The Journal of Community and Supportive Oncology

Primary Outcome

Our primary outcome was to assess if patients would complete a decision aid with full disclosure. Of 27 patients, only one (4%) chose not to complete the decision aid after starting. She was a 55-year-old African American woman who had recently started first-line treatment for metastatic colorectal cancer. She had been told at another institution that she had lost too much weight and was too ill to benefit from chemotherapy, but with counseling she regained the weight and had a performance status of 2 at VCU. In her pretest, she answered that she thought a woman with metastatic colorectal cancer spread to bones and lymph glands could be cured, with a chance of cure of 50%. Once presented with the information (good treatments that prolong life and control symptoms but no chance of cure and 9% of patients with metastatic colorectal cancer alive at 5 years), she said that she did not want to finish the questions. She did complete her HHI, which did not change, and was not distressed (see Table 2, patient 12).

In the pretest, almost all the patients, including the patient above, reported wanting full disclosure about cancer, prognosis, treatment, and side effects. In response to questions beginning “How much do you want to know about …” 27 of 27 answered “Tell me all” to the questions about “your cancer,” “your prognosis,” “treatment benefits,” and “treatment side effects.” Only one of 27 answered otherwise: “Tell me a little” about cancer, and “Tell me some” about prognosis.

Secondary Outcomes

Participants were overoptimistic about the results of palliative chemotherapy, as shown in Table 3. Most (14/27, 52%) people thought a person with “metastatic cancer (breast, colorectal, lung, prostate—specific to that person's disease) spread to the bones and lymph glands” could be cured. After the decision aid, more people recognized that their cancer could not be cured (17/25, 63%) but eight of 25 (32%, P = 0.15, Fischer's exact test) still thought a person with metastatic disease could be cured. Patients were particularly overoptimistic about the chance of their symptoms being helped by chemotherapy: 87% thought their symptoms would be helped by chemotherapy, and 60% thought a patient would have at least 50% shrinkage of their cancer before the exercise, which declined only slightly after the decision aid. (While the correct answer varies by disease, the number helped by chemotherapy is usually less than 50%, and response rates are always less than 50%.)

There was no change in responses to the HHI after the intervention as we have previously reported.¹⁸ Participants did not appear to be visibly distressed by the intervention. A psychologist and chaplain were made available, but no one requested their services. In our small clinic, the primary nurses and doctors have frequent interactions during visits and chemotherapy. No patient was reported to be distressed in any way, during that visit or subsequent visits.

The comments recorded by the patients or the interviewers at the end of the exercise showed that most patients would share the information, as shown in Table 4.

In some cases, the average prognosis and treatment benefit, although small, was bigger than the person thought before the exercise. Nearly all found it helpful. Some illustrative comments are shown in Table 2.

We did not formally measure the time to complete the screening questions, pre- and posttests, pre- and post-HHI, and decision aid; but in most cases it took less than 20 minutes to complete the whole package including the pre- and post-tests. Review of the decision aid with the patient always took less than 5 minutes, even when we were reading it with the patient and family. This is consistent with work showing that oncologists state that completing an advance directive will take too much time but, in fact, it takes less than 10 minutes.^{[19] and [20]}

Discussion

Historical data show that patients know little about their prognosis and the effect that treatment will have on their cancer. Yet, this knowledge is essential to making informed choices about treatment benefits, risks, and even costs. When tested in randomized controlled trials, decision aids led to more involvement in decision making.^{[21] and [22]} However, there were no decision aids available about metastatic incurable disease, despite some promising early starts^{[23], [24], [25], [26], [27] and [28]} and only one about first-line treatment,²⁹ so we made a simple one. A successful decision aid may allow patients to discuss their situations with their physicians and develop management strategies that best concur with personal goals and preferences and help patients make plans in other areas of life.

Our findings suggest that most people do want honest information, even if the news is bad. We found that 27 of 27 enrolled patients initially reported wanting to know all the available information about their cancer, prognosis, treatment benefits, and treatment side effects. Also, 26 of 27 patients were able to complete the decision aid fully, our main outcome measure. While approximately 10% of available patients were excluded from accrual by their oncologists or oncology nurses due to preexisting distress, fear of distress in the patient or family member, uncontrolled symptoms, or psychiatric illness, in general there was excellent acceptance of the study by patients and oncologists. In this pilot study we did not investigate the attitudes of nonparticipants nor were we able to collect sociodemographic data to determine nonresponse bias, that is, whether certain types of patients are more likely to decline participation in the study.

Participants in the study were overoptimistic about their chances of cure, potential treatment response, symptom relief, and survival. None of these patients had curable disease, but 63% thought that a person with metastatic cancer of their type could be cured and gave the average chance of cure as 52%. Inaccurate assessment of cure rates decreased postintervention. At the pretest 14/27 (52%) believed a person with cancer similar to theirs could be cured, which changed to 8/26 (31%) at the posttest. This agrees with other studies that showed that patients mistook palliative radiation for curative radiation about one-third of the time, even when provided with accurate information.^{[1], [30] and [31]}

Knowledge of prognosis and planning for the future is important as there is evidence of benefit to having the discussion about treatment outcomes. Recent data show improved quality of care, improved quality of life, and improved caregiver quality of life if the physician discusses death with the patient and family.⁵ Transplant patients with advanced directives had more than a twofold survival advantage over those without them.²⁷ Conversely, over- or underestimating survival or treatment benefit can lead to bad health outcomes. Stem-cell transplant patients who were overoptimistic lived no longer than those with realistic views.^{[32], [33] and [34]} Cancer patients who overestimated their survival were more likely to die a “bad” death (defined as death in an intensive care unit, on a ventilator, or with multiple hospitalizations and emergency room visits) without achieving life extension.³⁵ It may be that the 16%–20% of patients with incurable solid tumors who start a new chemotherapy regimen within 2 weeks of death,³⁶ when they are unlikely to benefit, simply do not know the prognosis or treatment effect or have different perspectives.³⁷ Alternatively, we do not know how many patients decline second- and nth-line chemotherapy without knowing the full benefits and risks and who might choose chemotherapy if they knew second- or nth-line chemotherapy improved survival, pain scores, or quality of life. For instance, 40% of breast cancer patients will have some disease control from fourth-line chemotherapy for up to 4 months even if there is no evidence of improved survival.³⁸

