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New Efficient and Practicable Adaptive Designs for Calibrating Items Online.

ABSTRACT: When calibrating new items online, it is practicable to first compare all new items according to some criterion and then assign the most suitable one to the current examinee who reaches a seeding location. The modified D-optimal design proposed by van der Linden and Ren (denoted as D-VR design) works within this practicable framework with the aim of directly optimizing the estimation of item parameters. However, the optimal design point for a given new item should be obtained by comparing all examinees in a static examinee pool. Thus, D-VR design still has room for improvement in calibration efficiency from the view of traditional optimal design. To this end, this article incorporates the idea of traditional optimal design into D-VR design and proposes a new online calibration design criterion, namely, excellence degree (ED) criterion. Four different schemes are developed to measure the information provided by the current examinee when implementing this new criterion, and four new ED designs equipped with them are put forward accordingly. Simulation studies were conducted under a variety of conditions to compare the D-VR design and the four proposed ED designs in terms of calibration efficiency. Results showed that the four ED designs outperformed D-VR design in almost all simulation conditions.

SUBMITTER: He Y

PROVIDER: S-EPMC6906388 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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New Efficient and Practicable Adaptive Designs for Calibrating Items Online.

He Yinhong Y Chen Ping P Li Yong Y

Applied psychological measurement 20190130 1

When calibrating new items online, it is practicable to first compare all new items according to some criterion and then assign the most suitable one to the current examinee who reaches a seeding location. The modified D-optimal design proposed by van der Linden and Ren (denoted as D-VR design) works within this practicable framework with the aim of directly optimizing the estimation of item parameters. However, the optimal design point for a given new item should be obtained by comparing all ex ...[more]

PMID: 31853155

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Project description:BackgroundAdaptive clinical trials are growing in popularity as they are more flexible, efficient and ethical than traditional fixed designs. However, notwithstanding their increased use in assessing treatments for COVID-19, their use in critical care trials remains limited. A better understanding of the relative benefits of various adaptive designs may increase their use and interpretation.MethodsUsing two large critical care trials (ADRENAL.Clinicaltrialsgov number, NCT01448109. Updated 12-12-2017; NICE-SUGAR.Clinicaltrialsgov number, NCT00220987. Updated 01-29-2009), we assessed the performance of three frequentist and two bayesian adaptive approaches. We retrospectively re-analysed the trials with one, two, four, and nine equally spaced interims. Using the original hypotheses, we conducted 10,000 simulations to derive error rates, probabilities of making an early correct and incorrect decision, expected sample size and treatment effect estimates under the null scenario (no treatment effect) and alternative scenario (a positive treatment effect). We used a logistic regression model with 90-day mortality as the outcome and the treatment arm as the covariate. The null hypothesis was tested using a two-sided significance level (α) at 0.05.ResultsAcross all approaches, increasing the number of interims led to a decreased expected sample size. Under the null scenario, group sequential approaches provided good control of the type-I error rate; however, the type I error rate inflation was an issue for the Bayesian approaches. The Bayesian Predictive Probability and O'Brien-Fleming approaches showed the highest probability of correctly stopping the trials (around 95%). Under the alternative scenario, the Bayesian approaches showed the highest overall probability of correctly stopping the ADRENAL trial for efficacy (around 91%), whereas the Haybittle-Peto approach achieved the greatest power for the NICE-SUGAR trial. Treatment effect estimates became increasingly underestimated as the number of interims increased.ConclusionsThis study confirms the right adaptive design can reach the same conclusion as a fixed design with a much-reduced sample size. The efficiency gain associated with an increased number of interims is highly relevant to late-phase critical care trials with large sample sizes and short follow-up times. Systematically exploring adaptive methods at the trial design stage will aid the choice of the most appropriate method.

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New Efficient and Practicable Adaptive Designs for Calibrating Items Online.

Publications

New Efficient and Practicable Adaptive Designs for Calibrating Items Online.

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