Patients' preferences for treatment outcomes for advanced non-small cell lung cancer: A conjoint analysis

Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA.
Lung cancer (Amsterdam, Netherlands) (Impact Factor: 3.96). 02/2012; 77(1):224-31. DOI: 10.1016/j.lungcan.2012.01.016
Source: PubMed


Treatment decisions for advanced non-small cell lung cancer (NSCLC) are complex and require trade-offs between the benefits and risks experienced by patients. We evaluated the benefits that patients judged sufficient to compensate for the risks associated with therapy for NSCLC.
Participants with a self-reported diagnosis of NSCLC (n=100) were sampled from an online panel in the United Kingdom. Eligible and consenting participants then completed a self-administered online survey about their disease and their treatment preferences were assessed. This involved respondents choosing among systematically paired profiles that spanned eight attributes: progression-free survival [PFS], symptom severity, rash, diarrhoea, fatigue, nausea and vomiting, fever and infection, and mode of treatment administration (infusion and oral). A choice model was estimated using mixed-logit regression. Estimates of importance for each attribute level and attribute were then calculated and acceptable tradeoffs among attributes were explored.
A total of 89 respondents (73% male) completed all choice tasks appropriately. Increases in PFS together with improvements in symptom severity were judged most important and increased with PFS benefit - 4 months: 5.7; 95% CI: 3.5-7.9; 5 months: 7.1; 95% CI: 4.4-9.9; and 7 months: 10.0; 95% CI: 6.1-13.9. However, improvements in PFS were viewed as most beneficial when disease symptoms were mild and as detrimental when patients had severe symptoms. Fatigue (5.0; 95% CI: 2.7-7.3) was judged to be the most important risk, followed by diarrhoea (2.8; 95% CI: 0.7-4.9), nausea and vomiting (2.1; 95% CI: 0.1-4.1), fever and infection (2.1; 95% CI: 0.2-4.1), and rash (2.0; 95% CI: 0.2-3.9). Oral administration was preferred to infusion (1.8; 95% CI: 0.0-3.6). Patients with mild and moderate symptoms traded PFS for less risks or more convenience if the severe symptoms were not experienced.
This study demonstrates the value of conjoint analysis in the study of patient preferences for cancer treatments. In this small sample of patients with NSCLC from the UK, we demonstrate that the value of improvements in PFS is conditional upon the severity of disease symptoms; and that risks are valued differently.

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Available from: Ateesha Farah Mohamed, Jan 21, 2015
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    • "Healthcare researchers have begun to use conjoint analysis as early as the 1980s [12]. Since then, healthcare researchers have continued to use this technique to elicit patients' preferences, rankings, and ratings in the treatment and delivery of different types of health services [5] [15] [16] [17] [19]. "
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    ABSTRACT: Objective The purpose of this study is to examine Indonesian men and women's satisfaction rating of the different attributes associated with location, convenience, accessibility, and affordability outpatient care. A secondary goal of this study is to assess whether attributes associated with location, convenience, accessibility, and affordability outpatient care differs among different segments of individuals. Methods A conjoint analysis of attributes associated with access to and utilization of outpatient care was conducted using the 2007 Indonesian Family and Life Survey. Results Results from the conjoint analysis revealed that type of facility was the most important determinant of preference, while one-way travel time was the least important determinant. Other attributes of considerable importance include the waiting time, the cost of treatment, and province where the facility is located. Conclusion Indonesians who utilizes the outpatient care are responsive to the type, cost, quality, and location of the outpatient care. The findings of this study also suggest that priority should be given to patients who visited facilities located in Sumatera and other provinces like Sunda, Kalimantan, Sulawesi, Maluku, and Papua.
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    • "For each attribute, an effects coding system (1, 0, −1 for a three-level attribute instead of a dummy coding 1, 0, 0) was used to estimate a parameter for each attribute level [19] [20]. With this system, the omitted categories were estimated as the negative sum of the included categories [16] [21], making the mean effect of the model zero. The standard errors for each omitted category were also estimated using the variance–covariance matrix. "
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    ABSTRACT: The aim of the study was to quantify patient preferences for outcomes associated with oral antidiabetic medications (OAMs) in Sweden and Germany through a discrete-choice experiment. Adults taking OAMs who had a self-reported physician's diagnosis of type 2 diabetes mellitus (T2DM) made a series of nine choices between pairs of hypothetical profiles. Each profile had a predefined range of attributes: blood glucose control, frequency of mild-to-moderate hypoglycaemia, annual severe hypoglycaemic events, annual weight gain, pill burden and frequency of administration, and cost. Choice questions were based on an experimental design with known statistical properties. Bivariate probit analysis estimated the probabilities of choice of medication administration from patient characteristics and, conditional on that choice, preferences for treatment outcomes. The final sample consisted of 188 Swedish and 195 German patients. For both countries, weight gain was the most important attribute, followed by blood glucose control. Avoiding a 5-kg weight gain was 1.5 times more important in Sweden and 2.3 times more important in Germany than achieving moderate blood glucose control, thereby, suggesting that blood glucose control is relatively more important to Swedish than to German patients. Least important outcomes were the number of daily pills (Sweden) and frequency of mild-to-moderate hypoglycaemia (Germany). Patients in both Sweden and Germany preferred OAMs not associated with weight gain.
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    • "A number of researchers have highlighted the benefits of using AHP to explore user needs in healthcare [29,30], and in particular for including patient opinions in health technology assessment [31,32], choosing treatments [33], and improving patient centred healthcare [34,35]. Other methods that have attempted to elicit and quantify user needs in healthcare are conjoint analysis (CA) [36] , discrete choice experiments [37] and best-worst scaling [38]. A growing number of articles have focused on comparing AHP with these methods, and in particular with CA. "
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    ABSTRACT: Background The rigorous elicitation of user needs is a crucial step for both medical device design and purchasing. However, user needs elicitation is often based on qualitative methods whose findings can be difficult to integrate into medical decision-making. This paper describes the application of AHP to elicit user needs for a new CT scanner for use in a public hospital. Methods AHP was used to design a hierarchy of 12 needs for a new CT scanner, grouped into 4 homogenous categories, and to prepare a paper questionnaire to investigate the relative priorities of these. The questionnaire was completed by 5 senior clinicians working in a variety of clinical specialisations and departments in the same Italian public hospital. Results Although safety and performance were considered the most important issues, user needs changed according to clinical scenario. For elective surgery, the five most important needs were: spatial resolution, processing software, radiation dose, patient monitoring, and contrast medium. For emergency, the top five most important needs were: patient monitoring, radiation dose, contrast medium control, speed run, spatial resolution. Conclusions AHP effectively supported user need elicitation, helping to develop an analytic and intelligible framework of decision-making. User needs varied according to working scenario (elective versus emergency medicine) more than clinical specialization. This method should be considered by practitioners involved in decisions about new medical technology, whether that be during device design or before deciding whether to allocate budgets for new medical devices according to clinical functions or according to hospital department.
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