Patients consistently tell us to be truthful, compassionate, and clear and to stay the course with them.^{[39] and [40]} Despite nearly all American patients stating that they want full disclosure about their prognosis, treatment options, and expected outcomes, most patients do not receive such information⁴¹ or receive such information far too late in their course.⁴² Even if terminally ill patients with cancer requested survival estimates, doctors would provide such estimates only 37% of the time, often an overestimate;⁷ and a recent meta-analysis showed that cancer physicians consistently overestimated prognosis by at least 30%.⁴³ Honest information respects the autonomy of a patient to make decisions based on what is known about the outcomes of such decisions.⁴⁴ Such information should not be forced on a patient, but the patient should be told that the information is available and that he or she has the right to accept or decline the information.⁴⁵

When we started this project, colleagues were concerned about whether patients would want such information, that patients would be distressed by poor prognosis, that patients would give up hope, and that the procedures would take too much time. We also were concerned about the effect of giving such bad news on the provider, when prior research showed negative effects on the information-giver's mood and affect from such encounters⁴⁶ and that doctors in general protect themselves by not giving bad news.⁴⁷ Completion of the decision aid was difficult for the interviewers, too. Some commented on how hard it was to give “bad” information about chance of cure and expected survival, even for patients they did not know. While patients may be more comfortable having advance directive discussions with a doctor they do not know rather than their oncologist,⁴⁸ it can still be hard for the provider. Surprisingly, it rarely took more than 20 minutes to discuss the information including the tests since the information was preprinted.

Patients vary in their approach to decision making, but the decisions should at least start with good information. Based on these preliminary findings, the piloted intervention is significant because it can lead to measurable impacts on knowledge about prognosis and appears to be judged helpful. We do not know the impact of full and truthful information on patient knowledge, decision making, hope, attendant choices about advanced medical directives, chemotherapy use, or hospice use. The next steps are to make the information available directly to patients on the Internet, which is in progress. The purpose is not to increase or decrease the use of palliative chemotherapy or hospice care; the lack of research into the decisions fully informed patients make precludes any such prediction. Since the intervention appears to be successful in this pilot trial, it will be tested in conjunction with standard care in a randomized clinical trial with measurement of quality of care, quality of life, and health-care cost outcomes.

Acknowledgments

This research was supported by VCU School of Medicine Research Year Out, GO8 LM0095259 from the National Library of Medicine (T. J. S., L. L., J. K.), and R01CA116227-01 (T. J. S.) from the National Cancer Institute.

Primary Outcome

Our primary outcome was to assess if patients would complete a decision aid with full disclosure. Of 27 patients, only one (4%) chose not to complete the decision aid after starting. She was a 55-year-old African American woman who had recently started first-line treatment for metastatic colorectal cancer. She had been told at another institution that she had lost too much weight and was too ill to benefit from chemotherapy, but with counseling she regained the weight and had a performance status of 2 at VCU. In her pretest, she answered that she thought a woman with metastatic colorectal cancer spread to bones and lymph glands could be cured, with a chance of cure of 50%. Once presented with the information (good treatments that prolong life and control symptoms but no chance of cure and 9% of patients with metastatic colorectal cancer alive at 5 years), she said that she did not want to finish the questions. She did complete her HHI, which did not change, and was not distressed (see Table 2, patient 12).

In the pretest, almost all the patients, including the patient above, reported wanting full disclosure about cancer, prognosis, treatment, and side effects. In response to questions beginning “How much do you want to know about …” 27 of 27 answered “Tell me all” to the questions about “your cancer,” “your prognosis,” “treatment benefits,” and “treatment side effects.” Only one of 27 answered otherwise: “Tell me a little” about cancer, and “Tell me some” about prognosis.

Secondary Outcomes

Participants were overoptimistic about the results of palliative chemotherapy, as shown in Table 3. Most (14/27, 52%) people thought a person with “metastatic cancer (breast, colorectal, lung, prostate—specific to that person's disease) spread to the bones and lymph glands” could be cured. After the decision aid, more people recognized that their cancer could not be cured (17/25, 63%) but eight of 25 (32%, P = 0.15, Fischer's exact test) still thought a person with metastatic disease could be cured. Patients were particularly overoptimistic about the chance of their symptoms being helped by chemotherapy: 87% thought their symptoms would be helped by chemotherapy, and 60% thought a patient would have at least 50% shrinkage of their cancer before the exercise, which declined only slightly after the decision aid. (While the correct answer varies by disease, the number helped by chemotherapy is usually less than 50%, and response rates are always less than 50%.)

There was no change in responses to the HHI after the intervention as we have previously reported.¹⁸ Participants did not appear to be visibly distressed by the intervention. A psychologist and chaplain were made available, but no one requested their services. In our small clinic, the primary nurses and doctors have frequent interactions during visits and chemotherapy. No patient was reported to be distressed in any way, during that visit or subsequent visits.

The comments recorded by the patients or the interviewers at the end of the exercise showed that most patients would share the information, as shown in Table 4.

In some cases, the average prognosis and treatment benefit, although small, was bigger than the person thought before the exercise. Nearly all found it helpful. Some illustrative comments are shown in Table 2.

We did not formally measure the time to complete the screening questions, pre- and posttests, pre- and post-HHI, and decision aid; but in most cases it took less than 20 minutes to complete the whole package including the pre- and post-tests. Review of the decision aid with the patient always took less than 5 minutes, even when we were reading it with the patient and family. This is consistent with work showing that oncologists state that completing an advance directive will take too much time but, in fact, it takes less than 10 minutes.^{[19] and [20]}

Discussion

Historical data show that patients know little about their prognosis and the effect that treatment will have on their cancer. Yet, this knowledge is essential to making informed choices about treatment benefits, risks, and even costs. When tested in randomized controlled trials, decision aids led to more involvement in decision making.^{[21] and [22]} However, there were no decision aids available about metastatic incurable disease, despite some promising early starts^{[23], [24], [25], [26], [27] and [28]} and only one about first-line treatment,²⁹ so we made a simple one. A successful decision aid may allow patients to discuss their situations with their physicians and develop management strategies that best concur with personal goals and preferences and help patients make plans in other areas of life.

Our findings suggest that most people do want honest information, even if the news is bad. We found that 27 of 27 enrolled patients initially reported wanting to know all the available information about their cancer, prognosis, treatment benefits, and treatment side effects. Also, 26 of 27 patients were able to complete the decision aid fully, our main outcome measure. While approximately 10% of available patients were excluded from accrual by their oncologists or oncology nurses due to preexisting distress, fear of distress in the patient or family member, uncontrolled symptoms, or psychiatric illness, in general there was excellent acceptance of the study by patients and oncologists. In this pilot study we did not investigate the attitudes of nonparticipants nor were we able to collect sociodemographic data to determine nonresponse bias, that is, whether certain types of patients are more likely to decline participation in the study.

