Cancer Patient Preferences for Quality and Length of Life

Abstract

Background

Optimal patient decision making requires integration of patient values, goals, and preferences with information received from the physician. In the case of life-threatening illness such as cancer, the weights placed on quality of life (QOL) and length of life (LOL) represent critical values. The objective of this study is to describe cancer patient values regarding QOL and LOL, and explore associations with communication preferences.

Methods

Patients with advanced cancer completed a computer-based survey prior to the initial consultation with a medical oncologist. Assessments included sociodemographics, physical and mental health state, values regarding quality and length of life, communication preferences and cancer-related distress.

Results

Seven hundred forty three advanced cancer patients were enrolled. Among 459 advanced cancer patients, fifty-five percent of patients equally valued QOL and LOL, 27% preferred QOL, and 18% preferred LOL. Patients with a QOL preference had lower levels of cancer-related distress (p < 0.001). QOL preference was associated with older age (p = 0.001), male gender (p = 0.003), and higher education (p = 0.062). Patients who preferred LOL over QOL desired a more supportive and less pessimistic communication style from their oncologists.

Conclusions

These data indicate that a values preference for length vs. quality of life may be simply measured, and is associated with wishes regarding the nature of oncologist communication. Awareness of these values during the clinical encounter could improve decision making by influencing the style and content of the communication between oncologists and their patients.

Introduction

Patients with advanced cancer exist in a unique medical context in which they are facing mortality and may be considering treatment options that have significant potential for toxicity. In addition, therapeutic choices are characterized by uncertain outcomes, and may be varied and complex, including supportive care alone, standard treatments (e.g. chemotherapy, radiation, biologic), and investigational approaches. Quality patient decision making requires an adequate patient understanding of treatment options, including potential benefit and harm. The physician serves as the primary source of medical information for cancer patients;^{1, 2} as such, the communication between doctor and patient is of critical importance to quality decision making.^3-5

In a survey of cancer patients who were considering participation in phase I trials, we observed discordance between patient and oncologist perceptions about the content of their consultations⁶. In particular, patients were far less likely to report discussion of quality of life issues than their physicians. This lack of agreement is a potential source of concern insofar as nearly all of the patients taking part in the survey valued quality of life at least as highly as length of life.

Adequate communication about the impact of treatment on quality of life is of particular importance given that patient preference for either quality of life or length of life can influence patient treatment decision making. For example, among cancer patients with advanced disease, an individual's preference for length of life over quality of life is associated with treatment preference for chemotherapy over watchful waiting⁷. Further, a number of sociodemographic factors are associated with preference for quality or length of life. Preference for quality of life is associated with older age^8-9, and having no children^7,8. In contrast, preference for length of life is associated with being young, having children, and good functional health status^8,10. Despite its importance for cancer patient treatment decision making, few studies have explored how individual preference for quality or length of life influences the way in which patients wish their doctors to present prognostic and treatment-related information.

To approach the understanding of how patient preferences impact decision making, we have applied the Cognitive-Social Health Information Processing (C-SHIP) model^11,12. This is a comprehensive framework which delineates the cognitive and affective factors involved in health information processing^11,12. These factors include the individual's self-construals (e.g. perceived vulnerability to disease), affect (e.g. anxious preoccupation with cancer), values and goals (e.g. about the doctor-patient encounter and importance of quality of life), as well as goals related to achieving desired treatment outcomes (e.g. expressing one's concerns and values)¹¹. The C-SHIP model emphasizes the role of distress in information processing and decision-making¹³. Since distress can impact a patient's ability to process critical prognostic and treatment-related information relevant to treatment choice¹⁴, examination of its relationship to an individual's values (e.g. importance of quality of life and length of life) and communication preferences is essential.

In an effort to improve the matching of consultation content with individual patient values, we developed a computer-based communication aid. We are conducting a randomized clinical trial to assess the impact of this intervention on the content of oncology consultations and patient satisfaction with physician communication. In this manuscript we describe the baseline characteristics of the study population in an effort to define the cognitive-affective profiles and communication preferences among those cancer patients who prefer quality of life and those that prefer length of life.

