Patients' preferences for characteristics associated with treatments for osteoarthritis
J. Ratcliffe1,
M. Buxton2,
T. McGarry3,
R. Sheldon3 and
J. Chancellor4
1School of Health and Related Research, University of Sheffield, Sheffield, 2Health Economics Research Group, Brunel University, Uxbridge, 3Accent Marketing and Research, Turnham Green and 4Innovus Research (UK) Ltd, High Wycombe, Bucks, UK.
Correspondence to: J. Ratcliffe, Sheffield Health Economics Group, School of Health and Related Research, University of Sheffield, Regent Court, 30 Regent Street, Sheffield S1 4DA, UK. E-mail: j.ratcliffe{at}sheffield.ac.uk
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Abstract
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Objective. The purpose of this study was to investigate patient preferences for attributes associated with the efficacy and side-effects of treatment for osteoarthritis.
Methods. A stated preference design questionnaire was administered to a sample of 412 individuals diagnosed with osteoarthritis (OA).
Results. Statistically significant attributes in influencing treatment preferences were the level of joint aches, the level of physical mobility and the risk of experiencing serious side-effects from treatment. Respondents were relatively more concerned about the risk of serious side-effects (even with a very low probability) than mild to moderate side-effects (at a much higher probability). Data segmentation revealed some variations in preferences according to respondent characteristics. The importance of joint aches increased according to the severity of the symptoms of osteoarthritis, indicating that this attribute is more troublesome to those respondents with more severe symptoms. Older respondents were more willing than younger respondents to accept an increased risk of experiencing serious side-effects for an improvement in the symptoms of OA. Individuals in lower income brackets appeared to attach greater importance to joint aches and the level of mobility experienced than those in higher income brackets. Respondents who had previously experienced gastrointestinal side-effects from treatment were, as expected, more tolerant of them than those who had not.
Conclusion. The use of conjoint analysis to assess patient preferences provides a useful insight to the likely attitudes of patients to novel treatments for osteoarthritis.
KEY WORDS: Osteoarthritis, Patient preferences, Stated preference, Conjoint analysis, Side-effects, Risk.
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Introduction
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The inclusion in clinical trials of outcomes valued by patients has been recognized as important if clinical decision making is truly to reflect the agency relationship between clinicians and patients [1]. Osteoarthritis (OA) provides a good example of this tenet. Whilst radiographic evidence of OA is present in most people older than 65 yr [2], this diagnostic criterion is poorly correlated with symptoms troublesome enough for patients to report. Nonetheless, the prevalence of OA symptoms is sufficient to represent a major limitation to lifestyle. In a community survey conducted in 1993 of 1201 randomly selected adults aged 2089 yr living in West Glamorgan [3], 19.1% of the 827 respondents reported they had been treated by a doctor for arthritis in the previous 12 months. This compared with back pain (20.2%), hypertension (13.1%), insomnia (10.3%), anxiety (8.8%), depression (6.6%), angina (7.4%), asthma (6.6%), sciatica (6.3%) and diabetes (2.8%). In addition, the decrement in health status reported by the arthritis sufferers within this community survey, using the SF-36 health status instrument [4] (particularly in the physical functioning, rolephysical and bodily pain domains) demonstrated that the health burden of arthritis is substantial.
Recommendations at the OMERACT III consensus conference for outcome measures in future Phase III trials in knee, hip or hand OA include an obligatory, inner core set of outcomes (pain, physical function, patient global assessment) as well as imaging in studies of duration of 1 yr or more [5]. Notably, a middle core to measure health-related quality of life is also strongly recommended. There are two disease-specific instruments purpose-designed for use in OA: the Western Ontario and McMaster Universities OA Index (WOMAC) [6] and the Lequesne Index [7]. The most commonly used, the WOMAC, consists of a set of questions concerning pain, stiffness and physical function. Although WOMAC addresses the most important manifestations of OA as a disease, the treatment of OA may also have an effect on overall patient quality of life. Many patients are treated with non-steroidal anti-inflammatory drugs (NSAIDs) to manage their symptoms. However, the high prevalence of a range of gastrointestinal side-effects caused by NSAIDs is well documented [810].
