Context.— Previous studies have documented that cancer patients tend to overestimate
the probability of long-term survival. If patient preferences about the trade-offs
between the risks and benefits associated with alternative treatment strategies
are based on inaccurate perceptions of prognosis, then treatment choices may
not reflect each patient's true values.
Objective.— To test the hypothesis that among terminally ill cancer patients an
accurate understanding of prognosis is associated with a preference for therapy
that focuses on comfort over attempts at life extension.
Design.— Prospective cohort study.
Setting.— Five teaching hospitals in the United States.
Patients.— A total of 917 adults hospitalized with stage III or IV non–small
cell lung cancer or colon cancer metastatic to liver in phases 1 and 2 of
the Study to Understand Prognoses and Preferences for Outcomes and Risks of
Treatments (SUPPORT).
Main Outcome Measures.— Proportion of patients favoring life-extending therapy over therapy
focusing on relief of pain and discomfort, patient and physician estimates
of the probability of 6-month survival, and actual 6-month survival.
Results.— Patients who thought they were going to live for at least 6 months were
more likely (odds ratio [OR], 2.6; 95% confidence interval [CI], 1.8-3.7)
to favor life-extending therapy over comfort care compared with patients who
thought there was at least a 10% chance that they would not live 6 months.
This OR was highest (8.5; 95% CI, 3.0-24.0) among patients who estimated their
6-month survival probability at greater than 90% but whose physicians estimated
it at 10% or less. Patients overestimated their chances of surviving 6 months,
while physicians estimated prognosis quite accurately. Patients who preferred
life-extending therapy were more likely to undergo aggressive treatment, but
controlling for known prognostic factors, their 6-month survival was no better.
Conclusions.— Patients with metastatic colon and lung cancer overestimate their survival
probabilities and these estimates may influence their preferences about medical
therapies.
MOST METASTATIC solid tumors, including lung and colon cancer, are incurable
and life expectancy is short. Cancer patients and their physicians are often
faced with a fundamental choice between cancer-directed therapy and supportive
care that emphasizes symptom management rather than control of the underlying
disease. Even in incurable solid tumors, cancer-directed therapy may prolong
average life expectancy by several months and palliate symptoms in some but
is often associated with treatment-related toxic effects. There is substantial
variability in the choices that are made about these alternatives. For example,
among patients diagnosed as having metastatic colon cancer in 1990, 42% received
chemotherapy as a component of their treatment, while 58% did not.1
We believe that patient preferences should drive choices between alternative
therapies, especially when life expectancy varies little and quality-of-life
considerations are prominent. Cancer patients' ability to participate in making
decisions about their care may be limited by several factors, however. Some
patients may be too ill or too overwhelmed emotionally to play a major role
in establishing the goals of therapy. Among those who wish to participate,
potential barriers include lack of physician support for patient decision
making, insufficient patient knowledge of the likely outcomes of their disease,
and lack of information concerning the effect of alternative strategies on
outcomes. Several studies have documented that cancer patients' understanding
of their prognosis is imperfect and that they tend to overestimate the probability
of long-term survival.2-6
For example, in 1 survey of patients with metastatic cancer, 37 believed that
treatment would cure them and 60% thought that it would control their metastatic
disease.5
Patients' preferences for care reflect their values, their understanding
of their illness, and their understanding of the risks and benefits associated
with treatment choices.7-10
Studies of cancer patients' values regarding trade-offs between quality and
quantity of life have shown substantial interpatient variability.11-13 If patients do not
understand their prognoses accurately, then their decisions about trade-offs
between treatment choices may not reflect their true values.
We examined the relationship between cancer patients' estimates of their
prognosis and their life-support and treatment preferences. The objectives
of the analysis were to determine (1) whether patients' prognostic estimates
were independent predictors of their treatment choice, (2) whether patients'
prognostic estimates were concordant with their physicians' estimates, (3)
whether patients' or physicians' estimates were more accurate, and (4) whether
patients' treatment preferences influenced their medical outcomes.
