Safety signal identification is founded on the recognition of a deviation from the expected background pattern of clinical events in a population of patients exposed to a product over time. A lack of understanding of the cumulative population of patients exposed, duration of exposure, and subpopulations of exposure by dose level is probably the largest pervasive deficiency in preregistrational safety event processing and interpretation. 

The population exposed is of equal importance to the presence of the event itself. The purpose of this article is to introduce a concept to aid in the determination of the total number of exposed patients (i.e., a rate denominator) in clinical trials. In this way, we will improve our understanding of adverse events (AEs) and their impact both on immediate patient safety for patients in the trial and, ultimately, potential drug efficacy when launched on the market. 

Current regulations and guidance documents only tangentially address exposure understanding as a dynamic and concurrent part of AE evaluation, especially as a prospective part of event evaluation. Current emphasis is on end-of-study or end-of-program summary. The explanation perhaps has less to do with intellectual resistance than a historical difficulty in pragmatically having exposure information available on a timely basis. 

Implicit in the structure of expectedness is a deviation in the frequency of an event. However, based on our experience, the submission of IND safety alerts, triggered by a change in frequency, rarely occur. During clinical trial drug development phases, patients progress from healthy volunteers, to relative health, to uncomplicated patients, to populations of patients reflective of patients with potentially higher risk. It would seem logical that an increase in the rate of AE occurrence would be observed through the development life cycle. But who knows, since the use of a radical concept called the rate seems to not yet be fully accepted in the pharmaceutical and biotechnology industries. 

The concept of a rate is widely used in a variety of industries to determine the significance of an event's occurrence. The use of rates to better understand and interpret the significance of AE occurrence, however, has been minimal. 

Typically, AE data is not fully studied or its ramifications understood until the end of the trial when all the trial data is available. If AE rates could be more accurately tracked and analyzed as a trial progresses, significant trial decisions could be made earlier. These decisions could improve patient safety and safeguard patient health. It could also be used to provide insight into a drug's performance earlier in the trial process. This insight could lead to critical trial changes, such as modifying the patient population or dose rates, among others. 

Why as an industry are we not embracing the interpretation of AE occurrence earlier in the drug development process? Among the most significant challenges in the interpretation of summary AE data is that both the actual number of AEs that have occurred (numerator) and the population of exposed patients (denominator) is unknown or difficult to gain access to during the course of the trial. Yet the key to unleashing this wealth of information has been in our hands all along. IVR technology already captures the necessary data; the only requisite is to recognize the power of the data that the technology holds. Using this technology, it's likely that the integrated use of exposure information will be established as a cornerstone of modern event processing. 

Identifying the actual number of exposed patients and the total duration of exposure is challenging.

  Some of the problems that have been identified include:

under-reporting of AEs by patients and practitioners

The challenges are especially daunting for spontaneous AE reporting.

  In the spontaneous arena, reporting is voluntary for health care providers and patients. Therefore, there is likely to be gross under-reporting, and in this case, even the numerator is unknown. Likewise, the population of exposed patients is unknown because there is no accurate way to determine how many patients receive prescription medication. There are firms that provide prescription data and sales data, but that data merely provides estimates of exposure and is inherently inaccurate. However, in this industry, accuracy is absolutely vital to patient health and is critical for determining denominator data for AE reporting. 

AE reporting is affected by many things besides the safety profile of a particular product. Some external factors that affect AE reporting include, but are not limited to:

adverse publicity associated with a particular product

litigation associated with similar products on the market.

In other words, reporting rate does not equal incidence. 

Before you can understand how an IVR system can aid in understanding AE profiles, we need to take a look at what an IVR system is and what information it captures. 

IVR systems utilize touch-tone telephony and user-friendly voice prompts to capture critical patient and drug supply management data in a centralized database. IVR systems were first used to optimize clinical trial management for pharmaceutical, biotechnology, and medical device trials in 1989 and is a widely used technology in this industry today. Frequent users of IVR systems include sponsors, investigators, site coordinators, monitors, and distribution centers. 