Participants in the study were overoptimistic about their chances of cure, potential treatment response, symptom relief, and survival. None of these patients had curable disease, but 63% thought that a person with metastatic cancer of their type could be cured and gave the average chance of cure as 52%. Inaccurate assessment of cure rates decreased postintervention. At the pretest 14/27 (52%) believed a person with cancer similar to theirs could be cured, which changed to 8/26 (31%) at the posttest. This agrees with other studies that showed that patients mistook palliative radiation for curative radiation about one-third of the time, even when provided with accurate information.^{[1], [30] and [31]}

Knowledge of prognosis and planning for the future is important as there is evidence of benefit to having the discussion about treatment outcomes. Recent data show improved quality of care, improved quality of life, and improved caregiver quality of life if the physician discusses death with the patient and family.⁵ Transplant patients with advanced directives had more than a twofold survival advantage over those without them.²⁷ Conversely, over- or underestimating survival or treatment benefit can lead to bad health outcomes. Stem-cell transplant patients who were overoptimistic lived no longer than those with realistic views.^{[32], [33] and [34]} Cancer patients who overestimated their survival were more likely to die a “bad” death (defined as death in an intensive care unit, on a ventilator, or with multiple hospitalizations and emergency room visits) without achieving life extension.³⁵ It may be that the 16%–20% of patients with incurable solid tumors who start a new chemotherapy regimen within 2 weeks of death,³⁶ when they are unlikely to benefit, simply do not know the prognosis or treatment effect or have different perspectives.³⁷ Alternatively, we do not know how many patients decline second- and nth-line chemotherapy without knowing the full benefits and risks and who might choose chemotherapy if they knew second- or nth-line chemotherapy improved survival, pain scores, or quality of life. For instance, 40% of breast cancer patients will have some disease control from fourth-line chemotherapy for up to 4 months even if there is no evidence of improved survival.³⁸

Patients consistently tell us to be truthful, compassionate, and clear and to stay the course with them.^{[39] and [40]} Despite nearly all American patients stating that they want full disclosure about their prognosis, treatment options, and expected outcomes, most patients do not receive such information⁴¹ or receive such information far too late in their course.⁴² Even if terminally ill patients with cancer requested survival estimates, doctors would provide such estimates only 37% of the time, often an overestimate;⁷ and a recent meta-analysis showed that cancer physicians consistently overestimated prognosis by at least 30%.⁴³ Honest information respects the autonomy of a patient to make decisions based on what is known about the outcomes of such decisions.⁴⁴ Such information should not be forced on a patient, but the patient should be told that the information is available and that he or she has the right to accept or decline the information.⁴⁵

When we started this project, colleagues were concerned about whether patients would want such information, that patients would be distressed by poor prognosis, that patients would give up hope, and that the procedures would take too much time. We also were concerned about the effect of giving such bad news on the provider, when prior research showed negative effects on the information-giver's mood and affect from such encounters⁴⁶ and that doctors in general protect themselves by not giving bad news.⁴⁷ Completion of the decision aid was difficult for the interviewers, too. Some commented on how hard it was to give “bad” information about chance of cure and expected survival, even for patients they did not know. While patients may be more comfortable having advance directive discussions with a doctor they do not know rather than their oncologist,⁴⁸ it can still be hard for the provider. Surprisingly, it rarely took more than 20 minutes to discuss the information including the tests since the information was preprinted.

Patients vary in their approach to decision making, but the decisions should at least start with good information. Based on these preliminary findings, the piloted intervention is significant because it can lead to measurable impacts on knowledge about prognosis and appears to be judged helpful. We do not know the impact of full and truthful information on patient knowledge, decision making, hope, attendant choices about advanced medical directives, chemotherapy use, or hospice use. The next steps are to make the information available directly to patients on the Internet, which is in progress. The purpose is not to increase or decrease the use of palliative chemotherapy or hospice care; the lack of research into the decisions fully informed patients make precludes any such prediction. Since the intervention appears to be successful in this pilot trial, it will be tested in conjunction with standard care in a randomized clinical trial with measurement of quality of care, quality of life, and health-care cost outcomes.

Acknowledgments

This research was supported by VCU School of Medicine Research Year Out, GO8 LM0095259 from the National Library of Medicine (T. J. S., L. L., J. K.), and R01CA116227-01 (T. J. S.) from the National Cancer Institute.

Primary Outcome

Our primary outcome was to assess if patients would complete a decision aid with full disclosure. Of 27 patients, only one (4%) chose not to complete the decision aid after starting. She was a 55-year-old African American woman who had recently started first-line treatment for metastatic colorectal cancer. She had been told at another institution that she had lost too much weight and was too ill to benefit from chemotherapy, but with counseling she regained the weight and had a performance status of 2 at VCU. In her pretest, she answered that she thought a woman with metastatic colorectal cancer spread to bones and lymph glands could be cured, with a chance of cure of 50%. Once presented with the information (good treatments that prolong life and control symptoms but no chance of cure and 9% of patients with metastatic colorectal cancer alive at 5 years), she said that she did not want to finish the questions. She did complete her HHI, which did not change, and was not distressed (see Table 2, patient 12).

In the pretest, almost all the patients, including the patient above, reported wanting full disclosure about cancer, prognosis, treatment, and side effects. In response to questions beginning “How much do you want to know about …” 27 of 27 answered “Tell me all” to the questions about “your cancer,” “your prognosis,” “treatment benefits,” and “treatment side effects.” Only one of 27 answered otherwise: “Tell me a little” about cancer, and “Tell me some” about prognosis.

Secondary Outcomes

Participants were overoptimistic about the results of palliative chemotherapy, as shown in Table 3. Most (14/27, 52%) people thought a person with “metastatic cancer (breast, colorectal, lung, prostate—specific to that person's disease) spread to the bones and lymph glands” could be cured. After the decision aid, more people recognized that their cancer could not be cured (17/25, 63%) but eight of 25 (32%, P = 0.15, Fischer's exact test) still thought a person with metastatic disease could be cured. Patients were particularly overoptimistic about the chance of their symptoms being helped by chemotherapy: 87% thought their symptoms would be helped by chemotherapy, and 60% thought a patient would have at least 50% shrinkage of their cancer before the exercise, which declined only slightly after the decision aid. (While the correct answer varies by disease, the number helped by chemotherapy is usually less than 50%, and response rates are always less than 50%.)

