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Improving efficiency in clinical trials using auxiliary information: Application of a multi-state cure model.

ABSTRACT: In clinical trials, an intermediate marker measured after randomization can often provide early information about the treatment effect on the final outcome of interest. We explore the use of recurrence time as an auxiliary variable for estimating the treatment effect on overall survival in phase three randomized trials of colon cancer. A multi-state model with an incorporated cured fraction for recurrence is used to jointly model time to recurrence and time to death. We explore different ways in which the information about recurrence time and the assumptions in the model can lead to improved efficiency. Estimates of overall survival and disease-free survival can be derived directly from the model with efficiency gains obtained as compared to Kaplan-Meier estimates. Alternatively, efficiency gains can be achieved by using the model in a weaker way in a multiple imputation procedure, which imputes death times for censored subjects. By using the joint model, recurrence is used as an auxiliary variable in predicting survival times. We demonstrate the potential use of the proposed methods in shortening the length of a trial and reducing sample sizes.

SUBMITTER: Conlon AS

PROVIDER: S-EPMC4480062 | biostudies-literature | 2015 Jun

REPOSITORIES: biostudies-literature

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Improving efficiency in clinical trials using auxiliary information: Application of a multi-state cure model.

Conlon A S C AS Taylor J M G JM Sargent D J DJ

Biometrics 20150113 2

In clinical trials, an intermediate marker measured after randomization can often provide early information about the treatment effect on the final outcome of interest. We explore the use of recurrence time as an auxiliary variable for estimating the treatment effect on overall survival in phase three randomized trials of colon cancer. A multi-state model with an incorporated cured fraction for recurrence is used to jointly model time to recurrence and time to death. We explore different ways in ...[more]

PMID: 25585942

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Project description:Background/aims The goal of this article is to illustrate the utility of multi-state models in cancer clinical trials. Our specific aims are to describe multi-state models and how they differ from standard survival methods, to illustrate how multi-state models can facilitate deeper understanding of the treatment effect on multiple paths along the disease process that patients could experience in cancer clinical trials, to explain the differences between multi-state models and time-dependent Cox models, and to briefly describe available software to conduct such analyses. Methods Data from 717 newly diagnosed acute myeloid leukemia patients who enrolled in the CALGB 10603 trial were used as an illustrative example. The current probability-in-state was estimated using the Aalen-Johansen estimator. The restricted mean time in state was calculated as the area under the probability-in-state curves. Cox-type regression was used to evaluate the effect of midostaurin on the various clinical paths. Simulation was conducted using a newly constructed shiny application. All analyses were performed using the R software. Results Multi-state model analyses of CALGB 10603 suggested that the overall improvement in survival with midostaurin seen in the primary analysis possibly resulted from a higher complete remission rate in combination with a lower risk of relapse and of death after complete remission in patients treated with midostaurin. Simulation results, in a three-state illness-death without recovery model, demonstrate that multi-state models and time-dependent Cox models evaluate treatment effects from different frameworks. Conclusion Multi-state models allow detailed evaluation of treatment effects in complex clinical trial settings where patients can experience multiple paths between study enrollment and the final outcome. Multi-state models can be used as a complementary tool to standard survival analyses to provide deeper insights to the effects of treatment in trial settings with complex disease process.

Project description:Introduction"Lean" is a set of management principles which focus on increasing value and efficiency by reducing or avoiding waste (e.g., overproduction, defects, inventory, transportation, waiting, motion, over processing). It has been applied to manufacturing, education, and health care, leading to optimized process flow, increased efficiency and increased team empowerment. However, to date, it has not been applied to neuroimaging research.MethodsLean principles, such as Value stream mapping (e.g. a tool with which steps in the workflow can be identified on which to focus improvement efforts), 5S (e.g. an organizational method to boost workplace efficiency and efficacy) and Root-cause analysis (e.g. a problem-solving approach to identify key points of failure in a system) were applied to an ongoing, large neuroimaging study that included seven research visits per participant. All team members participated in a half-day exercise in which the entire project flow was visualized and areas of inefficiency were identified. Changes focused on removing obstacles, standardization, optimal arrangement of equipment and root-cause-analysis. A process for continuous improvement was also implemented. Total time of an experiment was recorded before implementation of Lean for two participants and after implementation of Lean for two participants. Satisfaction of team members was assessed anonymously on a 5-item Likert scale, ranging from much worsened to much improved.ResultsAll team members (N = 6) considered the overall experience of conducting an experiment much improved after implementation of Lean. Five out of six team members indicated a much-improved reduction in time, with the final team member considering this somewhat improved. Average experiment time was reduced by 13% after implementation of Lean (from 142 and 147 minutes to 124 and 128 minutes).DiscussionLean principles can be successfully applied to neuroimaging research. Training in Lean principles for junior research scientists is recommended.

Project description:The discharge summary (DS) is a document that contains the diagnosis, comorbidities, procedures, complications, and future treatment plan for a particular patient after an inpatient hospital stay. The DS is completed by junior medical staff and is delivered to the general practitioner (GP). DS completion is time consuming and tedious, and DSs are usually not completed within the recommended time frame after a patient is discharged. Time spent completing DSs correlate to junior doctor overtime, which costs the hospital money in overtime pay. Information that is required in the DS is generally already entered into numerous electronic information systems in the hospital, including the "electronic patient journey board" which lists all the patients in a given ward with their clinical information. This information is constantly updated by all staff in the hospital. A program was developed that transferred this information directly into the patient DS. Ten junior doctors in two departments kept daily records for one week of the time spent compiling DSs, the time at work and the actual overtime claimed, before and after the introduction of the intervention. The mean (± SD) time for DS compilation per week reduced by 2.8 (± 2.4) hours from 10.0 (±3.5) hours (p<0.01) and the mean overtime worked per week reduced by 2.8 (± 3.1) hours from 8.5 (± 4.4) hours (p<0.05). The mean overtime claimed reduced by 1.8 (± 2.8) hours from 5.3 (± 5.4) hours per week (p<0.05), resulting in reduction in mean overtime payment of $114.95 from $290.57 per doctor, per week. Extrapolating to the 60 ward based junior doctors, the potential annual savings for the hospital budget are over $350,000. Additionally, the number of DSs completed within 48 hours increased from 45% to 58%. In summary, the transfer of electronic data from the electronic patient journey board to the discharge summary program has yielded improvements in DS completion rates and overtime worked by medical staff, resulting in significant reduction in overtime costs.

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Improving efficiency in clinical trials using auxiliary information: Application of a multi-state cure model.

Publications

Improving efficiency in clinical trials using auxiliary information: Application of a multi-state cure model.

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