Patients enrolled in phases 1 and 2 of the Study to Understand Prognoses
and Preferences for Outcomes and Risks of Treatments (SUPPORT) were eligible
for the study. Although the study included hospitalized patients with any
of 9 different diagnoses, only data pertaining to patients with non–small
cell lung cancer and colon cancer will be presented in this report. A full
description of the SUPPORT project objectives and methods has been published
previously.14
Phase 1 was a prospective observational study that described the process
of decision making and patient outcomes. Phase 2 was a cluster-randomized,
controlled clinical trial to test the effect of an intervention in which physicians
were provided with information about both prognosis and patient preferences
and in which a nurse attempted to facilitate communication to enhance decision
making. Enrollment, data collection, and interviewing were virtually identical
during the 2 phases.15 Phase 1 enrolled patients
from June 1989 through June 1991, and phase 2 enrolled patients from January
1992 through January 1994. Patients were enrolled at the time of hospitalization
at 1 of 5 medical centers (Beth Israel Hospital, Boston, Mass; Duke University
Medical Center, Durham, NC; MetroHealth Medical Center, Cleveland, Ohio; St
Joseph's Hospital/Marshfield Clinic, Marshfield, Wis; and University of California,
Los Angeles, Medical Center. No differences in any of 5 outcome measures (timing
of do not resuscitate orders, patient and physician agreement on preferences
regarding resuscitation, days spent in the intensive care unit while in coma
or receiving mechanical ventilation, frequency and severity of pain, and hospital
resource use) were found between phases 1 and 2.15
Therefore, in the analyses presented herein, patients from the 2 phases are
pooled in a single sample.
To be eligible for the diagnostic category of non–small cell lung
cancer, patients were required to be hospitalized with non–small cell
lung cancer, stage III or IV. To be eligible for the diagnostic category of
colon cancer, they were required to be hospitalized with colon cancer metastatic
to the liver. Patients whose conditions had been newly diagnosed (within 1
month) and who were hospitalized for the first time since diagnosis, as well
as patients who had been hospitalized for reasons unrelated to their cancer,
were ineligible. Patients were excluded from enrollment in SUPPORT if they
were non–English speaking, had a planned admission of less than 72 hours,
were pregnant, or had died or been discharged from the hospital within 48
hours of study entry.
All study patients were asked to identify a surrogate decision maker,
whom patients "would want to help [their] doctor[s] make a decision about
[their] medical care if [they] were too ill to do so." For patients who were
unable to designate a surrogate, the physician and/or next of kin was asked
to name a surrogate. Physicians interviewed were those identified by admission
records as the responsible physician and were confirmed by physician interview
to be the individual having primary responsibility for that patient's care.
Data were gathered prospectively by chart review, patient interview,
and surrogate interview. An attempt was always made to interview the surrogate
even if patient data were complete. Chart reviews were performed by trained
research nurses, while interviews were performed by trained interviewers.
Data collected by chart review included in this analysis were insurance, disease
type, disease stage, time from cancer diagnosis, number of readmissions to
the study hospital, resuscitations attempted, and death while receiving ventilatory
assistance.
Data collected by patient and/or surrogate interview included demographics
(age, sex, race, education, income); global quality of life (rated on a single-item
scale from 0-100); activities of daily living (ADL) using a modified Katz
ADL scale16,17; patient's estimates
of the probability of 2- and 6-month survival; and patient's preference for
receiving life-extending treatment. Regarding their prognoses, patients were
specifically asked, "What are the chances that you will live for 2 months
or more if the current plan of care stays the same?" and "How about 6 months
or more?" Patients were asked to choose from the following responses: "90%
or better," "about 75%," "about 50-50," "about 25%," "10% or less," or "don't
know." Regarding treatment preferences, they were specifically asked, "If
you had to make a choice at this time, would you prefer a course of treatment
that focuses on extending life as much as possible, even if it means having
more pain and discomfort, or would you want a plan of care that focuses on
relieving pain and discomfort, even if that means not living as long?" Response
options were "extend life as much as possible," "relieve pain or discomfort
as much as possible," and "don't know."