Sponsors use IVR systems for real-time updates. This real-time information can be used to monitor the performance of clinical sites, patient enrollment, drug supply re-ordering needs, and drug supply expiration. The data that IVR systems collect enable sponsors to manage clinical trial criteria. For example, IVR systems typically record inclusion/exclusion criteria, patient demographic data, randomization schemes, and enrollment levels. Real-time reports available to sponsors allow them to query for:  

status by protocol, site, country or region

Sponsors are now also using IVR systems to monitor patient compliance to diary reporting and medication regimens. For example, if patients request resupply late, they may receive reminder calls reinforcing the need for maintaining their medication regimen. Sponsors may receive updates on patient compliance trends. Some IVR systems automatically call patients if they do not report diary information in a timely fashion. 

Sites are also actively using IVR systems. Enrollment levels are monitored, clinical trial personnel receive training/communication updates, subjects can be unblinded for emergency reasons, and sites can be updated with reminders. 

Additionally, patients utilize the IVR system to report information that was traditionally captured in paper patient diaries. In this way, patient symptoms (including frequency and severity), quality of life information, and safety/efficacy information is available in real-time, rather than waiting for diary information to be recorded and provided at the next site visit. Another benefit that can be employed when utilizing IVR patient diaries is to force patients to select from a list of possible answers, reducing ambiguity. By forcing patients to select one of several prompted choices, the information provided may be more expeditiously reviewed and analyzed. 

While IVR systems are now routinely used for patient enrollment, randomization, and drug supply, few sponsors have tapped into the breadth of benefits that the captured data can deliver to their clinical trials. One decisive yet untapped benefit is to utilize the captured data to improve the understanding of AE reporting. As detailed earlier, IVR systems are used to track the dispensing of clinical compounds. The system records exactly how often supply requests are received from investigators. Therefore, an accurate calculation of exposure can be determined with a thoughtful analysis of this data, and those AE challenges that were previously discussed can be overcome. 

The first challenge is data accuracy. The total exposure calculation derived from an IVR system is likely to be much more accurate than the data reported directly from those responsible for coding and collecting AE data. This is because IVR systems, when used for drug supply management, must be engaged for redispensing. In this way, a highly accurate record of exposure is achieved. 

The next challenge is gaining the numerator (incident occurrence) and denominator (population exposure) data. IVR systems capture the critical variables that will give you visibility into the characteristics of the patient population and will provide insight into possible unreported AEs. These variables include: 

Numerator data—Incident reporting: Patient discontinuations.

 IVR systems capture patient discontinuation information. A patient dropping out of the clinical trial or discontinuing the medication indicates a possible AE. By utilizing the discontinuation information captured in the IVR system, an AE can be recorded at the time patient withdrawal is known. 

 Because IVR systems capture requests for resupply, they may be used to track discontinuations or changes in dosing patterns. For instance, the absence of a resupply request would obviously suggest an issue, possibly a discontinuation or noncompliance with a medication regimen. Discontinuations during trials are often a predictor of similar trends and AEs for approved drugs. 

Denominator data—Population exposure: Age and gender

. IVR systems are able to provide records of the ages of exposed patients (and ages of those experiencing AEs). In the same manner, IVR systems are able to provide the gender data for both the exposed population in a clinical trial and also the population experiencing AEs. 

. IVR systems are often used for randomization. In other words, randomization schedules can be built into IVR systems so that patients are randomized according to predetermined disease variables. This stratification can then be analyzed for both the exposed population and the population experiencing adverse events. 

. IVR systems can be programmed to capture the geographic distribution of the exposed patient population. This is especially useful in global trials. For example, if clinical trial enrollment lags in one region, one should expect that the frequency of AEs reported in that region would also lag. By comparison, if enrollment is similar in multiple regions, one would expect AE patterns to be similar in those regions. If AE patterns differ (while enrollment remains similar), there are at least two possible causes:  

There may be some ethnic, racial or other patient difference associated with the AEs being reported.