This research was motivated by the lack of published findings on how patients themselves (as distinct from those who may make decisions on their behalf) value improvements in symptoms relative to the risk of experiencing side-effects in the treatment of OA. The nature of this relationship needs to be better understood in the light of recent innovations in the drug therapy available for symptomatic treatment of OA. The recently introduced cyclooxygenase 2 (COX-2)-specific inhibitors celecoxib and rofecoxib provide similar efficacy to traditional NSAIDs but are associated with a reduced incidence of gastrointestinal side-effects [11]. As clinical experience accrues with such new agents, and as other new OA treatments are introduced, the available options for this efficacy vs side-effect trade-off may well change.
The main objective of the study was to estimate the relative importance attached to aspects of treatment efficacy and the risk of different categories of side-effects for patients diagnosed with OA. The economic technique of conjoint analysis (CA) [12] was used to assess patient preferences. CA is one of a number of stated preference techniques used to determine individual preferences in hypothetical controlled experimental conditions. Such techniques have been used extensively by market researchers [13, 14] to determine consumer preferences for a range of goods and services in the transport sector [15, 16] and has been recommended to the UK Treasury as a tool for determining consumers preferences for improvements in the quality of provision in public services [17].
In recent years CA has begun increasingly to be applied within the health-care sector. For example, it has been used to establish the relative importance of aspects of the process of care in addition to or in isolation from treatment outcomes [1820], to establish priorities for health-care expenditure [21, 22] and to examine aspects of the doctorpatient relationship [23]. CA can be used to establish the relative importance of different attributes (i.e. characteristics or features) in the provision of treatments or services, patients strength of preference for those characteristics and the overall utility or satisfaction gained from different treatment algorithms or service configurations. To our knowledge this is the first CA study to be undertaken which focuses specifically upon patient preferences for the effectiveness and the side-effect profile for the treatment of patients with OA.
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Methods
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Establishing the attributes and their levels
To identify the key attributes, or features, of importance to patients with OA, an initial literature review was undertaken followed by the convening of four face-to-face in-depth interviews with rheumatologists. The four qualitative interviews focused on clinicians perspectives of what were the important aspects of treatment effectiveness and tolerability/safety for patients with OA.
Four focus group discussions were conducted with patients diagnosed as suffering from OA. Respondent recruitment for the focus groups was undertaken through consultation with Arthritis Care, a charity supporting people with arthritis. Between six and eight respondents attended each group, giving 28 respondents in total. The group composition consisted of three female-only groups and a group consisting of seven female and one male respondent. Of these respondents, three were aged 4554 yr, five were aged 5564 yr and 20 were aged 65 yr and over. One moderator was responsible for undertaking all of the groups to ensure consistency. The focus groups lasted between 45 and 90 min and were taped, with the tapes from each of the focus groups transcribed to assist the analysis.
The characteristics that emerged from this process were formulated into attributes and levels by the research team. The following attributes were identified for the study: (i) the level of joint aches experienced; (ii) the level of joint pains experienced; (iii) the level of mobility; (iv) the risk of experiencing mild to moderate side-effects from the treatment received for OA; and (v) the risk of experiencing serious side-effects from the treatments received for OA.
The CA approach requires that the chosen attribute levels should be realistic and credible to respondents and capable of being traded off against each other [12] [trade-offs arise where respondents are prepared to substitute a deterioration in one attribute for an improvement in another attribute within a discrete choice experimental design]. The levels chosen for each of the attributes included in the study and their codings are detailed in Table 1.
Producing scenarios and pairing scenarios for comparison
The computer software package Speed version 2.1 was used to produce a fractional factorial design. The Speed software uses a formal system for representing factorials and fractional factorials developed using statistical design theory [24]. Such a design reduces the possible combinations of attributes and their respective levels (or scenarios) to a manageable number for the purposes of a telephone interview [25]. Speed produces an orthogonal main effects design that ensures that there is no multicollinearity between the independent variables. In this study the independent variables are represented by the characteristics of alternative treatments for OA.
The Speed software produced 16 scenarios for comparison. For ease of completion and understanding, a discrete-choice experimental design was used in which respondents were offered a series of pairwise comparisons between scenarios and asked to indicate their preferred option. A random number generator was used to place the 16 scenarios into three sets of eight pairwise choices (Appendix 1).
For each pair of scenarios, respondents were asked to indicate what choice they would make when receiving treatment for their OA if asked to choose between two treatments with different efficacy and side-effect profiles. (See Appendix 2 for an example of a discrete-choice question included within one of the choice sets.)
