Filling the gaps Breast and Lung ca

I N T R O D U C T I O NBreast and lung cancers are among the most

common cancers in the US population and ac-

count for a large proportion of cancer diag-

noses and deaths (1). Great efforts have been

devoted to the prevention and treatment of

these cancers (1,2). To help understand benefits

and costs of prevention and treatment strate-

gies, outcomes researchers have described, in-

terpreted, and predicted the impact of various

interventions on end points that matter to deci-

sion makers (2,3).

As the field of outcomes research developed,

researchers proposed ways to define this area of

inquiry conceptually and proposed frameworks

for describing key end points and for assessing

the impact of outcomes research on decision

making (2–4). Key end points that have been

described in the oncology literature include

survival, quality of life, mental health, cost, and

satisfaction (2–4). In recent years, cancer thera-

py objectives have changed from solely increas-

ing survival time for patients to that of improv-

ing patients’ quality of life and improving the

use of scarce resources. However, very little re-

search has been reported concerning whether

outcomes studies that investigate these key end

points actually affect decisions related to prac-

tice or policy (2).

To help understand the effects of outcomes re-

search on practice and policy, the Agency for

Health Care Policy and Research proposed a

four-level outcomes pyramid for assessing a

study’s “impact” (3,4). The agency proposed that

outcomes research can be classified, in ascend-

ing order, as that which “(1) adds to the knowl-

edge base only, (2) affects practice policies, (3)

influences the delivery of care, and (4) leads to

change in health outcomes” (4, p.1). An example

of a level 1 study is one that provides factual in-

formation. In contrast, a level 2 study is one that

may contribute to a policy or guideline change

as a direct result of the research and in turn po-

tentially could affect practice patterns. A level 3

study is one that directly changes what practi-

tioners or patients do or directly results in prac-

tice pattern changes (ie, delivery of care). A level

4 study not only directly leads to practice

changes, but also directly leads to change in

health outcomes. In such studies, the authors re-

port evidence that the practice change directly

leads to change in health outcomes. Researchers

who used this four-level framework concluded

Drug Information Journal, Vol. 39, pp. 335–344, 2005 • 0092-8615/2005Printed in the USA. All rights reserved. Copyright © 2005 Drug Information Association, Inc.

Opportunities for Filling Gaps in Breast and Lung

Cancer Outcomes Research*

Jon C. Schommer, PhDProfessor

Jose William Castellanos, MDPhD candidate

Luz Dalia Sanchez, MDPhD candidate

Samuel Wagner, PhDAdjunct Professor

Xin Ye, MSPhD candidate

University of MinnesotaCollege of Pharmacy,

Minneapolis, Minnesota

The purpose of this study was to describe re-ported research regarding breast and lungcancer outcomes in terms of (a) level of im-pact, (b) survival outcomes, (c) quality-of-lifeoutcomes, (d) mental health outcomes, (e)cost outcomes, and (f) satisfaction outcomes.In addition, we describe the impact that typeof cancer (breast or lung), year of publication(1999 to 2003), drug intervention (yes or no),surgical intervention (yes or no), and studytype (clinical trial, cohort study, or other) hadon the outcomes we studied. A total of 2,993articles were identified and reviewed, with1,470 (49.1%) of these articles related tobreast cancer outcomes and 1,523 (50.9%)

articles related to lung cancer outcomes. Themajority (85%) of the articles we reviewedwere coded as having only level 1 impact (addsto the knowledge base only). Logistic regres-sion analysis revealed that lung cancer studieswere more likely than breast cancer studies tohave outcomes at a level of impact greaterthan level 1 and to focus on survival outcomes.However, breast cancer studies were more likelythan lung cancer studies to focus on quality oflife, mental health, and satisfaction. The re-sults identified areas of inquiry for which re-searchers could provide more comprehensiveinvestigation to fill potential gaps in breastand lung cancer outcomes research.

Key WordsBreast cancer;

Lung cancer;Survival;

Quality of life;Cost;

Satisfaction;Patient outcomes

Correspondence AddressJon C. Schommer, PhD,

University of MinnesotaCollege of Pharmacy,

308 Harvard Street SE,Minneapolis, MN 55455

(email:[email protected]).

*Funding was provided by a Managed Care

Pharmaceutical OutcomesResearch Grant provided tothe University of Minnesotaby Pharmacia Corporation.

R E S E A R C H 335

R E S E A R C H336 Schommer et al.

that cancer outcomes research from 1989 to

1997 made substantial contributions at level 1;

they also concluded that cancer outcomes re-

search had little impact at levels 2 to 4 (3,4).

