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Original articles |
1 Royal Hospital for Sick Children, Yorkhill, Glasgow, UK
2 Decision Modelling Consultancy Ltd, Lymm, Cheshire, UK
Correspondence to:
Dr S Faisal Ahmed, Department of Child Health, Royal Hospital for Sick Children, Yorkhill, Glasgow G3 8SJ, UK; s.f.ahmed{at}clinmed.gla.ac.uk
Accepted for publication 25 March 2007
 | ABSTRACT |
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 TOP ABSTRACT METHODSRESULTSDISCUSSIONREFERENCES |
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Aims and objectives: To investigate the use of conjoint analysis in facilitating and understanding choice of growth hormone injection devices.
Method and subjects: 56 patients and their parents participated in an electronic, computer-based interview. The interview took a median time of 18 min (range 12–30) and allowed an immediate matching of injection devices to the family’s preferences.
Results: Amongst the key drivers of choice, lack of bruising was rated highest and designated an index of 100. Compared to this, the remaining attributes in order of desirability were: auto-injector (98), lack of pain (93), lightweight (88), silent (82), ready-mixed (77), ease of holding (69), telephone helpline (66), needle-free (62), small size (60), nurse support (47), hidden needle (45), stored in fridge (13) and home delivery (6). Out of the 17 families who had already chosen a device previously by discussion with the clinic nurse, the computer model placed their device either as first or second out of seven devices tested.
Conclusion: Adaptive or interactive conjoint analysis applied at the patient level can facilitate the choice-making process whilst providing an insight into the relative importance of the key features that influence choice.
Recombinant human growth hormone (rhGH) has been available for over 15 years for treatment of growth hormone (GH) deficiency.3 However, daily, subcutaneous injection of rhGH is financially and physically costly and failure to comply with treatment may be as high as 50% of all cases.4 5 To personalise therapy, the pharmaceutical industry has developed a number of different devices that vary in the method of subcutaneous injection, the injection product, the injection device and associated support services.6 However, the dramatic increase in the number of devices has made the overall process of providing unrestricted choice and shared decision making too time consuming in the clinic setting. In addition, there has been little progress in objectively identifying the factors that influence choice of device.
Conjoint analysis was originally developed for market research into consumer preferences, and is a method that investigates the relative importance of groups of attributes.7 More recently, it has been applied to various aspects of health care and has the potential to analyse patient preferences for various treatment alternatives.8–10
This paper reports on a novel application of conjoint analysis as a mechanism for facilitating choice of injection device in the outpatient clinic whilst improving our understanding of the factors that influence that choice.
| METHODS |
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| TOPABSTRACTMETHODSRESULTSDISCUSSIONREFERENCES |
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Conjoint analysis model
The values derived as above for each respondent can be added together to give the "value sum" associated with a defined device made up of several such attributes, still for that individual respondent, and that "value sum" can be compared with the "value sum" for other defined devices with different characteristics in order to model those of most appeal to the individual respondent. The conjoint analysis model developed for this application comprised 14 attributes related to device features and support services (table 1). Each attribute had between two and four utility levels. These attributes were identified as being important through previous discussions with patients, their parents and clinic staff. Some attributes (marked with an asterisk in table 1) were deliberately not used in the calculations to model the respondents’ device preferences as they are difficult to objectively quantify, but the data were nevertheless collected to assess their importance to the respondent. For all attributes, a higher level of utility indicates a greater preference for that aspect of the device.
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Module one: generation of utility values
At its core, conjoint requires the respondent to "trade-off" a series of alternative combinations of product features over several screens, similar to that shown in fig 1. These combinations are selected by the program according to that same respondent’s prior answers. As can be seen from fig 1, each respondent choice puts a relative weighting on each of the aspects shown on-screen, and after a series of such screens, in which different attributes are "traded-off", the program calculates the underlying utility values which "best fit" the answers given by this individual respondent. As the program cycles round, iteratively calculating values throughout the interview, it is able to display options that, more and more efficiently, arrive at an accurate representation of the respondent’s unique value structure in the form of "utility values". Averaged across the entire set of respondents (or any subset), the aggregated mean utilities identify the motivations for choice of device (table 3).8–10 To allow easier interpretation of the data, these mean values have been recalculated such that the lowest level in any one attribute is set equal to zero, and results are presented as "differences" from that lowest level. The 3.38 for "bruising" in table 3 is the difference in utility between the two extreme levels of this attribute: thus, the larger the "value span" of a given attribute, the greater the defined "motivational value". To help in interpreting the relative appeal of each attribute and level, the attribute "bruising" (with the greatest value span of 3.38) was given an index of 100, and the remaining attributes scaled against this figure. Mathematically, this is justified as these values are only used for modelling in an "additive" context.