There may be a pattern of under-reporting or a challenge with monitoring in a region. This provides clinical staff with an opportunity to intervene and improve AE reporting processes.

Thus, IVR systems generate denominator data (or at least denominator approximations). They also enable subset approximations (e.g., age, gender, disease stratification). A researcher who is actively involved in the summation and interpretation of AE data should consider the value that an IVR system offers in providing exposure data, both in patients who experience AEs and in the total patient population. 

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IVR systems can capitalize on exposure data and analyze adverse events much earlier.

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Service Configures Ready-Made Solution for Randomization and Trial Supply Management

announces its ClinPhone Compact, a new service created to meet the needs of many conventional study designs by offering a ready-made application for Interactive Voice Response and Interactive Web Response deployment.  

The new service is hosted by ClinPhone and utilizes its existing infrastructure offerings of 24/7 global support, unlimited language capabilities, and an extensive toll-free network of 82 countries. 

ClinPhone Compact allows pharmaceutical and biotechnology sponsors to perform central randomization, emergency code break, medication dispensation, and site and depot supplies management combined with real-time study progress reporting. Because the solution is ready-made, it minimizes the need for study-specific customization, including programming, testing, and validation. As such, this can lower sponsors' costs and accelerate timelines. 

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There has been a lot of recent discussion about adaptive designs with the release of a draft guidance from European regulators, and similar guidance is expected from the U.S. FDA. 

Adaptive or flexible designs involve design modifications based on the results of interim analyses. Although the use of interim analysis is not new, the option to make design modifications within a formal framework that is accepted by regulators is. Such designs can speed up drug development without lowering regulatory standards, but to execute them well requires good planning and the ability to maintain control of the processes involved. 

In this article we describe how the use of technologies such as interactive voice response (IVR) systems allows sponsors to take full advantage of some of the available opportunities for design changes while maintaining necessary standards. 

In Europe, a new CHMP (Committee for Medicinal Products for Human Use) guideline for consultation was issued on the use of adaptive clinical trials in March 2006.

  It describes the conditions under which adaptive designs can be used and the necessary criteria for running them, and draws attention to the areas where extra care is required. The guideline demonstrates the increasing acceptance of adaptive designs. 

In the United States, the FDA is also seeing the increased use of adaptive designs. And in July 2006, it announced plans to promote adaptive clinical trials and issue five new guidances.

Adaptive designs are reviewed in a recent paper by Philips and Keene

  from the Pharmaceutical Research and Manufacturers of America Working Group on Adaptive Designs. Both the guidelines and reviews emphasize the important areas of maintaining the blind and, where applicable, keeping the knowledge of the adaption away from investigators. 

Later we describe how the investigator and associated staff might be affected by an intervention, why this might be serious, and how you can guard against the problem while still gaining the advantages of reduced time and increased cost–benefit. 

Historically, clinical trials were planned, executed, and analyzed in three distinct steps. The practicalities of managing the complexities of a trial and the drive to reduce decision times and costs have forced statisticians to examine the process for greater flexibility. Without adversely affecting requirements for the unbiased and independent selection of patients and the random allocation of treatments, adaptive methods allow changes to the original design. Importantly, these changes are beneficial to the decision process and costs. 

Early work in this area included two-stage trials and sequential alterations to the sample size, but  recently a wider framework for adaptive designs has been developed and is becoming widely accepted. Possible adaptive designs now include: 					

Dropping or adding treatment arms (or doses)

Changes to the treatment allocation ratio

Change in objectives (e.g., switching from noninferiority to superiority trials)

Adjustment to the measurement time points

Changes to the interim analysis schedules

For example, a sponsor company might commence a study with several dose options then, with the approval of an independent data monitoring committee (DMC), change the allocation as it becomes clear an arm is unlikely to be suitable. At the end of the trial, more patients are exposed to the drug and dose level of interest than to those that will not be taken forward. Thus, patients and drug supplies are used efficiently. 

