results increases confidence in our estimates, there remains a risk of residual and unmeasured confounding, as is the case with all observational analyses. Conclusions: We successfully benchmarked an observational analysis against the B-31 and N9831 trials. By aligning protocols and using appropriate methodological approaches, we show that observational data can be used to estimate similar results as randomized trials of cancer treatments, like trastuzumab. This opens the door to using observational data to complement results from randomized trials of cancer treatments which can provide quick, cheap, and robust evidence to support decision-making where trials leave evidence gaps.

This poster presents the results of a targeted review of assessment documents published on the European Medicines Agency (EMA) website including | ORAL

Studying the effect of duration of treatment on prognostic outcomes using real-world data is challenging because only people who survive for a long time can receive a treatment for a long time. Specifying a target trial helps overcome such challenge. We aimed to estimate the effect of different durations of treatment with antihypertensive drugs with anticholinergic properties (AC AHT) on the risk of vascular dementia and Alzheimer’s disease by emulating a target trial using the UK CPRD GOLD database (2001-2017). Comparing treatment for 3-6 years vs ≤3 years yielded null results for both types of dementia. Comparing a longer duration of treatment, >6 years vs ≤3 years, yielded a 10-year risk ratio of 0.69 (95% CI, 0.54-0.90) for vascular dementia and 0.91 (95% CI, 0.77-1.10) for Alzheimer’s disease. For illustration, we performed an analysis that failed to emulate a target trial by assigning exposure categories using postbaseline information, obtaining implausible beneficial estimates. Our findings indicate a modest benefit of longer duration of treatment with AC AHT on vascular dementia and highlight the value of the target trial emulation to avoid selection bias in the evaluation of the effect of different durations of treatment. This article is part of a Special Collection on Pharmacoepidemiology.

PURPOSE: No consensus about the effectiveness of prostate-specific antigen (PSA) screening exists among clinical guidelines, especially for the elderly. Randomized trials of PSA screening have yielded different results, partly because of variations in adherence, and it is unlikely that new trials will be conducted. Our objective was to estimate the effect of annual PSA screening on prostate cancer (PC) mortality in Medicare beneficiaries age 67-84 years. METHODS: This is a large-scale, population-based, observational study of two screening strategies: annual PSA screening and no screening. We used data from 537,599 US Medicare (2001-2008) beneficiaries age 67-84 years who had a good life expectancy, no previous PC, and no PSA test in the 2 years before baseline. We estimated the 8-year PC mortality and incidence, treatments for PC, and treatment complications of PSA screening. RESULTS: In men age 67-74 years, the estimated difference in 8-year risk of PC death between PSA screening and no screening was –2.3 (95% CI, –4.1 to –1.1) deaths per 1,000 men (a negative risk difference favors screening). Treatment complications were more frequent under PSA screening than under no screening. In men age 75-84 years, risk difference estimates were closer to zero. CONCLUSION: Our estimates suggest that under conventional statistical criteria, annual PSA screening for 8 years is highly compatible with reductions of PC mortality from four to one fewer PC deaths per 1,000 screened men age 67-74 years. As with any study using real-world data, the estimates could be affected by residual confounding.

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