Information obtained by physician interview included physician estimates
for the patient's likelihood of survival at 6 months. This question was asked
prior to providing any computer-based prognostic information to physicians
in phase 2. Physicians were asked to respond with a number ranging from 0%
to 100% to the question "What is the probability that this patient will live
for 6 months or more?"
Bivariable analysis and a logistic regression model were used to test
the relationship between patient-prognostic estimates and their treatment
preferences. The fit of a logistic regression model was assessed by the Hosmer-Lemeshow
goodness-of-fit test.18 A secondary analysis
with stratification by the physician-prognostic estimates was performed. Physician-prognostic
estimates were measured as a continuous variable. For purposes of comparison
with patients, these estimates were grouped into 5 categories similar to those
available to the patients (≥90%, 61%-89%, 40%-60%, 11%-39%, and 10%). Correlation
coefficients for patient estimate vs physician estimate of 6-month survival
did not differ between categorized and continuous physician-prognostic variables,
so only the results of the analyses using categorized estimates are reported
herein.
The accuracy of patient and physician estimates of the probability of
the patients' being alive at 6 months were compared using receiver operating
characteristic (ROC) curves.19 In this technique,
the discrimination of a test or prediction is assessed by plotting the sensitivity
of the test (the true-positive rate) against 1 minus the specificity (the
false-positive rate). The points on the ROC curve are generated by calculating
the sensitivity and specificity of the test or prediction at various criteria
of positivity. The greater the area under the curve (on a scale of 0.5-1),
the better the discrimination of the test or prediction. The sensitivity of
the patients' estimates at a criterion of positivity of 90%, for example,
represented, among patients who lived 6 months or more, the ratio of the number
who estimated their probability of being alive at 6 months at 90% or higher
to the total number of patients in the subset. The specificity of the "test"
at this criterion of positivity represented, among patients who lived less
than 6 months, the ratio of the number of patients who estimated their probability
of being alive at 6 months at less than 90% to the total number of patients
in this subset.
The degree of correlation between patient and physician prognostic estimates
was evaluated with a
τ-b statistic.20 Bivariable
analysis and a logistic regression model were used to test the relationship
between patients' treatment preferences and their 6-month survival. The relationship
between treatment preference and occurrence of adverse events was evaluated
with a τ2 statistic.
When information on income, education, functional status, or quality
of life was not available, we substituted surrogate reports, calibrated to
patient response or imputed values using methods described previously.21
Only patients for whom patient or surrogate prognostic estimates were
available were included in the analysis (n=917, 63% of otherwise eligible
subjects). For some patients, data on perceived prognosis were not available
because they were cognitively impaired, intubated, or otherwise too ill to
participate in the interview. All analyses involving patient estimates of
prognoses were performed on 2 data sets. The first data set included only
those patients who provided an estimate of the probability of their being
alive at 6 months (n=546). In the second data set (n=917), when patient estimates
were missing but a surrogate estimate of the probability of being alive at
6 months was available, this value, adjusted in accordance with the observed
relationship between surrogate and patient estimates in the group for whom
both variables were present, was used to replace missing values (n=271). When
a surrogate estimate of the probability of the patient's being alive at 6
months was not available, the patient's estimate of the probability of the
patient's being alive at 2 months, cubed, was used in place of the missing
value (n=33). (The 6-month probability of being alive would equal the cube
of the 2-month probability of being alive if the hazard function were constant.)
For the remaining patients used in this analysis, only a surrogate estimate
of the probability of being alive at 2 months was available, and this value
cubed was used in place of the missing value (n=67). There were no meaningful
differences between the 2 data sets in any of the analyses; therefore, only
the results from the second data set are reported here.
The characteristics of all eligible subjects are shown in Table 1. The average age of study patients was 62 years. Most (84%)
were white, and 62% were male. Thirty-nine percent of the patients had metastatic
colon cancer and 61% had lung cancer, most of which were stage IV. At 6 months
of follow-up, 500 (55%) of the 917 patients had died. Characteristics of patients
not included in the analysis because data on their 6-month survival estimates
were not available proved to be similar to study patients except that a higher
proportion had non–small cell lung cancer and their 6-month survival
was poorer. These patients also had slightly lower incomes, fewer years of
education, and poorer scores on quality-of-life and ADL scales. Characteristics
of patients for whom data on preferences for life-extending therapy were missing
(34%) were similar to patients for whom this information was available, although
they were also more likely to have non–small cell lung cancer, their
mean age was 1 year higher, and they had slightly poorer 6-month survival,
lower incomes, and poorer scores on quality-of-life and ADL scales.