The framework allows the traditional Phase II dose finding trial and subsequent Phase III confirmatory trial to be combined into a single "seamless" trial (See Figure 1). Generally, adaptive designs offer the ability to make rapid decisions and to react to changes external to the trial. However, approaches like this cannot be employed without caution. Care has to be taken to ensure that plans include the right statistical methods, that the delivery and packaging of supplies can cope with the changes, and that any changes do not influence the trial results. This latter point is expanded in the European guidelines: 

Figure 1: The Adaptive Design: A Single, Seamless Trial			

"If the staff involved in the recruitment, selection, and execution of the trial become aware of a change, their behavior, whether conscious or not, can influence the results." 

As you will read below, technology approaches such as IVR systems used to control treatment allocation and pack dispensing help address the challenge of keeping study site staff unaware of design changes when they are implemented. 

Readers will be well aware of the dangers inherent in breaking the blind (assessments can no longer be accepted as unbiased and randomization schemes may be compromised for future patients). A related fear with adaptive designs is that different patients might be recruited before and after the adaption if investigators become aware of it (e.g., recruiters might avoid patients considered more at risk until they are confident that inefficacious trial regimens have been dropped). 

The procedures for running a well-controlled and well-executed trial are already established, but unless care is taken, the added complexity of dropping treatment arms or altering allocation ratios can risk impairing the blind or alerting the site staff to the existence of the changes—which may influence their future patient recruitment and selection decisions. 

Consider, for example, a study involving four treatments using a simple randomization list. Without central control of medication pack allocation, supply kits are prelabelled with patient numbers and sites allocate the next available patient number to newly randomized patients. The contents of kits are dictated by a predefined randomization list, so a study including four treatments (A, B, C, and D) might be supplied in such a way that each subsequent group of four patients at a site contains one in each treatment group—the order of which is randomized. In this scenario, if a treatment arm is dropped mid-study, it would immediately become apparent to the investigators, as they would be required to skip certain future patient numbers in their allocations. It may also reveal the block size, increasing the possibility that they guess the treatment groups the current patients belong to. The importance of keeping the block size hidden from investigators is specifically mentioned in regulatory guidance.

Any hint that there is "something going on" has the potential to change the required unbiased selection of patients, which in turn can affect the validity and usability of the study. 

The CHMP guidelines are quite specific: After an intervention, analysis plans should include tests to determine whether the outcomes before and after are the same. If arms are stopped, "all attempts should be taken to maintain the blind and restrict knowledge that recruitment to the arm has been prematurely stopped." So, whether the blind is knowingly broken or not, we need to be confident that there is not a significant effect from the alteration on the validity of the trial. 

By employing a technology solution with a central distribution method—such as by applying an IVR system to manage trial supplies and randomization—the knowledge of any change to treatments can be kept from investigators. Thus, there is no potential for bias and no reason to expect any observed differences between the before and after results (something the regulators will be specifically looking at). There are two aspects to the protection provided by this approach. 

The first is separation of the randomization and dispensing steps. By using pack labels that are not linked to patient numbers in any way, knowledge of alterations in treatment allocation can be restricted. By using a pack list with scrambled (double randomized

 ) numbers (see Figure 2), the supplies allocated by the investigator are completely disconnected, so there is no hint of the randomization block size and, importantly, any information about changes in treatment cannot be detected by site staff. By leaving gaps in the pack list numbering system, it is even possible to randomize a new treatment midstudy without the investigator knowing. 

Figure 2: Benefits of Double Randomization			

There is another feature of IVR solutions that facilitates the execution of adaptive trials: the automated management of the supply chain and maintenance of site stock levels. Even if the investigator cannot deduce anything from the altered numbering in an IVR trial, he or she might deduce something is up if there is a sudden surge in supply deliveries to the site—which could happen if site inventories are adjusted to cope with the altered allocation. But IVR systems can be configured to maintain low stock levels, with regular resupplies as needed. So the investigator is unlikely to deduce anything related to the change. The adjustment of the stock levels to account for the changed situation is automatically handled by the IVR system. 

