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Extrapolating Survival from Randomized Trials Using External Data: A Review of Methods.

ABSTRACT: This article describes methods used to estimate parameters governing long-term survival, or times to other events, for health economic models. Specifically, the focus is on methods that combine shorter-term individual-level survival data from randomized trials with longer-term external data, thus using the longer-term data to aid extrapolation of the short-term data. This requires assumptions about how trends in survival for each treatment arm will continue after the follow-up period of the trial. Furthermore, using external data requires assumptions about how survival differs between the populations represented by the trial and external data. Study reports from a national health technology assessment program in the United Kingdom were searched, and the findings were combined with "pearl-growing" searches of the academic literature. We categorized the methods that have been used according to the assumptions they made about how the hazards of death vary between the external and internal data and through time, and we discuss the appropriateness of the assumptions in different circumstances. Modeling choices, parameter estimation, and characterization of uncertainty are discussed, and some suggestions for future research priorities in this area are given.

SUBMITTER: Jackson C

PROVIDER: S-EPMC5424081 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Extrapolating empirical long-term survival data: the impact of updated follow-up data and parametric extrapolation methods on survival estimates in multiple myeloma.

Project description:BackgroundIn economic evaluations, survival is often extrapolated to smooth out the Kaplan-Meier estimate and because the available data (e.g., from randomized controlled trials) are often right censored. Validation of the accuracy of extrapolated results can depend on the length of follow-up and the assumptions made about the survival hazard. Here, we analyze the accuracy of different extrapolation techniques while varying the data cut-off to estimate long-term survival in newly diagnosed multiple myeloma (MM) patients.MethodsEmpirical data were available from a randomized controlled trial and a registry for MM patients treated with melphalan + prednisone, thalidomide, and bortezomib- based regimens. Standard parametric and spline models were fitted while artificially reducing follow-up by introducing database locks. The maximum follow-up for these locks varied from 3 to 13 years. Extrapolated (conditional) restricted mean survival time (RMST) was compared to the Kaplan-Meier RMST and models were selected according to statistical tests, and visual fit.ResultsFor all treatments, the RMST error decreased when follow-up and the absolute number of events increased, and censoring decreased. The decline in RMST error was highest when maximum follow-up exceeded six years. However, even when censoring is low there can still be considerable deviations in the extrapolated RMST conditional on survival until extrapolation when compared to the KM-estimate.ConclusionsWe demonstrate that both standard parametric and spline models could be worthy candidates when extrapolating survival for the populations examined. Nevertheless, researchers and decision makers should be wary of uncertainty in results even when censoring has decreased, and the number of events has increased.

| S-EPMC10226243 | biostudies-literature

Extrapolation of Survival Curves from Cancer Trials Using External Information.

Project description:BackgroundEstimates of life expectancy are a key input to cost-effectiveness analysis (CEA) models for cancer treatments. Due to the limited follow-up in Randomized Controlled Trials (RCTs), parametric models are frequently used to extrapolate survival outcomes beyond the RCT period. However, different parametric models that fit the RCT data equally well may generate highly divergent predictions of treatment-related gain in life expectancy. Here, we investigate the use of information external to the RCT data to inform model choice and estimation of life expectancy.MethodsWe used Bayesian multi-parameter evidence synthesis to combine the RCT data with external information on general population survival, conditional survival from cancer registry databases, and expert opinion. We illustrate with a 5-year follow-up RCT of cetuximab plus radiotherapy v. radiotherapy alone for head and neck cancer.ResultsStandard survival time distributions were insufficiently flexible to simultaneously fit both the RCT data and external data on general population survival. Using spline models, we were able to estimate a model that was consistent with the trial data and all external data. A model integrating all sources achieved an adequate fit and predicted a 4.7-month (95% CrL: 0.4; 9.1) gain in life expectancy due to cetuximab.ConclusionsLong-term extrapolation using parametric models based on RCT data alone is highly unreliable and these models are unlikely to be consistent with external data. External data can be integrated with RCT data using spline models to enable long-term extrapolation. Conditional survival data could be used for many cancers and general population survival may have a role in other conditions. The use of external data should be guided by knowledge of natural history and treatment mechanisms.

| S-EPMC6190619 | biostudies-literature

External validity of randomized controlled trials in older adults, a systematic review.

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| S-EPMC5367677 | biostudies-literature

Combining Non-randomized and Randomized Data in Clinical Trials Using Commensurate Priors.

Project description:Randomization eliminates selection bias, and attenuates imbalance among study arms with respect to prognostic factors, both known and unknown. Thus, information arising from randomized clinical trials (RCTs) is typically considered the gold standard for comparing therapeutic interventions in confirmatory studies. However, RCTs are limited in contexts wherein patients who are willing to accept a random treatment assignment represent only a subset of the patient population. By contrast, observational studies (OSs) often enroll patient cohorts that better reflect the broader patient population. However, OSs often suffer from selection bias, and may yield invalid treatment comparisons even after adjusting for known confounders. Therefore, combining information acquired from OSs with data from RCTs in research synthesis is often criticized due to the limitations of OSs. In this article, we combine randomized and non-randomized substudy data from FIRST, a recent HIV/AIDS drug trial. We develop hierarchical Bayesian approaches devised to combine data from all sources simultaneously while explicitly accounting for potential discrepancies in the sources' designs. Specifically, we describe a two-step approach combining propensity score matching and Bayesian hierarchical modeling to integrate information from non-randomized studies with data from RCTs, to an extent that depends on the estimated commensurability of the data sources. We investigate our procedure's operating characteristics via simulation. Our findings have implications for HIV/AIDS research, as well as elucidate the extent to which well-designed non-randomized studies can complement RCTs.

| S-EPMC5404417 | biostudies-literature

Challenges of using external data in clinical trials- an illustration in patients with COVID-19.