The distribution of patient 6-month survival estimates and their preferences
for life-extending therapy and actual survival stratified by their prognostic
estimates are shown in Table 2.
Because there were relatively few patients in each category below 75%, patient-estimated
prognosis at 6 months was treated as a dichotomous variable (≥90% vs <90%)
in analyses of the relationship between survival estimates and preference
for life-extending therapy (Table 3).
In unadjusted analyses, the patients who estimated their probability of being
alive at 6 months to be 90% or better were more likely to favor life-extending
therapy (odds ratio [OR], 2.6; 95% confidence interval [CI], 1.8-3.7) (Table 3). The OR for this comparison was
unchanged in logistic regression models that examined the need to adjust for
age, sex, race, education, income, insurance, disease site and stage, time
since diagnosis, functional status, and global quality of life (Hosmer-Lemeshow P=.54 for final model). A statistically significant relationship
between patient-prognostic estimates and treatment preferences was observed
in both phase 1 and phase 2. In addition, no significant difference in patient
preference or in the relationship between patient-prognostic estimates and
preference was observed between intervention and control patients in phase
2.
Stratification of patients by physician estimate of survival demonstrated
that the relationship between perceived prognosis and desire for life-extending
therapy was particularly strong among patients whose physicians believed them
to have a poor prognosis. For example, among patients whose physicians estimated
a less than 10% probability of 6-month survival, the odds of patients who
thought they had a 90% probability of 6-month survival choosing life-extending
therapy were 8.5 times higher than those of patients in this category who
thought they had less than 90% probability of surviving
6 months (Table 3). The progressive increase in the
OR for the relationship of self-perceived prognosis to desire for life-extending
therapy with declining physician estimates of the probability of 6-month survival
was statistically significant (P=.02).
Comparison of Patient and Physician Estimates of Survival
Patients were substantially more optimistic about their prognoses than
their physicians were (τ-b = 0.29) (Table 4). In 82% of physician-patient pairs,
the patients' estimate of their chance of living 6 months was higher than
the physicians'; in 59%, the patient estimate exceeded the physician estimate
by 2 prognostic categories or more.
Patient- and Physician-Prognostic Estimate Accuracy Comparison
The percentage of patients actually surviving 6 months or more within
each physician- and patient-prognostic category is shown in Table 5. Although patients who believed they had at least a 90%
probability of surviving 6 months lived longer than patients who were less
optimistic about their prognoses (Table
5), physician estimates were better calibrated than those of patients.
The discrimination of physician and patient estimates of the probability of
surviving 6 months were compared with ROC curves. Using this technique, physician
estimates of survival were significantly more accurate than patient estimates,
with ROC curve areas of 0.78 and 0.66, respectively (P<.0001).
Life-Extending Therapy Preference and Outcomes
The relationship between patient treatment preference and the occurrence
of adverse events is shown in Table 6.
Patients who preferred life-extending therapy were 1.6 (95% CI, 1.04-2.39)
times more likely to experience a readmission to the hospital, an attempted
resuscitation, or death while receiving ventilatory assistance (42/146 [29%])
than patients who preferred therapy directed at pain relief (29/159 [18%])
(P=.03). In bivariable analysis, patients who preferred
life-extending therapy were more likely to be alive at 6 months (P=.005). However, in a logistic regression model that controlled for
age, race, sex, education, income, insurance status, site and stage of disease,
functional status, overall quality of life, and physician-prognostic estimates,
there was no statistically significant difference in 6-month survival between
those who favored life-extending therapy and those who did not (Hosmer-Lemeshow P=.17 for final model).
In a large cohort of terminally ill cancer patients, we found that how
patients estimated their prognoses influenced their treatment preferences.
