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Multivariate Analyses to Assess Treatment Effectiveness in Advanced Head and Neck Cancer
Urjeet Patel, MD;
Edward Spitznagel, PhD;
Jay Piccirillo, MD
Arch Otolaryngol Head Neck Surg. 2002;128:497-503.
ABSTRACT
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Objective To assess relative benefit of combined radiotherapy and surgery over
single-modality treatment for advanced-stage squamous cell carcinoma of the
aerodigestive tract by means of several multivariable analyses to control
for patient variables.
Design Medical chart review.
Setting University medical center.
Patients and Methods The study included 532 patients receiving initial therapy between January
1, 1980, and December 31, 1989. Three multivariate techniques (multiple logistic
regression, propensity score stratification, and conjunctive consolidation)
were used to compare outcomes for treatment groups.
Main Outcome Measure Five-year survival.
Results Survival for radiation, surgery, and combined treatment groups were
24%, 40%, and 46%, respectively. With the use of multiple logistic regression
to control patient variables, the radiation group had a significantly lower
survival than the combined therapy group (risk ratio, 2.24; 95% confidence
interval, 1.32-3.80), while there was no statistical difference for the surgery
group compared with the combined therapy group (risk ratio, 1.26; 95% confidence
interval, 0.78-2.03). When analyzed by propensity score, 5-year survival was
higher in each quintile for the combined therapy group than for the group
who received radiation alone (P = .002). There was
no significant difference in survival between the surgery and combined treatment
groups (P = .25). Conjunctive consolidation was used
to create a clinical staging system to compare outcomes across treatment groups.
In each clinical severity stage, radiation alone had a lower survival than
combined therapy (P = .001), while no statistical
difference was noted between surgery and combined therapy (P = .50).
Conclusions All 3 statistical techniques showed a significantly lower survival for
patients treated with radiation alone vs combined therapy. No significant
difference was noted between surgery and combined therapy. Propensity score
analysis and conjunctive consolidation are useful techniques to control prognostic
variables in cancer database studies and should be used in future outcome
studies that address more current treatment dilemmas in head and neck oncology.
INTRODUCTION
COMBINED SURGERY and postoperative radiotherapy are often used to treat
stages III and IV squamous cell carcinoma of the upper aerodigestive tract.1 The choice of combined treatment over single-modality
treatment often hinges on clinical factors such as primary site, tumor size,
and extent of regional metastasis. Some centers report improved locoregional
control with combined therapy, while others report improved survival; however,
controversy exists over the benefit of combined therapy in various clinical
settings.2-4
The criterion standard for assessing the merits of a given treatment
is the prospective randomized trial. Accordingly, such trials have long been
advocated; however, these trials in head and neck cancer treatment are inherently
problematic and rare. The main problem is the heterogeneity of the study population
in terms of tumor stage, primary site, histologic grade, age, and small sample
size for any given research trial. In addition, it is often difficult to randomize
patients to treatments that are so markedly different, as patients are hesitant
to leave such grossly dissimilar options to chance alone. Therefore, studies
of treatment effectiveness in head and neck cancer are often relegated to
the realm of observational studies, where patients are not randomized to particular
treatments.
The goal of the observational study is to measure treatment effectiveness.
One of the major difficulties in the analysis of results from observational
studies is that the same clinical variables that affect patient outcomes (age,
stage, comorbidity, etc) also impact on treatment choice.5
This may lead to treatment selection bias. Thus, an observational study must
also seek to control selection bias to accurately measure treatment effect.
The goal of this study was to use observational data to assess the relative
benefit of combined surgery and radiotherapy over single-modality treatment
for advanced-stage squamous cell carcinoma of the upper aerodigestive tract.
To accomplish this goal, multiple statistical techniques were used to control
for selection bias.
PATIENTS AND METHODS
POPULATION UNDER STUDY
We studied 532 patients with newly diagnosed TNM stage III or IV biopsy-proved
squamous cell carcinoma who were first treated at Washington University Medical
Center, St Louis, Mo, between January 1, 1980, and December 31, 1989. These
patients were initially identified by means of records from the pathology
department of Barnes-Jewish Hospital, St Louis. Patients with American Joint
Committee on Cancer (AJCC) TNM stage III or IV squamous cell carcinoma of
the oral cavity, oropharynx, or larynx who were initially treated with radiotherapy,
surgery, or combined radiation and surgery were included in the study population.6 Patients with metastatic disease at the time of diagnosis
were excluded from the study, as were patients who received therapy other
than the 3 above-mentioned treatment options. Baseline and follow-up information
was obtained from inpatient medical records as well as records from the Departments
of Otolaryngology and Radiation Oncology. Full 5-year follow-up information
was obtained for all 532 patients. Supplemental date of death and death certificate
information was obtained from Equifax National Death Search (Arlington, Va).
COLLECTION OF DATA
Specially designed data extraction forms were used to ensure uniform
data collection from the medical records. Data collected from the pretreatment
interval and at the time of presentation included basic demographic information,
risk factors, medical history, symptom type and duration, complete anatomic
description of the tumor including the TNM classification with the 1992 AJCC
criteria,6 pathological description of the
biopsy specimen, and details of subsequent therapy. The zero-time for each
patient was chosen as the date of first antineoplastic intervention directed
at the primary site. Follow-up data, including development of recurrence,
new primary, and subsequent treatment, were also collected. Patient and tumor
status at last follow-up or death was obtained.
CLASSIFICATION OF DATA
To maintain scientific accuracy and ensure high quality of data, imperfections
in data obtained from retrospective studies must be managed in a systematic
and consistent manner. The general methods for such management have been previously
described.7-9
SYMPTOM SEVERITY
To study the prognostic importance of symptoms for a specific cancer
type, the presence of symptoms and their relationship to the primary cancer
must be clearly established. To manage possible discrepancies in the medical
record, 2 conventions were consistently applied. If a symptom was recorded
by at least 1 examiner, the symptom was regarded as present. If different
periods of duration were reported, the longer duration was recorded.
The details of symptom severity staging as used in our study have been
previously described.8 Briefly, the symptoms
of dysphagia, otalgia, neck lump, and weight loss were found to be independent
predictors of survival. Accordingly, a symptom severity staging system was
developed on the basis of the presence of these symptoms. Stage was defined
as none if none of the 4 symptoms was recorded, mild if 1 of the 4 symptoms
was recorded, moderate if 2 of the 4 symptoms were recorded, and severe if
3 or 4 of the 4 symptoms were recorded.
COMORBIDITY
The presence of concomitant disease unrelated to the disease under study
is termed comorbidity. Comorbidity has been shown
to clearly impact on survival and treatment selection in several types of
cancer.10-12 The
Kaplan-Feinstein index was used to classify comorbidity for this study.13 This scheme was used to classify the patients' comorbidity
as none, mild, moderate, or severe (grades 0, 1, 2, or 3, respectively). When
a patient's condition was described in the medical record as too sick to tolerate
standard antineoplastic therapy, a grade of 3 was assigned regardless of other
illnesses. Prognostic comorbidity was defined as grade 3, signifying the presence
of concomitant illness that significantly reduces a patient's life expectancy.
CANCER STAGING
The staging criteria for all tumors were reviewed according to the AJCC
cancer staging manual.6 All information obtained
before the zero-time was used to assess accuracy of the recorded stage as
dictated by the AJCC rules. In the case of staging discrepancies between written
notes by different physicians where the medical record lacked sufficient anatomic
information to accurately restage the tumor, the stage assigned by the most
senior otolaryngologist or radiation oncologist was recorded. Information
regarding the presence of cervical adenopathy was lacking in 4 members of
the final cohort. It was known that they had stage III or IV disease based
on T stage alone; subsequently, they were included in the study. These members
were omitted from aspects of data analysis requiring exact node status information.
PATHOLOGICAL EXAMINATION
The histologic grade of the primary tumor was recorded from the biopsy
or primary specimen for all patients, and grades were grouped into categories
of well, moderately, and poorly differentiated. If both biopsy specimen and
primary tumor were available, the biopsy specimen was used to define the histologic
grade. Specimens graded as moderately to well differentiated were recorded
as moderate, and those graded as moderately to poorly differentiated were
recorded as poor. Histopathologic grade was absent for 4 patients; these members
were omitted from data analyses requiring pathological information.
PRIMARY TREATMENT
Information regarding each patient's initial treatment included type
of treatment (radiotherapy, surgery, or combined treatment), type of surgical
procedure, timing of radiotherapy (preoperative or postoperative), and therapeutic
complications. Subsequent treatment was defined as treatment initiated secondary
to failure of primary therapy and was also recorded.
FOLLOW-UP AND OUTCOME
Each patient was monitored for persistence, recurrence, and development
of new primary cancer. Follow-up was considered complete when either a patient's
death was documented or a minimum of 5 years' survival was obtained. The primary
outcome measure presented in this study was 5-year survival.
DATA ANALYSIS
The primary objective of data analysis was to estimate any possible
benefit on 5-year survival of combined therapy over either radiation or surgery
alone. The possible benefit was estimated by 3 separate multivariable statistical
techniques: multivariate logistic regression, propensity score stratification,14 and conjunctive consolidation.12
The information from the data extraction forms was entered into a Paradox
database (Borland International, Scotts Valley, Calif). The specially designed
database screens were equipped with internal validity checks that facilitated
reliable and efficient data entry. Periodic review for internal consistency
and comparison with separate databases was performed to ensure accuracy of
data entry. Sorting, tabulation, and statistical analyses were performed with
the SAS system, release 6.12 (SAS Institute Inc, Cary, NC).
Logistic Regression
The impact of covariates and initial treatment options on 5-year survival
was evaluated by multiple logistic regression (PROC LOGIST function). The
logistic regression modeled the dependent variable of 5-year survival from
the independent patient, tumor, and treatment variables. A regression model
was fit with the use of the following covariates: age group, sex, race, prognostic
comorbidity, symptom severity, pathological findings, tumor size, presence
of adenopathy, primary site, and initial treatment choice. Of the 532 patients,
7 were eliminated from the regression model because of missing information
as described in the "Cancer Staging" and "Pathological Examination" subsections.
The multivariable regression had an area under the receiver operating characteristic
curve of 0.72. This means that the regression model was fairly accurate in
discriminating survivors from nonsurvivors on the basis of covariate information.
Adjusted risk ratios and corresponding 95% confidence intervals and P values were obtained according to reference groups for
each variable.
Propensity Score
The goal of propensity analysis is to reduce the effect of selection
bias between 2 treatment options as described by Rosenbaum and Rubin.15-17 Selection bias is
clearly problematic in observational studies when clinical covariates (age,
comorbidity, tumor stage, etc) impact on both treatment (radiation, surgery,
or combined therapy) and outcome (5-year survival). Propensity score stratification
seeks to replace the wide host of confounding covariates that may be present
in an observational study with a single variable function of these covariates.
The covariates are summarized into a single probability function called the propensity score that describes the likelihood of receiving
treatment A (surgery plus radiation, for example) vs treatment B (radiation
alone). The propensity score can be estimated through logistic regression
of the covariates on treatment choice. Accordingly, each individual has a
propensity score that represents the probability of being treated with combined
therapy rather than radiation alone. The propensity score is then used in
further analysis as the single confounding variable.
The study population is then stratified into a discrete number of groups,
usually 5, on the basis of the propensity score. Stratification into 5 quintiles
has been shown by Rosenbaum18 to eliminate
more than 90% of selection bias by covariates. Within each propensity stratum,
there will generally be a number of patients who received combined therapy
or radiation alone. The rationale behind the propensity score scheme is as
follows: If 2 patients have the same propensity score, then it follows that
they have the same likelihood of receiving combined treatment as radiation
alone on the basis of their given covariates. If the 2 patients receive different
treatments, then the choice of treatment can be considered random. The same
principle holds for 2 groups with similar propensity scores. Within a given
propensity stratum, the group of patients receiving combined therapy will
have a distribution of propensity scores similar to that of patients who received
surgery alone. Subsequently, the patients composing one treatment group can
be considered to be randomly chosen from the entire propensity stratum with
regard to their confounding covariate data. Within a propensity stratum, the
multivariate distribution of covariates should differ only randomly between
the 2 treatment groups as if they had been randomly assigned a treatment option.
Thus, use of this technique with stratification into quintiles eliminates
selection bias between 2 treatment groups.15
In our study, propensity score analysis was first used to assess treatment
effect between patients receiving combined therapy vs radiation alone. To
identify variables that were unbalanced between the 2 treatment groups, bivariate
screening was performed for all potential confounding covariates that potentially
impact on treatment decision. A multiple logistic regression was performed
in a stepwise fashion to determine important predictors of treatment selection.