We are not aware of any recent analysis that

described breast and lung cancer outcomes re-

search in terms of key end points that were

studied or in terms of the impact of the studies.

Thus, the objective of this study was to de-

scribe outcomes research published from 1999

through 2003 regarding breast and lung can-

cer in terms of six descriptive variables: (a) lev-

el of impact, (b) survival outcomes, (c) quality-

of-life outcomes, (d) mental health outcomes,

(e) cost outcomes, and (f ) satisfaction out-

comes. In addition, our objective was to de-

scribe the effect that type of cancer (breast or

lung), year of publication (1999 to 2003), drug

intervention (yes or no), surgical intervention

(yes or no), and study type (clinical trial, co-

hort study, or other) had on the six outcomes

we studied. The results of this study can be

useful for identifying gaps that exist in breast

and lung cancer research regarding level of im-

pact and key end points.

M E T H O D SSAMPLE

The sample of published articles selected for this

study consisted of articles identified via a struc-

tured, predefined MEDLINE search to identify

potentially relevant, peer-reviewed references

from January 1999 through December 2003.

The search sample consisted of all the articles

retrieved by MEDLINE limited to the English

language and human subjects and that included

at least one of the outcomes we investigated (see

Appendix 1 for the complete list of the key words

used in the search). Exclusion criteria in this re-

view included studies dealing primarily with bi-

ology or pathology of cancer, studies focusing

mainly on surrogate indica-tors of outcomes

such as tumor size or blood counts, clinical re-

views, letters, comments, and editorials.

DEPENDENT VARIABLES

Six components related to cancer outcomes

served as dependent variables (see Appendix 2).

The first, called level of impact, was defined as

follows: adds to the knowledge base only (level

1), affects practice policies (level 2), influences

the delivery of care (level 3), and leads to change

in health outcomes (level 4) (3,4). The second

dependent variable we studied was called sur-

vival and included any study that mentioned

overall survival, disease-free survival, quality of

life–adjusted survival, or mortality as outcomes.

The third, called quality of life, included any

study that reported findings related to overall

quality of life, health-related quality of life, can-

cer-specific quality of life, or cancer site–specif-

ic quality of life. The fourth dependent variable,

mental health, included any study that included

any mental health–related outcomes (typically,

depression). The fifth dependent variable was

called cost and included any study that in-

cluded per-patient costs, per-institution costs,

or system costs as outcomes. Finally, the satis-

faction dependent variable included any study

that mentioned satisfaction with care or satis-

faction with the health delivery system from

patient, payer, or societal perspectives. Each

variable was coded as 1 if it was reported as an

outcome in the study and as 0 if it was not re-

ported as an outcome in the study reviewed.

INDEPENDENT VARIABLES

Type of cancer was either breast cancer (refer-

ence category) or lung cancer. We chose these

two cancers for study because they are among

the most common types of cancers affecting

humans and because one type (breast cancer)

represented a disease that is associated with rel-

atively long survival and the other type repre-

sented a disease that typically results in relative-

ly short survival. Our assumption was that the

unique and distinct aspects of these two types of

cancer could affect the types of outcomes re-

search produced in the literature.

Year of publication was 1999, 2000, 2001,

2002, or 2003, with the year 1999 serving as the

reference category. We started with 1999 to

build on reviews reported previously in the liter-

ature (3,4). Drug intervention was defined as any

study that included a drug as treatment modali-

ty in the study. Surgical intervention included any

Breast and Lung Cancer Outcomes Research Gaps R E S E A R C H 337

Drug Information Journal

study that used a surgical treatment as the in-

tervention. Our assumption was that studies

that included the effects of drug or surgical in-

terventions on outcomes could be different

from studies that described outcomes in the ab-

sence of such interventions. Study type included

clinical trial, cohort study, or other study type

(eg, case-control studies, cross-sectional stud-

ies, surveys, or interviews). The other category

served as the reference category. We used this

categorization to reflect the prescriptive nature

of both clinical and cohort designs when ap-

plied to cancer outcomes research compared to

the less-rigid guidelines for conducting other

types of studies in this research domain.