Specifically, patients who believed that they would survive for at least 6
months favored life-extending therapy over comfort care at more than double
the rate of those who believed that there was at least a small chance (as
little as 10%) that they would not live 6 months. This association was most
marked in patients who were optimistic about their probability of surviving
6 months despite physician estimates to the contrary. In addition, we found
that patients greatly overestimated their chances of surviving 6 months, while
physician-prognostic estimates were more accurate. Finally, we found that
patients who expressed a preference for life-extending therapy were more likely
to undergo aggressive treatment, but controlling for known prognostic factors,
their 6-month survival was no better.
This study confirms the findings of a number of smaller case series
showing that cancer patients tend to overestimate their prognoses.2-6
But it is the first to demonstrate that patients' beliefs about their prognoses
are associated with their overall treatment preferences. One of the justifications
that has been offered for withholding prognostic information from patients
with a terminal disease is that, while promoting quality of life by maintaining
hope, it will have little effect on medical choices.22
Our findings suggest that the most fundamental medical choice patients with
incurable cancer face—the decision between life-extending therapy and
comfort care—may be highly influenced by their understanding of their
prognoses. These results are consistent with the empiric data demonstrating
that patient preferences about resuscitation depend on the nature of the expected
outcomes.23-28
Our findings take this work one step further by showing that a patient's preference
about overall therapy goals is also associated with his or her perceived prognosis.
What are the implications of this study for patient care? One possible
interpretation of the findings is that enhanced communication from physician
to patient about prognosis could help patients make better-informed treatment
decisions that are more consonant with their values. The observed relationship
between patient preferences for life-extending therapy and their likelihood
of experiencing aggressive therapy and adverse events suggests that this communication
might also diminish patient suffering at the end of life. Our data suggest
that these efforts might be particularly helpful in patients with poorer prognoses.
Most importantly, we found that it is not necessary for patients to have a
precise understanding of their prognoses to alter their treatment preferences.
Study patients who simply understood that there was at least a 10% probability
that they might not survive 6 months expressed substantially different treatment
preferences from those who did not. This suggests that it may be quite possible
both to maintain patient hope and to provide sufficient prognostic information
so that patients would be able to make treatment decisions consistent with
their underlying values.29,30
One important lesson from phase 2 of SUPPORT is that an understanding
of the sources of patients' beliefs, and preferences, and of the processes
by which they arrive at decisions about their care may be critical to designing
interventions that are effective in changing end-of-life patterns of care
and communication. Although our data provide compelling evidence that the
cancer patients participating in this study did not have an accurate understanding
of their prognoses, data were not collected that would allow us to identify
what was driving these estimates. In particular, we do not know what sources
of prognostic information patients used, how and whether physicians provided
accurate prognostic estimates to patients, and why patients did not understand
them or chose not to believe them. Analysis of the accuracy of patient-prognostic
estimates in the SUPPORT cohort as a whole showed no association with study
site, disease category, or whether patients were in the intensive care unit.31 This suggests that there may be pervasive and fundamental
barriers to effective communication about prognosis among seriously ill patients
that should be more fully investigated if effective interventions to address
them are to be designed. Our findings with respect to influence of patient-prognostic
estimates on their treatment preferences, and, in turn, of those preferences
on actual patterns of care, highlight the critical importance of gaining a
better understanding of the sources of these estimates.
Several limitations of this study design should be noted. First, because
data on patient- or surrogate-perceived prognosis were available for only
63% of otherwise eligible study subjects, the results may not be generalizable
to all patients with these incurable solid tumors. And although the patients
for whom data on perceived prognosis or treatment preference were missing
resembled those with complete data with respect to most sociodemographic and
clinical variables, these patients did have slightly lower incomes, quality-of-life
scores and ADL, and a higher proportion had lung cancer and died within 6
months of study enrollment. Questions asked of the terminally ill patients
in this study are difficult ones and, despite considerable investment in study
procedures designed to minimize missing data, response rates were not ideal.
We attempted to address this problem by including subjects for whom only surrogates'
estimates of prognoses were available, and it was reassuring to find that
this did not alter any of the findings of the study. But it is important to
recognize that our results describe the beliefs and preferences of only those
patients who were willing to share their views with the study team and should
not be generalized to the entire population.