Methods

Patient Selection

This report describes baseline survey data obtained as part of an intervention study to evaluate the efficacy of a computer-based communication aid on treatment decision making in advanced cancer patients. The parent study is a three-arm randomized clinical trial. Two of the arms include the survey items that form the basis for this report.

Participants were advanced cancer patients at three United States academic cancer centers. Eligibility criteria included: 1) first outpatient consultation with a medical oncologist; 2) documented metastatic malignancy; 3) 18 years of age or older; 4) able to read and verbally communicate in English; 5) ability to provide written informed consent to participate. Potential study participants were ascertained between January 2005 and January 2007 by review of new patient schedules and medical information forwarded at the time of scheduling, before the initial medical oncologist consultation.

Procedure

Following ascertainment, consent to contact potential participants was obtained from the physician with whom the patient was scheduled. A research assistant then contacted patients by phone to explain the study. After providing verbal consent, participants were given the option to complete the computer-based survey at home, using a secure web-interface¹⁵, or arrive one hour before their appointment to complete at the clinical site. Participants using the secure website to complete the survey signed informed consent documents electronically. Participants were given a toll-free number for technical support and to ask any questions regarding the study. All participants provided written consent upon arrival for their physician appointments. The study was approved by the Institutional Review Boards at the participating centers.

Measures

Patient Characteristics included gender, age, marital status, education, employment status, race, ethnicity, cancer diagnosis, and treatment history.

Physical and Emotional Health State was assessed using the Short-Form (SF)-12 and the Revised Impact of Events Scale (RIES). The SF-12¹⁶ is a highly reliable shortened version of the SF-36,¹⁷ and is intended to provide indices of quality of life similar to the longer instrument while reducing participant burden. Subscales include a Physical Component Scale (PCS) and Mental Component Scale (MCS). Cancer-Related Distress was measured using the RIES, a well-validated 15-item questionnaire measuring event-related intrusion and avoidance.¹⁸

Quality of Life and Length of Life Preferences were assessed with three items to determine the relative value that an individual assigns to quality of life (QOL) and quantity of life (LOL). This instrument, designed and refined based on prior research^{6, 19} with the target population, asked participants to select from among 4 choices about whether QOL or LOL was more important (QOL is all that matters, QOL is more important but LOL matters, LOL is more important but QOL matters, LOL is all that matters). Participants were also asked to rate the importance of QOL and LOL as independent domains on 5-point scales (not at all, somewhat, moderately, quite a bit, very important).

Communication Preferences were assessed using an eight-item survey developed based on a review of relevant literature and existing doctor-communication assessments.^20-24 Participants were asked to rank their agreement with each of these eight preferences regarding physician communication about prognostic and probabilistic information on a 5-point scale from “Strongly Agree” to “Strongly Disagree.”

Statistical Considerations

We defined QOL and LOL preferences in two ways. We initially defined QOL vs. LOL preference based upon the single 4-point survey item that required patients to prioritize QOL and LOL. In an effort to discriminate patient preferences further given infrequent selection of the extreme values of this single-item measure, we defined three preference groups based upon independent rating of QOL and LOL on the two 5 point scales. For this composite analysis, we categorized patients with regard to whether they selected equal ratings for QOL and LOL on the independent measures, or whether they rated one of these preferences higher on one of the measures. The analyses described below utilize the composite measure.

For unadjusted analyses comparing the quality of life preference groups with potential confounders, we used multiple linear regressions with robust standard errors of the continuous confounders to assess statistically significant differences among groups. We used robust standard errors to weaken the ANOVA assumption of normally distributed errors. We used Wald tests of the regression parameters to test the null hypothesis that there were no differences in mean responses among the three quality of life preference groups. For categorical confounders, we used Fisher's Exact tests. We used correlations to assess the relationship of the communication preference variables with the RIES distress score.

