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Optimal treatment allocation for placebo-treatment comparisons in trials with discrete-time survival endpoints.

ABSTRACT: In many randomized controlled trials, treatment groups are of equal size, but this is not necessarily the best choice. This paper provides a methodology to calculate optimal treatment allocations for longitudinal trials when we wish to compare multiple treatment groups with a placebo group, and the comparisons may have unequal importance. The focus is on trials with a survival endpoint measured in discrete time. We assume the underlying survival process is Weibull and show that values for the parameters in the Weibull distribution have an impact on the optimal treatment allocation scheme in an interesting way. Additionally, we incorporate different cost considerations at the subject and measurement levels and determine the optimal number of time periods. We also show that when many events occur at the beginning of the trial, fewer time periods are more efficient. As an application, we revisit a risperidone maintenance treatment trial in schizophrenia and use our proposed methodology to redesign it and compare merits of our optimal design.

SUBMITTER: Moerbeek M

PROVIDER: S-EPMC4793279 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature

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Optimal treatment allocation for placebo-treatment comparisons in trials with discrete-time survival endpoints.

Moerbeek Mirjam M Wong Weng-Kee WK

Statistics in medicine 20150628 27

In many randomized controlled trials, treatment groups are of equal size, but this is not necessarily the best choice. This paper provides a methodology to calculate optimal treatment allocations for longitudinal trials when we wish to compare multiple treatment groups with a placebo group, and the comparisons may have unequal importance. The focus is on trials with a survival endpoint measured in discrete time. We assume the underlying survival process is Weibull and show that values for the pa ...[more]

PMID: 26119759

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Project description:BackgroundThe choice of antiepileptic drug for an individual should be based upon the highest quality evidence regarding potential benefits and harms of the available treatments. Systematic reviews and meta-analysis of randomised controlled trials should be a major source of evidence supporting this decision making process. We summarise all available individual patient data evidence from randomised controlled trials that compared at least two out of eight antiepileptic drugs given as monotherapy.MethodsMultiple treatment comparisons from epilepsy monotherapy trials were synthesized in a single stratified Cox regression model adjusted for treatment by epilepsy type interactions and making use of direct and indirect evidence. Primary outcomes were time to treatment failure and time to 12 month remission from seizures. A secondary outcome was time to first seizure.ResultsIndividual patient data for 6418 patients from 20 randomised trials comparing eight antiepileptic drugs were synthesized. For partial onset seizures (4628 (72%) patients), lamotrigine, carbamazepine and oxcarbazepine provide the best combination of seizure control and treatment failure. Lamotrigine is clinically superior to all other drugs for treatment failure but estimates suggest a disadvantage compared to carbamazepine for time to 12 month remission [Hazard Ratio (95% Confidence Interval) = 0.87(0.73 to 1.04)] and time to first seizure [1.29(1.13 to 1.48)]. Phenobarbitone may delay time to first seizure [0.77(0.61 to 0.96)] but at the expense of increased treatment failure [1.60(1.22 to 2.10)]. For generalized onset tonic clonic seizures (1790 (28%) patients) estimates suggest valproate or phenytoin may provide the best combination of seizure control and treatment failure but some uncertainty remains about the relative effectiveness of other drugs.ConclusionFor patients with partial onset seizures, results favour carbamazepine, oxcarbazepine and lamotrigine. For generalized onset tonic clonic seizures, results favour valproate and phenytoin.

Project description:BackgroundPrediction models for time-to-event outcomes are commonly used in biomedical research to obtain subject-specific probabilities that aid in making important clinical care decisions. There are several regression and machine learning methods for building these models that have been designed or modified to account for the censoring that occurs in time-to-event data. Discrete-time survival models, which have often been overlooked in the literature, provide an alternative approach for predictive modeling in the presence of censoring with limited loss in predictive accuracy. These models can take advantage of the range of nonparametric machine learning classification algorithms and their available software to predict survival outcomes.MethodsDiscrete-time survival models are applied to a person-period data set to predict the hazard of experiencing the failure event in pre-specified time intervals. This framework allows for any binary classification method to be applied to predict these conditional survival probabilities. Using time-dependent performance metrics that account for censoring, we compare the predictions from parametric and machine learning classification approaches applied within the discrete time-to-event framework to those from continuous-time survival prediction models. We outline the process for training and validating discrete-time prediction models, and demonstrate its application using the open-source R statistical programming environment.ResultsUsing publicly available data sets, we show that some discrete-time prediction models achieve better prediction performance than the continuous-time Cox proportional hazards model. Random survival forests, a machine learning algorithm adapted to survival data, also had improved performance compared to the Cox model, but was sometimes outperformed by the discrete-time approaches. In comparing the binary classification methods in the discrete time-to-event framework, the relative performance of the different methods varied depending on the data set.ConclusionsWe present a guide for developing survival prediction models using discrete-time methods and assessing their predictive performance with the aim of encouraging their use in medical research settings. These methods can be applied to data sets that have continuous time-to-event outcomes and multiple clinical predictors. They can also be extended to accommodate new binary classification algorithms as they become available. We provide R code for fitting discrete-time survival prediction models in a github repository.

Dataset Information

Optimal treatment allocation for placebo-treatment comparisons in trials with discrete-time survival endpoints.

Publications

Optimal treatment allocation for placebo-treatment comparisons in trials with discrete-time survival endpoints.

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