There was no change in responses to the HHI after the intervention as we have previously reported.¹⁸ Participants did not appear to be visibly distressed by the intervention. A psychologist and chaplain were made available, but no one requested their services. In our small clinic, the primary nurses and doctors have frequent interactions during visits and chemotherapy. No patient was reported to be distressed in any way, during that visit or subsequent visits.

The comments recorded by the patients or the interviewers at the end of the exercise showed that most patients would share the information, as shown in Table 4.

In some cases, the average prognosis and treatment benefit, although small, was bigger than the person thought before the exercise. Nearly all found it helpful. Some illustrative comments are shown in Table 2.

We did not formally measure the time to complete the screening questions, pre- and posttests, pre- and post-HHI, and decision aid; but in most cases it took less than 20 minutes to complete the whole package including the pre- and post-tests. Review of the decision aid with the patient always took less than 5 minutes, even when we were reading it with the patient and family. This is consistent with work showing that oncologists state that completing an advance directive will take too much time but, in fact, it takes less than 10 minutes.^{[19] and [20]}

Discussion

Historical data show that patients know little about their prognosis and the effect that treatment will have on their cancer. Yet, this knowledge is essential to making informed choices about treatment benefits, risks, and even costs. When tested in randomized controlled trials, decision aids led to more involvement in decision making.^{[21] and [22]} However, there were no decision aids available about metastatic incurable disease, despite some promising early starts^{[23], [24], [25], [26], [27] and [28]} and only one about first-line treatment,²⁹ so we made a simple one. A successful decision aid may allow patients to discuss their situations with their physicians and develop management strategies that best concur with personal goals and preferences and help patients make plans in other areas of life.

Our findings suggest that most people do want honest information, even if the news is bad. We found that 27 of 27 enrolled patients initially reported wanting to know all the available information about their cancer, prognosis, treatment benefits, and treatment side effects. Also, 26 of 27 patients were able to complete the decision aid fully, our main outcome measure. While approximately 10% of available patients were excluded from accrual by their oncologists or oncology nurses due to preexisting distress, fear of distress in the patient or family member, uncontrolled symptoms, or psychiatric illness, in general there was excellent acceptance of the study by patients and oncologists. In this pilot study we did not investigate the attitudes of nonparticipants nor were we able to collect sociodemographic data to determine nonresponse bias, that is, whether certain types of patients are more likely to decline participation in the study.

Participants in the study were overoptimistic about their chances of cure, potential treatment response, symptom relief, and survival. None of these patients had curable disease, but 63% thought that a person with metastatic cancer of their type could be cured and gave the average chance of cure as 52%. Inaccurate assessment of cure rates decreased postintervention. At the pretest 14/27 (52%) believed a person with cancer similar to theirs could be cured, which changed to 8/26 (31%) at the posttest. This agrees with other studies that showed that patients mistook palliative radiation for curative radiation about one-third of the time, even when provided with accurate information.^{[1], [30] and [31]}

Knowledge of prognosis and planning for the future is important as there is evidence of benefit to having the discussion about treatment outcomes. Recent data show improved quality of care, improved quality of life, and improved caregiver quality of life if the physician discusses death with the patient and family.⁵ Transplant patients with advanced directives had more than a twofold survival advantage over those without them.²⁷ Conversely, over- or underestimating survival or treatment benefit can lead to bad health outcomes. Stem-cell transplant patients who were overoptimistic lived no longer than those with realistic views.^{[32], [33] and [34]} Cancer patients who overestimated their survival were more likely to die a “bad” death (defined as death in an intensive care unit, on a ventilator, or with multiple hospitalizations and emergency room visits) without achieving life extension.³⁵ It may be that the 16%–20% of patients with incurable solid tumors who start a new chemotherapy regimen within 2 weeks of death,³⁶ when they are unlikely to benefit, simply do not know the prognosis or treatment effect or have different perspectives.³⁷ Alternatively, we do not know how many patients decline second- and nth-line chemotherapy without knowing the full benefits and risks and who might choose chemotherapy if they knew second- or nth-line chemotherapy improved survival, pain scores, or quality of life. For instance, 40% of breast cancer patients will have some disease control from fourth-line chemotherapy for up to 4 months even if there is no evidence of improved survival.³⁸

Patients consistently tell us to be truthful, compassionate, and clear and to stay the course with them.^{[39] and [40]} Despite nearly all American patients stating that they want full disclosure about their prognosis, treatment options, and expected outcomes, most patients do not receive such information⁴¹ or receive such information far too late in their course.⁴² Even if terminally ill patients with cancer requested survival estimates, doctors would provide such estimates only 37% of the time, often an overestimate;⁷ and a recent meta-analysis showed that cancer physicians consistently overestimated prognosis by at least 30%.⁴³ Honest information respects the autonomy of a patient to make decisions based on what is known about the outcomes of such decisions.⁴⁴ Such information should not be forced on a patient, but the patient should be told that the information is available and that he or she has the right to accept or decline the information.⁴⁵

When we started this project, colleagues were concerned about whether patients would want such information, that patients would be distressed by poor prognosis, that patients would give up hope, and that the procedures would take too much time. We also were concerned about the effect of giving such bad news on the provider, when prior research showed negative effects on the information-giver's mood and affect from such encounters⁴⁶ and that doctors in general protect themselves by not giving bad news.⁴⁷ Completion of the decision aid was difficult for the interviewers, too. Some commented on how hard it was to give “bad” information about chance of cure and expected survival, even for patients they did not know. While patients may be more comfortable having advance directive discussions with a doctor they do not know rather than their oncologist,⁴⁸ it can still be hard for the provider. Surprisingly, it rarely took more than 20 minutes to discuss the information including the tests since the information was preprinted.

Patients vary in their approach to decision making, but the decisions should at least start with good information. Based on these preliminary findings, the piloted intervention is significant because it can lead to measurable impacts on knowledge about prognosis and appears to be judged helpful. We do not know the impact of full and truthful information on patient knowledge, decision making, hope, attendant choices about advanced medical directives, chemotherapy use, or hospice use. The next steps are to make the information available directly to patients on the Internet, which is in progress. The purpose is not to increase or decrease the use of palliative chemotherapy or hospice care; the lack of research into the decisions fully informed patients make precludes any such prediction. Since the intervention appears to be successful in this pilot trial, it will be tested in conjunction with standard care in a randomized clinical trial with measurement of quality of care, quality of life, and health-care cost outcomes.