To adjust for age, education, sex, race, SF-12 PCS, and MCS scores in analyses relating quality of life preferences with communication preferences and distress, we used propensity score adjustment through propensity score-based weighting with doubly robust estimation²⁵. We calculated robust standard errors using the sandwich estimator of Huber²⁶; our standard errors account for the uncertainty associated with the estimation of the propensity scores. Propensity score-based weighting is an effective means of adjusting for potential confounders because it allows for the creation of an adjusted population in which the balance of confounders between quality of life preference groups can be assessed directly. If the average covariate characteristics between quality of life preference groups in the adjusted population are similar, then it is unlikely that the covariates are confounding the adjusted inferences. The adjusted mean communication preference scores presented in Table 4 and adjusted mean distress scores presented in Figure 1 represent estimates of mean responses in the adjusted population.

Table 4.

Relationship between communication preferences, distress, and LOL/QOL preference

	Total RIES	Adjusted Mean (SE)			p-value
	Correlation
	p	QOL	Equal	LOL	p
I want the doctor to speak in a positive manner	0.096 p=0.040	3.65 (.09)	4.03 (.05)	4.19 (.09)	<.001
I want to hear general terms (for example, “the treatment is likely to work”) rather than statistics (for example, “the treatment has a 75% likelihood of working”).	0.034 p=0.468	3.22 (.12)	3.63 (.07)	3.76 (.12)	.002
I want the doctor to soften the blow when giving me bad news	0.321 p<0.001	2.17 (.10)	2.74 (.08)	2.93 (.16)	<.001
I want the doctor to speak to me in an emotionally supportive way	0.289 p<0.001	3.83 (.08)	4.15 (.04)	4.24 (.07)	<.001
I want to hear detailed statistics	0.074 p=0.112	3.87 (.10)	3.96 (.06)	3.81 (.10)	.358
I want the doctor to speak matter-of-factly (for example, give me the cold hard facts)	-0.217 p<0.001	3.92 (.10)	3.82 (.07)	3.88 (.10)	.721
I want to hear averages about people like me	0.034 p=0.463	4.05 (.07)	4.01 (.05)	4.01 (.10)	.910
I want to hear the doctor's opinion about my case in particular	-0.087 p=0.064	4.70 (.05)	4.66 (.04)	4.64 (.07)	.788

QOL, quality of life preferred; LOL, length of life preferred; Scores represent average responses, 1 = Strongly Disagree, 5 = Strongly Agree (standard deviation)

Figure 1.

QOL-LOL preferences among patients with different cancer types.

To develop the propensity score model, we used a multinomial logistic regression of the nominal three-category quality of life variable. The age, SF-12, education, sex, and race terms were entered into the model using restricted cubic splines for age, SF-12 PCS, and SF-12 MCS²⁷ and interactions of sex with age and education. The appropriateness of the model was measured by it ability to balance the covariates among the three groups, as demonstrated in Table 3.

Table 3.

Relationship between patient characteristics and values preferences.

	Values Preference
	QOL	Equal	LOL	p
N	123	252	84
Age mean (SD)	62.6 (10.5)	59.2 (11.9)	57.3 (11.9)	.001
PCS -12	43.5 (10.6)	44.6 (11.0)	41.6 (10.4)	.075
MCS-12	44.5 (5.4)	42.9 (6.3)	43.0 (7.9)	.031
Male	34%	49%	17%	.003
Female	20%	61%	19%
Race				.67
Black	17%	59%	24%
White	27%	55%	18%
Other	35%	50%	14%
Education				.062
High School or Less	18%	62%	19%
Some College	28%	51%	21%
College or more	32%	52%	16%

QOL, quality of life preferred; LOL, length of life preferred

Results

Participant Characteristics

1932 patients were contacted and 1101 (57%) agreed to be assigned a password to consider participation in this three-arm study; 743 patients (68%) provided informed consent and completed baseline surveys. 471 patients were randomized to the two intervention arms, and 12 patients with missing data are not included in these analyses. Therefore, 459 patients form the basis of this report. While all 459 patients were used to derive propensity scores, each communication preference response had an additional one or two missing values. Patient demographics are shown in Table 1. The sample was primarily White (91%), 51% male, and well-educated (70% some college or more).

Table 1.