Selecting the sample and administering the questionnaire
A pilot survey (n = 30) was conducted in advance of the main study to check that patients understood the questions and were completing the choice tasks as instructed. Small changes were made to the detail of the questions on the sociodemographic characteristics of respondents based upon the pilot study. No changes were deemed necessary to the CA exercise itself. To obtain a general population sample of patients with OA, a general population sample of older respondents (aged
55 yr) was first identified using a market research database. This meant that all households contacted for this research included at least one person aged
55 yr. Households were contacted by a telephone interviewer and asked for their consent to participation in the research. Consenting households were then asked to indicate whether anyone in the household was diagnosed as having arthritis. If the answer to this question was yes, the respondent was then presented with a series of recruitment questions by the telephone interviewer that were designed to identify those individuals with OA (as opposed to any other form of arthritis).
The recruitment questionnaire asked respondents (i) whether they had been diagnosed by a clinician as having OA [here, clinician also included alternative medicine practitioners]; (ii) what was the location of their arthritis (respondents had to spontaneously mention the knees, hips or spine/lower back to be included in the research); and (iii) what medications they were taking (respondents were excluded if they had been prescribed medications traditionally associated with other forms of arthritis, e.g. rheumatoid arthritis).
The recruitment questions were devised upon the advice of our clinical colleague and were specifically designed to maximize the likelihood of identifying patients living with OA. Respondents who passed all stages of the recruitment questionnaire were deemed highly likely to have OA and, if willing, were included in the research. A relatively large target sample (for a study of this nature) of 400 respondents was set to allow sufficient observations to adequately reflect the sampling frame and enable investigation of preferences according to a series of subgroup classifications. Respondents were recruited by telephone, posted material to be used in the interview and then telephoned again a few days later to take part in the interview.
The main questionnaire comprised three sections. In the first section of the questionnaire respondents were asked to indicate the severity of their OA symptoms using the global assessment scale conventionally employed in clinical trials of OA [5]. In the second section of the questionnaire, respondents were presented with eight pairwise choices comprising one of the three choice sets generated for the CA exercise. Each choice set contained a test for logical consistency, in which one of the eight discrete-choice questions presented one scenario which was clearly superior on all attribute levels to the comparator scenario and hence should rationally be the chosen option. In the final section of the questionnaire, sociodemographic details were requested.
Data analysis
The data from the CA exercise were analysed using the random effects probit model, which takes account of the repeated measurement aspect of the data (whereby multiple responses are obtained from the same individual) [26]. The function to be estimated was of the following form:
where V is the utility or satisfaction associated with alternative modes of service delivery, 15 are the parameter estimates of the model to be estimated as described in Table 1, and e and u are the unobservable error terms; e is due to differences amongst observations and u is the error term due to differences amongst respondents. The estimated coefficients and their statistical significance (or otherwise) indicate the relevant importance of the different attributes on individual preferences.
In general, the greater the size of the coefficient, the greater the importance of the attribute in determining overall utility or satisfaction (though where different units of measurement of the attributes are used, care must be taken in interpreting the results). A positive sign on a coefficient indicates that as the level of the attribute increases so does the utility derived, and the converse applies for a negative sign on a coefficient. The marginal rate of substitution (MRS) provides an indication of the extent to which respondents are prepared to trade an improvement in one attribute for a detriment in another attribute at the aggregate level. The MRS between a pair of attributes can be estimated by the ratio of the relevant parameter estimates, e.g. the MRS between the level of joint aches experienced and the chance of experiencing mild to moderate side-effects is estimated by dividing the coefficient attached to joint aches by the coefficient attached to mild/moderate side-effects. A non-parametric bootstrapping approach can be used to generate confidence intervals around MRS of interest [27].
For each respondent, tests were also carried out to determine if any of the attributes were dominant [28]. A dominant attribute implies that the scenario with the higher level of this attribute is always chosen, irrespective of the levels of the remaining attributes.
To ascertain to what extent preferences vary across subgroups of respondents, the data were segmented (providing subgroups had more than 30 observations, which is the minimum number of observations recommended for analysis [24, 29]).
The data were segmented according to the following characteristics: (i) severity of OA symptoms (very good, good, fair, poor, very poor); (ii) age (<61, 6170, 7180, >80 yr); (iii) annual household income (<5, 510, 1125, >25 thousand UK pounds); and (iv) most troublesome side-effect experienced (gastrointestinal side effects, no gastrointestinal side effects).