Acknowledgments

This research was supported by VCU School of Medicine Research Year Out, GO8 LM0095259 from the National Library of Medicine (T. J. S., L. L., J. K.), and R01CA116227-01 (T. J. S.) from the National Cancer Institute.

Anchor-Based Analysis

Spearman correlations between changes in the BPI-SF worst pain item and changes in potential anchors are presented in Table 2. For all potential anchors, the highest correlations with the BPI-SF worst pain rating were obtained at the baseline to week 25 interval. All potential anchors correlated significantly (P < 0.001) with the BPI-SF worst pain rating with the exception of the FACT-G Social/Family Well-Being scale. However, correlations were low (<0.30) for several potential anchors: ECOG Performance Status, FACT-B Overall QOL item, FACT-G Emotional Well-Being, and FACT-G Functional Well-Being. Therefore, the week 25 interval and the following anchors were selected for the MID analysis: BPI-SF current pain rating, EQ-5D Index score, EQ-5D Pain item, FACT-B TOI, FACT-G Physical Well-Being, and FACT-G total score. Correlation coefficients between the changes in the selected anchors and changes in the BPI-SF worst pain ratings range from 0.329–0.647.

Mean changes in the BPI-SF worst pain rating that correspond to a one-category change in anchors from baseline to week 25 are presented in Table 3. BPI-SF current pain ratings >5 and EQ-5D Index scores <0.40 were excluded from their respective analysis due to small sample sizes. A one-category increase in the anchor scores was associated with an absolute value of change in the BPI-SF worst pain item ranging from 0.26–2.42. A one-category decrease in the anchor score was associated with an absolute value of change in the BPI-SF worst pain item ranging from 0.56–3.16. Changes associated with improvement and worsening in anchors were not symmetrical, nor was there a consistent trend across anchors. For example, for the EQ-5D pain item, the magnitude of change in BPI-SF worst pain was greater for a one-category increase in the anchor than for a one-category decrease in the anchor. In contrast, for the EQ-5D Index score, the magnitude of change in BPI-SF worst pain was greater for a one-category decrease in the anchor than for a one-category increase in the anchor.

The regression of changes in anchors on changes in the BPI-SF worst pain item is shown in Table 4. Changes in each anchor are significantly (P < 0.05) associated with changes in BPI-SF worst pain rating. A one-point increase in BPI-SF current pain rating and EQ-5D Pain item is associated with a 0.817 and 1.805 increase in BPI-SF worst pain, respectively, while a one-point increase in EQ-5D Index score, FACT-B TOI, FACT-G Physical Well-Being, and FACT-G total is associated with a 3.548, 0.098, 0.163, and 0.048 decrease in BPI-SF worst pain rating, respectively. Likewise, a two-point increase in BPI-SF current pain rating and EQ-5D Pain item is associated with a 1.634 and 3.610 increase in BPI-SF worst pain, respectively, while a two-point increase in EQ-5D Index score, FACT-B TOI, FACT-G Physical Well-Being, and FACT-G total is associated with a 7.096, 0.196, 0.326, and 0.096 decrease in BPI-SF worst pain rating, respectively. The change in anchor-by-baseline anchor interaction was statistically significant only for BPI current pain and FACT-G Physical Well-Being. The interaction tests whether the anchor–BPI-SF slope differs as a function of baseline anchor score; therefore, a lack of significance suggests that the association between BPI-SF worst pain and other anchors does not differ by baseline anchor rating.

A post hoc confirmatory analysis was done replicating these analyses using data from the baseline to week 49 interval (n = 1,250). Results indicate a slightly stronger correlation between the anchors and the change scores. (Spearman's correlations range from 0.372 for FACT-TOI to 0.644 for BPI-SF current pain rating.) Mean change scores of BPI-SF worst pain ratings by each of the six anchors and regression coefficients were similar to those for the baseline to week 25 interval. For instance, mean change scores for the EQ-5D Pain item for stable patients ranged from 0.25–0.56, 1.58–295 for an improvement of one category, and 1.75–2.80 for a worsening of one category compared with 0.50–0.51, 1.71–1.98, and 2.56–3.16, respectively, for the baseline to week 25 interval.

Distribution-Based Analysis

The distribution-based estimates for the BPI-SF worst pain rating are presented in Table 5. There appears to be consistency with the 1 SEM estimates, the 0.50 effect size, and the 0.50 Guyatt's statistic.

Integrating Anchor-Based and Distribution-Based Mid Estimates

The distribution-based analyses suggest that the MDC for the worst pain rating, defined as the smallest change that can be reliably differentiated from random fluctuation, is between 1.3 and 1.6 points (see Table 5). This represents the lower bound for establishing the MID.

The results from regression analyses can be used to translate changes between anchors and corresponding changes in BPI-SF worst pain. This strategy can be particularly informative when the MID for an anchor is known. This is the case for the EQ-5D Health State Index, where the MID has been estimated at 0.06 for U.S. Index scores and 0.07 for U.K. Index scores.²³ A one-point change in EQ-5D Index translates to a change of −3.548 in BPI-SF worst pain, so a 0.07-point change in EQ-5D Index (the MID for the measure) corresponds to a change of −0.248 in BPI-SF worst pain. In contrast, a one-point change in BPI-SF worst pain (which is smaller than the MID based upon the distribution-based analyses) translates to a change of 0.036 for the EQ-5D Index score (considerably smaller than the MID of 0.07). However, a two-point change in BPI-SF worst pain rating corresponds to a 0.072 change in EQ-5D Index score, which is almost identical to the MID for that measure. This suggests that a two-point change may be a reasonable estimate for the MID of the BPI-SF worst pain rating.

Discussion

Data from both distribution-based and anchor-based approaches were used to develop estimates of the MID for the BPI-SF worst pain rating. Results from these approaches are similar, providing reasonably strong support for establishing a two-point MID for the BPI-SF worst pain rating. Further, the results suggest that this estimate of MID is, for the most part, independent of baseline BPI-SF worst pain ratings. However, there is some evidence to suggest that the direction of change (improvement or worsening) may be important to consider. A number of reports have suggested that a smaller change may be required to be considered clinically important when a patient is improving compared with worsening.¹³ Also, when considered as a percentage, a one-point change in any scale has a different value for an increase versus a decrease; eg, a change from 2 to 3 is an increase of 50%, while a change from 3 to 2 is a decrease of 33%. Nonetheless, these findings provide important information to researchers for interpreting changes in the BPI-SF worst pain ratings.

