Designs for adding a treatment arm to an ongoing clinical trial.

BACKGROUND:For many disease areas, there are often treatments in different stages of the development process. We consider the design of a two-arm parallel group trial where it is planned to add a new experimental treatment arm during the trial. This could potentially save money, patients, time and resources; however, the addition of a treatment arm creates a multiple comparison problem. Current practice in trials when a new treatment arm has been added is to compare the new treatment only to controls randomised concurrently, and this is the setting we consider here. Furthermore, for standard multi-arm trials, optimal allocation randomises a larger number of patients to the control arm than to each experimental treatment arm. METHODS:In this paper we propose an adaptive design, the aim of which is to adapt the sample size of the trial when the new treatment arm is added to control the family-wise error rate (FWER) in the strong sense, whilst maintaining the marginal power of each treatment-to-control comparison at the level of the original study. We explore optimal allocation for designs where a treatment arm is added with the aim of increasing the overall power of the study, where we define the overall power to be the probability of detecting all treatments that are better than the control. RESULTS AND CONCLUSIONS:An increase in sample size is required to maintain the marginal power for each pairwise comparison when adding a treatment arm if control of the FWER is required at the level of the type I error in the original study. When control of the FWER is required in a single trial which adds an additional experimental treatment arm, but control of the FWER is not required in separate trials, depending on the design characteristics, it may be better to run a separate trial for each experimental treatment, in terms of the number of patients required. An increase in overall power can be achieved when optimal allocation is used once a treatment arm has been added to the trial, rather than continuing with equal allocation to all treatment arms

Improving the efficiency of clinical trial designs by using historical control data or adding a treatment arm to an ongoing trial

The most common type of confirmatory trial is a randomised trial comparing the experimental treatment of interest to a control treatment. Confirmatory trials are expensive and take a lot of time in the planning, set up and recruitment of patients. Efficient methodology in clinical trial design is critical to save both time and money and allow treatments to become available to patients quickly. Often there are data available on the control treatment from a previous trial. These historical data are often used to design new trials, forming the basis of sample size calculations, but are not used in the analysis of the new trial. Incorporating historical control data into the design and analysis could potentially lead to more efficient trials. When the historical and current control data agree, incorporating historical control data could reduce the number of control patients required in the current trial and therefore the duration of the trial, or increase the precision of parameter estimates. However, when the historical and current data are inconsistent, there is a potential for biased treatment effect estimates, inflated type I error and reduced power. We propose two novel weights to assess agreement between the current and historical control data: a probability weight based on tail area probabilities; and a weight based on the equivalence of the historical and current control data parameters. For binary outcome data, agreement is assessed using the posterior distributions of the response probability in the historical and current control data. For normally distributed outcome data, agreement is assessed using the marginal posterior distributions of the difference in means and the ratio of the variances of the current and historical control data. We consider an adaptive design with an interim analysis. At the interim, the agreement between the historical and current control data is assessed using the probability or equivalence probability weight approach. The allocation ratio is adapted to randomise fewer patients to control when there is agreement and revert back to a standard trial design when there is disagreement. The final analysis is Bayesian utilising the analysis approach of the power prior with a fixed weight. The operating characteristics of the proposed design are explored and we show how the equivalence bounds can be chosen at the design stage of the current study to control the maximum inflation in type I error. We then consider a design where a treatment arm is added to an ongoing clinical trial. For many disease areas, there are often treatments in different stages of the development process. We consider the design of a two-arm parallel group trial where it is planned to add a new treatment arm during the trial. This could potentially save money, patients, time and resources. The addition of a treatment arm creates a multiple comparison problem. Dunnett (1955) proposed a design that controls the family-wise error rate when comparing multiple experimental treatments to control and determined the optimal allocation ratio. We have calculated the correlation between test statistics for the method proposed by Dunnett when a treatment arm is added during the trial and only concurrent controls are used for each treatment comparison. We propose an adaptive design where the sample size of all treatment arms are increased to control the family-wise error rate. We explore adapting the allocation ratio once the new treatment arm is added to maximise the overall power of the trial

Evaluation of iwi and hapū participation in the resource consents processes of six district councils

This working paper analyses the processes adopted by councils for involving hapū/iwi in plan implementation, including the resource consents process. Three topic issues were investigated to assess plan implementation — urban amenity, storm water, and issues of importance to iwi. Questions were asked about the capacity of hapū/iwi to engage in the resource consent process, which resource issues were of concern to them, their relationship with council and consent applicants, and their perception of the consent process. Most resources listed in the questionnaire were of concern to hapū/iwi, with water quality, wāhi tapu and heritage the most commonly cited. In conclusion, we found a general dissatisfaction on the part of hapū/iwi with councils’ performance with respect to both Treaty relationships and consent processing under the RMA. A further contributing factor to the poor relationships found between hapū/iwi and councils, was the lack of clarity over the role of hapū and iwi in resource management. In several districts, diverging responses from hapū/iwi and councils to questions about level of understanding and commitment suggests there is a need for more effective communication. These problems are compounded by the generally low capacity of hapū/iwi to participate in resource consent processes. These findings suggest that there is much to be done to improve relationships and behaviour of these key stakeholder groups in the plan implementation process if key provisions in the RMA related to hapū/iwi interests are to be fulfilled. The differences shown in reciprocal perceptions have serious implications for establishing a sound working partnership between councils and hapū/iwi in their areas. Making clear these discrepancies is a first step towards taking the measures needed for building a better partnership. Further, the capacity of hapū/iwi to participate could be better utilised if there was greater integration between regional and district councils on issues of significance and processes for iwi involvement