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| RESULTS |
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| TOPABSTRACTMETHODSRESULTSDISCUSSIONREFERENCES |
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Importance rating of attributes: aggregate results
Lack of bruising was the highest rated attribute by the respondents and was designated an index of 100 (table 3). Compared to this, the other attributes in order of high to low desirability, in aggregate, were: auto-injector (98), lack of pain (93), lightweight (88), silent (82), ready-mixed (77), ease of holding (69), telephone helpline (66), needle-free (62), small size (60), nurse support (47), hidden needle (45), stored in fridge (13) and home delivery (6). It is clear that the most highly valued "device features" were automatic injection and weight, but that each of these may be outweighed by the more subjective aspects of likelihood of bruising and pain felt.
Modelling choice of device: individual results
Using the device specifications shown in table 2, "value sums" were calculated for seven devices and top three modelled. Device D was calculated as the most popular followed by G and F (table 4). There were clear differences in the specifications of these devices and especially in the top six attributes (table 2). On comparison of the modelled results with the actual devices already being used by the 17 families with children on rhGH, 11 had already been using devices that were regarded by the model as first or second preference (table 5). Only two had "sub-optimum" devices, and of these one was modelled as a third choice and so clearly was still seen as meeting the patient’s requirements. As noted above, the model was set up to select from a total of seven devices as possible candidates for the "Top 3" so that the correlation is even more impressive than might appear from the above figures. Four out of 17 respondents who were on rhGH treatment were using device E and in three out of these four, device D was modelled as the first choice. The only differences between devices E and D are that the latter has a relatively shorter needle and its preparation does not require any mixing.
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| DISCUSSION |
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What is already known on this topic
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Our aggregated data clearly show that bruising and pain remain two of the greatest fears of patients and their parents and are strong motivators for device choice. We felt that we could not specify the utility values for these attributes for the devices included in the study as these attributes are dependent greatly on perception. However, it is clear that when considering device design, manufacturers would benefit from features that would be perceived as causing the least amount of bruising and pain (ie, look unthreatening), even if this may be at the expense of more tangible but less highly regarded features. Clinical staff, like manufacturers, can also draw useful conclusions, for example that reassurance and training to minimise pain are obvious priorities, but operation, weight and sound, as defined, are all aspects with almost as much value to the patient.
Furthermore, the data obtained from this report can also be used to weigh the resources required in any device alteration against the gain in "patient appeal". For instance, it is likely that the expertise and the expense involved in making a device sound-free is greater than reducing the size of the device, thus encouraging the manufacturer towards concentrating on the second alteration. Similar inferences can be drawn regarding device specification changes that involve combining improvements; since the utility values calculated can be added together, the total increase in utility of two or more simultaneous changes can be identified.
There is scarce information on how patients view community-based support services, yet these may be resource intensive for many pharmaceutical companies. Our data suggest that, whilst the availability of a telephone helpline was rated quite highly, the need for nurse support at home was much less highly valued. Home delivery of drugs, a service often hailed by firms, was rated as of least importance. It is, of course, possible that the need for some of these additional support features may be influenced by geographical and socioeconomic factors.
To some extent, certain attributes may be correlated with each other, such as automatic injection and noise, needle-free and noise, or weight and size. Therefore, some care is needed in mathematically applying all of these values to a device, since ideally all attributes are orthogonal. The test–retest reliability of the model was not assessed as it was felt that the response at the second interview may be biased and that the interview process would have been too demanding for most families attending the outpatient clinic. A previous study has found a high degree of reproducibility within a 2-month period.7
In summary, we describe a short, objective method of providing a personalised "short list" from the wide range of rhGH injection devices available, via a method that also provides helpful information to guide the future development of devices.
| ACKNOWLEDGMENTS |
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| FOOTNOTES |
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Published Online First 5 April 2007
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Related Article
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  Petrou and McIntosh Measuring the benefits of growth hormone therapy in children: a role for preference-based approaches? Arch. Dis. Child., February 1, 2008; 93(2): 95 - 97. [Full Text] [PDF]
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