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This section of our technical library presents information and documentation relating to Clinical IVR Development and custom IVR software and products. Business phone systems and toll free answering systems (generally 800 numbers and their equivalent) are very popular for service and sales organizations, allowing customers and prospects to call your organization anywhere in the country. The PACER and WIZARD IVR System is just one of many DSC call center phone system features..

What is IVR Software?. An Interactive Voice Response (IVR) processes inbound phone calls, plays recorded messages including information extracted from databases and the internet, and potentially routes calls to either inhouse service agents or transfers the caller to an outside extension.

Contact DSC today. to learn more about our IVR services and IVR application development software.

Optimizing the Supply Chain Through Trial Simulation
Page 3

By: Damian McEntegart, Nikki Dowlman, Martin Lang, Graham Nicholls
Stephen Bacon, Jeremy Star, Bill Byrom

Applied Clinical Trials

Model input

The model will need to accept inputs for a significant number of variables that impact on the supply chain and drug availability (Figure 2). Some of these parameters will be fixed for a particular run, by their very nature: method of randomization, block size, pack types, visit schedule, etc. Others are programmed to emulate the "real-world" variation, e.g., recruitment rates, withdrawal rates, and up/down titration rates for each visit. Each simulation runs through every day of the duration of study, mimicking the events that could be expected for that day. So at the start of the trial, countries and sites are activated randomly over a defined period of time and initial supplies are requested in a way identical to that occurring in a real trial using an IVR system. Subjects are recruited on a daily basis using the recruitment rates given for each site. Their subsequent visits are randomly sampled from the target visit date range defined for the study. Subject withdrawals occur in a random manner on a "daily" basis according to the defined withdrawal rates. At the end of each day, site and depot stocks are assessed for the requirements to raise a re-supply order using the predefined re-supply algorithms, mimicking the IVR system. The time taken for medication deliveries to arrive at site are sampled from an underlying uniform distribution. The model must be able to imitate all of the functionality observed in the medication management application used by the IVR system and all of the randomization methodologies that can be applied with IVR. It is also possible to automatically register milestones in the study and make sensible adjustments to input parameters. For example, at the end of recruitment it may be desirable to reduce the buffer stock held at a site as there are no more "new" subjects to recruit and therefore fewer "unexpected" supply events likely to take place.

Model output

In using the simulation tool, it is important to run a sufficient number of simulations in order to identify any possible outliers. This may mean running the model over a thousand times in order to ensure that all likely outcomes are simulated. As discussed earlier, while each simulation will produce a different result for all of the output variables, the true power of the tool is demonstrated when reviewing the summary statistics as a whole. Examining the data in this way will provide a range (minimum and maximum and confidence interval) as well as a mean estimate. In our experience we have found the most useful summary statistics to be:

  • The overage in medication required, i.e., the amount of medication remaining in the supply chain (site or local depot) at the end of the study
  • The number of deliveries required
  • When additional supplies (from an additional packaging run) will be required to prevent supply chain stocks from running too low
  • The probability of being unable to supply one or more subjects (at randomization or dispensing visits)
  • The expected incidence of forced randomization,8 if it is allowed (i.e., if the desired treatment is unavailable the subject is forced onto an alternative treatment group)
  • The number of subjects affected and the length of time before supplies are available at site , if medication is unavailable for dispensation
  • The ranges of study durations.
Reviewing these data for each scenario (a single defined set of input variables) enables the end-user to explore "what if" analyses to help understand the behavior of the supply strategy, for example, to help balance overage reductions with increased risk of supply failures to aid in study optimization. The user can also evaluate a particular supply strategy in comparison to others. In generating awareness of an outcome's likeliness, appropriate contingency plans can be devised or the supply strategy adjusted to reduce the odds of the situation occurring. Sensitivity analyses to test the ability of the strategy to cope with alternative assumptions about the trial progression can provide a valuable measure of the robustness of the model's results.

Simulation example

To illustrate the usage of the model, simulations of a 1,000-subject study were performed. The protocol design was chosen to mimic a number of likely practical challenges that one could expect to face in a genuine study. The following overview details these challenges.

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