The American Journal of Managed Care July 2009
A Multiattribute Decision Model for Bipolar Disorder: Identification of Preferred Mood-Stabilizing Medications
This research develops a multiattribute decision model to aid in the selection of preferred mood-stabilizing agents for the treatment of bipolar disorder.
Objective: To develop a multiattribute decision model (MADM) to aid in the selection of preferred medications for the treatment of bipolar disorder.
Study Design: Data were obtained via a selfadministered online survey among psychiatric pharmacist specialists. These survey data were used to construct a MADM based on multiattribute utility technology.
Methods: Anticonvulsant mood stabilizers, atypical antipsychotics, olanzapine-fluoxetine combination, and lithium carbonate were evaluated using a MADM. Attributes included in the model were effectiveness, safety and tolerability, cost, monitoring burden, and dosing frequency. A survey instrument was developed to score the relative importance of each attribute and the factor scores for each medication. Four iterations of the model were performed to ascertain the medication with the highest total utility score when the effectiveness factors were weighted to consider the following: overall effectiveness, effectiveness in acute mania, effectiveness in acute bipolar depression, and effectiveness in maintenance treatment. Sensitivity analyses were performed to evaluate the stability of the MADM results.
Results: When overall effectiveness, effectiveness in acute mania, or effectiveness in maintenance treatment was considered, lithium carbonate had the highest total utility score. When effectiveness in acute bipolar depression was considered, lamotrigine had the highest total utility score. When considering only atypical antipsychotics, aripiprazole was associated with the highest total utility score for all iterations of the MADM.
Conclusion: The use of a MADM may be a beneficial tool to assist in making formulary or preferred therapeutic agent decisions.
(Am J Manag Care. 2009;15(7):e42-e52)
This article describes the process of developing a multiattribute decision model (MADM) to compare mood stabilizers for the treatment of bipolar disorder.
- A MADM takes multiple attributes (eg, effectiveness, safety and tolerability, and cost) into account when determining the most favorable decision alternative.
- According to this model, lithium carbonate and lamotrigine are the mood-stabilizing medications with the highest utility scores.
- Among the atypical antipsychotics, aripiprazole is associated with the highest utility score.
- MADMs based on multiattribute technology are a versatile method that may have applications in the formulary decision-making process.
Treatment of bipolar disorder is associated with significant costs, a large portion of which is related to the cost of chronic medication administration.4 Medication formularies are increasingly being used to help control the cost of treatment for various medical conditions, as well as for psychiatric disorders such as bipolar disorder. To thoroughly evaluate the utility of a medication, it is important that formulary decisions take into account factors other than just medication cost in the formulary development process. A number of factors such as safety and effectiveness should be considered in addition to acquisition cost. A multiattribute decision model (MADM) attempts to take multiple factors into account when determining what medication alternative has the highest utility.
The MADM presented herein is a form of evaluation using multiattribute utility technology (MAUT). Multiattribute utility technology analysis is a broad technique originally developed to aid in making decisions regarding competing social programs.5 The MAUT framework has been extended to a number of other applications such as differentiating among medication interventions for medical conditions, including migraine,6 angina,7 and schizophrenia.8
The objective of this research was to develop a MADM to differentiate mood-stabilizing medications for the treatment of bipolar disorder. We synthesized data from a variety of sources, including focus group discussions, an online survey of psychiatric pharmacist specialists, and relevant clinical information in construction of our MADM.
Development of a MADM involves determining the perspective of the model, identifying the treatment alternatives, ascertaining the relevant attributes to be considered, quantifying each attribute for each for this MADM was determined to be a hypothetical psychiatric hospital. Lithium, anticonvulsant medications, atypical antipsychotics, and the olanzapine-fluoxetine combination were identified as the possible treatment alternatives. The attributes relevant to a comparison of mood-stabilizing medications were determined by a focus group discussion involving clinical psychiatric pharmacist specialists and academic pharmacy practice researchers. The attributes important to differentiating the various mood-stabilizing medications identified by the focus group were effectiveness, safety and tolerability, cost, monitoring burden, and dosing frequency. The next step in developing our MADM was to determine the relative weight or relative importance of each of these attributes and to assign a score for each medication on each of these attributes. To complete these steps of the analysis, we developed a survey instrument to be administered to a group of psychiatric pharmacist specialists.
The survey instrument was developed as a self-administered online survey. The target respondent population was psychiatric pharmacist specialists. A psychiatric pharmacist specialist is a pharmacist who has received specialized training or practices in the area of clinical psychopharmacology. Survey respondents were recruited from among the membership of the College of Psychiatric and Neurological Pharmacists, an organization representing more than 700 healthcare professionals in the United States and Canada. Subjects were recruited via the organization’s e-mail Listserv. The initial invitation to participate in the survey was followed by a second e-mail invitation 1 week later. Online responses were collected for 14 days. The survey instrument consisted of 3 parts, namely, demographic information, attribute weights, and attribute factor scores. Only completed surveys were included in this analysis.