DATA CODING

To develop a data coding strategy for this study,

two researchers were trained to conduct coding

for a relatively small number of articles (n = 30)

to test interjudge reliability and determine the

number of judges needed in this study. The

judges were trained on the rules and procedures

for coding, and they independently scored each

article. Interjudge reliabilities were then calcu-

lated by using the Perreault and Leigh reliability

(5) index as follows:

I = {[(F/N) − (1/k)][k/(k − 1)]}1/2

where F is the observed frequency of agreement

between judges, N is the total number of judg-

ments, and k is the number of categories.

The two judges had a high level of agreement

in their scoring (interjudge reliability scores

were greater than 0.95 for study design, final

end point, and impact levels, respectively). In

light of reliability scores well above the recom-

mended level of 0.90, each article for this study

was coded by one researcher. One reviewer ana-

lyzed articles from breast cancer, and another

reviewed articles from lung cancer. Both review-

ers discussed and agreed with the inclusion and

exclusion criteria. Also, they conferred with

each other on any unclear or ambiguous coding

decisions. A third researcher conducted a re-

view of a small subset of articles (n = 30) to veri-

fy consistency in coding.

Following this process, each article was classi-

fied in one of the four impact categories (3,4).

In addition to level of impact, the reviewers clas-

sified using mutually agreed upon definitions

information contained in each article accord-

ing to the other five outcomes we investigated:

survival, quality of life, mental health, cost, and

satisfaction.

DATA ANALYSIS

Data were entered into SPSS for Windows statis-

tical software. Frequencies and descriptive sta-

tistics were calculated for each variable to help

identify miscoded data and outliers. Logistic re-

gression analysis was used for testing relation-

ships among study variables. Goodness of fit for

competing logistic regression models was as-

sessed based on the change in −2 log likelihood

and model improvement χ2 statistics. The best-

fitting model for each dependent variable was

chosen based on goodness of fit and parsimony

of interpretation (6). After the best-fitting mod-

el was determined, the odds ratio [exp (β)] and

corresponding 95% confidence interval

exp[β − 1.96(SEβ)] to exp[β + 1.96(SEβ)]

were computed for the regression coefficient of

each significant predictor variable.

R E S U LT SA total of 2,993 articles were identified and re-

viewed, with 1,470 (49.1%) of these articles relat-

ed to breast cancer outcomes, and 1,523 (50.9%)

articles related to lung cancer outcomes.

Table 1 shows that 84.7% of the studies we re-

viewed were at level 1 impact. The most com-

mon outcome studied in the articles we re-

viewed was survival (77.5%), followed by quality

of life (14.8%), cost (7.9%), mental health

(6.2%), and satisfaction (4.0%). Of the 2,993

studies we reviewed, only 810 (27.1%) included a

drug intervention as a treatment variable, and

341 (11.4%) included a surgical intervention as

a treatment variable. In terms of study type, 863

(28.8%) were cohort studies, and 549 (18.3%)

were clinical trials. However, the majority of the

studies (52.8%) were other types of studies,

such as case-control studies, cross-sectional

studies, surveys, or interviews.


Table 2 contains logistic regression results

for the associations of type of cancer, year of

publication, drug intervention, surgical inter-

vention, and study type with the six types of

cancer outcomes. The results showed that

lung cancer studies were more likely than

breast cancer studies to have outcomes at a

level of impact greater than level 1 and to fo-

cus on survival outcomes. However, breast

cancer studies were more likely than lung can-

cer studies to focus on quality of life, mental

health, and satisfaction.

In terms of year of publication, articles pub-

lished during the years 2000, 2001, and 2002

were more likely to report greater than level 1

outcomes compared to the reference year

(1999). However, articles published in 2003

were less likely to report greater than level 1

outcomes compared to the reference year

(1999). Also, Table 2 shows that articles pub-

lished during 2000 were more likely to focus on

mental health outcomes compared to the other

years we studied (1999, 2001, 2002, and 2003).

Of the studies we reviewed, 27% utilized a

drug as an intervention. Studies that used drugs

as an intervention were more likely to report im-

pacts greater than level 1 and were more likely

to focus on quality of life as an outcome. Howev-

er, drug intervention studies were less likely to

focus on mental health outcomes and satisfac-

tion than studies that did not utilize drugs as a

treatment intervention. Of the studies we re-

viewed, 11% utilized surgery as an intervention.

Studies that used surgery as an intervention

were more likely to report impacts greater than

level 1 and were more likely to focus on satisfac-

tion outcomes. However, surgery intervention

studies were less likely to focus on survival out-

comes than those that did not utilize surgery as

a treatment intervention.

Finally, clinical trials and cohort studies were

more likely to focus on survival outcomes and

less likely to focus on mental health, cost, and

satisfaction outcomes compared to other types

of studies. In addition, cohort studies were the

most likely study type to result in greater than

level 1 impact but the least likely study type to

focus on quality-of-life outcomes.

Variable Coding Frequency Percentage

Level of Impact

Level 1 (0) 2,536 84.7Level 2 (1) 368 12.3Level 3 (1) 89 3.0Level 4 (1) 0 0.0

Survival mentioned as an outcome

Yes (1) 2,320 77.5No (0) 673 22.5

Quality of life mentioned as an outcome

Yes (1) 444 14.8No (0) 2,549 85.2

Mental health mentioned as an outcome

Yes (1) 187 6.2No (0) 2,806 93.8

Cost mentioned as an outcome

Yes (1) 236 7.9No (0) 2,757 92.1

Satisfaction mentioned as an outcome

Yes (1) 119 4.0No (0) 2,874 96.0

Type of cancer

Breast cancer (1) 1,470 49.1Lung cancer (2) 1,523 50.9

Year of publication

1999 (1999) 639 21.32000 (2000) 622 20.82001 (2001) 620 20.72002 (2002) 618 20.62003 (2003) 494 16.5

Drug intervention

Yes (1) 810 27.1No (0) 2,183 72.9

Surgical intervention

Yes (1) 341 11.4No (0) 2,652 88.6

Study type

Clinical trial (2) 549 18.3Cohort study (1) 863 28.8Other* (0) 1,581 52.8

*Other included case-control studies, cross-sectional studies, surveys, andinterviews.

T A B L E 1Descriptive Summary of Articles Reviewed

(N = 2,993)



L I M I T A T I O N SBefore the results are discussed, some limita-

tions of this study should be noted. The litera-

ture search relied on MEDLINE indexing, which

may vary by clinical setting and journal subjects.

Therefore, some breast and lung cancer out-

comes research might have been missed by our

searches. Another limitation relates to our cod-

ing of the articles we found. It is possible that an-

other set of researchers might code the articles

differently from the way we did. Also, we catego-

rized studies into the various levels of impact

based on their potential impact as described by

the authors of the reviewed article and did not

require reported findings that showed actual im-

pact. Finally, we studied the effect that year of

publication could have on our dependent vari-

ables. However, it should be noted that varia-

tions in review and publication schedules at dif-

ferent journals could make these comparisons

suspect.

D I S C U S S I O NThe results of this study showed that lung can-

cer research has been more likely than breast

cancer research to focus on higher level impact

research studies (greater than level 1) and had a

greater focus on survival outcomes. In compari-

son to lung cancer, breast cancer has a more fa-

vorable survival time after diagnosis, and the re-

search in breast cancer has focused more on

quality of life, mental health, and satisfaction.

Our findings also revealed that there are vari-

ations in outcomes research depending on the

year of publication. This might reflect changes

in research funding priorities, accessibility to

data, or new discoveries that necessitate differ-

ent types of research. However, it is also likely

that differences in review and publication

schedules for different journals could make our

findings suspect. Because we did not find a sys-

tematic trend for the effect of year of publica-

tion and our dependent variables, we view the

findings for year of publication with caution.

Our findings also uncovered that drug inter-

vention studies published from 1999 to 2003 re-

sulted in higher level impact (greater than level

1) and quality-of-life outcomes than non-drug

studies but did not focus as much on mental

health outcomes and satisfaction. It appears that

there might be a gap in research that presents an

opportunity for more focus on mental health

outcomes and on humanistic outcomes such as

satisfaction in future drug intervention studies.

Clinical trials and cohort studies appear suit-

ed for research devoted to survival outcomes.

However, they tend to be limited in their focus

on mental health, cost, and satisfaction out-

comes. We suggest that efforts to augment clini-

cal trials and cohort studies with other types of

studies that would provide more information on

mental health, cost, and satisfaction outcomes

would be useful in cancer outcomes research.

Taken together, these results can help define

areas of inquiry for which researchers could

provide more comprehensive investigation to fill

potential gaps in cancer outcomes research.

The overall goal of this study was to identify

gaps in breast and lung cancer outcomes re-

search. Based on the findings, it is clear that

study designs are selected based on the type of

research questions asked. We suggest that some

important ways to fill in research gaps and to ex-

pand the impact of cancer outcomes research

would be for drug intervention studies to in-

clude more focus on mental health outcomes

and satisfaction. Also, we suggest that efforts to

include mental health, cost, and satisfaction

outcomes within (or complementary to) clinical

trials and cohort studies could be helpful for

understanding and improving the treatment of

breast cancer and lung cancer.

