Patient or Physician Preferences for Decision Analysis
Identifieur interne : 002E76 ( Istex/Corpus ); précédent : 002E75; suivant : 002E77Patient or Physician Preferences for Decision Analysis
Auteurs : Paul S. Heckerling ; Marion S. Verp ; Nancy AlbertSource :
- Medical Decision Making [ 0272-989X ] ; 1999-01.
English descriptors
- Teeft :
- Abnormal child, Abnormal fetus, Abnormality, Abortion, Actual test choice, Actual test choices, Alternative rules, Amniocentesis, Best outcome, Chononic villus sampling, Chorionic, Chorionic villi sampling, Chorionic villus, Chorionic villus sampling, Chromosomal abnormality, Clin epidemiol, Confidence interval, Cutoff, Cutoff value, Decision analyses, Decision analysis, Decision models, Decision rule, Decision rules, Decision tree, Equal weights, Fetus, First tnmester, Genetic abnormality, Genetic testing, Health outcomes, High preference rating, John wiley, Lexicographic, Likely outcome, Linear rating scales, Little impact, Logistic regression, Lower preference rating, Median preference rating, Minor effects, Normal child, Normal fetus, Obstet gynecol, Obstetnc vanables, Obstetric, Obstetric confounders, Obstetrical genetics clinics, Odds ratio, Odds ratios, Other test, Outcome probabilities, Patient choice, Patient decision, Patient decision analysis, Patient preferences, Physician, Physician choice, Physician preferences, Physician sample sizes, Physician variables, Preference, Preference rating, Preference ratings, Preference scale, Preferred test, Prenatal, Prenatal outcome, Prenatal outcomes, Prenatal test, Prenatal test choice, Prenatal test choices, Prenatal testing, Prenatal tests, Probability estimates, Prospective studies, Rating, Regrettable outcome, Results support, Sensitivity analyses, Separate models, Similar fashion, Sociodemographic, Spontaneous abortion, Spontaneous abortions, Standard gambles, Such preferences, Test choice, Test choices, Therapeutic abortion, Therapeutic abortions, Unadjusted analyses, Utility rules, Villus, Worst outcome.
Abstract
The choice between amniocentesis and chononic villus sampling for prenatal genetic testing involves tradeoffs of the benefits and risks of the tests. Decision analysis is a method of explicitly weighing such tradeoffs. The authors examined the relationship between prenatal test choices made by patients and the choices prescribed by deci sion-analytic models based on their preferences, and separate models based on the preferences of their physicians. Preferences were assessed using written scenarios describing prenatal testing outcomes, and were recorded on linear rating scales. After adjustment for sociodemographic and obstetric confounders, test choice was signifi cantly associated with the choice of decision models based on patient preferences (odds ratio 4.44; CI, 2.53 to 7.78), but not with the choice of models based on the preferences of the physicians (odds ratio 1.60; CI, 0.79 to 3.26). Agreement between decision analyses based on patient preferences and on physician preferences was little better than chance (kappa = 0.085 ± 0.063). These results were robust both to changes in the decision-analytic probabilities and to changes in the model structure itself to simulate non-expected utility decision rules. The authors conclude that patient but not physician preferences, incorporated in decision models, correspond to the choice of amniocentesis or chorionic villus sampling made by the patient. Nevertheless, because patient preferences were assessed after referral for genetic testing, prospec tive preference-assessment studies will be necessary to confirm this association. Key words: decision analysis; patient preferences; physician preferences; prenatal genetic testing. (Med Decis Making 1999;19:66-77)
Url:
DOI: 10.1177/0272989X9901900109
Links to Exploration step
ISTEX:C5044293006C5AB9D07CC0419C64E55038D80BA1Le document en format XML
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Heckerling</name></author><author wicri:is="90%"><name sortKey="Verp, Marion S" sort="Verp, Marion S" uniqKey="Verp M" first="Marion S." last="Verp">Marion S. Verp</name></author><author wicri:is="90%"><name sortKey="Albert, Nancy" sort="Albert, Nancy" uniqKey="Albert N" first="Nancy" last="Albert">Nancy Albert</name></author></analytic><monogr></monogr><series><title level="j">Medical Decision Making</title><idno type="ISSN">0272-989X</idno><idno type="eISSN">1552-681X</idno><imprint><publisher>Sage Publications</publisher><pubPlace>Sage CA: Thousand Oaks, CA</pubPlace><date type="published" when="1999-01">1999-01</date><biblScope unit="volume">19</biblScope><biblScope unit="issue">1</biblScope><biblScope unit="page" from="66">66</biblScope><biblScope unit="page" to="77">77</biblScope></imprint><idno type="ISSN">0272-989X</idno></series></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0272-989X</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="Teeft" xml:lang="en"><term>Abnormal child</term><term>Abnormal fetus</term><term>Abnormality</term><term>Abortion</term><term>Actual test choice</term><term>Actual test choices</term><term>Alternative rules</term><term>Amniocentesis</term><term>Best outcome</term><term>Chononic villus sampling</term><term>Chorionic</term><term>Chorionic villi sampling</term><term>Chorionic villus</term><term>Chorionic villus sampling</term><term>Chromosomal abnormality</term><term>Clin epidemiol</term><term>Confidence interval</term><term>Cutoff</term><term>Cutoff value</term><term>Decision analyses</term><term>Decision analysis</term><term>Decision models</term><term>Decision rule</term><term>Decision rules</term><term>Decision tree</term><term>Equal weights</term><term>Fetus</term><term>First tnmester</term><term>Genetic abnormality</term><term>Genetic testing</term><term>Health outcomes</term><term>High preference rating</term><term>John wiley</term><term>Lexicographic</term><term>Likely outcome</term><term>Linear rating scales</term><term>Little impact</term><term>Logistic regression</term><term>Lower preference rating</term><term>Median preference rating</term><term>Minor effects</term><term>Normal child</term><term>Normal fetus</term><term>Obstet gynecol</term><term>Obstetnc vanables</term><term>Obstetric</term><term>Obstetric confounders</term><term>Obstetrical genetics clinics</term><term>Odds ratio</term><term>Odds ratios</term><term>Other test</term><term>Outcome probabilities</term><term>Patient choice</term><term>Patient decision</term><term>Patient decision analysis</term><term>Patient preferences</term><term>Physician</term><term>Physician choice</term><term>Physician preferences</term><term>Physician sample sizes</term><term>Physician variables</term><term>Preference</term><term>Preference rating</term><term>Preference ratings</term><term>Preference scale</term><term>Preferred test</term><term>Prenatal</term><term>Prenatal outcome</term><term>Prenatal outcomes</term><term>Prenatal test</term><term>Prenatal test choice</term><term>Prenatal test choices</term><term>Prenatal testing</term><term>Prenatal tests</term><term>Probability estimates</term><term>Prospective studies</term><term>Rating</term><term>Regrettable outcome</term><term>Results support</term><term>Sensitivity analyses</term><term>Separate models</term><term>Similar fashion</term><term>Sociodemographic</term><term>Spontaneous abortion</term><term>Spontaneous abortions</term><term>Standard gambles</term><term>Such preferences</term><term>Test choice</term><term>Test choices</term><term>Therapeutic abortion</term><term>Therapeutic abortions</term><term>Unadjusted analyses</term><term>Utility rules</term><term>Villus</term><term>Worst