In addition, although not specific to the BPI worst pain rating, the findings of this study are consistent with other published MID analyses for a similar item. A recent review of three studies concluded that, for a numerical rating scale of pain intensity ranging 0–10 similar in content to the BPI-SF worst pain rating, changes of around two points represent “meaningful,” “much better,” or “much improved” reductions in chronic pain.²⁴

Several factors contribute to the overall strength of the current results. First, as frequently recommended in the literature,¹¹ both anchor-based and distribution-based methods were used to estimate the MID for the worst pain rating. Second, analyses were based on a large sample, totaling over 1,500 patients for the baseline to week 25 assessment interval. A larger sample size will generally provide a broader distribution of responses, which will likely increase the generalizability of the results. Third, multiple anchors were used to evaluate changes in BPI-SF worst pain ratings. Fourth, analyses were performed across several assessment intervals to determine the strongest relationship between BPI-SF ratings and other anchors. Finally, the regression analyses provide important information about whether baseline differences influence the relationship between BPI-SF and other PRO measures.

Nevertheless, these analyses are not without certain limitations. The sample for the current analyses consisted entirely of breast cancer patients. It is unclear to what extent these results will be relevant for other patient populations. Further research is needed to determine whether the MID for the BPI-SF worst pain rating established in this sample has broader applicability. Also, it must be noted that the recall period varied across assessments. The BPI-SF focuses on the past 24 hours, the FACT uses the past week, and the EQ-5D uses the present moment. It is unclear to what extent these differences in recall periods may have influenced the current results. Finally, the baseline to week 25 interval was used to determine the MID for the BPI-SF worst pain rating based on the higher correlations for this interval. Data from baseline to week 49 are consistent with these results, providing some confirmatory evidence to suggest that these MID estimates are stable.

In conclusion, the findings of the present analyses suggest that the MID estimate for the BPI-SF worst pain rating is two points. This value provides guidance to researchers using the BPI-SF worst pain rating on how to interpret baseline differences as well as change scores in the BPI-SF worst pain rating. Additional analyses could be done in other populations to confirm these findings.

Acknowledgments

The authors acknowledge the contributions of Betsy Tschosik, Health Outcomes Solutions, for assistance in the preparation of this manuscript and Vidya Setty of Amgen, Inc., for editing assistance.

Anchor-Based Analysis

Spearman correlations between changes in the BPI-SF worst pain item and changes in potential anchors are presented in Table 2. For all potential anchors, the highest correlations with the BPI-SF worst pain rating were obtained at the baseline to week 25 interval. All potential anchors correlated significantly (P < 0.001) with the BPI-SF worst pain rating with the exception of the FACT-G Social/Family Well-Being scale. However, correlations were low (<0.30) for several potential anchors: ECOG Performance Status, FACT-B Overall QOL item, FACT-G Emotional Well-Being, and FACT-G Functional Well-Being. Therefore, the week 25 interval and the following anchors were selected for the MID analysis: BPI-SF current pain rating, EQ-5D Index score, EQ-5D Pain item, FACT-B TOI, FACT-G Physical Well-Being, and FACT-G total score. Correlation coefficients between the changes in the selected anchors and changes in the BPI-SF worst pain ratings range from 0.329–0.647.

Mean changes in the BPI-SF worst pain rating that correspond to a one-category change in anchors from baseline to week 25 are presented in Table 3. BPI-SF current pain ratings >5 and EQ-5D Index scores <0.40 were excluded from their respective analysis due to small sample sizes. A one-category increase in the anchor scores was associated with an absolute value of change in the BPI-SF worst pain item ranging from 0.26–2.42. A one-category decrease in the anchor score was associated with an absolute value of change in the BPI-SF worst pain item ranging from 0.56–3.16. Changes associated with improvement and worsening in anchors were not symmetrical, nor was there a consistent trend across anchors. For example, for the EQ-5D pain item, the magnitude of change in BPI-SF worst pain was greater for a one-category increase in the anchor than for a one-category decrease in the anchor. In contrast, for the EQ-5D Index score, the magnitude of change in BPI-SF worst pain was greater for a one-category decrease in the anchor than for a one-category increase in the anchor.

The regression of changes in anchors on changes in the BPI-SF worst pain item is shown in Table 4. Changes in each anchor are significantly (P < 0.05) associated with changes in BPI-SF worst pain rating. A one-point increase in BPI-SF current pain rating and EQ-5D Pain item is associated with a 0.817 and 1.805 increase in BPI-SF worst pain, respectively, while a one-point increase in EQ-5D Index score, FACT-B TOI, FACT-G Physical Well-Being, and FACT-G total is associated with a 3.548, 0.098, 0.163, and 0.048 decrease in BPI-SF worst pain rating, respectively. Likewise, a two-point increase in BPI-SF current pain rating and EQ-5D Pain item is associated with a 1.634 and 3.610 increase in BPI-SF worst pain, respectively, while a two-point increase in EQ-5D Index score, FACT-B TOI, FACT-G Physical Well-Being, and FACT-G total is associated with a 7.096, 0.196, 0.326, and 0.096 decrease in BPI-SF worst pain rating, respectively. The change in anchor-by-baseline anchor interaction was statistically significant only for BPI current pain and FACT-G Physical Well-Being. The interaction tests whether the anchor–BPI-SF slope differs as a function of baseline anchor score; therefore, a lack of significance suggests that the association between BPI-SF worst pain and other anchors does not differ by baseline anchor rating.

A post hoc confirmatory analysis was done replicating these analyses using data from the baseline to week 49 interval (n = 1,250). Results indicate a slightly stronger correlation between the anchors and the change scores. (Spearman's correlations range from 0.372 for FACT-TOI to 0.644 for BPI-SF current pain rating.) Mean change scores of BPI-SF worst pain ratings by each of the six anchors and regression coefficients were similar to those for the baseline to week 25 interval. For instance, mean change scores for the EQ-5D Pain item for stable patients ranged from 0.25–0.56, 1.58–295 for an improvement of one category, and 1.75–2.80 for a worsening of one category compared with 0.50–0.51, 1.71–1.98, and 2.56–3.16, respectively, for the baseline to week 25 interval.

Distribution-Based Analysis

The distribution-based estimates for the BPI-SF worst pain rating are presented in Table 5. There appears to be consistency with the 1 SEM estimates, the 0.50 effect size, and the 0.50 Guyatt's statistic.