Patient Demographics (N = 459)

		N	%
Median age (years)	60
Age range	26-89
Male		232	51%
Race
White		416	91%
Black		29	6%
Other		14	3%
Ethnicity
Non-Hispanic		452	99%
Hispanic		6	1%
Education
High School		136	30%
Some College		145	32%
College degree or More		178	39%
Cancer Diagnosis
Lung		83	18%
Colorectal		68	15%
Breast		53	12%
Pancreas		32	7%
Prostate		33	7%
Renal		24	5%
Ovarian		21	5%
Other*		145	32%
Years since diagnosis
Median	1
Mean (SD)	3.1 (4.3)
Range	0 - 41
Prior systemic therapy		276	60%

^*Other includes cancers accounting for less than 5% of all diagnoses within the study population, including bladder, bone, cervical, endometrial, esophageal, head and neck, lymphoma,

Preferences regarding quality and length of life are summarized in Table 2. Approximately half of the patient participants (55%) equally valued QOL and LOL based upon the composite measure. Of those patients with a preference, quality of life was selected more commonly than length of life, 27% vs. 18%. This finding is consistent with the selection of QOL as more important on the single-item scale (80% of patients) where patients were required to commit to a preference. Older age (p<.001) and male gender (p<.004) were positively associated with a preference for QOL. To remove possible confounding by these variables in the subsequent communication preference analyses, we used propensity score adjustment. In the propensity weighted population, older age and male gender were no longer associated with a preference for QOL, indicating successful adjustment. In the adjusted population, all p-values were greater than 0.32. Compared to patients with other diseases, those with prostate cancer preferred QOL (p=0.021).

Table 2.

Preferences for Quality and Length of Life

Single-Item measure	N	%
Quality of life is all that matters	67	15%
Quality of life is more important, but length of life matters	299	65%
Length of life is more important, but quality of life matters	89	19%
Length of life is all that matters	4	1%
Composite Measure	Mean	S.D.
How important is quality of life to you?	4.4	0.8
How important is length of life to you?	4.2	1.0
	N	%
Quality of life more important	123	27%
Equally important	252	55%
Length of life more important	84	18%

Given a potential relationship between affect and values, an association between patient distress and QOL vs. LOL preferences was investigated. As shown in Figure 2, those patients with a preference for quality of life had lower levels of cancer-related distress than those patients with a length of life preference or no preference. There was a significant association between the mental health component summary score of the SF12 (p=0.031) and a marginal association between the physical health component summary score of the SF12 (p=0.075), and preferences for quality or length of life, with inferior quality of life on these measures associated with a preference for length of life. There was no association between length of time since diagnosis or previous receipt of systemic therapy, and QOL vs. LOL preferences.

Figure 2.

Cancer related distress and quality of life versus length of life preferences.

The center bars represent the means of the propensity score adjusted Total RIES measurements, while the whiskers represent the 95% confidence intervals. P<0.001 for a hypothesis test of the equality of the three means.

We next explored whether QOL/LOL preferences are associated with preferences for communication with the physician. As shown in Table 4, after propensity score-weighted adjustment, regression analyses indicate significant differences in preferences for style of communication between those patients preferring QOL and those preferring LOL. Specifically, compared to the other groups, individuals who indicated that they prefer LOL over QOL (testing for the equality of three means) were more likely to indicate that they would like the doctor to speak more positively (p<.001), to want the doctor to use general terms (p<.004), to want doctor to soften bad news (p<.001), and to want the doctor to speak in an emotionally supportive way (p<.001). In contrast, they were less likely to indicate that they wanted the doctor to give the worst possible results (p<.044).

Discussion

There is great variability in cancer patients' preferences regarding the content and format of communication from their physicians^28,29-31. Matching communication to patient preferences contributes to quality patient decision making and satisfaction^{32, 33}. Thus tools to assist physicians in identifying relevant patient preferences and guiding communication accordingly could improve clinical outcomes. The data we present indicate that a values preference for length vs. quality of life may be simply measured, and is associated with a desire for more supportive and less pessimistic communication from the oncologist.