The first three adaptive designs listed on the previous page are the most common, and it is possible to preprogram and validate them on an IVR system so that the sponsor can activate the change with a single telephone call, while masking the occurrence from frontline staff. A further aid in implementation is the speed with which IVR or its integration with an EDC system makes key data available for decision making. 

 This is easily done by a call to the system, which triggers it to mark the relevant entries in the randomization list as not available for allocation. The need  for more supplies of the nondropped treatments is controlled by the system's automated supply chain management. 

 The number of patients that the system will automatically close entry to is updated following sample size re-estimation. Supplies can continue to be distributed as needed up until the sample size changes. 

 With IVR the switch can be made instantly via a call. The new allocation is taken from a replacement randomization list, which can be prepared in advance but is only activated in an automated manner at the time of the switch. Similarly, it is very easy to alter the allocation ratio in studies using dynamic randomization allocation techniques to maintain the balance across the study. 

 In adaptive trials it is important to rapidly get the key data available to the DMC for decision making. In some cases (as in the case study in the sidebar), key endpoint data may be patient reported and come directly from the IVR system in addition to information about treatment group allocations. In other cases, the key endpoint data may come from an EDC solution with the data integrated with the IVR system, or may be captured on paper CRFs with the key endpoint data sent by mail or fax or collected via an additional IVR/IWR (interactive Web response) event. 

A Case Study in Adaptive Clinical Trial Design			

With the publishing of the latest CHMP guidelines and a more active response from FDA, it is now acceptable to use the established methodology for adaptive designs to run and manage very efficient studies. 

To open the door to all the benefits of adaptive design, the trial needs to be executed carefully to ensure that the right information is available at the right time in the right format for the right people. It is important that the data are collected, collated, and interpreted, and that the results are communicated to the DMC as efficiently as possible. 

An IVR system is a proficient way to gain the advantages of adaptive design. Through the IVR system or its integration with EDC, interim results can be rapidly compiled for the DMC. Then, any resulting study design changes can be followed up promptly, with any necessary steps related to treatment allocation and supplies performed accurately and speedily and without the site staff becoming aware of the change or the treatment allocation becoming visible. By using IVR and the double randomization method, not only can the blind be maintained, it can be shown to be maintained. 

Thus, by using an IVR system fully, the DMC can make timely recommendations and the research staff can readily take action in the knowledge that the changes are well managed and the results will conform to regulatory requirements. 

Interactive voice response systems are a good match for adaptive clinical trials and can help keep investigators in the dark.

Blinded by Science with Adaptive Designs

In clinical trials, we ask patients to self-report data relating to a number of areas including efficacy, side effects, quality of life, health economics, medication adherence, and treatment satisfaction. Recent draft guidance from the FDA defines a patient reported outcome (PRO) as a measurement of any aspect of a patient's health status that comes directly from the patient, without interpretation of the patient's responses by a physician or anyone else.

  Electronic solutions present significant advantages in the collection of PRO data compared to paper and pencil. In particular, electronic patient diaries can eliminate conflicting or ambiguous data inherent in paper diaries and, importantly, can measure and maintain the required diary reporting schedule so that trialists can demonstrate the contemporaneous nature of their PRO data. In fact, studies in paper diary completion have shown that many patients complete their diaries retrospectively, often just prior to a clinic visit; some even complete their diaries ahead of time.

  Both these behaviors put into question the validity of PRO data collected using paper and are cited as a concern in the draft FDA guidance.

In addition to enhancing the quality and integrity of data collected, the use of technology [which includes, for example, personal digital assistants (PDAs), interactive voice response (IVR) systems, mobile phone applications, and digital pens] can provide additional features that enable us to take new and enhanced measurements from patients. In fact, ePRO provides us with the potential to do far more than we ever could do using pencil and paper. The research community is actively exploring new possibilities and approaches that may become tomorrow's gold standards in assessing patients in both clinical trials and routine care. In this article, I overview a small selection of these new areas of innovation and comment on their potential application to clinical trials. 