Designs for adding a treatment arm to an ongoing clinical trial

For many disease areas, there are often treatments in different stages of the development process. We consider the design of a two-arm parallel group trial where it is planned to add a new experimental treatment arm during the trial. This could potentially save money, patients, time and resources; however, the addition of a treatment arm creates a multiple comparison problem. Current practice in trials when a new treatment arm has been added is to compare the new treatment only to controls randomised concurrently, and this is the setting we consider here. Furthermore, for standard multi-arm trials, optimal allocation randomises a larger number of patients to the control arm than to each experimental treatment arm

The trials and tribulations of team-nursing

Aim: The aim of this study was to review the team-nursing approach to care adopted by two general medical wards in a large private hospital. The delivery model of care was reviewed to determine the factors that enhance and/or hinder the timely delivery, continuity and communication of care.Method: All nursing and ancillary staff who worked on two medical wards at a private teaching hospital were invited to participate in the study. Thirty eight participants from the two wards took part in focus group discussions, individual interviews and completed the Staff Continuity of Care Questionnaire. Findings: Findings indicated that achieving functionally sound teamwork is a complex task that is affected by the interplay of a number of organisational, patient and staff factors. Its smooth application is further affected by the uncertain and changing conditions on the wards, which are difficult to control and impact on the smooth delivery of patient care. The findings revealed strengths and weaknesses in teamwork, communication of care, documentation and discharge planning. The results also highlighted factors that enhance and hinder the smooth delivery of care. This paper details the factors that influence the delivery of care from the perspectives of nursing staff and makes recommendations to enhance the delivery of patient care using a team-nursing approach.<br /

District plan implementation under the RMA: Confessions of a resource consent

This report focuses on results from Phase 2 of PUCM - the quality of plan implementation in six district councils selected for their range of plan quality and capacity to plan. Only those results considered to be important for assisting the six councils (and others) to improve implementation of their plans are included in this report. The findings and recommendations, both specific and general, ought to be instructive for other councils, thereby helping to improve their plans and implementation processes. Since hapu/iwi interests formed a key component of the research, the outcomes will help enhance their case for better consideration of their interests when dealing with local government. As well, many of the findings and recommendations relate to matters of governance and capacity building that require Government action, which until done will make it difficult for councils to achieve quality plans and implementation processes

A novel equivalence probability weighted power prior for using historical control data in an adaptive clinical trial design: a comparison to standard methods

A standard two‐arm randomised controlled trial usually compares an intervention to a control treatment with equal numbers of patients randomised to each treatment arm and only data from within the current trial are used to assess the treatment effect. Historical data are used when designing new trials and have recently been considered for use in the analysis when the required number of patients under a standard trial design cannot be achieved. Incorporating historical control data could lead to more efficient trials, reducing the number of controls required in the current study when the historical and current control data agree. However, when the data are inconsistent, there is potential for biased treatment effect estimates, inflated type I error and reduced power. We introduce two novel approaches for binary data which discount historical data based on the agreement with the current trial controls, an equivalence approach and an approach based on tail area probabilities. An adaptive design is used where the allocation ratio is adapted at the interim analysis, randomising fewer patients to control when there is agreement. The historical data are down‐weighted in the analysis using the power prior approach with a fixed power. We compare operating characteristics of the proposed design to historical data methods in the literature: the modified power prior; commensurate prior; and robust mixture prior. The equivalence probability weight approach is intuitive and the operating characteristics can be calculated exactly. Furthermore, the equivalence bounds can be chosen to control the maximum possible inflation in type I error

A novel equivalence probability weighted power prior for using historical control data in an adaptive clinical trial design: A comparison to standard methods.

Funder: National Institute of Health Research (NIHR) Cambridge Biomedical Research CentreA standard two-arm randomised controlled trial usually compares an intervention to a control treatment with equal numbers of patients randomised to each treatment arm and only data from within the current trial are used to assess the treatment effect. Historical data are used when designing new trials and have recently been considered for use in the analysis when the required number of patients under a standard trial design cannot be achieved. Incorporating historical control data could lead to more efficient trials, reducing the number of controls required in the current study when the historical and current control data agree. However, when the data are inconsistent, there is potential for biased treatment effect estimates, inflated type I error and reduced power. We introduce two novel approaches for binary data which discount historical data based on the agreement with the current trial controls, an equivalence approach and an approach based on tail area probabilities. An adaptive design is used where the allocation ratio is adapted at the interim analysis, randomising fewer patients to control when there is agreement. The historical data are down-weighted in the analysis using the power prior approach with a fixed power. We compare operating characteristics of the proposed design to historical data methods in the literature: the modified power prior; commensurate prior; and robust mixture prior. The equivalence probability weight approach is intuitive and the operating characteristics can be calculated exactly. Furthermore, the equivalence bounds can be chosen to control the maximum possible inflation in type I error