Project description:BackgroundTo improve the efficiency of clinical trials, leveraging external data on control and/or treatment effects, which is almost always available, appears to be a promising approach.MethodsWe used data from the experimental arm of the Covidicus trial evaluating high-dose dexamethasone in severely ill and mechanically ventilated COVID-19 patients, using published data from the Recovery trial as external data, to estimate the 28-day mortality rate. Primary approaches to deal with external data were applied.ResultsEstimates ranged from 0.241 ignoring the external data up to 0.294 using hierarchical Bayesian models. Some evidence of differences in mortality rates between the Covidicus and Recovery trials were observed, with an matched adjusted odds ratio of death in the Covidicus arm of 0.41 compared to the Recovery arm.ConclusionsThese indirect comparisons appear sensitive to the method used. None of those approaches appear robust enough to overcome randomized clinical trial data.Trial registrationCovidicus Trial: NCT04344730, First Posted: 14/04/2020; Recovery trial: NCT04381936.

| S-EPMC9753019 | biostudies-literature

Estimating the Efficiency Gain of Covariate-Adjusted Analyses in Future Clinical Trials Using External Data.

Project description:We present a framework for using existing external data to identify and estimate the relative efficiency of a covariate-adjusted estimator compared to an unadjusted estimator in a future randomized trial. Under conditions, these relative efficiencies approximate the ratio of sample sizes needed to achieve a desired power. We develop semiparametrically efficient estimators of the relative efficiencies for several treatment effect estimands of interest with either fully or partially observed outcomes, allowing for the application of flexible statistical learning tools to estimate the nuisance functions. We propose an analytic Wald-type confidence interval and a double bootstrap scheme for statistical inference. We demonstrate the performance of the proposed methods through simulation studies and apply these methods to estimate the efficiency gain of covariate adjustment in Covid-19 therapeutic trials.

| S-EPMC10433828 | biostudies-literature

Analytic methods for individually randomized group treatment trials and group-randomized trials when subjects belong to multiple groups.

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| S-EPMC4013262 | biostudies-other

Design and Evaluation of an External Control Arm Using Prior Clinical Trials and Real-World Data.

Project description:PURPOSE:We discuss designs and interpretable metrics of bias and statistical efficiency of "externally controlled" trials (ECT) and compare ECT performance to randomized and single-arm designs. EXPERIMENTAL DESIGN:We specify an ECT design that leverages information from real-world data (RWD) and prior clinical trials to reduce bias associated with interstudy variations of the enrolled populations. We then used a collection of clinical studies in glioblastoma (GBM) and RWD from patients treated with the current standard of care to evaluate ECTs. Validation is based on a "leave one out" scheme, with iterative selection of a single-arm from one of the studies, for which we estimate treatment effects using the remaining studies as external control. This produces interpretable and robust estimates on ECT bias and type I errors. RESULTS:We developed a model-free approach to evaluate ECTs based on collections of clinical trials and RWD. For GBM, we verified that inflated false positive error rates of standard single-arm trials can be considerably reduced (up to 30%) by using external control data. CONCLUSIONS:The use of ECT designs in GBM, with adjustments for the clinical profiles of the enrolled patients, should be preferred to single-arm studies with fixed efficacy thresholds extracted from published results on the current standard of care.

| S-EPMC6697596 | biostudies-literature

Meta-analysis of time-to-event outcomes from randomized trials using restricted mean survival time: application to individual participant data.

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| S-EPMC5695659 | biostudies-literature

Quality of reporting internal and external validity data from randomized controlled trials evaluating stents for percutaneous coronary intervention.

Project description:BackgroundStents are commonly used to treat patients with coronary artery disease. However, the quality of reporting internal and external validity data in published reports of randomised controlled trials (RCTs) of stents has never been assessed.The objective of our study was to evaluate the quality of reporting internal and external validity data in published reports of RCTs assessing the stents for percutaneous coronary interventions.MethodsA systematic literature review was conducted. Reports of RCTs assessing stents for percutaneous coronary interventions indexed in MEDLINE and the Cochrane Central Register of Controlled Trials and published between January 2003 and September 2008 were selected. A standardized abstraction form was used to extract data. All analyses were adjusted for the effect of clustering articles by journal.Results132 articles were analyzed. The generation of the allocation sequence was adequate in 58.3% of the reports; treatment allocation was concealed in 34.8%. Adequate blinding was reported in one-fifth of the reports. An intention-to-treat analysis was described in 79.5%. The main outcome was a surrogate angiographic endpoint in 47.0%. The volume of interventions per center was described in two reports. Operator expertise was described in five (3.8%) reports. The quality of reporting was better in journals with high impact factors and in journals endorsing the CONSORT statement.ConclusionThe current reporting of results of RCTs testing stents needs to be improved to allow readers to appraise the risk of bias and the applicability of the results.

| S-EPMC2679061 | biostudies-literature

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