Also, we believe that our findings are useful for

identifying research needs to help decision

makers make choices among various cancer

therapies as new therapies become available.

Therapy selection has extended beyond tradi-

tional safety and efficacy studies that use place-

bo controls. Cancer therapy objectives have

changed from solely increasing survival time for

patients to improving patients’ quality of life

and improving the use of scarce resources. Thus,

therapy selection now needs to consider effec-

tiveness issues in various patient populations,

key end points in addition to just survival time,


T A B L E 2Type of Outcome† Proportion Yes Model Model χ2

Level of impact greater than level 1 15.3% Constant, CA TYPE, 769.6, P < .001YEAR, DRUG, SURGERY, STUDY TYPE

Survival 77.5% Constant, CA TYPE, 293.1, P < .001SURGERY, STUDY TYPE

Quality of life 14.8% Constant, CA TYPE, 131.1, P < .001DRUG, STUDY TYPE

Mental health 6.2% Constant, CA TYPE, 168.9, P < .001YEAR, DRUG, STUDY TYPE

Cost 7.9% Constant, STUDY TYPE 30.0, P < .001

Satisfaction 4.0% Constant, CA TYPE, DRUG, 154.8, P < .001SURGERY, STUDY TYPE

*Independent variables for logistic regression models were cancer type (CA TYPE), year (YEAR), drug (DRUG), surgery (SURGERY), and study type (STUDY TYPE). The best-fitting models for each type of outcome were determined by −2 log likelihood, model improvement χ2, and parsimony of interpretation.†See Appendix 2 for definitions of each type of outcome.‡Reference categories used for logistic regression analysis included type of cancer (CA TYPE) with Type (1) = Breast cancer (reference category), Type (2) = Lung cancer; yearof publication (YEAR) with 1999 (reference category), 2000, 2001, 2002, 2003; drug intervention (DRUG) with 0 = A drug was not a treatment modality in the study (refer-

Logistic Regression Models* for Types of Cancer Outcomes Research (N = 2,993)



T A B L E 2Predictor Variables‡ Odds Ratio exp (β) 95% Confidence Interval for the Odds Ratio§

CA TYPE (2) 14.39 9.34–22.1

YEAR (2000) 1.86 1.32–2.63

YEAR (2001) 4.46 3.17–6.26

YEAR (2002) 1.04 0.70–1.55

YEAR (2003) 0.27 0.14–0.54

DRUG (1) 2.71 2.05–3.58

SURGERY (1) 2.08 1.51–2.87

STUDY TYPE (1) 1.46 1.09–1.97

STUDY TYPE (2) 1.10 0.76–1.55

CA TYPE (2) 2.68 2.18–3.29

SURGERY (1) 0.64 0.47–0.87

STUDY TYPE (1) 3.41 2.64–4.42

STUDY TYPE (2) 1.82 1.37–2.41

CA TYPE (2) 0.54 0.42–0.69

DRUG (1) 2.25 1.64–2.80

STUDY TYPE (1) 0.33 0.24–0.45

STUDY TYPE (2) 0.76 0.56–1.03

CA TYPE (2) 0.23 0.15–0.36

YEAR (2000) 2.15 1.32–3.50

YEAR (2001) 1.15 0.69–1.94

YEAR (2002) 1.35 0.82–2.22

YEAR (2003) 1.13 0.66–1.93

DRUG (1) 0.39 0.21–0.73

STUDY TYPE (1) 0.38 0.23–0.60

STUDY TYPE (2) 0.44 0.22–0.89

STUDY TYPE (1) 0.43 0.30–0.61

STUDY TYPE (2) 0.52 0.35–0.77

CA TYPE (2) 0.25 0.14–0.44

DRUG (1) 0.27 0.11–0.69

SURGERY (1) 3.66 2.21–6.06

STUDY TYPE (1) 0.15 0.07–0.33

STUDY TYPE (2) 0.25 0.08–0.83

ence category), 1 = A drug was a treatment modality in the study; surgical intervention (SURGICAL) with 0 = Surgery was not a treatment modality in the study (referencecategory), 1 = Surgery was a treatment modality in the study; study type (STUDY TYPE) with 0 = Study type other than a cohort study or a clinical trial (reference category),1 = Cohort study, 2 = Clinical trial (examples of other types of studies included case-control studies, cross-sectional studies, surveys, or interviews).§Confidence intervals for the odds ratio = exp[β − 1.96(SEβ)] to exp[β + 1.96(SEβ)].

