Dataset Information

Follow up after sample size re-estimation in a breast cancer randomized trial for disease-free survival.

ABSTRACT: BACKGROUND:While the clinical trials and statistical methodology literature on sample size re-estimation (SSRE) is robust, evaluation of SSRE procedures following the completion of a clinical trial has been sparsely reported. In blinded sample size re-estimation, only nuisance parameters are re-estimated, and the blinding of the current trial treatment effect is preserved. Blinded re-estimation procedures are well-accepted by regulatory agencies and funders. We review our experience of sample size re-estimation in a large international, National Institutes of Health funded clinical trial for adjuvant breast cancer treatment, and evaluate our blinded sample size re-estimation procedure for this time-to-event trial. We evaluated the SSRE procedure by examining assumptions made during the re-estimation process, estimates resulting from re-estimation, and the impact on final trial results with and without the addition of participants, following sample size re-estimation. METHODS:We compared the control group failure probabilities estimated at the time of SSRE to estimates used in the original planning, to the final un-blinded control group failure probability estimates for those included in the SSRE procedure (SSRE cohort), and to the final total control group failure probability estimates. The impact of re-estimation on the final comparison between randomized treatment groups is evaluated for those in the originally planned cohort (n?=?340) and for the combination of those recruited in the originally planned cohort and those added after re-estimation (n?=?509). RESULTS:Very little difference is observed between the originally planned cohort and all randomized patients in the control group failure probabilities over time or in the overall hazard ratio estimating treatment effect (originally planned cohort HR 1.25 (0.86, 1.79); all randomized cohort HR 1.24 95% CI (0.91, 1.68)). At the time of blinded SSRE, the estimated control group failure probabilities at 3?years (0.24) and 5?years (0.40) were similar to those for the SSRE cohort once un-blinded (3?years, 0.22 (0.16, 0.30); 5?years, 0.33 (0.26, 0.41)). CONCLUSIONS:We found that our re-estimation procedure performed reasonably well in estimating the control group failure probabilities at the time of re-estimation. Particularly for time-to-event outcomes, pre-planned blinded SSRE procedures may be the best option to aid in maintaining power. TRIAL REGISTRATION:ClinicalTrials.gov, NCT00201851 . Registered on 9 September 2005. Retrospectively registered.

SUBMITTER: Hade EM

PROVIDER: S-EPMC6708130 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Follow up after sample size re-estimation in a breast cancer randomized trial for disease-free survival.

Hade Erinn M EM Young Gregory S GS Love Richard R RR

Trials 20190823 1

<h4>Background</h4>While the clinical trials and statistical methodology literature on sample size re-estimation (SSRE) is robust, evaluation of SSRE procedures following the completion of a clinical trial has been sparsely reported. In blinded sample size re-estimation, only nuisance parameters are re-estimated, and the blinding of the current trial treatment effect is preserved. Blinded re-estimation procedures are well-accepted by regulatory agencies and funders. We review our experience of s ...[more]

PMID: 31443726

Similar Datasets

Project description:BackgroundCrossover designs are commonly utilised in randomised controlled trials investigating treatments for long-term chronic illnesses. One problem with this design is its inherent repeated measures necessitate the availability of an estimate of the within-person standard deviation (SD) to perform a sample size calculation, which may be rarely available at the design stage of a trial. Interim sample size re-estimation designs can be used to help alleviate this issue by adapting the sample size mid-way through the trial, using accrued information in a statistically robust way.MethodsThe AIM HY-INFORM study is part of the Informative Markers in Hypertension (AIM HY) Programme and comprises two crossover trials, each with a planned recruitment of 600 participants. The objective of the study is to test whether blood pressure response to first line antihypertensive treatment depends on ethnicity. An interim analysis is planned to reassess the assumptions of the planned sample size for the study. The aims of this paper are: (1) to provide a formula for sample size re-estimation in both crossover trials; and (2) to present a simulation study of the planned interim analysis to investigate alternative within-person SDs to that assumed.ResultsThe AIM HY-INFORM protocol sample size calculation fixes the within-person SD to be 8 mmHg, giving > 90% power for a primary treatment effect of 4 mmHg. Using the method developed here and simulating the interim sample size reassessment, if we were to see a larger within-person SD of 9 mmHg at interim, 640 participants for 90% power 90% of the time in the three-period three-treatment design would be required. Similarly, in the four-period four-treatment crossover design, 602 participants would be required.ConclusionsThe formulas presented here provide a method for re-estimating the sample size in crossover trials. In the context of the AIM HY-INFORM study, simulating the interim analysis allows us to explore the results of a possible increase in the within-person SD from that assumed. Simulations show that without increasing the planned sample size of 600 participants, we can reasonably still expect to achieve 80% power with a small increase in the within-person SD from that assumed.Trial registrationClinicalTrials.gov, NCT02847338. Registered on 28 July 2016.

