Dataset Information

Target Trial Emulation: A Design Tool for Cancer Clinical Trials.

ABSTRACT:

Purpose

To apply target trial emulation to explore the potential impact of eligibility criteria on the primary outcome of a randomized controlled trial.

Methods

Simulations of a real-world explanatory trial of transarterial radioembolization for advanced unresectable hepatocellular carcinoma with portal vein invasion were performed to examine the effects of cohort specification on survival outcomes and patient sample size. Simulations comprised 24 different permutations of the trial varied on three disease nonspecific eligibility parameters. Treatment and control arms for these emulated trials were drawn from the National Cancer Database and matched by treatment propensity. Target trial emulation served as the causal framework for this analysis, allowing the architecture of a true controlled experiment to address forms of bias routinely encountered in comparative effectiveness studies involving real-world observational data.

Results

Twenty-four propensity score-matched cohorts comprising a wider clinical spectrum of patients than specified by the original target trial were successfully generated using the National Cancer Database. The arms for each of the emulated trials demonstrated exchangeability across all eligibility criteria and other clinical covariates. Significant treatment benefits were associated with only a narrow range of eligibility criteria, indicating that the original target trial was well specified.

Conclusion

The impact of patient selection on treatment outcomes can be studied using target trial emulation. This analytical framework can furthermore serve to leverage existing real-world data to inform the task of cohort specification for a randomized controlled trial, facilitating a more data-driven approach for this important step in clinical trial design.

SUBMITTER: Kwee SA

PROVIDER: S-EPMC10166475 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

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Publications

Target Trial Emulation: A Design Tool for Cancer Clinical Trials.

Kwee Sandi A SA Wong Linda L LL Ludema Christina C Deng Chris K CK Taira Deborah D Seto Todd T Landsittel Douglas D

JCO clinical cancer informatics 20230101

<h4>Purpose</h4>To apply target trial emulation to explore the potential impact of eligibility criteria on the primary outcome of a randomized controlled trial.<h4>Methods</h4>Simulations of a real-world explanatory trial of transarterial radioembolization for advanced unresectable hepatocellular carcinoma with portal vein invasion were performed to examine the effects of cohort specification on survival outcomes and patient sample size. Simulations comprised 24 different permutations of the tri ...[more]

PMID: 36608311

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Project description:ImportanceNonrandomized studies using insurance claims databases can be analyzed to produce real-world evidence on the effectiveness of medical products. Given the lack of baseline randomization and measurement issues, concerns exist about whether such studies produce unbiased treatment effect estimates.ObjectiveTo emulate the design of 30 completed and 2 ongoing randomized clinical trials (RCTs) of medications with database studies using observational analogues of the RCT design parameters (population, intervention, comparator, outcome, time [PICOT]) and to quantify agreement in RCT-database study pairs.Design, setting, and participantsNew-user cohort studies with propensity score matching using 3 US claims databases (Optum Clinformatics, MarketScan, and Medicare). Inclusion-exclusion criteria for each database study were prespecified to emulate the corresponding RCT. RCTs were explicitly selected based on feasibility, including power, key confounders, and end points more likely to be emulated with real-world data. All 32 protocols were registered on ClinicalTrials.gov before conducting analyses. Emulations were conducted from 2017 through 2022.ExposuresTherapies for multiple clinical conditions were included.Main outcomes and measuresDatabase study emulations focused on the primary outcome of the corresponding RCT. Findings of database studies were compared with RCTs using predefined metrics, including Pearson correlation coefficients and binary metrics based on statistical significance agreement, estimate agreement, and standardized difference.ResultsIn these highly selected RCTs, the overall observed agreement between the RCT and the database emulation results was a Pearson correlation of 0.82 (95% CI, 0.64-0.91), with 75% meeting statistical significance, 66% estimate agreement, and 75% standardized difference agreement. In a post hoc analysis limited to 16 RCTs with closer emulation of trial design and measurements, concordance was higher (Pearson r, 0.93; 95% CI, 0.79-0.97; 94% meeting statistical significance, 88% estimate agreement, 88% standardized difference agreement). Weaker concordance occurred among 16 RCTs for which close emulation of certain design elements that define the research question (PICOT) with data from insurance claims was not possible (Pearson r, 0.53; 95% CI, 0.00-0.83; 56% meeting statistical significance, 50% estimate agreement, 69% standardized difference agreement).Conclusions and relevanceReal-world evidence studies can reach similar conclusions as RCTs when design and measurements can be closely emulated, but this may be difficult to achieve. Concordance in results varied depending on the agreement metric. Emulation differences, chance, and residual confounding can contribute to divergence in results and are difficult to disentangle.

Project description:BackgroundIf you want to know which of two or more healthcare interventions is most effective, the randomised controlled trial is the design of choice. Randomisation, however, does not itself promote the applicability of the results to situations other than the one in which the trial was done. A tool published in 2009, PRECIS (PRagmatic Explanatory Continuum Indicator Summaries) aimed to help trialists design trials that produced results matched to the aim of the trial, be that supporting clinical decision-making, or increasing knowledge of how an intervention works. Though generally positive, groups evaluating the tool have also found weaknesses, mainly that its inter-rater reliability is not clear, that it needs a scoring system and that some new domains might be needed. The aim of the study is to: Produce an improved and validated version of the PRECIS tool. Use this tool to compare the internal validity of, and effect estimates from, a set of explanatory and pragmatic trials matched by intervention.MethodsThe study has four phases. Phase 1 involves brainstorming and a two-round Delphi survey of authors who cited PRECIS. In Phase 2, the Delphi results will then be discussed and alternative versions of PRECIS-2 developed and user-tested by experienced trialists. Phase 3 will evaluate the validity and reliability of the most promising PRECIS-2 candidate using a sample of 15 to 20 trials rated by 15 international trialists. We will assess inter-rater reliability, and raters' subjective global ratings of pragmatism compared to PRECIS-2 to assess convergent and face validity. Phase 4, to determine if pragmatic trials sacrifice internal validity in order to achieve applicability, will compare the internal validity and effect estimates of matched explanatory and pragmatic trials of the same intervention, condition and participants. Effect sizes for the trials will then be compared in a meta-regression. The Cochrane Risk of Bias scores will be compared with the PRECIS-2 scores of pragmatism.DiscussionWe have concrete suggestions for improving PRECIS and a growing list of enthusiastic individuals interested in contributing to this work. By early 2014 we expect to have a validated PRECIS-2.

Project description:BackgroundReal-world observational data are an important source of evidence on the treatment effectiveness for patients hospitalized with coronavirus disease 2019 (COVID-19). However, observational studies evaluating treatment effectiveness based on longitudinal data are often prone to methodological biases such as immortal time bias, confounding bias, and competing risks.MethodsFor exemplary target trial emulation, we used a cohort of patients hospitalized with COVID-19 (n = 501) in a single centre. We described the methodology for evaluating the effectiveness of a single-dose treatment, emulated a trial using real-world data, and drafted a hypothetical study protocol describing the main components. To avoid immortal time and time-fixed confounding biases, we applied the clone-censor-weight technique. We set a 5-day grace period as a period of time when treatment could be initiated. We used the inverse probability of censoring weights to account for the selection bias introduced by artificial censoring. To estimate the treatment effects, we took the multi-state model approach. We considered a multi-state model with five states. The primary endpoint was defined as clinical severity status, assessed by a 5-point ordinal scale on day 30. Differences between the treatment group and standard of care treatment group were calculated using a proportional odds model and shown as odds ratios. Additionally, the weighted cause-specific hazards and transition probabilities for each treatment arm were presented.ResultsOur study demonstrates that trial emulation with a multi-state model analysis is a suitable approach to address observational data limitations, evaluate treatment effects on clinically heterogeneous in-hospital death and discharge alive endpoints, and consider the intermediate state of admission to ICU. The multi-state model analysis allows us to summarize results using stacked probability plots that make it easier to interpret results.ConclusionsExtending the emulated target trial approach to multi-state model analysis complements treatment effectiveness analysis by gaining information on competing events. Combining two methodologies offers an option to address immortal time bias, confounding bias, and competing risk events. This methodological approach can provide additional insight for decision-making, particularly when data from randomized controlled trials (RCTs) are unavailable.

Project description:BackgroundPrompt antibiotic administration for febrile neutropenia (FN) is standard of care, and targets of time to antibiotics (TTA) <60 min are common. We sought to determine the effect of TTA ≥60 versus <60 min on adverse outcomes (intensive care unit (ICU) admission or death) in children with cancer and FN. Effect modification by a decision rule that predicts infection (AUS-rule) and bacteraemia were also investigated.MethodsThe prospective, multi-centre (n = 8), Australian PICNICC study dataset was analysed. To control for confounding, we used outcome regression adjusted for propensity score modelled as restricted cubic spline with two degrees of freedom. The propensity score was estimated from a logistic regression model for the exposure on the confounders, identified a priori (age, sex, severely unwell, disease, chemotherapy intensity and site). TTA was defined as time from from emergency triage to first antibiotic dose.Findings1685 FN episodes in 976 patients were included. Median TTA was 53 min (IQR 37-77 min, 1542 (92%) <120 min). An adverse outcome occurred in 43 (2.6%) episodes (39 ICU; 5 deaths). The confounder-adjusted point estimate suggested a lower risk for adverse outcome associated with TTA ≥60 min (RR 0.62, 95% CI 0.32-1.21), but the wide 95% CI precluded definitive judgement about strength and direction of the effect (unadjusted RR 0.52; 95% CI 0.26, 1.05). Similarly, although the point estimates were suggestive of a null association or reduced risk for adverse outcome associated with TTA ≥60 min for all comparisons across bacteraemia or AUS-rule strata, the 95% CIs were imprecise.InterpretationFor children with FN, there was no definite evidence that TTA ≥60 min from hospital triage (but within 2 h), increased risk of adverse outcome or prolonged hospital admission. This study has important implications for FN TTA mandates, suggesting a more nuanced approach is required.FundingNational Health and Medical Research Council and Medical Research Future Fund.

Dataset Information

Target Trial Emulation: A Design Tool for Cancer Clinical Trials.

Purpose

Methods

Results

Conclusion

Publications

Target Trial Emulation: A Design Tool for Cancer Clinical Trials.

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