Generalizability is also limited by the fact that all patients participating
in this study were hospitalized for treatment of their disease or disease-related
complications at an academic medical center. The characteristics of hospitalized
patients may differ in systematic ways from those of ambulatory patients.
Furthermore, patients opting for care in an academic setting may be especially
interested in obtaining state-of-the-art or aggressive therapy. Future studies
in different settings would be valuable in elucidating the influence of these
factors on the relationship between patient perception of prognosis and treatment
preferences.
Finally, and most importantly, demonstration of an association between
self-perceived prognosis and treatment preference does not prove a causal
relationship. It is possible that the same personality traits or coping strategies
that lead certain patients to cling to overly optimistic views of their prognoses
may also lead those patients to opt for life-extending therapy. Although our
multivariable analysis suggests that the association between self-perceived
prognosis and treatment preference is not explained by sociodemographic, clinical,
or quality-of-life variables, this does not prove causation.
The real value of this observational study is not that it provides definitive
proof that improving cancer patients' understanding of their prognoses would
enable them to make better decisions about their treatment. Rather, it is
important preliminary evidence that could be used to justify an intervention
study to examine the issue. The potential costs of effectively educating incurable
cancer patients about their prognoses might include the loss of hope and associated
deleterious effect on quality of life. However, our data suggest that there
may be real benefits as well, including treatment choices that are more consonant
with patient preferences, which could lead to a diminished demand for aggressive,
toxic interventions driven by unrealistic expectations. Furthermore, our findings
indicate that physicians may be able to provide prognostic data that are sufficiently
accurate to achieve these benefits.
The results of the phase 2 SUPPORT intervention trial demonstrated that
providing prognostic information to physicians was not effective in changing
the patterns of care of the seriously ill.15
The results of this analysis suggest that to achieve the goals of making care
at the end of life consistent with patient values and minimizing futile therapy,
we may need to change what physicians tell patients about their prognoses
and be sure that patients hear and understand what their physicians have said.
Meticulous care would be required to design an ethical and informative study
of the impact educating cancer patients about prognosis has on a variety of
outcomes. The potential benefit of such findings to patients, their families,
and society could be substantial.
1.Steele GD. The National Cancer Data Base report on colorectal cancer.
Cancer.1994;74:1979-1989.Google Scholar 2.Siminoff LA, Fetting JH, Abeloff MD. Doctor-patient communication about breast cancer adjuvant therapy.
J Clin Oncol.1989;7:1192-1200.Google Scholar 3.Bernheim JL, Ledure G, Souris M, Razav D. Differences in perception of disease and treatment between cancer patients
and their physicians.
Monogr Ser Eur Organ Res Treatment Cancer.1987;17:285-295.Google Scholar 4.Pronzato P, Bertelli G, Losardo P, Landucci M. What do advanced cancer patients know of their disease? a report from
Italy.
Support Care Cancer.1994;2:242-244.Google Scholar 5.Eidinger RN, Schapira DV. Cancer patients' insight into their treatment, prognosis and unconventional
therapies.
Cancer.1984;53:2736-2740.Google Scholar 6.Yellen SB, Cella DF. Ignorance is bliss? beliefs about illness and perception of well-being. In: Program and abstracts of the Fourth International Society of
Behavioral Medicine; March 13-16, 1996; Washington, DC. Abstract 45B.
7.Forrow L, Wartman SA, Brock DW. Science, ethics, and the making of clinical decisions: implications
for risk factor intervention.
JAMA.1988;259:3161-3167.Google Scholar 8.Wanzer SH, Federman DD, Adelstein JS.
et al. The physician's responsibility toward hopelessly ill patients: a second
look.
N Engl J Med.1989;320:844-849.Google Scholar 9.Singer PA, Choudhry S, Armstrong J.
et al. Public opinion regarding end-of-life decisions: influence of prognosis,
practice and process.
Soc Sci Med.1995;41:1517-1521.Google Scholar 10.Mezey M, Kluger M, Maislin G, Mittelman M. Life-sustaining treatment decisions by spouses of patients with Alzheimer's
disease.