outcome</term></keywords></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">The choice between amniocentesis and chononic villus sampling for prenatal genetic testing involves tradeoffs of the benefits and risks of the tests. Decision analysis is a method of explicitly weighing such tradeoffs. The authors examined the relationship between prenatal test choices made by patients and the choices prescribed by deci sion-analytic models based on their preferences, and separate models based on the preferences of their physicians. Preferences were assessed using written scenarios describing prenatal testing outcomes, and were recorded on linear rating scales. After adjustment for sociodemographic and obstetric confounders, test choice was signifi cantly associated with the choice of decision models based on patient preferences (odds ratio 4.44; CI, 2.53 to 7.78), but not with the choice of models based on the preferences of the physicians (odds ratio 1.60; CI, 0.79 to 3.26). Agreement between decision analyses based on patient preferences and on physician preferences was little better than chance (kappa = 0.085 ± 0.063). These results were robust both to changes in the decision-analytic probabilities and to changes in the model structure itself to simulate non-expected utility decision rules. The authors conclude that patient but not physician preferences, incorporated in decision models, correspond to the choice of amniocentesis or chorionic villus sampling made by the patient. Nevertheless, because patient preferences were assessed after referral for genetic testing, prospec tive preference-assessment studies will be necessary to confirm this association. Key words: decision analysis; patient preferences; physician preferences; prenatal genetic testing. 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Heckerling MD</name></json:item><json:item><name>Marion S. Verp MD</name></json:item><json:item><name>Nancy Albert</name></json:item></author><articleId><json:string>10.1177_0272989X9901900109</json:string></articleId><arkIstex>ark:/67375/M70-MCLJCN8V-H</arkIstex><language><json:string>eng</json:string></language><originalGenre><json:string>other</json:string></originalGenre><abstract>The choice between amniocentesis and chononic villus sampling for prenatal genetic testing involves tradeoffs of the benefits and risks of the tests. Decision analysis is a method of explicitly weighing such tradeoffs. The authors examined the relationship between prenatal test choices made by patients and the choices prescribed by deci sion-analytic models based on their preferences, and separate models based on the preferences of their physicians. Preferences were assessed using written scenarios describing prenatal testing outcomes, and were recorded on linear rating scales. After adjustment for sociodemographic and obstetric confounders, test choice was signifi cantly associated with the choice of decision models based on patient preferences (odds ratio 4.44; CI, 2.53 to 7.78), but not with the choice of models based on the preferences of the physicians (odds ratio 1.60; CI, 0.79 to 3.26). Agreement between decision analyses based on patient preferences and on physician preferences was little better than chance (kappa = 0.085 ± 0.063). These results were robust both to changes in the decision-analytic probabilities and to changes in the model structure itself to simulate non-expected utility decision rules. The authors conclude that patient but not physician preferences, incorporated in decision models, correspond to the choice of amniocentesis or chorionic villus sampling made by the patient. Nevertheless, because patient preferences were assessed after referral for genetic testing, prospec tive preference-assessment studies will be necessary to confirm this association. Key words: decision analysis; patient preferences; physician preferences; prenatal genetic testing. 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Decision analysis is a method of explicitly weighing such tradeoffs. The authors examined the relationship between prenatal test choices made by patients and the choices prescribed by deci sion-analytic models based on their preferences, and separate models based on the preferences of their physicians. Preferences were assessed using written scenarios describing prenatal testing outcomes, and were recorded on linear rating scales. After adjustment for sociodemographic and obstetric confounders, test choice was signifi cantly associated with the choice of decision models based on patient preferences (odds ratio 4.44; CI, 2.53 to 7.78), but not with the choice of models based on the preferences of the physicians (odds ratio 1.60; CI, 0.79 to 3.26). Agreement between decision analyses based on patient preferences and on physician preferences was little better than chance (kappa = 0.085 ± 0.063). These results were robust both to changes in the decision-analytic probabilities and to changes in the model structure itself to simulate non-expected utility decision rules. The authors conclude that patient but not physician preferences, incorporated in decision models, correspond to the choice of amniocentesis or chorionic villus sampling made by the patient. Nevertheless, because patient preferences were assessed after referral for genetic testing, prospec tive preference-assessment studies will be necessary to confirm this association. Key words: decision analysis; patient preferences; physician preferences; prenatal genetic testing. 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<contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Heckerling</surname><given-names>Paul S.</given-names></name><degrees>MD</degrees></contrib></contrib-group>
<contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Verp</surname><given-names>Marion S.</given-names></name><degrees>MD</degrees></contrib></contrib-group>
<contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Albert</surname><given-names>Nancy</given-names></name></contrib></contrib-group>
<pub-date pub-type="ppub"><month>01</month><year>1999</year></pub-date><volume>19</volume><issue>1</issue><fpage>66</fpage><lpage>77</lpage><abstract><p>The choice between amniocentesis and chononic villus sampling for prenatal genetic testing involves tradeoffs of the benefits and risks of the tests. Decision analysis is a method of explicitly weighing such tradeoffs. The authors examined the relationship between prenatal test choices made by patients and the choices prescribed by deci sion-analytic models based on their preferences, and separate models based on the preferences of their physicians. Preferences were assessed using written scenarios describing prenatal testing outcomes, and were recorded on linear rating scales. After adjustment for sociodemographic and obstetric confounders, test choice was signifi cantly associated with the choice of decision models based on patient preferences (odds ratio 4.44; CI, 2.53 to 7.78), but not with the choice of models based on the preferences of the physicians (odds ratio 1.60; CI, 0.79 to 3.26). Agreement between decision analyses based on patient preferences and on physician preferences was little better than chance (kappa = 0.085 ± 0.063). These results were robust both to changes in the decision-analytic probabilities and to changes in the model structure itself to simulate non-expected utility decision rules. The authors conclude that patient but not physician preferences, incorporated in decision models, correspond to the choice of amniocentesis or chorionic villus sampling made by the patient. Nevertheless, because patient preferences were assessed after referral for genetic testing, prospec tive preference-assessment studies will be necessary to confirm this association. <italic>Key words:</italic> decision analysis; patient preferences; physician preferences; prenatal genetic testing. <bold> (Med Decis Making 1999;19:66-77)</bold></p></abstract><custom-meta-wrap><custom-meta xlink:type="simple"><meta-name>sagemeta-type</meta-name><meta-value>Other</meta-value></custom-meta><custom-meta xlink:type="simple"><meta-name>search-text</meta-name><meta-value>66 Modeling Clinicians' Decision ProcessesPatient or Physician Preferences for Decision AnalysisThe Prenatal Genetic Testing Decision SAGE Publications, Inc.1999DOI: 10.1177/0272989X9901900109 Paul S.Heckerling MD Marion S.Verp MD Nancy Albert The choice between amniocentesis and chononic villus sampling for prenatal genetic testing involves tradeoffs of the benefits and risks of the tests. Decision analysis is a method of explicitly weighing such tradeoffs. The authors examined the relationship between prenatal test choices made by patients and the choices prescribed by deci sion-analytic models based on their preferences, and separate models based on the preferences of their physicians. Preferences were assessed using written scenarios describing prenatal testing outcomes, and were recorded on linear rating scales. After adjustment for sociodemographic and obstetric confounders, test choice was signifi cantly associated with the choice of decision models based on patient preferences (odds ratio 4.44; CI, 2.53 to 7.78), but not with the choice of models based on the preferences of the physicians (odds ratio 1.60; CI, 0.79 to 3.26). Agreement between decision analyses based on patient preferences and on physician preferences was little better than chance (kappa = 0.085 ± 0.063). These results were robust both to changes in the decision-analytic probabilities and to changes in the model structure itself to simulate non-expected utility decision rules. The authors conclude that patient but not physician preferences, incorporated in decision models, correspond to the choice of amniocentesis or chorionic villus sampling made by the patient. Nevertheless, because patient preferences were assessed after referral for genetic testing, prospec tive preference-assessment studies will be necessary to confirm this association. Key words: decision analysis; patient preferences; physician preferences; prenatal genetic testing. (Med Decis Making 1999;19:66-77) The choice between amniocentesis (AMN) and chorionic villus sampling (CVS) for prenatal genetic testing involves tradeoffs of the benefits and risks of the two tests. AMN has a lower procedure-related spontaneous abortion rate,1 and lower false-positive3 4 and indeterminacy4,S rates than CVS. However, the results of AMN are frequently not available before the 18th gestational week, leading to increased maternal anxiety67 and decreased maternal-fetal bonding." In addition, termination of a pregnancy after an abnormal AMN requires a second-trimester abortion, which frequently involves induction of labor, and entails greater risk than a first-trimester procedure.910 Chorionic villus sampling provides the genetic diagnosis during the first trimester, minimizing maternal anxiety and providing the option of a safer, less traumatic first-trimester therapeutic abortion if the results are abnormal.91° However, CVS may be associated with an increased risk of spontaneous abortion, and slightly higher false-positive and in- determinacy rates, compared with AMN.11 In addition, CVS may be associated with an increased risk of limb-reduction defects (LRDs),12 especially when performed prior to 66 to 70 days gestational age, although not all studies support such an association. 13 Implicitly, if not explicitly, a woman choosing CVS over AMN is trading off a small increased risk to her pregnancy for the psychologic benefits of decreased anxiety, increased maternal-fetal bonding, and the option for a first-trimester therapeutic abortion in the event of a genetic abnormality. Decision analysis is a method for explicitly defining choices and outcomes under conditions of uncertainty. 14 15 In a prior work we found that decision-analytic results based on patient preferences for prenatal outcomes were associated with prenatal test choices actually made by the patient.16 However, a significant proportion of women did not choose the tests prescribed by de- Received November 5, 1997, from the Section of General Internal Medicine, Department of Medicine, University of Illinois (PSH), and the Department of Obstetrics and Gynecology, University of Chicago (MSV, NA), Chicago, Illinois. Revision accepted for publication September 22, 1998. Supported without restriction by grant HS06945 from the Agency for Health Care Policy and Research Address correspondence and reprint requests to Dr. Heck- erling. Department of Medicine (M/C 787), University of Illinois, 840 South Wood Street, Chicago, IL 60612; phone (312) 413-0312; fax (312) 413-8283 7167 cision-analytical models based on their preferences. Although there were several possible reasons for this discrepancy, one potential explanation was that in cases where decision analysis and test choice disagreed, the preferences used to select the test were the physicians' rather than the patients'. If so, then in these cases, decision-analytic models based on physician preferences might more accurately reflect the choices of prenatal tests made by their patients. To address this possibility, in our current study we constructed decision-analytic models based on patients' preferences for prenatal outcomes and separate models based on the preferences of their physicians. We sought to answer the following questions : First, among women choosing AMN or CVS for the indication of maternal age, how do decision-analytic models based on their preferences relate to actual test choices? Second, how do decision-analytic models based on the preferences of their physicians relate to test choices? And third, how do the choices of decision-analytic models based on patient preferences and on physician preferences compare? Methods PATIENT AND PHYSICIAN SAMPLES We enrolled 372 consecutive pregnant women referred by their obstetricians to the obstetrical genetics clinics of the University of Chicago Hospitals for prenatal counseling and testing for advanced maternal age. Patients presenting because of an abnormal fetus or child in a previous pregnancy, a family history of genetic disease, or an abnormal maternal serum screen (alpha-fetoprotein, chorionic gonadotropin, and unconjugated estriol) were excluded. The obstetrical genetics clinics consisted of a CVS clinic, in which patients presented at 10 to 11 weeks' gestation for counseling and CVS, and an AMN clinic, in which patients presented at 15 to 16 weeks' gestation for counseling and AMN. In the clinics, patients were counseled about their risks of a chromosomal abnormality, their risks of a spontaneous abortion after prenatal testing, their risks of an indeterminate result on CVS that would necessitate AMN for clarification, and their risk of a LRD after CVS. The risks of morbidity after first- and second-trimester therapeutic abortions were not routinely discussed unless the patient inquired. For each study patient, we attempted to enroll the physician who had referred that patient for counseling and testing. Physicians were mailed a questionnaire explaining that at least one of their patients was enrolled in a study concerning patient and physician preferences for prenatal genetic testing, and inviting them to participate. Physicians who did not return the questionnaire within eight weeks received a second questionnaire and a telephone call