Dummy variable interaction terms were then created between all of the attributes, and a dummy variable for each characteristic. Where there were more than two levels for a particular characteristic, one level was used as a base case and all subsequent levels were compared with the base case. The Wald statistic was calculated to test for statistically significant differences on the coefficients across subgroups.
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Results
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Characteristics of respondents and response patterns
A total of 412 telephone interviews were completed. The descriptive characteristics of responders are summarized in Table 2.
It can be seen that the majority of respondents (68%) were aged between 61 and 80 yr and female (72%). A total of 65% of respondents indicated that they were currently receiving medication(s) for their OA. Approximately 48% of respondents indicated that they had experienced side-effects from the medication(s) prescribed for their OA. Most respondents (75%) had retired from employment and 54% reported that they had no formal academic qualifications. [No formal academic qualifications indicates the absence of any of the following: 1, higher degree or degree equivalent and above; 2, other higher education below degree level; 3, A levels, vocational level 3 and equivalent trade apprenticeships; 4, General Certificate of Secondary Education, O level grade AC, vocational level 2 and equivalents; 5, qualifications at level 1 or below (National Vocational Qualification, General Certificate of Secondary Education or equivalent below grade C etc.); 6, other qualifications, level unknown.]
Assessment of OA symptoms
The results from the first section of the questionnaire (in which respondents were asked to indicate an assessment of their OA symptoms today) are also presented in Table 2.
The majority of respondents (57%) reported their rating of their OA symptoms as fair (moderate symptoms of OA and limitation of some normal activities). A small minority of respondents (3%) rated their OA symptoms as very good on the day that they participated in the survey. A similarly small minority of respondents (4%) rated their OA symptoms as very poor.
Conjoint analysis model estimation
The results of the random effects probit regression model for the total sample of respondents are presented in Table 3.
Of the 412 respondents, 21 (5%) failed the logical consistency test and the results of the random effects probit regression model when these individuals were excluded from the main CA data analysis are presented in Table 4.
The regression coefficients for both models have a negative sign, which indicates that respondents would prefer less severe symptoms and a lower probability of experiencing side-effects from medication(s) to more severe symptoms and a higher probability of experiencing side-effects. The relative size of the attribute coefficients indicates that the most important attribute was the extent of mobility achieved. Tables 3 and 4 indicate that the statistically significant attributes in influencing preferences for respondents as a whole were: (i) the level of joint aches experienced; (ii) the extent of physical mobility achieved; and (iii) the probability of experiencing serious side-effects as a consequence of receiving medication(s) for osteoarthritis.
As in other CA studies in health-care [1820], a relatively large percentage of respondents (34%) exhibited dominant preferences for one of the attributes included in the questionnaire (Table 5). In this exercise, the level of physical mobility was the most common dominant attribute.
For the main analysis, excluding inconsistent respondents only, the MRS between mobility and the risk of serious side-effects (1.67) was estimated by dividing the coefficient attached to mobility (-0.5419) by the coefficient attached to serious side-effects (-0.3197). The 95% confidence interval varied from 1.21 to 2.30. The MRS indicates that an improvement in mobility of one level is valued 1.7 times more highly than a 1% reduction in the chance of experiencing serious side-effects from treatment. Similarly, the MRS between mobility and joint aches (10.69) can be estimated by dividing the coefficient attached to mobility (-0.5419) by the coefficient attached to joint aches (-0.0507). This indicates that an improvement in mobility of one level is valued approximately 10 times more highly than an improvement of one level in joint aches. The 95% confidence interval varied from 9.36 to 12.33.
The results from the segmentation of respondents according to their own assessment of their OA symptoms are presented in Table A3.1 in Appendix 3. The results revealed that the importance attached to joint aches in influencing preferences (as indicated by the relative size of the coefficient attached to this attribute) increased as the severity of symptoms increased. The negative sign attached to the coefficient indicates that those individuals reporting more severe OA symptoms were more likely to express a preference in favour of mild joint aches than those individuals reporting less severe OA symptoms. Conversely, the relative importance attached to the level of mobility achieved decreased as the severity of symptoms increased, indicating that this attribute was less important in determining the preferences of those individuals reporting more severe symptoms. For joint aches, mobility and the risk of serious side-effects, the Wald test indicated that the differences in preferences between the severity groupings were statistically significant.
The segmentation of respondents according to age (Table A3.1 in Appendix 3) revealed that the relative importance attached to joint aches decreased as age increased, indicating that in general this attribute was not as important in influencing the preferences of older respondents relative to younger respondents. Similarly, the relative importance attached to the risk of serious side-effects decreased as age increased.