A logistic regression model was then fit with variables found to be significant
(P<.15) in the logistic analysis. The area under
the receiver operating characteristic curve for this regression model was
0.76, indicating good discrimination between patients receiving combined vs
single therapy. With this model, a propensity score was calculated for each
patient that predicts the likelihood of being initially treated with combined
surgery and radiation therapy vs radiation alone.19
Patients were then sorted by propensity score and clustered into quintiles
accordingly. Bivariate screening and logistic regression were then performed
within each quintile to identify any remaining bias among covariates after
stratification by propensity score. The effect of treatment assignment on
5-year survival was then analyzed within each quintile. The Mantel-Haenszel
odds ratio was calculated in addition to the Cochran-Mantel-Haenszel (CMH)  2. The Mantel-Haenszel odds ratio represents a composite of the 5 odds
ratios derived from each quintile, and the CMH 2 reflects
the statistical significance of the odds ratio. The Breslow-Day test, which
indicates whether there is homogeneity of the odds ratios among the 5 quintiles,
was also performed.20
The same process described above was then repeated for the comparison
of combined therapy vs surgery alone. A similar regression model was fit with
significant covariates. The area under the receiver operating characteristic
curve for this model was 0.67, indicating good discrimination of treatment
options. Similar analysis to estimate the effect of treatment assignment was
then performed after estimation of the propensity scores and stratification
into propensity quintiles.
Conjunctive Consolidation
An alternative multivariable technique produces clusters of patients
through conjunctive consolidation and is exemplified by the TNM staging systems.12 Use of conjunctive consolidation to evaluate treatment
effect has been previously described.12 Under
this system, patients are stratified according to values of the prognostic
covariates (such as TNM, age, comorbidity, etc). With the addition of each
new variable, the number of strata grows and the number of patients within
each stratum decreases. The problem with addition of numerous variables is
the exponential growth in the number of stratified groups of patients, making
further analysis of patients within each group difficult. Through conjunctive
consolidation, groups of categories are clustered by means of unions and intersections
of Boolean algebra.12 This process is the cross-table
analysis of the conjoined effect of 2 variables on the outcome of interest.
Each conjoined cell contains patients with similar values for the 2 variables
being conjoined. Adjacent cells can then be combined according to clinical
and statistical similarity. This allows for the inclusion of numerous clinical
factors while avoiding the subsequent increase in number of categories.
To assemble data for evaluating the prognostic covariates, patients
were combined according to a therapeutic "nil hypothesis," as previously described.12 This makes a tentative clinical assumption that treatment
had no effect on a patient's clinical course. With this assumption made, the
data were combined for all patients regardless of treatment. Patients were
then categorized according to their nontherapeutic prognostic covariates,
and survival outcomes were then analyzed for each category. After prognostic
factors were consolidated according to the nil hypothesis, the impact of different
treatment options was explored for patients within given stages.
In the present study, the technique of conjunctive consolidation was
first applied to combine 2 clinical variables, age group and prognostic comorbidity,
into a 3-category composite functional staging system. Patients younger than
55 years with no comorbidity were categorized as stage  , those aged
55 to 69 years with no comorbidity as stage ß, and those older than 69
years or with prognostic comorbidity as stage  . The cancer variables
of tumor size and presence of adenopathy were then combined to form a composite
tumor staging system (1, 2, and 3). Patients with T3,4 N0 disease were combined
with patients with T1 N1 disease into cancer stage 1; T2 N1 was classified
as stage 2; and T3,4 N1 was categorized as stage 3. The functional stage and
the tumor stage were next combined to create a composite clinical severity
staging system (A, B, C, and D). Stage 1 was classified as composite
stage A. Stages 2 and ß1 were combined into composite stage B.
Stages 3, ß2, ß3, 1, and 2 were combined into
stage C. Finally, stage 3 was classified as stage D. With the use of
conjunctive consolidation in this manner, the 4 covariates of age, comorbidity,
tumor size, and cervical adenopathy were conjoined into a 4-category composite
clinical severity staging system.