Project description:IntroductionExtrapolation of time-to-event data from clinical trials is commonly used in decision models for health technology assessment (HTA). The objective of this study was to assess performance of standard parametric survival analysis techniques for extrapolation of time-to-event data for a single event from clinical trials with limited data due to small samples or short follow-up.MethodsSimulated populations with 50,000 individuals were generated with an exponential hazard rate for the event of interest. A scenario consisted of 5000 repetitions with six sample size groups (30-500 patients) artificially censored after every 10% of events observed. Goodness-of-fit statistics (AIC, BIC) were used to determine the best-fitting among standard parametric distributions (exponential, Weibull, log-normal, log-logistic, generalized gamma, Gompertz). Median survival, one-year survival probability, time horizon (1% survival time, or 99th percentile of survival distribution) and restricted mean survival time (RMST) were compared to population values to assess coverage and error (e.g., mean absolute percentage error).ResultsThe true exponential distribution was correctly identified using goodness-of-fit according to BIC more frequently compared to AIC (average 92% vs 68%). Under-coverage and large errors were observed for all outcomes when distributions were specified by AIC and for time horizon and RMST with BIC. Error in point estimates were found to be strongly associated with sample size and completeness of follow-up. Small samples produced larger average error, even with complete follow-up, than large samples with short follow-up. Correctly specifying the event distribution reduced magnitude of error in larger samples but not in smaller samples.ConclusionsLimited clinical data from small samples, or short follow-up of large samples, produce large error in estimates relevant to HTA regardless of whether the correct distribution is specified. The associated uncertainty in estimated parameters may not capture the true population values. Decision models that base lifetime time horizon on the model's extrapolated output are not likely to reliably estimate mean survival or its uncertainty. For data with an exponential event distribution, BIC more reliably identified the true distribution than AIC. These findings have important implications for health decision modelling and HTA of novel therapies seeking approval with limited evidence.

Project description:Background/aimsThe use of adaptive designs has been increasing in randomized clinical trials. Sample size re-estimation is a type of adaptation in which nuisance parameters are estimated at an interim point in the trial and the sample size re-computed based on these estimates. The Secondary Prevention of Small Subcortical Strokes study was a randomized clinical trial assessing the impact of single- versus dual-antiplatelet therapy and control of systolic blood pressure to a higher (130-149 mmHg) versus lower (<130 mmHg) target on recurrent stroke risk in a two-by-two factorial design. A sample size re-estimation was performed during the Secondary Prevention of Small Subcortical Strokes study resulting in an increase from the planned sample size of 2500-3020, and we sought to determine the impact of the sample size re-estimation on the study results.MethodsWe assessed the results of the primary efficacy and safety analyses with the full 3020 patients and compared them to the results that would have been observed had randomization ended with 2500 patients. The primary efficacy outcome considered was recurrent stroke, and the primary safety outcomes were major bleeds and death. We computed incidence rates for the efficacy and safety outcomes and used Cox proportional hazards models to examine the hazard ratios for each of the two treatment interventions (i.e. the antiplatelet and blood pressure interventions).ResultsIn the antiplatelet intervention, the hazard ratio was not materially modified by increasing the sample size, nor did the conclusions regarding the efficacy of mono versus dual-therapy change: there was no difference in the effect of dual- versus monotherapy on the risk of recurrent stroke hazard ratios (n = 3020 HR (95% confidence interval): 0.92 (0.72, 1.2), p = 0.48; n = 2500 HR (95% confidence interval): 1.0 (0.78, 1.3), p = 0.85). With respect to the blood pressure intervention, increasing the sample size resulted in less certainty in the results, as the hazard ratio for higher versus lower systolic blood pressure target approached, but did not achieve, statistical significance with the larger sample (n = 3020 HR (95% confidence interval): 0.81 (0.63, 1.0), p = 0.089; n = 2500 HR (95% confidence interval): 0.89 (0.68, 1.17), p = 0.40). The results from the safety analyses were similar to 3020 and 2500 patients for both study interventions. Other trial-related factors, such as contracts, finances, and study management, were impacted as well.ConclusionAdaptive designs can have benefits in randomized clinical trials, but do not always result in significant findings. The impact of adaptive designs should be measured in terms of both trial results, as well as practical issues related to trial management. More post hoc analyses of study adaptations will lead to better understanding of the balance between the benefits and the costs.

Dataset Information

Follow up after sample size re-estimation in a breast cancer randomized trial for disease-free survival.

Publications

Follow up after sample size re-estimation in a breast cancer randomized trial for disease-free survival.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets