**January 2009, Volume 36,
Number 1**

*Online
Exclusive Article*

**Application of Multilevel
Growth-Curve Analysis in Cancer Treatment Toxicities: The Exemplar of Oral Mucositis and Pain**

*William N. Dudley, PhD, Deborah B. McGuire, PhD, RN, FAAN, Douglas E.
Peterson, DMD, PhD, and Bob Wong, PhD*

**Purpose/Objectives:** To introduce the use of a statistical technique known as multilevel
growth-curve analysis and illustrate how the method can be advantageous in
comparison with traditional repeated measures for the study of trajectories of
signs and symptoms in individual patients over time.

**Data
Sources:** Data were derived from use of the technique in a
randomized clinical trial of a psychoeducational
intervention to reduce severity of oral mucositis and
oral pain.

**Data
Synthesis:** The development of new biologic models that seek to
explain clustering of signs and symptoms or the appearance and resolution of
signs and symptoms motivates the need to use more sophisticated statistical
techniques to test such models.

**Conclusions:** The application of multilevel growth model to an existing data set
demonstrates that the model can be effective in the study of individual
differences in trajectories of change in signs and symptoms.

**Implications
for Nursing:** This method for the study of changes in patients’
signs and symptoms over time can be of particular interest to nursing, both
from a clinical point of view and as a way to test theoretical models that have
been proposed to capture patient experiences with signs and symptoms.

The study of symptom clusters has become an
important focus of oncology nursing research (Barsevick, 2007). Concurrently,
longitudinal studies of clinical phenomena in which individuals are measured
across time have become common. As the study of symptom clusters has matured,
research has evolved beyond describing symptom clusters to questioning the
underlying processes that lead to symptom clusters. These changes in research
foci have led to biologic models of symptom clustering (Lee* *et al.,
2004; Sonis, 2004a, 2004b) and a need for
sophisticated statistical methods to test such models.

Lee et al. (2004) proposed a general inflammatory
model in which cancer therapies (chemotherapy or radiotherapy) lead to the
release of cytokines that, in turn, generate specific clusters of symptoms in
patients receiving treatment. In a model specific to oral mucositis
(OM), Sonis (2004b) proposed a pathobiologic
model of OM that models the development and resolution of that serious side
effect of cancer therapy. Linkage of these models and related models represents
an important development in symptom cluster research. Both of the biologic
models propose a longitudinal chain of processes that underlie the clinical
phenomena under study. For the science to progress, researchers must use
statistical methods that can appropriately model individual trajectories of
change, capture interindividual variability in change
over time inherent in the models, and model factors that explain that
variation.

The traditional repeated-measures analysis of
variance (ANOVA), which uses ordinary least-squares estimation, has long been
the mainstay for statistical analyses of longitudinal clinical trials with
continuous outcomes (Maxwell & Delaney, 2004). Repeated-measures ANOVA is
highly effective in studying mean change and treatment group differences in mean
change over a limited number of occasions with balanced data. However, it is
less useful for the study of interindividual
variability in trajectories of change that practitioners commonly see in
clinical settings, specifically in the context of signs and symptoms related to
cancer treatment. An alternative to traditional repeated-measures ANOVA is one
of several growth-curve modeling approaches to examine interindividual
variability in trajectories of change. One commonly used method is the
multilevel growth model in which observations (sign or symptom severity) over
time are “nested” within a patient. The patient’s trajectories of change then
are linked to patient-related characteristics (e.g., age, gender) or
treatment-related characteristics (e.g., radiation dose) that can be thought of
as correlates of change.

The purpose of this article is to introduce
growth-curve modeling of longitudinal data via the use of multilevel modeling
and to illustrate the advantages of multilevel modeling with longitudinal
clinical data over the traditional repeated-measures ANOVA model. It focuses on
interindividual differences in trajectories of OM, a
significant side effect of cancer therapy (Peterson, Keefe, Hutchins, &
Schubert, 2006; Sonis et al., 2004).