requesting their participation. Ninety-six of the 137 eligible physicians (70.1%) agreed to participate. PREFERENCES AND LOCUS OF DECISION MAKING Patients were asked to complete a written questionnaire indicating their preference ratings for potential outcomes of prenatal testing. Outcomes included were: first- and second-trimester diagnosis (by CVS and AMN, respectively) of a normal child; spontaneous abortions of an abnormal fetus and of a normal fetus after AMN, after CVS, and without prenatal testing; first- and second-trimester therapeutic abortions after an abnormal prenatal test, with abortus confirmed as abnormal and with disconfirmed results (abortus normal); maternal bleeding or infection necessitating hospitalization after therapeutic abortion; and birth of a child with LRD after AMN, after CVS, and without prenatal testing. Each preference was assessed using a written vignette asking the patient to assume that she had experienced the outcome in question. Preferences were recorded in the obstetrical genetic clinic (AMN or CVS) to which the patient had been referred, prior to formal genetic counseling and testing. No patient was counseled on the basis of preferences from the questionnaire. The physicians received a similar questionnaire eliciting their preference ratings for all the outcomes of testing listed above. However, in the vignettes the physicians were asked to assume that their patients, not they, experienced the outcome in question. To control for any effects of vignette order on preference, both patients and physicians were randomized to receive questionnaires presenting either AMN- or CVS-related outcomes first. Preferences were recorded on linear rating scales" anchored at 100 units (representing the best outcome) and 0 units (representing the worst outcome). Patients and physicians were instructed to assume that the birth of a genetically abnormal child was equal to 0 unit. Patients and physicians indicated their preference ratings for each outcome by marking the appropriate point on the scale. The distance from the "0" point to the mark was measured to calculate each preference rating (that is, preference rating = 100 [distance from "0" to mark]/ [length of rating scale line]). Patients and physicians were also asked whether the decision to have AMN or CVS had been made principally by themselves, made by their physicians/ patients, or shared between them. Responses were recorded on a five-point Likert-type scale, with a spectrum ranging from "decision entirely mine" to "decision entirely physician's/patient's." For pur- 7268 poses of analysis, in cases in which the patient's and physician's perceptions of locus of decision making did not agree, the patient's perception was used. DECISION ANALYSES For each patient, we performed two decision analyses, the first using the patients' preference ratings, and the second using their physicians' ratings. We used a decision tree that modeled the choice of AMN and CVS, and included probability data important in making that choice.18,19 For each patient we used age-adjusted prevalences of chromosomal abnormalities at the time of CVS or AMN} 20,22 and age-adjusted rates and trimester-specific distributions of spontaneous abortions of both normal and abnormal abortuses.23,24 For the baseline analysis, we assumed that CVS increased the odds of a LRD birth by a factor of 4.7, based on results of a recent case-control study.25 However, we also performed analyses assuming that CVS did not increase the odds of a LRD birth.2' All probabilities used in the analysis are shown in appendix A. Expected preferences for AMN and CVS were calculated by folding back the decision tree twice, first using the patients' preference ratings, and then using their physicians' preference ratings. (We call all decision-analytic results "expected preferences" rather than "expected utilities" becausing rating-scale preferences do not conform to the axioms of utility theory 17) . For each analysis, the test with the higher expected preference was considered the decision-analytic choice for that patient or physician. We also calculated differences in expected preferences between the AMN and CVS strategies, to determine how much better one strategy was than the other. In addition, to explore the effect of over- or underestimation of probabilities by patients, we performed sensitivity analyses on all CVS-related outcome probabilities in the patient-preference-based models. All calculations were performed using SMLTREE decision analysis software. 21 NON-EXPECTED UTILITY RULES We also examined the effect of changes in the decision algorithm itself by substituting several non-expected utility decision rules for the decision-analytic model. Because these rules generally require less mental effort to implement than decision analysis, they may more closely approximate human decision-making strategies.29 We modeled six alternative decision rules, including an equal weights rule} 30 an elimination by aspects rule,31 a majority of confirming decisions rule/9 a satisficing rule/2 a lexicographic (and a lexicographic semi-order) rule,33 and a minimax regret rule.34 Each rule is described in detail in appendix B. Some of the rules (equal weights, majority of confirming decisions) are compensatory rules, in the sense that a high preference rating for a less likely outcome can compensate for a lower preference rating for a more likely outcome (as with decision analysis). Others (elimination by aspects, lexicographic, minimax regret) are noncompensatory rules, in the sense that a high preference rating for a less likely outcome cannot compensate for a lower preference rating for a more likely outcome. As in the decision-tree analyses, each decision rule was analyzed twice, first using the patients' preference ratings, and then using their physicians' preference ratings. An implicit assumption of the analysis was that patients and their physicians used the same decision rules to choose a prenatal test. STATISTICAL ANALYSIS Concordance of patient and physician decision-analytic choices was measured using the kappa coefficient and its asymptotic standard error. Comparisons of decision analysis and non-expected utility rule variables between patients choosing AMN and patients choosing CVS, adjusted for sociodemographic, obstetric, decision locus, and preference covariates, were made using unconditional logistic regression." Because of clustering of patients within physicians, generalized estimating equations36 were used to estimate regression parameters and their variances. We report generalized estimating equations based on a correlation structure assuming independence of patients within physicians; results based on an exchangeable correlation structure for a random effects model were essentially equivalent. Adjusted odds ratios and 95% confidence intervals (CI) were calculated from the logistic coefficients and their standard errors. Post-hoc power calculations were performed to determine detectability of odds ratio changes for AMN and CVS, given patient and physician sample sizes, and intercorrelations between patient and physician variables." Results PATIENT FACTORS Patient sociodemographic, obstetric, and locus-of-decision-making data are shown in table 1. Among the 372 patients, 288 (77.4%) chose AMN for prenatal testing, and 84 (22.6%) chose CVS. According to the patients, the choice of prenatal test was made entirely or mostly by the patient in 175 cases (48.6%), made entirely or mostly by the physician in 51 cases (14.1%), and shared equally between patient and physician in 134 cases (37.2%). 