Survey questions in this section were designed to obtain demographic information about survey participants. Demographic information collected included the following: sex, number of years in practice, academic degrees obtained, pharmacy residency information, board certifications obtained, and current practice setting.
Attribute weights were determined by asking respondents to weight how important each of the attributes (effectiveness, safety and tolerability, cost, monitoring burden, and dosing frequency) is when considering which medication should be used for a patient with bipolar disorder. Responses were scored on a 10-point scale (10 indicates very important, and 1 indicates not important at all) and were averaged for each attribute. The raw attribute weightings were converted to ratio weights and finally to percentage scores for use in the final model.
Attribute Factor Scores
For each attribute, factors were identified that could be used to quantify the attribute. In light of the differential effectiveness of medications in the different phases of bipolar disorder, we asked respondents to score each medication on the following 3 separate factors related to effectiveness: effectiveness in acute mania, effectiveness in acute bipolar depression, and effectiveness in maintenance treatment. For the safety and tolerability attribute, respondents were asked to score each medication separately on the factors of safety and tolerability. Monitoring burden and costs were each evaluated as a single factor. When rating each of the medications on the cost factor, respondents were asked to rate their “perception of the overall cost” associated with each medication. Dosing frequency was also evaluated as a single factor. Dosing frequency was included in the model to incorporate adherence as a component in the model, as the frequency of medication treatment alternative, and calculating the total utility score for each treatment alternative.5 The perspective administration has been found to be closely related to medication adherence.9
Raw factor scores were determined by asking the respondents to score each medication on all of the factors using a 5-point Likert-type scale. The responses were anchored for each factor such that a higher raw factor score indicated a more favorable performance on each attribute. If respondents did not know how to score a particular medication on a given factor, there was a “not sure” response available for them to select. “Not sure” responses were excluded from subsequent calculations on a casewise basis. The mean response scores were then calculated for each of the medications, and this mean response score was used as the raw factor score for each factor. For the dosing attribute, a single factor score based on consensus dosage recommendations and the US Food and Drug Administration–approved dosing frequency was used. The dosing factor score for each medication was assigned a value on a scale from 0 to 100 as given in Table 1. This method for attributing dosage factor scores was based on techniques used in a MADM analysis of medication treatments for schizophrenia.8
Calculation of Total Utility Score
The final steps of our MAUT analysis involved conversion of the raw factor scores to utility scores, calculation of the attribute utility scores, and calculation of the total utility score for each medication. The mean raw factor scores were converted to a common utility scale ranging from 0 (the worst plausible value for a factor) to 100 (the best plausible value for a factor) to obtain the factor utility score. Equation 1 was used to convert the raw factor scores to the factor utility scores. For all of the individual factors, except for the dosing factor, values of Vmax = 5 and Vmin = 1 were used. For the dosing factor, values of Vmax = 100 and Vmin = 30 were used.
Factor utility scores were weighted and summed, when appropriate, to determine the attribute utility score (see equation 2 herein). For example, because the safety and tolerability attribute score was related to 2 factor scores, the safety and tolerability factor scores (usafety and utolerability, respectively) were each weighted equally (ie, wsafety and wtolerability were both set to 0.50) to calculate the unified safety and tolerability attribute score (Ust). A similar procedure was used to calculate the effectiveness attribute utility score (Ue), as discussed in the following “Model Iterations” subsection. For the monitoring burden, cost, and dosing frequency attributes, the attribute utility score (Um, Uc, and Ud , respectively) is equivalent to the factor utility score, because only a single factor contributes to the attribute. Finally, attribute utility scores were scaled by their respective attribute weights and were summed to calculate the total utility score. The total utility score is represented in Equation 2.
Four main iterations of the model were performed to compare the total utility scores when effectiveness in acute mania, effectiveness in acute bipolar depression, effectiveness in maintenance treatment, and overall effectiveness were considered. Each of these iterations was performed by adjusting the effectiveness factor weightings (w in equation 2). For example, for the iteration of the model evaluating effectiveness in acute mania, wmania was set to 1, while wdepression and wmaintenance were set to 0. For the iteration evaluation of overall effectiveness, each of the effectiveness factors was weighted equally.
Sensitivity Analysis We also evaluated the stability of our model by varying the methods of calculating the monitoring and cost based on factor scores. Namely, we calculated the monitoring factor score using the number of monitoring interventions recommended instead of the survey response scores. We also calculated the cost factor score by inputting the average wholesale price for a 30-day supply of a typical dosage of each medication instead of using the perception of cost item from the survey. We then compared the results of these models with those of the original model to evaluate the model stability. To clarify the reporting of results, in cases where a medication is available in both generic and branded forms, we indicate brand in parentheses next to the chemical name for branded versions of the medication. In cases where a medication was only available in a branded formulation at the time of this research, the chemical name is used exclusively. All calculations were performed using SPSS version 14.0 (SPSS Inc, Chicago, IL) and Excel 2007 (Microsoft, Redmond, WA). This research project was approved by the University of Texas at Austin Institutional Review Board.