J Am Geriatr Soc.1996;44:144-150.Google Scholar 11.Singer PA, Tasch ES, Stocking C.
et al. Sex or survival: trade-offs between quality and quantity of life.
J Clin Oncol.1991;9:328-334.Google Scholar 12.Slevin ML, Stubbe L, Plant HJ.
et al. Attitudes to chemotherapy: comparing views of patients with cancer
with those of doctors, nurses and the general public.
BMJ.1990;300:1458-1460.Google Scholar 13.McQuellon RP, Muss HB, Hoffman SL.
et al. Patient preferences for treatment of metastatic breast cancer: a study
of women with early-stage breast cancer.
J Clin Oncol.1995;13:858-868.Google Scholar 14.Murphy DJ, Cluff LE. SUPPORT: Study to Understand Prognoses for Outcomes and Risks of Treatments,
study design.
J Clin Epidemiol.1990;43(suppl): 1S-108S.Google Scholar 15.The SUPPORT Principal Investigators. A controlled trial to improve care for seriously ill hospitalized patients.
JAMA.1995;274:1591-1598.Google Scholar 16.Katz S, Ford AB, Moskowitz RW, Jackson BA, Jaffe MW. Studies of illness in the aged, the index of ADL: a standardized measure
of biological and psychosocial function.
JAMA.1963;185:914-919.Google Scholar 17.Katz S, Apkom CA. A measure of primary sociobiological functions.
Int J Health Serv.1976;6:493-507.Google Scholar 18.Hosmer DW, Lemeshow S. Applied Logistic Regression. New York, NY: John Wiley & Sons Inc; 1989.
19.Hanley JA, McNeil BJ. The meaning and use of a receiver operating characteristic (ROC) curve.
Radiology.1982;143:29-36.Google Scholar 20.Goodman LA, Kruskal WH. Measures of association for cross-classification I, II, III and IV.
J Am Stat Assoc.1972;67:415-421.Google Scholar 21.Phillips RS, Hamel MB, Teno JM.
et al. Race, resource use and survival in seriously ill hospitalized adults.
J Gen Intern Med.1996;11:387-396.Google Scholar 22.Miyaji NT. The power of compassion: truth-telling among American doctors in the
care of dying patients.
Soc Sci Med.1993;36:249-264.Google Scholar 23.Schneiderman LJ, Kronick R, Kaplan RM, Anderson JP, Langer RD. Effects of offering advance directives on medical treatments and costs.
Ann Intern Med.1992;117:599-606.Google Scholar 24.Seckler AB, Meier DE, Mulvihill M, Paris BEC. Substituted judgment: how accurate are proxy predictions?
Ann Intern Med.1991;115:92-98.Google Scholar 25.Shmerling RH, Bedell SE, Lilienfeld A, Delbanco TL. Discussing cardiopulmonary resuscitation: a study of elderly outpatients.
J Gen Intern Med.1988;3:317-321.Google Scholar 26.Danis M, Southerland LI, Garrett JM.
et al. A prospective study of advance directives for life-sustaining care.
N Engl J Med.1991;324:882-888.Google Scholar 27.Emanuel LL, Barry MJ, Stoeckle JD.
et al. Advance directives for medical care: a case for greater use.
N Engl J Med.1991;324:889-895.Google Scholar 28.Murphy DJ, Burrows D, Santilli S, Kemp AW, Tenner S, Kreling B. The influence of the probability of survival on patients' preferences
regarding cardiopulmonary resuscitation.
N Engl J Med.1994;330:545-549.Google Scholar 29.Miyaji NT. Informed consent, cancer and truth in prognosis.
N Engl J Med.1994;331:810.Google Scholar 30.Holcombe RF. Informed consent, cancer and truth in prognosis.
N Engl J Med.1994;331:811.Google Scholar 31.Dawson NV, Arkee H, Conners AP.
et al. Physician-patient discussion does not increase agreement about prognosis
in severely ill patients.
J Gen Intern Med.1995;10(suppl 4):41.Google Scholar