Oral Mucositis

The current pathobiologic
model of OM supports variations in clinical expression and is supported by
substantial basic and clinical research (Sonis,
2002).** **Multilevel growth-curve models have the potential to integrate
patient-based variations in clinical expression of OM within the pathobiologic model. Selected patient cohorts, including
those receiving head and neck radiation (Elting, Cooksley, Chambers, & Garden, 2007) or hematopoietic
stem cell transplantation (HSCT) (Sonis et al.,
2004), typically demonstrate predictable peaks and troughs in severity of OM.
However, distinct differences in the expression of signs and symptoms often
occur across patients, even among those receiving similar treatment regimens,
such as high-dose chemotherapy in preparation for stem cell transplantation.
The variation may be seen in different trajectories of oral mucosal injury over
time (the peaks and troughs noted previously) across individual patients. Such
variation can include incidence and duration of clinically significant oral
mucosal injury and can affect dose delivery of multicycle
chemotherapy (Peterson, Jones, & Petit, 2007). In addition, the number of
patients with solid tumors who experience OM is substantially higher than the
number of patients undergoing head and neck radiation and HSCT combined (Avritscher, Cooksley, & Elting, 2004; Elting et al*.*,
2003). In this model, clinical changes in oral tissue occur because of an
underlying biologic process; also, individual trajectories of change are quite
variable, and the variability may be the result of a host of patient-related
(e.g., age, oral health) and treatment-related (e.g., type of treatment
regimen) factors. To test model-related hypotheses, a statistical model must
quantify individual trajectories of change and correlate the trajectories to
patient-related and treatment-related variables. It also must have the
potential to relate changes in one sign or symptom to patterns of change in
other signs or symptoms (as a researcher might do in a study of symptom
clustering over time). The multilevel growth model discussed in this article is
one statistical model that is consistent with those requirements.

Multilevel Growth Models

**Multilevel
Growth Models in the Study of Oral Mucositis**

A need exists for novel analytic approaches designed
to integrate the modeling of OM among individual patients, vis-a-vis the collective patient experience, by quantifying
individual trajectories of oral mucosal injury over time. As more and more
researchers employ repeated-measure, longitudinal designs to study cancer signs
and symptoms, the authors anticipate that the availability of longitudinal data
will create a shift toward the use of new models for the study of change. The
technique described in this article, multilevel growth-curve modeling, is one
commonly used approach to the study of change over time.

Multilevel growth-curve modeling also can contribute
to an enhanced understanding of the OM experience within a constellation of
signs and symptoms in patients undergoing high-dose cancer therapies. The
concept of symptom clusters has emerged as an important paradigm in oncology
(Barsevick, 2007; Dodd, Miaskowski, & Paul, 2001;
Kim, McGuire, Tulman, & Barsevick, 2005; Lee et
al*.*, 2004; Miaskowski & Aouizerat, 2007).** **In that context, OM pathogenesis
and clinical outcomes could be contributory to, or an outcome of,
molecular-based toxicities such as fatigue associated with tumor necrosis
factor-a, interleukin (IL)-6, IL-8, and epidermal growth factor (Lee et al.).**
**The symptom clusters can exhibit considerable variation across patients
with cancer, even among those receiving comparable treatment regimens.
Multilevel growth-curve modeling may help to elucidate and integrate data on OM
with data related to the collective symptom experience across patients.

**Statistical
Basis**

The methods presented herein are based on the
seminal work by Bryk and Raudenbush
(1992) and subsequent work of numerous methodologists (Curran, 2000; Singer
& Willett, 2003; Verbeke & Molenberghs, 2000). A number of approaches to growth-curve
modeling exist. Multilevel growth-curve modeling is used commonly because it is
generalizable to other approaches, such as individual
growth-curve modeling or latent curve growth-curve modeling. The approaches
share a common statistical model discussed in detail later.

As the name suggests, a multilevel model consists of
a number of hierarchically nested regression models in which model parameters
(i.e., regression coefficients, standard errors, variance components, and
covariance components) are computed simultaneously. Typical longitudinal
multilevel modeling involves two different levels of equations: level 1 and
level 2. The level 1 equations capture within-subject variability, in this case
individual change over time, whereas level 2 equations capture between-subject
variability. The authors describe both levels of equations in the following
sections. After the authors present the statistical model, they discuss how the
statistical model relates to the symptom experience over time in a sample of
patients.

**Level
1 equations: **In the level 1 equation, each individual subject’s
change over time is a separate regression equation. In other words, each
subject’s outcome on the dependent variable(s) (erythema
and pain in this example) is regressed onto the variable of time of measurement
(e.g., day 1, day 2, day 3). The result is a
regression equation (which may be linear or nonlinear) that represents each
individual subject’s growth curve. The coefficients that make up the regression
equation are the individual subject’s growth-curve parameters. With standard
Cartesian coordinates, the Y intercept is the value in the outcome variable
where the growth curve (either individual or group mean) intersects the
abscissa axis (typically at a baseline day = 0). The linear rate of growth is
termed slope, which is the amount of change in the dependent variable per unit
of time. A quadratic term describes the amount of acceleration or deceleration
(nonlinear increase or decrease) of the same dependent variable per unit of
time squared.

Equation 1 is the general level 1 regression
equation that captures individual change over time in some outcome (in this
case, the authors used erythema to illustrate the
process). Unlike repeated-measures ANOVA, which aggregates information and
loses individual differences, multilevel models retain individual information
and develop separate regression equations for each subject. The subscript “i” indicates an individual. The subscript “t” indicates
time, which could be actual days from a baseline zero or, as more commonly
encountered in clinical research, an ordinal series of time (e.g., first
treatment, second treatment).

Equation 1: Y_{ti }= p_{0i} + p_{1i} a_{ti} + p_{2i} a_{ti}^{2} + e_{ti}

From equation 1, Y_{ti} is subject i’s erythema score at measurement
t; a_{ti} is the day of measurement postchemotherapy (e.g., 0, 1, 2, 3) for the erythema score and represents linear change. The a_{ti}^{2}
term is the time of measurement squared and represents curvilinear change over
time. e_{ti} is the
difference between the observed erythema score at
time t for subject i (Y_{ti})
and the predicted erythema score. e_{ti} is a residual value that indicates an
individual subject’s variability. Because the authors must estimate a separate
level 1 equation for each subject, the timing of measurement occasions and the
number of measurement occasions may vary over subjects. Thus, multilevel models
can handle unbalanced designs as opposed to traditional repeated-measures
ANOVA. Unbalanced designs refer to data collection processes in which the
number of measurement occasions differs from one patient to another. The discrepancy
may be a result of missing data or duration of treatment regimen conditions
that often are encountered in longitudinal clinical studies. The ability to
handle varying times of measurement and number of measurement occasions is
critical in the longitudinal study of clinical phenomena.

Each subject’s level 1 equation, called a growth
curve, consists of a function of growth parameters: a Y intercept, p_{0i};
a slope, p_{1i}; a quadratic term, p_{2i}; and an error term, e_{ti}. The Y intercept, p_{0i}, is an
individual subject i’s predicted erythema
score where time is zero (i.e., a_{ti}_{ }=
0). Y intercepts are estimated and interpreted where the other variables in the
equation are set to zero (Biesanz, Deeb-Sossa, Papadakis, Bollen, & Curran, 2004; Cohen, Cohen, West, &
Aiken, 2003; Wainer, 2000). The linear change rate,
or slope of the growth curve for individual subject i,
is p_{1i. }The slope is the predicted linear rate of change in erythema scores per unit of time, t. In the quadratic
model, the slope has a special meaning. It is the rate of change at the
intercept. That is, it is the slope of the line passing through the intercept
and tangent to the curve represented by the quadratic term (Singer &
Willett, 2003). The quadratic growth parameter for subject i
is p_{2i}, and it is the rate of acceleration (or deceleration if
negative) in erythema scores per unit of time
squared, t^{2}.

To illustrate the Y intercept and slope concepts, Figure 1 shows a linear growth curve fitted to a
hypothetical subject’s erythema scores for the first
four time points (day 0, day 1, day 2, and day 3) using the equation described
earlier. For simplification purposes, the figure does not show the quadratic
term. The circles represent the measured erythema
score at days t = 0, 1, 2, and 3, and the dotted line
is the subject’s growth curve. The Y intercept, p_{0i};
slope, p_{1i}; and residuals, e_{ti},
for the subject’s growth curve are labeled.

**Level
2 equations: **Estimation of the growth parameters (i.e.,
intercept, slope, and quadratic term) in level 1 equations
involves a different set of regression equations. Each growth parameter is
modeled by a regression equation that captures the population main effect plus
the variability resulting from each individual. The level 2 equations for the
current example consist of three regression equations. As shown here, equation
2 examines subjects’ intercept values, equation 3 estimates subjects’ linear
slope parameter values, and equation 4 estimates
subjects’ quadratic parameter values.

Equation 2: p_{0l}** **= b_{00 }+
m_{0l}** **

Equation 3: p_{1l}** **= b_{10 }+
m_{1l}

Equation 4: p_{2l}** **= b_{20} +
m_{2l}

Recalling equation 1,

Y_{ti }= p_{0}_{l}** **+_{ }p_{1}_{l }a_{t}_{l }+ p_{0}_{l} a_{ }_{t}_{l}^{2}_{ }+ e_{t}_{l }_{}

the authors substitute the bs and m’s for the
growth parameters to yield equation 5.

Y_{ti }= (b_{00 }+ m_{0l}) + (b_{10}** **+ m_{1l})a_{t}_{l }+** **(b_{20} + m_{2}_{l}) + e_{ }_{t}_{l}** **_{}

Figure 2 shows a
hypothetical grand mean linear growth curve shown as a solid line with the
individual subject’s growth curve shown as a dashed line. The coefficients of
the level 2 equations are labeled.

Figure 2 illustrates the
relationship between the individual’s trajectory of erythema
and the mean (across all individuals) trajectory of erythema.
In the figure, the dashed line is identical to the line portrayed in Figure 1. The solid line in Figure
2 portrays the mean trajectory, and the parameters of interest include the
parameters for the mean trajectory (the b terms) as well as the parameters for
the deviation of the individual from the mean (the m terms). The parameters are
described in the following sections.

b**-terms**** fixed effects:** Three b terms exist: b_{00}, b_{10}, and b_{20}. In repeated-measures ANOVA terminology, the terms
represent population main effects. In growth-modeling terminology, b_{00} is the grand mean intercept. The interpretation of
this mean intercept for growth modeling is different from repeated-measures
ANOVA, which interprets the intercept as the value aggregated across all
subjects and all time points. Therefore, it is the average value of erythema regardless of time. For growth modeling, the
intercept typically is set to represent the initial or beginning value of the
dependent variable at time 0 (a_{ti} = 0).
The statistical testing of b_{00} determines
whether the intercept value differs from zero. Other interpretations of the
intercept can be accomplished by centering the data on a time point that is not
zero. For example, if the time variable was centered on the mean time value, in
the current example 1.5 days (a_{ti} = 1.5),
then the intercept would be consistent with the repeated-measures ANOVA
results, because the mean intercept value would be calculated at the mean time
value. b_{10}_{ }is the grand mean
slope, or average linear rate of change per unit of time for the population
growth curve. Testing b_{10 }against zero is similar to orthogonal linear
contrasts in repeated-measures ANOVA terminology (Biesanz*
*et al., 2004). b_{20} is the grand mean
quadratic term, or average change in slope value per squared unit of time.

m**-terms**** random effects:** Figure 2 illustrates two random coefficients:
m_{0i} is the random coefficient for the intercept, whereas m_{1i}
is the random coefficient for slope. The term m_{0i }is the difference
between the individual’s Y intercept (p_{0i}) and the overall
grand mean intercept (b_{10}), whereas the term u_{1i}_{ }is the deviation
between the individual’s slope and the overall grand mean slope (b_{10}).
Referring to Figure 2, the individual’s Y intercept
is higher than the grand mean intercept, whereas the individual’s slope is
shallower than the grand mean slope.