Cognitive function testing in clinical trials is conventionally performed at the site using dedicated hardware and software. This can make testing large numbers of patients prohibitive, especially if cognition is not the primary measure of interest. However, ePRO solutions in mobile phone, PDA, and IVR applications have been successful in delivering cognitive testing batteries. This affords the possibility of measuring cognition remotely without requiring patients to attend a clinic, and can provide a cost-effective means of testing very large samples of patients. 

The examples I present in this article use an IVR test battery that has been used in a number of published studies.

  This battery comprises four tests measuring aspects of attention and memory (see Table 1).					

 Table 1. Cognitive function test battery delivered using interactive voice response system.			

An early validation study with this system

  investigated the comparability of the IVR test battery with the corresponding tests delivered conventionally via a PC and the ability of telephone testing to detect the known effects of aging on cognition. A sample of 138 volunteers aged 11 to 78 years were tested at their homes on a single occasion and performed both sets of tests in a randomized cross-over study design. In all cases, volunteers were able to follow the requirements of tests delivered using both the PC and IVR systems, indicating that the test battery can be successfully administered over the telephone. In addition, results from each volunteer showed high concordance between tests administered over the telephone and the corresponding tests delivered via the PC-based system. Finally, the IVR test battery was seen to detect the same pattern of age-related effects on cognition as observed using conventional testing in this study and others.  For example, as illustrated in Figure 1, volunteers in higher age bands showed slower reaction times in the simple and time test

—a known phenomenon associated with the aging population. 					

Figure 1. Effects of aging on simple reaction times measured using a telephone test battery.3			

Another study used the IVR test battery to explore the sedative effects of midazolam in patients with dental phobia.

    In the United Kingdom, dental phobia affects between 7% and 15% of the population, and as a result many sufferers attend a dental hospital for dental surgery because in this environment they are able to receive midazolam to help them undergo the procedures. In this study, 18 patients requiring a molar extraction on two occasions were treated with midazolam prior to surgery and, in a randomized cross-over design, received flumazenil or placebo following surgery on each occasion. Patients used the clinic telephone to undergo cognitive tests pre-midazolam at 1 and 2 hours postdose. They continued telephone tests from home at 3, 4, 5, 6, and 7 hours postdose. Midazolam was shown to slow reaction times and decrease attention in each of the four tests administered. Effects did not return to baseline by 7 hours. Despite the small sample size, the IVR test battery was able to show that these deteriorations were statistically significant. In addition, when subjects received flumazenil after surgery, the telephone tests detected significant partial reversal of the effects of midazolam, as illustrated in Figure 2, for the digit recognition test. These results suggest that telephone testing is sensitive enough to detect drug-related changes in cognition and their reversal, even when used in the less controlled environment of the patient's home.					

Figure 2. Effects of midazolam and its temporary reversal using flumazenil on numeric working memory speed/accuracy in dental phobia patients using a telephone test battery.4			

These studies indicate that the measurements required by computerized cognitive tests can be made using simple electronic solutions other than site-based hardware. This opens up greater opportunities for clinical trials where beneficial changes in cognition can add great additional value to the primary information collected. These approaches are perhaps most applicable to normal and mildly impaired populations, including, for example, patients with depression, epilepsy, chronic heart failure, hypertension, diabetes, insomnia, and cancer. 

Most of us are taught our handwriting by a teacher who teaches us a specific script and writing style. However, although taught, we all develop individual differences in the way we write. Some would say that these individual expressions reflect aspects of our personality. For example, handwriting analysts would argue that backward-sloping handwriting suggests introversion, words close together suggest sociability, and missing i-dots and t-crosses suggest forgetfulness or carelessness. Where is this leading? Interestingly, handwriting effects are observed in some neurological conditions. For example, some Parkinson's disease patients exhibit micrographia (i.e., very small handwriting). 

Metrics taken from handwriting have, in fact, been used as endpoints in clinical studies. The digital pen provides an effective means of recording not only the output of a handwriting exercise but also information on speed and writing technique. For example, in schizophrenic patients studies have illustrated that reduction in handwriting area is correlated with, and could even be a predictor of, dopamine (D2) receptor occupancy.