Integrating Anchor-Based and Distribution-Based Mid Estimates

The distribution-based analyses suggest that the MDC for the worst pain rating, defined as the smallest change that can be reliably differentiated from random fluctuation, is between 1.3 and 1.6 points (see Table 5). This represents the lower bound for establishing the MID.

The results from regression analyses can be used to translate changes between anchors and corresponding changes in BPI-SF worst pain. This strategy can be particularly informative when the MID for an anchor is known. This is the case for the EQ-5D Health State Index, where the MID has been estimated at 0.06 for U.S. Index scores and 0.07 for U.K. Index scores.²³ A one-point change in EQ-5D Index translates to a change of −3.548 in BPI-SF worst pain, so a 0.07-point change in EQ-5D Index (the MID for the measure) corresponds to a change of −0.248 in BPI-SF worst pain. In contrast, a one-point change in BPI-SF worst pain (which is smaller than the MID based upon the distribution-based analyses) translates to a change of 0.036 for the EQ-5D Index score (considerably smaller than the MID of 0.07). However, a two-point change in BPI-SF worst pain rating corresponds to a 0.072 change in EQ-5D Index score, which is almost identical to the MID for that measure. This suggests that a two-point change may be a reasonable estimate for the MID of the BPI-SF worst pain rating.

Discussion

Data from both distribution-based and anchor-based approaches were used to develop estimates of the MID for the BPI-SF worst pain rating. Results from these approaches are similar, providing reasonably strong support for establishing a two-point MID for the BPI-SF worst pain rating. Further, the results suggest that this estimate of MID is, for the most part, independent of baseline BPI-SF worst pain ratings. However, there is some evidence to suggest that the direction of change (improvement or worsening) may be important to consider. A number of reports have suggested that a smaller change may be required to be considered clinically important when a patient is improving compared with worsening.¹³ Also, when considered as a percentage, a one-point change in any scale has a different value for an increase versus a decrease; eg, a change from 2 to 3 is an increase of 50%, while a change from 3 to 2 is a decrease of 33%. Nonetheless, these findings provide important information to researchers for interpreting changes in the BPI-SF worst pain ratings.

In addition, although not specific to the BPI worst pain rating, the findings of this study are consistent with other published MID analyses for a similar item. A recent review of three studies concluded that, for a numerical rating scale of pain intensity ranging 0–10 similar in content to the BPI-SF worst pain rating, changes of around two points represent “meaningful,” “much better,” or “much improved” reductions in chronic pain.²⁴

Several factors contribute to the overall strength of the current results. First, as frequently recommended in the literature,¹¹ both anchor-based and distribution-based methods were used to estimate the MID for the worst pain rating. Second, analyses were based on a large sample, totaling over 1,500 patients for the baseline to week 25 assessment interval. A larger sample size will generally provide a broader distribution of responses, which will likely increase the generalizability of the results. Third, multiple anchors were used to evaluate changes in BPI-SF worst pain ratings. Fourth, analyses were performed across several assessment intervals to determine the strongest relationship between BPI-SF ratings and other anchors. Finally, the regression analyses provide important information about whether baseline differences influence the relationship between BPI-SF and other PRO measures.

Nevertheless, these analyses are not without certain limitations. The sample for the current analyses consisted entirely of breast cancer patients. It is unclear to what extent these results will be relevant for other patient populations. Further research is needed to determine whether the MID for the BPI-SF worst pain rating established in this sample has broader applicability. Also, it must be noted that the recall period varied across assessments. The BPI-SF focuses on the past 24 hours, the FACT uses the past week, and the EQ-5D uses the present moment. It is unclear to what extent these differences in recall periods may have influenced the current results. Finally, the baseline to week 25 interval was used to determine the MID for the BPI-SF worst pain rating based on the higher correlations for this interval. Data from baseline to week 49 are consistent with these results, providing some confirmatory evidence to suggest that these MID estimates are stable.

In conclusion, the findings of the present analyses suggest that the MID estimate for the BPI-SF worst pain rating is two points. This value provides guidance to researchers using the BPI-SF worst pain rating on how to interpret baseline differences as well as change scores in the BPI-SF worst pain rating. Additional analyses could be done in other populations to confirm these findings.

Acknowledgments

The authors acknowledge the contributions of Betsy Tschosik, Health Outcomes Solutions, for assistance in the preparation of this manuscript and Vidya Setty of Amgen, Inc., for editing assistance.

Anchor-Based Analysis

Spearman correlations between changes in the BPI-SF worst pain item and changes in potential anchors are presented in Table 2. For all potential anchors, the highest correlations with the BPI-SF worst pain rating were obtained at the baseline to week 25 interval. All potential anchors correlated significantly (P < 0.001) with the BPI-SF worst pain rating with the exception of the FACT-G Social/Family Well-Being scale. However, correlations were low (<0.30) for several potential anchors: ECOG Performance Status, FACT-B Overall QOL item, FACT-G Emotional Well-Being, and FACT-G Functional Well-Being. Therefore, the week 25 interval and the following anchors were selected for the MID analysis: BPI-SF current pain rating, EQ-5D Index score, EQ-5D Pain item, FACT-B TOI, FACT-G Physical Well-Being, and FACT-G total score. Correlation coefficients between the changes in the selected anchors and changes in the BPI-SF worst pain ratings range from 0.329–0.647.

Mean changes in the BPI-SF worst pain rating that correspond to a one-category change in anchors from baseline to week 25 are presented in Table 3. BPI-SF current pain ratings >5 and EQ-5D Index scores <0.40 were excluded from their respective analysis due to small sample sizes. A one-category increase in the anchor scores was associated with an absolute value of change in the BPI-SF worst pain item ranging from 0.26–2.42. A one-category decrease in the anchor score was associated with an absolute value of change in the BPI-SF worst pain item ranging from 0.56–3.16. Changes associated with improvement and worsening in anchors were not symmetrical, nor was there a consistent trend across anchors. For example, for the EQ-5D pain item, the magnitude of change in BPI-SF worst pain was greater for a one-category increase in the anchor than for a one-category decrease in the anchor. In contrast, for the EQ-5D Index score, the magnitude of change in BPI-SF worst pain was greater for a one-category decrease in the anchor than for a one-category increase in the anchor.