The results across income levels (Table A3.2 in Appendix 3) indicate that joint aches are relatively more important in influencing the preferences of those on low incomes in comparison with those on higher incomes (the negative sign attached to the coefficient indicating that respondents prefer mild joint aches to more severe joint aches). The relative importance of the risk of serious side-effects from medication in influencing preferences increased as income increased, those respondents in higher income brackets expressing stronger preferences away from treatments with relatively high risks of serious side-effects. The results of segmentation according to experience of gastrointestinal side-effects from treatment (Table A3.2 in Appendix 3) show no notable differences in preferences across attributes.
The results from all four data-segmented models revealed some consistent results. The Wald test indicated that, regardless of the characteristic used for segmentation, the level of mobility and the risk of serious side-effects from OA medication(s) were consistently statistically significant attributes in influencing preferences.
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Discussion
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The results of the CA exercise confirmed that patients are prepared to trade off improvements in the symptoms of OA (in particular those relating to physical mobility) as a consequence of the treatment they receive with the risk of experiencing complications due to the side-effects of medications. Focus group discussions prior to the main study had suggested that OA sufferers are well aware of the potential of NSAID treatment not just for nuisance gastrointestinal side-effects but also for rare but life-threatening ulcer complications. Respondents in the main study were relatively more concerned about the risk of serious side-effects than mild to moderate side-effects even when the probability of receiving serious side-effects was extremely small. This demonstrates that the need for effective but safer alternatives to conventional NSAIDs is recognized by patients.
The potential problems associated with estimating confidence intervals around ratios, such as the marginal rates of substitution between pairs of attributes, have been well recognized not only in the discipline of health economics [30, 31] but also in the bioassay literature [32]. Unfortunately when the denominator of the ratio is close to zero, parametric methods of confidence interval estimation, such as Fieller's theorem, break down and the confidence interval becomes unbounded. In this situation it may be more appropriate to use the bootstrapping method [27], a non-parametric approach to confidence interval estimation which estimates the sampling distribution of a statistic through a large number of simulations based upon sampling with replacement from the original data. The empirical estimate of the sampling distribution can then be used to construct confidence intervals. We recommend that confidence intervals around MRS are routinely calculated and further research is undertaken as to the most appropriate methods for doing so.
Analysis of preferences by subgroup revealed some variation in preferences. The importance of joint aches increased according to the severity of the symptoms of OA, indicating that this attribute is more troublesome to those respondents with more severe symptoms. Perhaps unsurprisingly, older respondents were less risk-averse in relation to the chance of serious potentially life-threatening side-effects than younger respondents and were correspondingly more willing to trade an increased risk of experiencing serious side-effects for an improvement in the symptoms of OA. Those individuals in lower income brackets appeared to attach greater importance to joint aches and the level of mobility experienced than those in higher income brackets. Conversely, those individuals in higher income brackets were relatively more concerned about the risk of experiencing serious side-effects from treatment and were not so readily prepared to trade between this attribute and the symptoms of OA as those in low income brackets. Analysis of preferences according to whether or not the respondent had experienced gastrointestinal side-effects indicated that those respondents who had experienced these side-effects were, as expected, more tolerant of them than those who had not.
It is possible that the pattern that we observed of a large percentage of respondents displaying dominant choices is a consequence of the choices presented. Alternative levels for some or all of the attributes presented may have encouraged these respondents to trade off the dominant attributes. However, it is important to ensure that the levels chosen for the attributes appear plausible to respondents and the attribute levels included within this exercise were carefully chosen to reflect realistic levels for patients receiving treatment for their OA.
As with most conjoint analyses, the sample size here was based upon a rule of thumb because the traditional calculations for sample size determination cannot readily be applied. This means, however, that non-significant findings should be interpreted with caution since the study may lack the power to demonstrate differences that might be important in policy terms.
This paper has highlighted the application of the CA technique to measuring patients preferences for characteristics of treatment. This CA study is the first, to our knowledge, relating to treatment for OA. The study has demonstrated that patients attach importance to both the clinical efficacy and the risks of side-effects from treatment for OA. Using CA to assess patient preferences provides a useful tool to analyse likely attitudes to novel treatments for OA.