Handwriting tests have also been used to study the effects of alcohol. In one study,

  20 healthy female volunteers aged 19 to 20 years were randomized to receive a large vodka and orange juice cocktail or placebo on two separate occasions according to a cross-over design. On each occasion, volunteers were subjected to a battery of performance tests, including a handwriting test in which they were required to quickly write down a number of defined words, with each word being written a number of times. The study showed the usual performance deterioration associated with alcohol, and also that similar effects were observed in the handwriting test. On average, the length of written words increased after consuming alcohol—mean word length increasing by 23% at 45 minutes postdose. This is illustrated in Figure 3, which shows the increase in length of the written word "black" for the median subject. 					

Figure 3. Mean word length increased 23% at 45 minutes postdose.7 (Reprinted with permission from Dr. Tiplady, University of Edinburgh.)			

Interestingly, as well as having an influence on handwriting, drugs and conditions also have an effect on the way we speak. Voice acoustic measures have been shown to be sensitive indicators of disease severity and therapeutic response in many CNS disorders, including Parkinson's disease, depression, and schizophrenia. This is well illustrated by one study that investigated the relationship between voice metrics and depression severity.

  In this study, researchers captured and digitized voice samples from seven depression rating interview videos (one patient with mild depression, six with moderate/severe depression). These videos are commonly used in Investigator training events for depression clinical trials to enable the Investigators to standardize the way they rate patients using the Hamilton Depression Rating Scale (HAM-D). Voice samples comprised the first 10 seconds of uninterrupted speech, and voice acoustic measures were made on the middle 5-second interval starting at the onset of a word, using commercially available software. The measures investigated were speaking rate (number of syllables spoken in the 5-second interval), percent pause time (sum duration of pauses over 250 ms in duration, expressed as a percentage of the 5-second interval), and pitch variation (a measure of how highly intonated or monotonic the voice is). Results showed strong correlations between the HAM-D score and both speaking rate and pitch variation. Speech slowed and became more monotonic with increasing severity of depression (Figure 4).					

Figure 4. Correlation between voice acoustics measures and Hamilton Depression Rating Scale scores.8			

Normally, these kinds of voice acoustics measures are made in a specialist voice laboratory, but a recent study showed that voice samples suitable for voice acoustics analysis could be reliably captured remotely via the telephone using an IVR system.

  This research indicates that in the future it may be possible to make these measurements from patients in a simple and cost-effective manner in large clinical studies. More work is underway in validating and understanding these endpoints for use in future clinical trials. 

One challenge identified in the recent FDA PRO draft guidance

 is that PRO instruments that require patients to rely upon memory, especially recall over a period of time, may threaten the accuracy of the data collected. Try to remember what you ate for dinner a week ago. Unless it was a special occasion, you are unlikely to be able to. However, in clinical trials we often ask patients to rate their condition relative to a pretreatment baseline condition that occurred weeks earlier. This is the basis of the patient global impression of improvement (PGI-I) score in which patients rate themselves on a 7-point scale from 1 (very much better) to 7 (very much worse). A new approach using an electronic instrument developed by Healthcare Technology Systems called MERET (Memory Enhanced Retrospective Evaluation of Treatment) helps to address these concerns regarding the accuracy of recall. At baseline, patients describe their feelings and experiences related to their condition in their own voice and words. This is captured using an IVR system. At further time points this baseline recording can be played back to the patient, enabling them to accurately recall their pretreatment condition and rate themselves relative to it using the same 7-point scale as the conventional PGI-I. Interestingly, not only does the content of the recorded message help the patient to anchor recall of their baseline status, but the emotion, intonation, choice of words, and hesitation provide additional cues about their condition at baseline. As we saw with the voice acoustics study in depression, aspects of voice such as pitch variability and speaking rate correlate well with depression severity, and so these additional measures, subconsciously interpreted by the patient when listening to their pretreatment recording, provide enhanced insight into their baseline condition.					