The regression of changes in anchors on changes in the BPI-SF worst pain item is shown in Table 4. Changes in each anchor are significantly (P < 0.05) associated with changes in BPI-SF worst pain rating. A one-point increase in BPI-SF current pain rating and EQ-5D Pain item is associated with a 0.817 and 1.805 increase in BPI-SF worst pain, respectively, while a one-point increase in EQ-5D Index score, FACT-B TOI, FACT-G Physical Well-Being, and FACT-G total is associated with a 3.548, 0.098, 0.163, and 0.048 decrease in BPI-SF worst pain rating, respectively. Likewise, a two-point increase in BPI-SF current pain rating and EQ-5D Pain item is associated with a 1.634 and 3.610 increase in BPI-SF worst pain, respectively, while a two-point increase in EQ-5D Index score, FACT-B TOI, FACT-G Physical Well-Being, and FACT-G total is associated with a 7.096, 0.196, 0.326, and 0.096 decrease in BPI-SF worst pain rating, respectively. The change in anchor-by-baseline anchor interaction was statistically significant only for BPI current pain and FACT-G Physical Well-Being. The interaction tests whether the anchor–BPI-SF slope differs as a function of baseline anchor score; therefore, a lack of significance suggests that the association between BPI-SF worst pain and other anchors does not differ by baseline anchor rating.

A post hoc confirmatory analysis was done replicating these analyses using data from the baseline to week 49 interval (n = 1,250). Results indicate a slightly stronger correlation between the anchors and the change scores. (Spearman's correlations range from 0.372 for FACT-TOI to 0.644 for BPI-SF current pain rating.) Mean change scores of BPI-SF worst pain ratings by each of the six anchors and regression coefficients were similar to those for the baseline to week 25 interval. For instance, mean change scores for the EQ-5D Pain item for stable patients ranged from 0.25–0.56, 1.58–295 for an improvement of one category, and 1.75–2.80 for a worsening of one category compared with 0.50–0.51, 1.71–1.98, and 2.56–3.16, respectively, for the baseline to week 25 interval.

Distribution-Based Analysis

The distribution-based estimates for the BPI-SF worst pain rating are presented in Table 5. There appears to be consistency with the 1 SEM estimates, the 0.50 effect size, and the 0.50 Guyatt's statistic.

Integrating Anchor-Based and Distribution-Based Mid Estimates

The distribution-based analyses suggest that the MDC for the worst pain rating, defined as the smallest change that can be reliably differentiated from random fluctuation, is between 1.3 and 1.6 points (see Table 5). This represents the lower bound for establishing the MID.

The results from regression analyses can be used to translate changes between anchors and corresponding changes in BPI-SF worst pain. This strategy can be particularly informative when the MID for an anchor is known. This is the case for the EQ-5D Health State Index, where the MID has been estimated at 0.06 for U.S. Index scores and 0.07 for U.K. Index scores.²³ A one-point change in EQ-5D Index translates to a change of −3.548 in BPI-SF worst pain, so a 0.07-point change in EQ-5D Index (the MID for the measure) corresponds to a change of −0.248 in BPI-SF worst pain. In contrast, a one-point change in BPI-SF worst pain (which is smaller than the MID based upon the distribution-based analyses) translates to a change of 0.036 for the EQ-5D Index score (considerably smaller than the MID of 0.07). However, a two-point change in BPI-SF worst pain rating corresponds to a 0.072 change in EQ-5D Index score, which is almost identical to the MID for that measure. This suggests that a two-point change may be a reasonable estimate for the MID of the BPI-SF worst pain rating.

Discussion

Data from both distribution-based and anchor-based approaches were used to develop estimates of the MID for the BPI-SF worst pain rating. Results from these approaches are similar, providing reasonably strong support for establishing a two-point MID for the BPI-SF worst pain rating. Further, the results suggest that this estimate of MID is, for the most part, independent of baseline BPI-SF worst pain ratings. However, there is some evidence to suggest that the direction of change (improvement or worsening) may be important to consider. A number of reports have suggested that a smaller change may be required to be considered clinically important when a patient is improving compared with worsening.¹³ Also, when considered as a percentage, a one-point change in any scale has a different value for an increase versus a decrease; eg, a change from 2 to 3 is an increase of 50%, while a change from 3 to 2 is a decrease of 33%. Nonetheless, these findings provide important information to researchers for interpreting changes in the BPI-SF worst pain ratings.

In addition, although not specific to the BPI worst pain rating, the findings of this study are consistent with other published MID analyses for a similar item. A recent review of three studies concluded that, for a numerical rating scale of pain intensity ranging 0–10 similar in content to the BPI-SF worst pain rating, changes of around two points represent “meaningful,” “much better,” or “much improved” reductions in chronic pain.²⁴

Several factors contribute to the overall strength of the current results. First, as frequently recommended in the literature,¹¹ both anchor-based and distribution-based methods were used to estimate the MID for the worst pain rating. Second, analyses were based on a large sample, totaling over 1,500 patients for the baseline to week 25 assessment interval. A larger sample size will generally provide a broader distribution of responses, which will likely increase the generalizability of the results. Third, multiple anchors were used to evaluate changes in BPI-SF worst pain ratings. Fourth, analyses were performed across several assessment intervals to determine the strongest relationship between BPI-SF ratings and other anchors. Finally, the regression analyses provide important information about whether baseline differences influence the relationship between BPI-SF and other PRO measures.

Nevertheless, these analyses are not without certain limitations. The sample for the current analyses consisted entirely of breast cancer patients. It is unclear to what extent these results will be relevant for other patient populations. Further research is needed to determine whether the MID for the BPI-SF worst pain rating established in this sample has broader applicability. Also, it must be noted that the recall period varied across assessments. The BPI-SF focuses on the past 24 hours, the FACT uses the past week, and the EQ-5D uses the present moment. It is unclear to what extent these differences in recall periods may have influenced the current results. Finally, the baseline to week 25 interval was used to determine the MID for the BPI-SF worst pain rating based on the higher correlations for this interval. Data from baseline to week 49 are consistent with these results, providing some confirmatory evidence to suggest that these MID estimates are stable.

In conclusion, the findings of the present analyses suggest that the MID estimate for the BPI-SF worst pain rating is two points. This value provides guidance to researchers using the BPI-SF worst pain rating on how to interpret baseline differences as well as change scores in the BPI-SF worst pain rating. Additional analyses could be done in other populations to confirm these findings.

Acknowledgments

The authors acknowledge the contributions of Betsy Tschosik, Health Outcomes Solutions, for assistance in the preparation of this manuscript and Vidya Setty of Amgen, Inc., for editing assistance.