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Appendix 1. Choice set designs
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Choice |
Scenario |
ACHES |
PAINS |
MOBILITY |
MILDMOD |
SERIOUS |
|
Choice set A
|
1 |
1 |
Treatment A: 3 |
Treatment A: 1 |
Treatment A: 2 |
Treatment A: 25 |
Treatment A: 0.2 |
|
7 |
Treatment B: 2 |
Treatment B: 2 |
Treatment B: 3 |
Treatment B: 50 |
Treatment B: 0.2 |
2 |
8 |
Treatment A: 2 |
Treatment A: 1 |
Treatment A: 1 |
Treatment A: 25 |
Treatment A: 0.2 |
|
14 |
Treatment B: 1 |
Treatment B: 3 |
Treatment B: 3 |
Treatment B: 25 |
Treatment B: 0.5 |
3 |
13 |
Treatment A: 1 |
Treatment A: 3 |
Treatment A: 1 |
Treatment A: 50 |
Treatment A: 0.2 |
|
9 |
Treatment B: 2 |
Treatment B: 1 |
Treatment B: 1 |
Treatment B: 50 |
Treatment B: 0.5 |
4 |
2 |
Treatment A: 3 |
Treatment A: 2 |
Treatment A: 1 |
Treatment A: 25 |
Treatment A: 2 |
|
15 |
Treatment B: 1 |
Treatment B: 2 |
Treatment B: 2 |
Treatment B: 50 |
Treatment B: 0.2 |
5 |
11 |
Treatment A: 3 |
Treatment A: 1 |
Treatment A: 2 |
Treatment A: 50 |
Treatment A: 0.5 |
|
12 |
Treatment B: 1 |
Treatment B: 1 |
Treatment B: 1 |
Treatment B: 75 |
Treatment B: 0.2 |
6 |
3 |
Treatment A: 3 |
Treatment A: 2 |
Treatment A: 1 |
Treatment A: 75 |
Treatment A: 0.5 |
|
5 |
Treatment B: 3 |
Treatment B: 1 |
Treatment B: 3 |
Treatment B: 50 |
Treatment B: 2 |
7 |
4 |
Treatment A: 3 |
Treatment A: 1 |
Treatment A: 3 |
Treatment A: 75 |
Treatment A: 0.2 |
|
16 |
Treatment B: 1 |
Treatment B: 1 |
Treatment B: 1 |
Treatment B: 50 |
Treatment B: 2 |
8 |
6 |
Treatment A: 2 |
Treatment A: 3 |
Treatment A: 1 |
Treatment A: 50 |
Treatment A: 0.2 |
|
10 |
Treatment B: 2 |
Treatment B: 3 |
Treatment B: 2 |
Treatment B: 75 |
Treatment B: 2 |
Choice set B
|
1 |
14 |
Treatment A: 1 |
Treatment A: 3 |
Treatment A: 3 |
Treatment A: 25 |
Treatment A: 0.5 |
|
9 |
Treatment B: 2 |
Treatment B: 1 |
Treatment B: 1 |
Treatment B: 50 |
Treatment B: 0.5 |
2 |
2 |
Treatment A: 3 |
Treatment A: 2 |
Treatment A: 1 |
Treatment A: 25 |
Treatment A: 2 |
|
1 |
Treatment B: 3 |
Treatment B: 1 |
Treatment B: 2 |
Treatment B: 25 |
Treatment B: 0.2 |
3 |
5 |
Treatment A: 3 |
Treatment A: 1 |
Treatment A: 3 |
Treatment A: 50 |
Treatment A: 2 |
|
15 |
Treatment B: 1 |
Treatment B: 2 |
Treatment B: 2 |
Treatment B: 50 |
Treatment B: 0.2 |
4 |
7 |
Treatment A: 2 |
Treatment A: 2 |
Treatment A: 3 |
Treatment A: 50 |
Treatment A: 0.2 |
|
12 |
Treatment B: 1 |
Treatment B: 1 |
Treatment B: 1 |
Treatment B: 75 |
Treatment B: 0.2 |
5 |
10 |
Treatment A: 2 |
Treatment A: 3 |
Treatment A: 2 |
Treatment A: 75 |
Treatment A: 2 |
|
11 |
Treatment B: 3 |
Treatment B: 1 |
Treatment B: 2 |
Treatment B: 50 |
Treatment B: 0.5 |
6 |
8 |
Treatment A: 2 |
Treatment A: 1 |
Treatment A: 1 |
Treatment A: 25 |
Treatment A: 0.2 |
|
16 |
Treatment B: 1 |
Treatment B: 1 |
Treatment B: 1 |
Treatment B: 50 |
Treatment B: 2 |
7 |
3 |
Treatment A: 3 |
Treatment A: 2 |
Treatment A: 1 |
Treatment A: 75 |
Treatment A: 0.5 |
|
4 |
Treatment B: 3 |
Treatment B: 1 |
Treatment B: 3 |
Treatment B: 75 |
Treatment B: 0.2 |
8 |
6 |
Treatment A: 2 |
Treatment A: 3 |
Treatment A: 1 |
Treatment A: 50 |
Treatment A: 0.2 |
|
13 |
Treatment B: 1 |
Treatment B: 3 |
Treatment B: 1 |