Clinical trials using MERET have provided encouraging results. One study in 74 depressed patients

  indicated that MERET was more sensitive to detecting treatment-related improvements than the standard PGI-I. In this study, depressed patients received up to four weeks of treatment with duloxetine or placebo. MERET recordings were made prior to entering the double-blind phase, and patients were asked to provide improvement ratings four weeks later.  Patients self-rated changes on the PGI-I first, then listened to baseline MERET recordings before providing a second rating. Although in this study both PGI-I and MERET ratings showed significant improvements on active treatment compared to placebo, MERET scores showed greater separation and effect size compared to the conventional PGI-I. Using pre-treatment experiential anchors in this way appears to enhance the ability of patients to perceive the magnitude of their change in clinical condition. Unlike formal voice acoustics analysis, MERET and similar concepts are applicable for use in today's clinical trials and could apply to studies in any therapy area where patient-rated change in condition is measured. 

Many clinical trials today collect self-report data from patients. Electronic methods for collecting this PRO data provide quality and integrity enhancements over paper, and enable additional measurements to be made from patients that have been impossible or impractical using pencil and paper. As a consequence, the research community is actively investigating new ways of collecting complex data that may provide us with greater insight into disease states. A few of these have been illustrated in this article, but many more are in development and testing in the academic community. Some of this research may in fact unearth tomorrow's gold standards. 

 is product strategy director with ClinPhone Group Limited, Lady Bay House, Meadow Grove, Nottingham NG2 7EW, United Kingdom, +44 115 955 7333, email: 

 1. Food and Drug Administration. Draft Guidance for Industry.  Patient-reported outcome measures: use in medical product development to support labeling claims. (FDA, Rockville, MD, Feb 2006).   

 2.  A.A. Stone, S. Shiffman, J.E. Schwartz, J.E. Broderick, M.R. Hufford, "Patient Non-compliance with Paper Diaries," 

 3.  K.A. Wesnes, T. Ward, G. Ayre, C. Pincock, "Development and Validation of a System for Evaluating Cognitive Functioning over the Telephone for Use in Late Phase Drug Development," 

 4. N.M Girdler, J.P. Lyne, R. Wallace, N. Neave, A. Scholey, K.A. Wesnes, C. Herman, "A Randomized, Controlled Trial of Cognitive and Psychomotor Recovery from Midazolam Sedation Following Reversal with Oral Flumazenil," 

 5.  P. McCue, A.B. Scholey, C. Herman, K.A. Wesnes, "Validation of a Telephone Cognitive Assessment Test Battery for Use in Chronic Fatigue Syndrome," 

 6.  U. Kuenstler, U. Juhnhold, W.H. Knapp, H.J. Gertz, "Positive Correlation Between Reduction of Handwriting Area and D2 Dopamine Receptor Occupancy During Treatment with Neuroleptic Drugs," 

 7.  K. Farquhar, K. Lambert, B. Tiplady, P. Wright, "Handwriting as an Index of Ethanol Effects," 

 8.  M. Cannizzaro, B. Harel, N. Reilly, P. Chappell, P. Snyder, "Voice Acoustical Measurement of the Severity of Major Depression," 

 9.  M. Cannizzaro, N. Reilly, J. Mundt, P. Snyder, "Remote Capture of Human Voice Acoustical Data by Telephone: A Methods Study," 

10. J.C. Mundt, D.J. DeBrota, H.K. Moore, J.H. Greist, "Memory Enhanced Retrospective Evaluation of Treatment (MERET): Anchoring Patients' Perceptions of Clinical Change in the Past," 45th Annual Meeting of the New Clinical Drug Evaluation Unit Program, Boca Raton, FL USA. Abstract I-30 (2005). 

Electronic solutions such as IVR systems, PDAs, and digital pens exhibit advantages over paper and pencil in PRO data collection.

Supplements-10-18-2007

Press One for Safety

eShowcase: ClinPhone Compact

Blinded by Science with Adaptive Designs

Innovative ePRO: Tapping into the Potential