Each individual has his or her own random
coefficient m_{0i} and m_{1i} terms. The statistical testing of
the variability of the terms is the key difference between multilevel modeling
and traditional repeated-measures ANOVA. If statistically significant
variability exists in any of the growth parameters (intercepts, slopes, and
quadratic terms), a researcher can add predictor variables to the level 2
equations to explain the variability. The ability to use patient-level
predictor variables allows multilevel models to explore individual differences.

Using Raudenbush amd Bryk’s (2002) terminology, a
model that describes the variability among growth parameters without predictor
variables is called an unconditional model. An unconditional model that adds
predictor variables to explain any significant variance in growth parameters is
called a conditional model. The authors present a numerical example of
unconditional and conditional growth modeling in the next section.

**Relationship
of the Statistical Model to Clinical Phenomena**

The model discussed earlier is a representation of
how a sample of patients might change over time with regard to a single sign or
symptom. The level 1 model captures the process of change in an individual.
What clinicians might see as an absence of a sign or symptom at the start of
therapy followed by a rapid development and resolution of erythema
for a given patient would be captured as growth parameters for that patient.
The parameters would indicate an intercept of zero and a quadratic term that is
highly negative (the slope term is less important in a quadratic model than in
a linear model). Just as each patient might show a different pattern of rise
and fall of erythema, the level 1 parameters (the Y
intercept, p_{0i}; slope, p_{1i}; and quadratic term, p_{2i})
would differ. In addition, if the clinical phenomena were known to show a
variable expression, a researcher would expect that the measures on individual
variability in the statistical model (the m terms discussed earlier) would show
a high degree of variability. Finally, just as a clinician might see that the
progression of erythema could differ depending on
gender or previous history, the conditional model discussed earlier could test
that association. In those ways, the statistical model can be congruent with
the clinical picture and can serve as a rigorous test of hypotheses that are
developed from clinician experiences or from a biologic model such as that
proposed by Sonis (2004a). Thus, with an appreciation
of the fundamentals of growth-curve modeling, researchers can formulate
questions about changes in signs or symptoms in a more rigorous fashion and
develop hypotheses that can be subjected to statistical analyses.

Example of Growth-Curve Modeling Using Oral Mucositis and Pain Data

**Parent
Study **

To illustrate the growth-curve modeling approach to
studying change over time, the authors employed individual growth-curve
modeling to clinicians’ observational ratings of erythema
and patients’ self-reported ratings of oral pain, the defining components of OM
(McGuire* *et al., 1993). In the parent study (McGuire, Yeager,* *et
al., 1998), a sample of 153 patients received high-dose chemotherapy in
preparation for bone marrow or stem cell transplantation (n = 133) or for
leukemia induction therapy (n = 20). Although the study was a randomized
clinical trial testing the effects of a psychoeducational
intervention for reducing duration and severity of OM and pain, data from the
experimental and usual control groups were aggregated for the purposes of this
analysis. After patients completed chemotherapy, researchers collected data
from patients in their hospital rooms on designated study days (three times per
week) in a manner designed to capture developing, peaking, and resolving OM and
pain. Trained nurses and a dentist conducted observational ratings of OM
(including erythema) using the 20-item Oral Mucositis Index (McGuire* *et al., 2002). Patients
self-reported ratings of oral pain using the Brief Pain Inventory (Cleeland, 1989). The erythema
score was computed as the mean of severity of erythema
(rated on a scale ranging from 0 [normal] to 3 [severe] across nine sites in
the mouth [upper and lower labial mucosa; right and left buccal
mucosa; dorsal, lateral, and ventral tongue; floor of the mouth; and soft
palate]). Erythema and oral pain scores were similar
to total average scores reported in earlier studies (McGuire* *et al*.*,
1993; Schubert, Williams, Lloid, Donaldson, & Chapko, 1992). The focus here is on erythema
as opposed to ulceration because ulceration was less prominent than erythema in the parent study data.

**Modeling
**

The unconditional and conditional growth-curve
models were estimated, as recommended by Byrk, Raudenbush, and Congdon (2002).
In the process, the researchers estimated a quadratic form of the trajectories
of erythema over eight time points that were defined
as study days. The quadratic form was chosen because previous reports of OM
have indicated this type of trajectory (McGuire* *et al*.*, 1993; Sonis, 2004a; Woo, Sonis, Monopoli, & Sonis, 1993). Models
were conducted with no centering, so the intercept is equivalent to the level
of erythema and pain at the beginning of the study,
the linear slope indicates the rate of change per unit of time, and the
quadratic term indicates the curvature (acceleration or deceleration) of erythema and pain scores.

The first analysis conducted was an unconditional
model to inferentially test that the intercept, slope, and quadratic terms were
different from zero and to investigate whether the individual differences in
the growth parameters had sufficient variability. The second analysis consisted
of adding the predictor variable of gender to explain residual variance
(variability), thus creating a conditional model. The models’ equations with
intercept, linear, and quadratic parameters for erythema
are shown next. Parameter estimates for both erythema
and self-reported OM pain are shown in Table 1.

Level 1: Y_{ti} = p_{0}_{l }+** **p_{1}_{l}** **a_{tl}_{ }+ p_{2}_{l}** **a_{tl}^{2}** **+ e_{tl}

Level 2: p_{0}_{l} =** **b_{00} + m_{0i}

_{
}p_{1}_{l }=** **b_{10} + m_{1l}_{}

_{
}p_{2}_{l} =** **b_{20} + m_{2}_{l}

**Unconditional
model results: **The model for erythema
demonstrated no centering of data, which allowed b_{00 }to represent the
mean intercept at the beginning of the study. The estimate b_{00}_{ }= 0.0313 was not statistically significant from zero
(i.e., patients began the study with no erythema on
average). b_{01}_{ }was the mean
linear rate across time, and the estimate b_{00}_{ }= 0.1433 was statistically significant from zero,
indicating an increase in erythema at the outset of
the study. The estimate b_{20} = -0.0065 was negative and differed significantly
from zero, indicating that, on average, subjects’ erythema
first rose and then declined (recall that the quadratic term is a nonlinear
change, which can be seen by the downward curvature of erythema
scores in Figure 3). Similar results were obtained
with unconditional modeling of self-reported OM pain over time (see Table 2). Thus, in erythema
and oral pain, the overall process of change was similar, the intercept was
zero, severity increased at the start of the study, and resolution (or partial
resolution) occurred as the study progressed.

Investigation of the variance components among
subjects’ linear slope and quadratic random effects (i.e., *T*_{11 }and
*T*_{22}) revealed that both the linear slope and quadratic random
effects differed significantly from zero, indicating variability in linear
growth rates and quadratic effects among subjects that may be accounted for by
additional predictor variables. The nonsignificant
random effect of intercept indicates no variability in initial levels of erythema that could be accounted for by predictor
variables. Thus, the random effect for intercept was dropped from the
unconditional and conditional model. Explaining the significant variance among
subjects’ growth parameters (e.g., linear slope, quadratic effect) demonstrates
how growth modeling better represents individual differences in forms of change
over time compared to repeated-measures ANOVA approaches.

**Conditional
model results: **By adding the predictor variable of gender to the
unconditional model for erythema and pain, the
researchers obtained the following equations.