7369 Patient decision-analytic expected-preference and test-choice data are shown in table 2. Based on patient preferences, and assuming an increased risk of LRD after CVS, decision analysis indicated AMN as the test of choice in 234 cases (66.5%) and CVS as the test of choice in 118 cases (33.5%). Assuming no increased risk of LRD after CVS did not materially affect these results. Under both assumptions, decision-analytic choice and actual test choice agreed in 255 cases (72.4%) and disagreed in 97 cases (27.6%). Based on decision analyses incorporating patient preferences, difference in expected preference (a measure of how strongly one test ought to be preferred to the other) and decision-analytic choice (a measure of which test had the higher expected preference) were both significantly associated with actual test choices made by the patients (table 3). In analyses adjusted for sociodemographic and obstetric factors, having a decision analysis yielding CVS as the test of choice increased the patients' odds of Table 1 . Patient Sociodemographic, Obstetric, and Locus-of-decision-making Data Table 2 . Patient Decision-analytic Expected-preference and Test-choice Data *Based on a preference scale from 0 to 100, with 100 the best outcome and 0 the worst outcome. CVS = chonomc mllus samplng; AMN = amniocentesis. choosing CVS 4.4-fold (CI, 2.51 to 7.58). In adjusted analyses, a five-unit difference in expected preference between CVS and AMN increased the probability of choosing CVS 30% (CI, 12% to 52%); a ten- unit difference increased the probability 70% (CI, 26% to 131%). These results are predicated on an assumption of increased risk of LRD after CVS; however, an assumption of no increased risk had little impact on the results (table 3). Adjustment for physician preferences and for locus of decision making had little effect on these results (data not shown). PHYSICIAN FACTORS Sociodemographic, obstetric, practice, and locus-of-decision-making data for the 96 physicians are shown in table 4. Each physician saw an average of 3.3 study patients, ranging from 1 patient to 26 patients. According to the physicians, in their practices the choice of prenatal test was generally made entirely or mostly by the patient for 53 physicians (56.4%), entirely or mostly by them for 6 (6.4%), and shared between them and their patients for 35 (37.2%). Physicians were more likely than patients to believe that prenatal test choice was made mostly or entirely by the patient (p = 0.04). Physician decision-analytic expected-preference and test-choice data are shown in table 5. Based on decision analyses incorporating physician preferences, and for assumptions of both increased risk 7470 Table 3 . Odds Ratios for Choice of Chononic Villus Sampling (CVS) versus Amniocentesis (AMN) Based on Patient Decision-analytic Expected-preference and Test-choice Variables *Ad~usted for patient socioeconomic and obstetnc vanables of LRD after CVS and of no increased risk, decision analysis indicated AMN as the test of choice in 179 cases (65.3%) and CVS as the test of choice in 95 cases (34.7%). Concordance between decision-analytic choices based on patient preferences and on physician preferences was little better than chance (kappa = 0.085 ± 0.063 assuming an increased risk of LRD after CVS; kappa = 0.079 ± 0.062 assuming no increased risk). Physician decision-analytic choices and actual test choices made by the patients agreed in 173 cases (63.1%) and disagreed in 101 (36.9%). Decision-analytic test choice based on physician preferences did not predict actual test choice in un- adjusted analyses or in analyses adjusted for patient preferences (table 6). A physician decision-analytic choice of CVS did not predict patient choice of CVS either on the assumption of an increased risk of LRD after CVS (odds ratio 1.49; CI, 0.69 to 3.22; p = 0.31) or on the assumption of no increased risk (odds ratio 1.47; CI, 0.68 to 3.17; p = 0.33). Similarly, strength of physician preference for one test or the other, as measured by difference in expected preferences for CVS and AMN, did not predict choice of prenatal test. Even among cases where decision analysis based on patient preferences and test choice disagreed, physician decision-analytic choice did not predict test choice (odds ratio for CVS 1.21; CI, 0.36 to 4.01; p = 0.76). Adjustment for locus of decision making did not materially alter these results (data not shown). SENSITIVITY ANALYSES To explore the effect of patient over- or underestimation of outcome probabilities on test choice, we performed sensitivity analyses in which we varied all CVS-related probabilities in the decision analyses incorporating patient preferences. CVS-related probabilities were varied across a range of both plausible and implausible values to move them closer to or further from AMN-related probabilities. Probabilities in the decision analyses incorporating physician preferences were held fixed at their baseline values, since physicians would presumably base their decisions on the most plausible values for these variables based on the medical literature. As shown in table 7, variations in CVS-related probabilities for spontaneous abortion, abnormal or indeterminate test results, test sensitivity and specificity, and maternal morbidity due to therapeutic abortion had only minor effects on the associations of patient decision-analytic choice and actual test choice. When all probabilities favored CVS, a patient decision analysis yielding CVS as the test choice increased the odds of the patient's choosing CVS 6.9- fold (CI, 3.40 to 14.0). Conversely, when all probabilities favored AMN, a patient decision analysis yielding CVS increased the odds of the patient's 7571 choosing CVS 3.9-fold (CI, 2.24 to 6.81). A physician decision analysis yielding CVS did not increase the odds of the patient's choosing CVS whether patient probabilities favored CVS (odds ratio 1.25; CI, 0.57 to 2.71; p = 0.52) or favored AMN (odds ratio 1.43; CI, 0.67 to 3.06; p = 0.31). These results are based on an assumption of increased risk of LRD after CVS; however, an assumption of no increased risk did not significantly alter the results. NON-EXPECTED UTILITY RULES Variations in the decision rule itself generally had only minor effects on the associations of decision rule choice with actual test choice (table 8). For most decision rules (except minimax regret and satisficing with AMN considered first), a patient preference-based decision rule yielding CVS increased the odds of the patient's choosing CVS. This was true both for compensatory strategies such as equal weights (odds ratio 3.09; CI, 1.67 to 5.72) and majority of confirming decisions (odds ratio 3.83; CI, 1.98 to 7.40), and for noncompensatory strategies such Table 4 o Physician Sociodemographic, Obstetric, Practice, and Locus-of-decision-making Data *For either fetal matunty or genetic diagnosis. Table 5 . Physician Decision-analytic Expected-preference and Test-choice Data *Using the physician as the unit of analysis; based on a preference scale from 0 to 100, with 100 the best outcome and 0 the worst outcome. CVS = choriomc mllus samphng; AMN = amniocentesis. tusing the patient as the unit of analysis. as elimination by aspects (odds ratio 3.12; CI 1.68 to 5.80), lexicographic (odds ratio 6.03; CI, 2.73 to 13.3), and lexicographic semi-order (odds ratio 4.99; CI, 2.25 to 11.1). In addition, the confidence interval for the odds ratio for each of these rules included the odds ratio for decision analysis based on patient preferences (odds ratio 4.36 assuming an increased risk of LRD after CVS; odds ratio 4.44 assuming no increased risk). Thus, for patient-based analyses, decision analysis and non-expected utility rules (except for satisficing and minimax regret) yielded similar results. A physician preference-based decision rule yielding CVS as the test of choice did not significantly increase the odds of the patient's choosing CVS (table 8). In addition, for all rules examined, the confidence interval for the odds ratio included the odds ratio for decision analysis based on physician preferences (odds ratio 1.49 assuming an increased risk of LRD after CVS; odds ratio 1.47 assuming no increased risk). Thus, for physician-based analyses, as for patient-based analyses, decision analysis and non-expected utility rules yielded generally similar findings. Nevertheless, for several rules, including equal weights (odds ratio 2.04; CI, 0.98 to 4.27), majority of confirming decisions (odds ratio 1.87; CI, 0.93 to 3.77), lexicographic (odds ratio 4.40; CI, 0.95 to 20.3), and lexicographic semi-order (odds ratio 7672 Table 6 o Odds Ratios for Patient Choice of Chorionic Villus Sampling (CVS) versus Amniocentesis (AMN) Based on Physician Decision-analytic Expected-preference and Test-choice Variables *Ad~usted for patient expected-preference and decision-analytic choice vanables. 2.05; CI, 0.96 to 4.35), there were nonsignificant trends toward increased odds of patients' choosing CVS based on physician preferences. POWER ANALYSES Post-hoc analyses showed that based on patient and physician sample sizes, and on intercorrelations between patient and physician preferences, our study had 81.5% power to detect odds ratios for CVS of 1.5 for physician decision-analytic results, adjusted for patient values for these variables. Discussion We found that decision-analytic results based on patients' preferences for prenatal outcomes were strongly associated with their choices of AMN or CVS for prenatal genetic testing. Decision analyses based on physicians' preferences often disagreed with those of their patients, and were not associated with their patients' choices of test, Thus, based on decision analysis, patients' preferences, and not those of their physicians, were the principal determinants of the tests they chose for prenatal genetic diagnosis. In our study, the patients were not counseled on the basis of their preferences or decision-analytic results. Nevertheless, having a decision analysis prescribing CVS as the test of choice increased the patients' odds of choosing CVS more than fourfold. These results do not imply that the patients actually used decision analysis or similar mental heuristics to weigh their preferences for likely prenatal outcomes (e.g., the birth of a normal child), unlikely outcomes (e.g., test-related spontaneous abortion; a chromosomally-abnormal gestation), and remote outcomes (therapeutic abortion of a normal fetus due to test error; the birth of a child with LRD) in choosing between AMN and CVS. Indeed, it is likely that the patients used far simpler mental heuristics to reach their decisions.3g'39 In support of this possibility, we found that across a variety of simple non-expected utility rules, a rule prescribing CVS remained strongly associated with choice of CVS, with odds ratios similar in magnitude to those for decision analysis. Thus, it is likely that decision-analytic models based on patient preferences incorporate elements of simpler heuristics that drive test choices in the prenatal domain. Decision analyses based on physician preferences did not predict patient test choices either in unad- justed analyses or in analyses that adjusted for patient preferences. Even among cases where decision analysis based on patient preferences and actual test choice disagreed, where physician preferences most conceivably could have determined test choice, physician decision-analytic choice did not predict test choice. Failure to find such an association did not reflect inadequate statistical power. Based on patient and physician sample sizes, and on intercorrelations between patient and physician preferences, our 7773 Table 7 o Odds Ratios for Patient Choice of Chononic Villus Sampling (CVS) versus Amniocentesis (AMN) Based on Patient and Physician Decision-analytic Test-choice Variables, at Different Values of Probabilities of CVS-related Outcomes* *Assuming odds ratio for limb-reduction defect (LRD) after CVS (compared mth after AMN) = 4.7. t Adjusted for patient socioeconomic and obstetnc vanables. ~Adjusted for patient decision-analytic choice. §Lower estimate (arrin) represents the maternal age-adjusted probability of a chromosomal abnormality at the time of AMN, upper estimate (cvs+) represents the age-adjusted probability of a chromosomal abnormality at the time of CVS plus the difference m abnormality rates at CVS and AMN. Table 8 o Odds Ratios for Patient Choice of Chonomc Villus Sampling (CVS) versus Amniocentesis (AMN) Based on Test Choices of Non-expected-utility Decision Rules incorporating Patient and Physician Preferences *See appendix B for a descnption of the decision rules t Adjusted for patient socioeconomic and obstetnc vanables. $Ad~usted for patient decision-rule choice. §CVS examined first. DAMN examined first IlUsing a just-noticeable difference of 5 preference units. study had over 80% power to detect odds ratios of 1.5 for physician decision-analytic variables. In addition, although some trends toward associations between physician preferences and test choice emerged for several non-expected utility rules, these were in no case statistically significant, and in all cases compatible with decision-analytic results. Thus, although we cannot exclude a contribution of physician preferences to patient decision making, our findings most likely reflect a true predominance 7874 of patient over physician preferences in determining an appropriate prenatal test. Our results support a model of patient autonomy, in which physicians may provide alternatives but patients make choices based on their preferences. In such circumstances, the tradeoffs normally involved in a decision would be based on patients' values and not on physicians' values. Patient- and physician-based decision analyses agreed upon a test of choice in 58% of cases, which was little better than chance. Because patient- and physician-based analyses incorporated equivalent maternal probabilities, lack of agreement between them could reflect only differences in preferences for prenatal outcomes. These preference differences were sufficient to cause patient and physician decision models to disagree on test choices in almost 42% of cases. Our results support those of Boyd et al.