Methods for Examining Cancer Symptom Clusters Over Time Canhua Xiao, Deborah Watkins Bruner, Bonnie Mowinski Jennings, Alexandra L. Hanlon

Correspondence to Canhua Xiao E-mail: [email protected] Canhua Xiao Postdoctoral Fellow Nell Hodgson Woodruff School of Nursing Emory University, 1520 Clifton Road NE Room 225, Atlanta, GA 30322-4207 Deborah Watkins Bruner Professor Robert W. Woodruff Chair in Nursing Nell Hodgson Woodruff School of Nursing Emory University, 1520 Clifton Road NE Room 225, Atlanta, GA 30322-4207 Bonnie Mowinski Jennings Professor Nell Hodgson Woodruff School of Nursing Emory University, 1520 Clifton Road NE Room 225, Atlanta, GA 30322-4207

Abstract: In this article, we address statistical techniques appropriate for examining longitudinal changes in cancer symptom clusters. When the cluster structure is not pre-determined, researchers may examine symptom clusters either at each time point or use composite scores to examine the symptom clusters across time points. When the cluster structures are pre-determined, the statistical techniques depend on the research assumptions or purposes. Multilevel modeling, generalized estimating equations, latent growth curve modeling, and multivariate repeated-measure analysis of variance are good choices for exploring whole cluster changes over time. Alternately, confirmatory factor analysis and path analysis are appropriate techniques for examining changes in symptom relationships within clusters over time. Each technique is described, with examples and strengths and weaknesses.

ß 2014 Wiley Periodicals, Inc. Keywords: symptom clusters; cancer; longitudinal designs; multilevel modeling; generalized estimating equations; latent growth curve modeling Research in Nursing & Health, 2014, 37, 65–74 Accepted 8 October 2013 DOI: 10.1002/nur.21572 Published online in Wiley Online Library (wileyonlinelibrary.com).

Alexandra L. Hanlon Research Associate Professor School of Nursing University of Pennsylvania Philadelphia, PA

Clinical and research evidence indicates that many cancer patients experience multiple symptoms (Dodd, Miaskowski, & Paul, 2001; Gift, Jablonski, Stommel, & Given, 2004). Simultaneous experience of more than one symptom negatively affects patients' functional status and quality of life more than experience of individual symptoms (Barsevick, Whitmer, Nail, Beck, & Dudley, 2006; Dodd et al., 2001). In 2001, Dodd and colleagues coined the concept of symptom clusters to address the synergistic effect of multiple symptoms on cancer patient outcomes (Dodd et al., 2001). Since then, symptom clusters have been defined as groups of at least two symptoms that are related to each other temporally or biologically (Dodd et al., 2001; H. J. Kim, McGuire, Tulman, & Barsevick, 2005). Researchers have realized that studying cancer symptom clusters may help healthcare professionals better understand the complexity of concurrent symptoms and guide the development of interventions to manage symptom clusters. Although the significance of cancer symptom cluster research is recognized, methodological challenges remain

in examining the dynamic aspects of patient symptom profiles longitudinally (Miaskowski, Dodd, & Lee, 2004). Given the greater methodological complexity when examining symptom clusters in longitudinal research, it is important to understand both methodological and clinical implications of statistical methods appropriate to such analyses. Researchers have addressed statistical methods that are useful for identifying symptom clusters (H. J. Kim et al., 2005; Kirkova, Aktas, Walsh, & Davis, 2011), yet the focus has been on analyzing cross-sectional data. As yet unreported, to our knowledge, is an appraisal of the statistical methods appropriate for examining symptom clusters in longitudinal research. The purpose of this article is to critically review statistical methods used to examine symptom clusters in cancer patients over time. Each statistical method and its clinical relevance to cancer symptom cluster studies was explored in existing literature to determine how researchers examined longitudinal changes in symptom clusters among oncology patients.