Level 1: Y_{ti }= p_{0}_{l} + p_{1}_{l} a_{tl} + p_{2}_{l} (a_{tl}^{2}) + e_{tl}

** **Level 2: p_{0}_{l} = b_{00}

p_{1}_{l} = b_{10 }+ b_{11 }(gender) + m_{1}_{l}

** **p_{2}_{l} = b_{20 }+ b_{21 }(gender) + m_{2}_{l}

For the conditional model, gender was coded as
female = 0 and male = 1. Like the unconditional model, no centering of data
occurred, which allowed b_{00} to represent the mean intercept at the beginning of
the study. Investigation of the conditional erythema model
indicated that, similar to the unconditional model for erythema,
the estimate b_{00}_{ }= 0.0322 was not statistically significantly
different** **from zero. Interpreting the other growth parameters requires
some care. b_{10}_{ }was the mean
linear rate across time when gender = 0 (i.e., female), and the estimate b_{01
}= 0.1158 was statistically significantly different** **from zero. The
estimate b_{20} = –0.0052 was the average curvature when gender = 0 (female) and was
significantly different from zero. b_{11 }= 0.0638 was the additional linear slope when gender
= 1 (male) and was significantly different from zero. The additional linear
slope effect for being male is illustrated in Figure 4,
where the males show a faster rise in erythema
severity than females. The estimate b_{21 }= –0.0030 was the additional
quadratic estimate when gender = 1 (male) and was significantly different from
zero. The additional quadratic effect for being male also is illustrated in Figure 3, where the males show a sharper decline in erythema severity past the zenith (i.e., more curvature).
Investigation of the variance components among subjects’ linear slope and
quadratic random effects (i.e., t_{11} and t_{22}) revealed
that both the linear slope and quadratic random effects differed significantly
from zero, indicating variability in linear growth rates and quadratic effects
among subjects that may be accounted for by additional predictor variables
besides gender. Self-reported ratings of oral pain showed very similar results;
the growth parameters are included in Table 2.

Discussion

This article delineates the utility of multilevel
growth-curve modeling to the study of change over time. The authors
demonstrated that utility by the application of multilevel models to repeated
measures of OM (clinician-rated erythema and patient
self-reported ratings of oral pain). The results for erythema
and pain were consistent with previous reports in the literature (McGuire et
al., 1993; Schubert et al., 1992; Sonis, 2004b; Woo
et al., 1993). The quadratic models of change also resulted in significant
models commensurate with published reports of patterns of OM based on typical
mean scores (McGuire et al., 1993; Schubert et al.; Sonis,
2004b; Woo et al.). In addition, the curve parameters of erythema
were associated with gender, which also is consistent with reports of factors
associated with OM (Avritscher* *et al*.*,
2004). Another important outcome of the analyses is that the results help
support or extend understanding of the pathobiologic
model of OM (Sonis, 1998, 2004b), including clinical
manifestations, correlates, and risk factors.

This article is the first report, to the authors’
knowledge, to examine the utility of multilevel growth-curve analysis in
studying changes in OM over a clinical trajectory. Future studies could employ
similar methods to test predictions based on the evolving pathobiologic
model of OM (Anthony, Bowen, Garden, Hewson, & Sonis, 2006; Sonis, 2007; Sonis et al*.*, 2007; Sonis,
1998, 2004b), with the aim of adding to existing knowledge about this
critically important side effect of high-dose chemotherapy. For example,
multilevel growth-curve analysis might be used to predict whether individual
trajectories of change in erythema and ulceration are
related to patient-related (e.g., demographic) or treatment-related (e.g.,
diagnosis, treatment regimen) variables or to underlying mechanistic processes
indicated by biologic measures such as cytokine levels. Thus, this analytic
strategy could contribute to an enhanced understanding of pathobiologically
based individual variations in the clinical expression of OM.

Another critical advantage of using multilevel
growth-curve analysis is that analyses could lead to fuller integration of
mechanistic and etiologic models such as Sonis’
(2004a) OM model into the broader context of symptom clusters in patients with
cancer (Barsevick, 2007; Kim* *et al., 2005; Lee et al., 2004; Miaskowski & Aouizerat,
2007).** **For example, the pathobiologic model of
mucositis suggests that the complex processes
underlying the development of OM also may be implicated in the development of
other signs and symptoms that are observed concurrently with mucositis, such as pain, sleeping alterations, fatigue, and
emotional distress (Gaston-Johansson, Fall-Dickson, Bakos,
& Kennedy, 1999; Lee et al.; McGuire, 2002; McGuire et al., 1993; McGuire,
Owen, & Peterson, 1998; Miaskowski & Aouizerat). With increased knowledge of underlying
causative mechanisms and new ways to analyze change over time in multiple signs
or symptoms, the interrelationships of pathobiology
and clinical trajectories may be explored in ways that advance understanding of
symptom clusters more rapidly.