Communication skill in the cancer context is particularly critical given that patients are commonly facing mortality and “bad news,” treatment outcomes are characterized by uncertainty, and treatment is associated with significant potential for morbidity. Previous reports have identified a variety of patient characteristics that bear on their wishes regarding physician communication. For example, women and patients with higher levels of educational attainment have been shown to want more detailed information about their prognosis.^{31, 34, 35} Female gender is also associated with desire for a supportive communication style over a blunter approach, while patients with more education³¹ and older patients³⁶; have been shown to prefer a more fact-oriented style of communication). The data we present support the hypothesis that preference regarding quality and length of life is a key value that impacts treatment goals and desires regarding physician communication. We also observed that older age (p=0.001), male gender (p=0.004), and higher education (p=0.068) were associated with a preference for quality of life. Even after propensity score adjustment, the QOL/LOL preference was predictive of patient communication preferences.

It is notable that patients with less cancer-related distress were more likely to favor quality of life over length of life. The direction of causation in this relationship cannot be inferred from these data. It is possible that increased distress is associated with greater difficulty in processing quality of life issues when faced with a life-threatening illness, and therefore a focus on length of life is preferred. It is also plausible that a greater concern for one's length of life leads to greater anxiety in the context of an immediate threat to longevity. In either case, high levels of distress can negatively impact risk information processing and communication, and ultimately decision making. The fact that those patients who favor length of life seek a more supportive communication style and content is consistent with a desire to minimize additional distress in this patient subgroup. Further, an alternate explanation exists; patients who rely more heavily on blunting or denial as a way to manage their distress may be more inclined to avoid thinking about end of life and report a preference for length of life without fully considering the range of options, namely, shorter but enhanced quality of life. This subgroup of individuals may be more inclined to prefer provider communications that “soften the blow” and avoid discussion of a difficult reality, and experience heightened distress when provided too much threat-relevant information³⁷. Further research is required to more finely delineate the relationship between these cognitive and affective influences on downstream events.

Although patients who value length of life over quality of life prefer a more supportive communication style, this does not imply that communication of “bad news” should be avoided. Withholding of negative prognostic information may result in loss of trust in the physician, decreased compliance, communication barriers between partners, patient isolation and loss of control, and a lost opportunity to adapt to new circumstances^38-40. Furthermore, recent data suggest that prognostic disclosure may be associated with increased hope, even in the face of poor prognosis⁴¹. Our data support the recommendation that communication must be tailored such that medical information is conveyed in a manner that serves the cognitive and affective needs of individual patients^{28, 38-40}.

Our findings also show that patients who value LOL over QOL have lower scores on indicators of mental health with a trend for these patients to score lower on indicators of physical health. We previously reported that the decision to take part in an experimental therapy program (perhaps indicative of a length of life focus) was related to a patient's reference point regarding the threat that their cancer posed to their quality adjusted survival.⁴² Further exploration of the relationship between health state and preferences regarding quality and length of life is warranted.

Because the study population in this report is racially homogenous and relatively well educated, it is difficult to generalize these results to a more diverse population. It is possible that the relationships we observed between distress, QOL and LOL preferences, and communication preferences may differ in various socioeconomic or racial groups. Furthermore, this study is limited in that the assessment was conducted at a single time point, and does not capture potential variations in values and goals over the course of illness. Finally, the method we used to classify patients' preferences for length vs. quality of life assumed that a 1-point difference on a 5-point scale represents a precise, meaningful difference. If such differences were not meaningful, there would be greater noise in our classification of patients' preference, thus leading to an underestimation of the relationships reported here. It is important to note that before this classification method is adopted as a component of routine care, further work to establish its discriminant ability is appropriate. Nevertheless, this study represents one of the largest surveys of cancer patient preferences reported to date, and patient ascertainment procedures sought to identify and enroll all eligible subjects.

In summary, this study provides evidence that patient differences in the value placed on quality and length of life can be easily assessed, and these values are associated with preferences for the content and format of physician communication. We are conducting a prospective randomized clinical trial (NCT00244868) to assess whether reporting patient values and preferences to the oncologist before the initial consultation will impact communication content and format, and improve patient satisfaction with the consultation.