} 40 who showed that patients' and physicians' utilities for colostomy led to differing choices of surgery versus radiotherapy in a decision-analytic model of therapy for colorectal cancer. They also support those of Holmes et al., 41 who found significant discrepancies between patients' and physicians' utilities for clinical outcomes associated with estrogen- replacement therapy in postmenopausal women. There are several limitations to our study. First, and most important, women provided preferences for prenatal testing after they had decided which test to have. Thus, it is possible that some preferences were confounded by stage of decision making, and served to some extent as a post hoc justification for the choice. Studies have shown that for decisions under certainty, post-decision preferences may shift toward a chosen alternative compared with pre-de- cision preferences.42 It is possible that for decisions under uncertainty, similar processes may be operative as well. It is also possible that some women may have presented to their physicians when their pregnancies was too advanced for CVS, and favored AMN because it was their only option. Unfortunately, study constraints precluded assessment of patient preferences and pregnancy status at the time of the pre-referral visit. Prospective studies, with preference assessments performed prior to referral for genetic testing, will be necessary to confirm the associations we found. Second, because we did not obtain probability estimates for prenatal outcomes from patients, we cannot exclude the possibility that their probabilities were poorly calibrated, and confounded with their preferences. If that were the case, then decision models based on these probability estimates, rather than on empirical data, might have revealed levels of agreement with test choice different from that which we found. However, sensitivity analysis demonstrated that variation in probability estimates in patient decision models had little effect on the associations of decision-analytic results and test choice. In addition, an equal-weights analysis, which ignored all probabilities of prenatal events, had little impact on our findings. These results support our earlier findings that prenatal test choice is in most circumstances a utility-, and not a probability-, driven decision.18 Nevertheless, it remains possible that incorporation of more extreme (and unrealistic) probabilities into the patient-based models might have altered our results. Third, we assessed patients' and physicians' preferences using linear rating scales rather than standard reference gambles, the axiomatically correct method based on utility theory.43 As a result, our decision-analytic measures were based on expected preference rather than expected utility. Nevertheless, rating-scale preferences have shown moderate correlations with standard gambles, 41 and are related to them through power functions.45 As a result, expected utility will (on average) increase monotonically in expected preference, and alternatives of choice based on expected-preference and expected- utility models will be identical. We used rating scales rather than standard gambles in order to improve patient and physician comprehension of the questionnaire. Fourth, we assumed that patients' and physicians' preferences for the birth of a genetically abnormal child were valued at 0 units, representing the worst possible outcome. Although some decision makers might consider outcomes such as therapeutic abortion to be worse than the birth of an abnormal child, such preferences are not common among patients electing prenatal genetic testing.46 The frequency of such preferences among physicians is not known. Fifth, we studied patients and physicians from one institution in Chicago. Our results therefore may not be applicable to other patient populations in other health care settings. In addition, we cannot be sure that nonparticipating physicians had preferences similar to those of participating physicians. Nevertheless, we studied women from diverse socioeconomic and ethnic backgrounds who were seen by physicians in private and hospital-based practices, strengthening the generalizability of our findings. Finally, we studied women who presented for prenatal testing for the indication of advanced maternal age. Our results therefore cannot be generalized to women who seek testing because of a prior genetic abnormality. Because of their greater likelihood of requiring therapeutic abortion, such women more often choose CVS,8 " as decision models suggest that they should. 18,48 In conclusion, decision models based on patients' preferences for prenatal outcomes predicted their prenatal test choices after controlling for sociode- 7975 mographic and obstetric confounders. However, decision models based on physicians' preferences were not associated with their patients' choices of prenatal tests. Thus, in the arena of prenatal genetic diagnosis it was the patients' preferences, and not those of their physicians, that determined the tests they chose. Although there was substantial concordance between patient decision-analytic choice and test choice, this does not imply that patients should be counseled regarding AMN and CVS using decision-analytic methods. Although others have successfully used decision analysis to guide the choice of AMN or no prenatal testing,46 49 whether it would be similarly useful in guiding the choice between AMN or CVS is not known, and will require further study. The authors are indebted to the patients and physicians who participated in the study, and to the two anonymous reviewers for suggestions for improving the manuscript. References Rhoads GG, Jackson LG, Schlesselman SE, et al. The safety and efficacy of chorionic villus sampling for early prenatal diagnosis N Engl J Med. 1989;320:609-17. Canadian Collaborative CVS-Amniocentesis Clinical Trial Group. Multicentre randomized clinical trial of chorionic villus sampling and amniocentesis Lancet. 1989;1:1-6. Benn PA, Hsu Lyf, Carlson A., Tannenbaum HL The centralized prenatal genetics screening program of New York City. III. The first 7000 cases Am J Med Genet. 1985;20:369-384. NICHD CVS Study Group. 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Alan R Liss, 1987;151-69 APPENDIX A Probabilities Used in the Decision Analyses Probability * Assuming fetal heartbeat seen at 8 to 10 weeks' gestaUon. With AMN, 45% of spontaneous abortions were assumed to occur in the first tnmester, and 55% were assumed to occur in the second and third tnmesters. With CVS, 55% of spontaneous abortions were assumed to occur m the first tnmester, and 45% were assumed to occur in the second and third trimesters. tThe excesses over reported liveborn rates represent abnormal fetuses that would have spontaneously aborted but were instead terminated by therapeutic abortion because the abnormalities were detected by prenatal testmg. 8177 APPENDIX B Non-expected-utility Decision Rules We describe below the non-expected utility rules used in the alternative rules analysis. Because decision analysis yielded a preferred test for all patients and physicians with preference data, alternative rules were expected to do so as well. Thus, in cases where alternative rules did not yield a preferred test after full implementation of their algorithms, a test was chosen at random. 1. Equal-weight rule30: This rule examined all possible outcomes of prenatal testing with AMN and CVS, but ignored the relative probabilities of these outcomes. For each test, the preference ratings for each prenatal outcome were summed, and the test with the largest sum was chosen. If the summed preference ratings for each test were equal, a test was chosen at random. 2. Elimination-by-aspects rule This rule examined the outcomes of prenatal testing one at a time, in descending order of probability, until a test was chosen. First, for each test, the preference rating for the most likely outcome (birth of a normal child) were compared with a cutoff value (the median preference rating for that outcome). If the preference rating for one test was above the cutoff value and the preference rating for the other test was below the cutoff value, the test above the cutoff was chosen, and the process was terminated. If the preference ratings for both tests were either above the cutoff or below the cutoff, the next most likely outcome (spontaneous abortion of a normal abortus) was examined in a similar fashion, and so on. If after serially examining all outcomes no test was chosen, a test was chosen at random. 3. Majority of confirming decisions rule29: This rule examined all outcomes of prenatal testing by comparing outcome pairs between AMN and CVS. The preference ratings for each test were compared on each prenatal outcome, and the test with the majority of winning comparisons was chosen. If after processing all outcomes the numbers of winning comparisons for the tests were identical, a test was chosen at random. 