The association between clinical severity stage and 5-year survival
was examined. There was a strong relationship that was both clinically impressive
and statistically significant. The 2 for linear trend was P = .001. Next, treatment effect was examined within composite
clinical severity stage groups. Patients were grouped according to initial
treatment within composite stages. Survival rates were calculated for each
treatment group by composite stage. A 2 test of significance
was performed by comparing survival rates across the different therapies within
composite clinical severity staging groups. The Mantel-Haenszel odds ratio
was then calculated for each single modality treatment vs combined therapy
in addition to the CMH 2. The Breslow-Day test was also performed
to verify that the odds ratios derived from each stage were homogeneous.
RESULTS
The characteristics of the 532 study patients are presented in Table 1. The study population was 70% male,
and more than 75% were white. Most patients had either absent or mild comorbidity
as well as symptom severity. More than 65% had evidence of cervical adenopathy
and were equally divided between TNM stage III and stage IV disease. Disease
was most prevalent in the oropharynx (40%), followed by the larynx (35%) and
then the oral cavity (25%). The most common initial treatment plan was combined
surgery plus radiation (52%), followed by radiation alone (25%) and then surgery
alone (23%).
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Table 1. Sociodemographic and Clinical Characteristics and 5-Year Survival
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The relationship between covariates, including treatment and survival,
is also shown in Table 1. Patient
characteristics that were associated with decreased survival included increasing
age (P = .01), increasing comorbidity (P = .03), and increasing symptom severity (P
= .002). Presence of cervical adenopathy was associated with a significant
decrease in survival from 47% to 35% (P = .01). Laryngeal
cancer was associated with the highest 5-year survival at 47%, compared with
oral cavity and oropharyngeal disease at 37% and 33%, respectively. Looking
at treatment, radiation alone was associated with significantly lower survival
of 24% while survival rates for combined therapy and surgery alone were 46%
and 40%, respectively.
Multivariate logistic regression was used to assess the impact of covariates
and treatment on survival. Table 2
shows the adjusted risk ratios for covariates and treatment for the study
population. Increasing age and male sex were both related to a decreased 5-year
survival rate. Similarly, the presence of cervical adenopathy and tumor size
greater than stage 1 impact negatively on survival. With regard to primary
site, patients with cancer of the oral cavity and oropharynx had significantly
higher risk of death than the patients with laryngeal cancer. With regard
to treatment, the group of patients treated with radiation alone had a significantly
higher risk of death than those receiving combined therapy (risk ratio, 2.24;
95% confidence interval, 1.32-3.80). While treatment with surgery alone did
reflect an increased risk of death compared with patients receiving combined
treatment, this risk was not found to be statistically significant (risk ratio,
1.26; 95% confidence interval, 0.78-2.03).
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Table 2. Multivariate Logistic Regression: Impact of Covariates and
Primary Treatment on Death*
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Stratification by propensity score and assessment of the treatment effect
of each single-modality treatment and combined therapy on survival were performed.
The population receiving either radiation alone or combined therapy was examined
first (n = 410). The population was stratified into propensity quintiles as
previously described. Table 3
shows survival rates for both treatment groups after stratification. The percentage
of patients receiving combined therapy decreased from the first propensity
quintile to the fifth as predicted by the propensity model. In each of the
5 strata, patients receiving combined therapy had a higher 5-year survival
rate than the group receiving radiation alone. In quintiles 1 and 3, the difference
in survival was statistically significant. The P
value for the CMH 2 comparing survival between the treatment
groups while controlling for propensity quintile was .002, suggesting a strong
difference in survival between those receiving radiation alone vs combined
treatment.
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Table 3. Five-Year Survival of 403 Patients Within Propensity Strata:
Radiation vs Combined Therapy*
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Propensity score analysis was similarly performed for patients initially
receiving either surgery alone or combined therapy (n = 397). Table 4 shows survival rates for these treatment groups after propensity
score stratification. As expected, the percentage of patients receiving combined
therapy decreased from the first to the fifth quintile. Within quintile 2,
patients receiving surgery alone had a higher survival than those receiving
combined treatment, while in the remaining quintiles, patients with combined
treatment had more favorable survival. In none of the quintiles was the difference
in survival statistically significant. The P value
for the CMH 2 was .25, suggesting no significant difference
between treatment groups across quintiles.