Continued


and costs of therapy in relation to existing ther-

apies, a reference case, or budgetary con-

straints. Our findings reveal gaps in the litera-

ture that need to be filled so that improved

decision making about therapies can be made.

C O N C L U S I O N SThe majority (85%) of the articles we reviewed

had a level 1 impact in that they added to the

knowledge base only. Relatively few studies af-

fected practice policies (level 2) or influenced

the delivery of care (level 3). None of the articles

we reviewed led to direct change in health out-

comes (level 4). We conclude that there are gaps

in breast and lung cancer outcomes research

that need to be filled by more comprehensive

investigation.

A P P E N D I X 1KEY WORDS

1. exp Lung Neoplasms/ or lung cancer.mp (mp = title, abstract, name of substance, MeSH subject

heading)

2. exp ECONOMICS/ or exp ECONOMICS, DENTAL/ or exp ECONOMICS, HOSPITAL/ or exp ECO-

NOMICS, MEDICAL/ or exp ECONOMICS, NURSING/ or expe ECONOMICS, PHARMACEUTICAL/

3. exp “Costs and Cost Analysis”/

4. exp DISEASE-FREE SURVIVAL/ or exp SURVIVAL/ or exp SURVIVAL ANALYSIS/ or exp SURVIVAL

RATE/

5. exp Quality of Life/

6. exp Personal Satisfaction/ or Satisfaction.mp (mp = title, abstract, name of substance, MeSH sub-

ject heading)

7. exp Mental Health/

8. 2 or 3 or 4 or 5 or 6 or 7

9. 1 and 8

10. exp Health Services Accessibility/

11. exp “Outcome Assessment (Health Care)”/ or exp Health Services Research/

12. exp Quality of Health Care/

13. exp Quality Assurance, Health Care/

14. exp Quality Indicators, Health Care/

15. exp Practice Guidelines/

16. exp Decision Making/

17. 11 or 12 or 13 or 14 or 15 or 16

18. 9 and 17

19. 18

20. Limit 19 to (human and English language)

21. 20

22. Limit 21 to yr=1999

23. 20

24. Limit 23 to yr=2000

25. 20

26. Limit 25 to yr=2001

27. 20

28. Limit 27 to yr=2002

29. 20

30. Limit 29 to yr=2003



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A P P E N D I X 2DEPENDENT VARIABLES (COMPONENTS OF CANCER OUTCOMES RESEARCH)

Level of Impact: The primary outcome for each study (survival, quality of life, mental health, cost, or

satisfaction) was coded as follows: Level 1 adds to the knowledge base only; Level 2 affects practice

policies; Level 3 influences the delivery of care, or Level 4 leads to change in health outcomes. Level

1 was coded as 0; Levels 2–4 were coded as 1.

Survival: Included mentions of overall survival, disease-free survival, or mortality (1 = survival was an

outcome studied, 0 = survival was not an outcome studied).

Quality of Life: Included mentions of adjusted quality of life, generic quality of life, cancer-specific

quality of life, or cancer site–specific quality of life (1 = quality of life was an outcome studied, 0 =quality of life was not an outcome studied).

Mental Health: Included mentions of depression or other mental health–related outcomes (1 = men-

tal health was an outcome studied, 0 = mental health was not an outcome studied).

Cost: Included per-patient costs, per-institution costs, or system costs (1 = cost was an outcome

studied, 0 = cost was not an outcome studied).

Satisfaction: Included mentions of satisfaction with care or satisfaction with the health delivery sys-

tem from patient, payer, or societal perspectives (1 = satisfaction was an outcome studied, 0 = satis-

faction was not an outcome studied).

Independent Variables

Type of Cancer (CA TYPE):

Type (1) = Breast cancer (reference category)

Type (2) = Lung cancer

Year of Publication (YEAR):

1999 (reference category)

2000

2001

2002

2003

Drug Intervention (DRUG):

0 = A drug was not a treatment modality in the study (reference category)

1 = A drug was a treatment modality in the study

Surgical Intervention (SURGICAL):

0 = Surgery was not a treatment modality in the study (reference category)

1 = Surgery was a treatment modality in the study

Study Type (STUDY TYPE):

0 = Study type other* than a cohort study or a clinical trial (reference category)

1 = Cohort study

2 = Clinical trial

*Examples of other types of studies included case-control studies, cross-sectional studies, surveys, or interviews).


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