4. Satisficing rule This rule examined one test at a time until a test was chosen. Therefore, using this rule, it was possible to choose one test without ever examining the other. For the first test examined, the preference rating for each prenatal outcome was compared with a cutoff value (the median preference rating for that outcome). If any preference rating was below its cutoff value, that test was rejected, and the other test was then examined in a similar fashion. In the case where neither test exceeded cutoff values for all its preference ratings, a test was chosen at random. Because with this rule test choice may depend on which test is examined first, we implemented this rule both with AMN examined first and with CVS examined first. 5. Lexicographic rule33: As with elimination by aspects, this rule examined the outcomes of prenatal testing one at a time, in descending order of probability, until a test was chosen. First, for each test, the preference ratings for the most likely outcome were compared with each other. If the preference rating for one test was greater than the preference rating for the other test, that test was chosen, and the process was terminated. If the preference ratings for the two tests were identical, the next most likely outcome was examined in a similar fashion, and so on. If after serially examining all outcomes no test was chosen, a test was chosen at random. We also implemented a variation of this rule, the lexicographic semi-order rule, in which if the preference ratings for both tests were within a just noticeable difference (set at 5 preference units), they were considered to be identical. This assured that a test with marginally better preference ratings on a more probable outcome but much worse ratings on other less likely outcomes would not necessarily be chosen. 6. Minimax regret In a variation of the minimax loss algorithm, this rule examined the outcomes of prenatal testing one at a time, in descending order of "regret," until a test was chosen. Prenatal outcomes with lower median preference ratings were considered to be worse outcomes, and confer greater regret, than prenatal outcomes with higher median preference ratings. For each test, the preference ratings for the most regrettable outcome (therapeutic abortion of a normal fetus because of false-positive test results) were compared with each other. If the preference rating for one test was greater than the preference rating for the other test, that test was chosen, and the process was terminated. If the preference ratings for the two tests were identical, the next most regrettable outcome (spontaneous abortion of a normal fetus after prenatal testing) was examined in a similar fashion, and so on. 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</ref-list></back></article></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Patient or Physician Preferences for Decision Analysis</title><subTitle>The Prenatal Genetic Testing Decision</subTitle></titleInfo><titleInfo type="alternative" lang="en" contentType="CDATA"><title>Patient or Physician Preferences for Decision Analysis</title><subTitle>The Prenatal Genetic Testing Decision</subTitle></titleInfo><name type="personal"><namePart type="given">Paul S.</namePart><namePart type="family">Heckerling</namePart><namePart type="termsOfAddress">MD</namePart></name><name type="personal"><namePart type="given">Marion S.</namePart><namePart type="family">Verp</namePart><namePart type="termsOfAddress">MD</namePart></name><name type="personal"><namePart type="given">Nancy</namePart><namePart type="family">Albert</namePart></name><typeOfResource>text</typeOfResource><genre type="other" displayLabel="other" authority="ISTEX" authorityURI="https://content-type.data.istex.fr" valueURI="https://content-type.data.istex.fr/ark:/67375/XTP-7474895G-0">other</genre><originInfo><publisher>Sage Publications</publisher><place><placeTerm type="text">Sage CA: Thousand Oaks, CA</placeTerm></place><dateIssued encoding="w3cdtf">1999-01</dateIssued><copyrightDate encoding="w3cdtf">1999</copyrightDate></originInfo><language><languageTerm type="code" authority="iso639-2b">eng</languageTerm><languageTerm type="code" authority="rfc3066">en</languageTerm></language><abstract lang="en">The choice between amniocentesis and chononic villus sampling for prenatal genetic testing involves tradeoffs of the benefits and risks of the tests. Decision analysis is a method of explicitly weighing such tradeoffs. The authors examined the relationship between prenatal test choices made by patients and the choices prescribed by deci sion-analytic models based on their preferences, and separate models based on the preferences of their physicians. Preferences were assessed using written scenarios describing prenatal testing outcomes, and were recorded on linear rating scales. After adjustment for sociodemographic and obstetric confounders, test choice was signifi cantly associated with the choice of decision models based on patient preferences (odds ratio 4.44; CI, 2.53 to 7.78), but not with the choice of models based on the preferences of the physicians (odds ratio 1.60; CI, 0.79 to 3.26). Agreement between decision analyses based on patient preferences and on physician preferences was little better than chance (kappa = 0.085 ± 0.063). These results were robust both to changes in the decision-analytic probabilities and to changes in the model structure itself to simulate non-expected utility decision rules. The authors conclude that patient but not physician preferences, incorporated in decision models, correspond to the choice of amniocentesis or chorionic villus sampling made by the patient. Nevertheless, because patient preferences were assessed after referral for genetic testing, prospec tive preference-assessment studies will be necessary to confirm this association. Key words: decision analysis; patient preferences; physician preferences; prenatal genetic testing. (Med Decis Making 1999;19:66-77)</abstract><relatedItem type="host"><titleInfo><title>Medical Decision Making</title></titleInfo><genre type="journal" authority="ISTEX" authorityURI="https://publication-type.data.istex.fr" valueURI="https://publication-type.data.istex.fr/ark:/67375/JMC-0GLKJH51-B">journal</genre><identifier type="ISSN">0272-989X</identifier><identifier type="eISSN">1552-681X</identifier><identifier type="PublisherID">MDM</identifier><identifier type="PublisherID-hwp">spmdm</identifier><part><date>1999</date><detail type="volume"><caption>vol.</caption><number>19</number></detail><detail type="issue"><caption>no.</caption><number>1</number></detail><extent unit="pages"><start>66</start><end>77</end></extent></part></relatedItem><identifier type="istex">C5044293006C5AB9D07CC0419C64E55038D80BA1</identifier><identifier type="ark">ark:/67375/M70-MCLJCN8V-H</identifier><identifier type="DOI">10.1177/0272989X9901900109</identifier><identifier type="ArticleID">10.1177_0272989X9901900109</identifier><recordInfo><recordContentSource authority="ISTEX" authorityURI="https://loaded-corpus.data.istex.fr" valueURI="https://loaded-corpus.data.istex.fr/ark:/67375/XBH-0J1N7DQT-B">sage</recordContentSource></recordInfo></mods><json:item><extension>json</extension><original>false</original><mimetype>application/json</mimetype><uri>https://api.istex.fr/ark:/67375/M70-MCLJCN8V-H/record.json</uri></json:item></metadata><serie></serie></istex></record>Pour manipuler ce document sous Unix (Dilib)
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