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Table 4. Five-Year Survival of 393 Patients Within Propensity Strata:
Surgery vs Combined Therapy*
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Conjunctive consolidation was performed as previously described. Table 5 shows survival rates for all patients
according to composite stage (A, B, C, or D). A prognostic gradient was noted
in survival from stage A through stage D, with a significant 2
for linear trend (P = .001). Patients were separated
into treatment group, and survival rates were then compared within composite
staging groups (Table 6). In all
4 composite staging groups, survival rates were higher for patients receiving
combined therapy compared with radiation alone, with a statistically significant
difference noted in 3 of the 4 stages. The CMH 2 was highly
significant (P = .001). Comparing groups receiving
combined therapy vs surgery alone, survival was higher for combined therapy
in stages A, B, and D, while surgery alone was favored for stage C patients.
In only stage D was there a statistically significant difference in survival
between patients receiving surgery vs combined treatment. No statistical difference
was noted between the 2 groups by the CMH 2 (P = .50).
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Table 5. Five-Year Survival by Conjunctive Consolidation Staging*
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Table 6. Bivariate Analysis of 5-Year Survival and Treatment for 528
Patients by Conjunctive Consolidation Staging
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COMMENT
Our research demonstrates the usefulness of multivariate analysis with
regard to head and neck oncology observational studies. The relative benefit
of combined therapy over single-modality therapy was assessed by means of
multiple logistic regression, propensity score analysis, and conjunctive consolidation.
The 3 forms of analysis concurred in their findings that combined therapy
offered significantly higher survival at 5 years than radiotherapy alone.
In contrast, no significant difference was seen when combined therapy was
compared with surgical treatment alone. By using multivariate analysis to
eliminate selection bias, the difference in survival can be attributed to
treatment effect without the influence of confounding variables.
Previous studies have compared combined therapy with single-modality
treatment for various tumors in the head and neck region.21-24
While many of these studies seek to measure treatment effect, few of them
compare different treatment options while controlling for selection bias.
Patient variables such as comorbidity, pathological grade, symptom severity,
and age are often omitted from analysis despite the fact that these variables
may influence treatment choice as well as outcome. Subsequently, conclusions
are drawn regarding treatment effectiveness without adequately controlling
for potential selection bias. Without such control, the conclusions may not
accurately assess true treatment effectiveness.
While multivariate analysis does permit a more controlled estimate of
treatment effectiveness, there do exist potential inaccuracies in its formulation.
Each multivariate model is able to control only the study variables included
in the analysis. In our study, there was no variable to quantify the amount
of radiotherapy given to each patient, nor was there any measure in the quality
of the surgery performed. Subsequently, it is possible that variables exist
that would alter the measured treatment effects had they been included in
the multivariate analysis. In addition, a given multivariate analysis makes
use of statistical models to approximate the data being analyzed. The degree
to which a given model fits the data appropriately can vary and needs to be
considered when the results of statisitical analysis are interpreted. This
is especially true when different statistical tools yield varying results
when the same data are analyzed.
Multivariate analysis is a highly useful tool to measure treatment effect
in observational studies. Multiple logistic regression is a statistical technique
that is frequently used in analysis of observational study results. Propensity
score analysis and conjunctive consolidation are also highly effective at
controlling selection bias to measure treatment effectiveness. The use of
these techniques will improve the ability to accurately measure treatment
effectiveness in observational studies. These tools may be applied to more
current clinical dilemmas, such as chemoradiation protocols compared with
surgical resection for treatment of head and neck cancer.
AUTHOR INFORMATION
Accepted for publication October 11, 2001.
This study was supported in part by grant R01CA2072 from the National
Cancer Institute, Bethesda, Md (Dr Piccirillo).
This study was presented at the Fifth International Conference on Head
and Neck Cancer, San Francisco, Calif, July 31, 2000.
Corresponding author and reprints: Jay Piccirillo, MD, Department
of Otolaryngology, 660 S Euclid St, Box 8115, St Louis, MO 63110 (e-mail: piccirij{at}msnotes.wustl.edu).
From the Departments of Otolaryngology (Drs Patel and Piccirillo) and
Mathematics (Dr Spitznagel), Washington University, St Louis, Mo.
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