Treatment B: 50 |
Treatment B: 0.2 |
Choice set C
|
1 |
7 |
Treatment A: 2 |
Treatment A: 2 |
Treatment A: 3 |
Treatment A: 50 |
Treatment A: 0.2 |
|
16 |
Treatment B: 1 |
Treatment B: 1 |
Treatment B: 1 |
Treatment B: 50 |
Treatment B: 2 |
2 |
15 |
Treatment A: 1 |
Treatment A: 2 |
Treatment A: 2 |
Treatment A: 50 |
Treatment A: 0.2 |
|
14 |
Treatment B: 1 |
Treatment B: 3 |
Treatment B: 3 |
Treatment B: 25 |
Treatment B: 0.5 |
3 |
5 |
Treatment A: 3 |
Treatment A: 1 |
Treatment A: 3 |
Treatment A: 50 |
Treatment A: 2 |
|
8 |
Treatment B: 2 |
Treatment B: 1 |
Treatment B: 1 |
Treatment B: 25 |
Treatment B: 0.2 |
4 |
11 |
Treatment A: 3 |
Treatment A: 1 |
Treatment A: 2 |
Treatment A: 50 |
Treatment A: 0.5 |
|
10 |
Treatment B: 2 |
Treatment B: 3 |
Treatment B: 2 |
Treatment B: 75 |
Treatment B: 2 |
5 |
9 |
Treatment A: 2 |
Treatment A: 1 |
Treatment A: 1 |
Treatment A: 50 |
Treatment A: 0.5 |
|
1 |
Treatment B: 3 |
Treatment B: 1 |
Treatment B: 2 |
Treatment B: 25 |
Treatment B: 0.2 |
6 |
12 |
Treatment A: 1 |
Treatment A: 1 |
Treatment A: 1 |
Treatment A: 75 |
Treatment A: 0.2 |
|
2 |
Treatment B: 3 |
Treatment B: 2 |
Treatment B: 1 |
Treatment B: 25 |
Treatment B: 2 |
7 |
4 |
Treatment A: 3 |
Treatment A: 1 |
Treatment A: 3 |
Treatment A: 75 |
Treatment A: 0.2 |
|
6 |
Treatment B: 2 |
Treatment B: 3 |
Treatment B: 1 |
Treatment B: 50 |
Treatment B: 0.2 |
8 |
3 |
Treatment A: 3 |
Treatment A: 2 |
Treatment A: 1 |
Treatment A: 75 |
Treatment A: 0.5 |
|
13 |
Treatment B: 1 |
Treatment B: 3 |
Treatment B: 1 |
Treatment B: 50 |
Treatment B: 0.2 |
|
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Appendix 2. Example of discrete choice question included in the questionnaire
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Appendix 3
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View this table:
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TABLE A3.2. Results of data segmentation: model 3 = income; model 4 = experienced gastrointestinal side-effects or not
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Acknowledgments
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This research was undertaken whilst JR was based at the Health Economics Research Group, Brunel University. JC was based at Pharmacia Corporation at the time the research was commissioned. The research was fully funded through a contract with Pharmacia. The focus groups were led and interviewing was conducted by Accent Marketing and Research. We would like to thank Arthritis Care for allowing access to their members to participate in focus group discussions; Dr Simon Allard for his clinical advice, particularly on the text of the recruitment and main questionnaires; colleagues in Accent Marketing and Research for their involvement in the administration of the focus groups and interviews; Louise Longworth (Health Economics Research Group) for her assistance in the analysis; and an anonymous reviewer for useful comments on previous versions of the paper. The authors retained full control of the methods, analysis and reporting, and the views expressed should be ascribed to them alone.
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Submitted 1 May 2003;
Accepted 21 August 2003