Another potential use of this methodology is in the
analysis of other symptoms (e.g., fatigue) or combinations of signs and
symptoms (e.g., OM, pain, fatigue). It could be an important new approach to
analyzing potentially complex relationships among symptoms in patients with
cancer. Finally, this method offers useful advantages for current and future
work on uncovering processes that underlie the clustering of symptoms,
consistent with recommendations by numerous experts (Barsevick, 2007; Barsevick,
Whitmer, Nail, Beck, & Dudley, 2006; Kim et al*.*,
2005; National Institutes of Health, 2002). Relevant targets could include
proposed models for relationships among symptoms such as Lee et al.’s (2004)
cytokine model and Parker, Kimble, Dunbar, and Clark’s (2005) symptom
interactional framework.

**Limitations**

As with any research, the results presented herein
have some inherent limitations. First, they reflect a secondary data analysis
from a study testing the effects of a psychoeducational
intervention in reducing the duration and severity of OM and oral pain in
patients receiving high-dose chemotherapy, so the data were analyzed for
different purposes than intended in the original study. Second, considerable
data were missing beginning at about 14 days after initiation of chemotherapy
because of patient discharges from the hospital, which limited the researchers’
ability to apply the growth-curve techniques across the full trajectory of
signs and symptoms.** **Substantive studies may require the use of sensitivity
analyses to control for biases resulting from data that are not missing at
random (Diggle & Kenward,
1994; Troxel, Harrington, & Lipsitz,
1998).

Conclusion

This article illustrates the potential utility of
multilevel growth-curve modeling techniques in the study of change in signs and
symptoms over time. The results relative to the analysis of erythema
are consistent with previously published studies and extend the modeling by
delineating several patterns obscured by traditional analyses of mean scores.
Knowledge of these patterns may help clinicians approach assessment
differentially, depending on treatment and other factors. The multilevel
growth-curve modeling technique appears to be well suited to complex modeling
of multiple signs or symptoms and related outcomes. The method may enhance the
ability of researchers to analyze results of the complex data that emerge when
symptom clusters are being studied. The data include the process of change in
clinical signs and symptoms and the relationship of such processes to other
individual and clinical characteristics of patients, as well as to underlying
mechanistic models.

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*William N. Dudley, PhD, is a professor and the associate dean for
research in the School of Health and Human Performance at the University of
North Carolina–Greensboro; Deborah B. McGuire, PhD, RN, FAAN, is a professor
and the director of oncology specialty in the School of Nursing at the
University of Maryland in Baltimore; Douglas E. Peterson, DMD, PhD, is a
professor of oral medicine in the Department of Oral Health and Diagnostic
Sciences in the School of Dental Medicine and chair of the Head and Neck/Oral
Oncology Program in the Neag Comprehensive Cancer
Center at the University of Connecticut Health Center in Farmington; and Bob
Wong, PhD, is an assistant professor in the Emma Eccles Jones Nursing Research
Center in the College of Nursing at the University of Utah in Salt Lake City.
This research was supported in part by the National Institutes of Health (NIH)
grant #5R01CA114263-02, a grant from the Fetzer Institute (Ref. No. 99012801),
and NIH grant #1R01NR03929. Mention of specific products and opinions related
to those products do not indicate or imply endorsement by the Oncology Nursing Forum or the
Oncology Nursing Society. Dudley can be reached at wndudley@uncg.edu, with copy to editor at ONFEditor@ons.org. (Submitted
February 2008. Accepted for publication May 8, 2008.)
*

Digital Object Identifier: 10.1188/09.ONF.E11-E19