C

2014 Wiley Periodicals, Inc.

66

RESEARCH IN NURSING & HEALTH

Methods

study, the investigators used Wilcoxon signed ranks test (Lengacher et al., 2012) as an alternative to the paired t-test for comparing two time points when data are not normally distributed, which did not enable a longitudinal view.

The research examples were found by searching four databases: PubMed, Cumulative Index to Nursing and Allied Health (CINAHL), Psychological Information (PsycINFO), and Sociological Abstracts. Symptom cluster(s), or multiple symptom(s), along with cancer, oncology, neoplasm, or tumor, were used as the key words to identify articles published in English, between 1980 and 2012. Reference lists and bibliographies from published articles were used to find additional related publications. In the literature search, 83 articles were identified for initial review; of these, longitudinal changes in symptom clusters in cancer populations were addressed in 29 articles. Seven of the 29 articles were not used as examples, for the following reasons. The authors of two studies used qualitative methodologies (Lopez, Copp, Brunton, & Molassiotis, 2011; Molassiotis, Lowe, Blackhall, & Lorigan, 2011) rather than statistical methods. The researchers in one study (Dodd, Cho, Cooper, & Miaskowski, 2010) used a distinct conceptual approach in which patients with similar symptom profiles were grouped together within a specific symptom cluster, rather than grouping associated symptoms together to generate symptom clusters. In another study, investigators clustered symptoms based on severity change over time instead of clustering associated symptoms (Wang et al., 2006). Researchers in another two studies focused on the internal consistency of symptom clusters over time (Chan, Richardson, & Richardson, 2005; Gift, Stommel, Jablonski, & Given, 2003). In the seventh

Results The methods used for examining longitudinal changes fell into two main pathways, with a number of approaches possible within each pathway. The pathway choice depended on whether or not symptom cluster structures were predetermined. Pathway 1 was used when symptom cluster structures were not pre-determined by theories, clinical observations, or prior empirical investigations. The statistical approaches and techniques on pathway 1 are best when researchers are not certain whether symptom clusters exist in their population of interest. Pathway 2 was used when symptom cluster structures are pre-determined theoretically, clinically, or empirically. The statistical approaches and techniques on pathway 2 are more appropriate when researchers want to explore changes over time in known symptom cluster structures (see Table 1). Statistical approaches for a range of conditions in each pathway are displayed in Figure 1.

Pathway 1: When a Cluster Structure Is Not Pre-Determined Two approaches lend themselves to examining longitudinal changes of symptom clusters when the underlying cluster

Table 1. Longitudinal Symptom Cluster Studies in Cancer Patients

Statistical Methods Pathway 1: Cluster structure not pre-determined Approach 1: Identifying symptom clusters at each time point: Exploratory factor analysis

Number of Studies

6

1 1

Chen et al. (2011), Gleason et al. (2007), E. Kim et al. (2009), H. Kim et al. (2008), Molassiotis et al. (2010), Skerman et al. (2012) Chow et al. (2007); Chow et al. (2010a); Hadi et al. (2008); Hird et al. (2010) Atay et al. (2012); Dodd et al. (2011); Reyes-Gibby et al. (2007) Capp et al. (2009) Xiao et al. (2013)

1 1

Xiao (2011) Jarden et al. (2009)

1 1

Steel et al. (2010) Chan et al. (2011)

1

Chow et al. (2010b)

2

Hayduk et al. (2010); Olson et al. (2008)

Principal component analysis

4

Cluster analysis

3

Integrated visualization and clustering Approach 2: Identifying symptom clusters across time points Pathway 2: Cluster structure pre-determined Tactic 1: Assuming unchangeable relationships Using a single composite symptom cluster score Random regression model Generalized estimating equations Using original individual symptom scores Latent growth curve model in structural equation modeling Multivariate repeated-measure analysis of variance Tactic 2: Assuming changeable relationships Exploring cluster structure changes Confirmatory factor analysis Examining causal relationship changes Path analysis in structural equation modeling

Research in Nursing & Health

Author and Year of the Study

SYMPTOM CLUSTERS OVER TIME/ XIAO ET AL.

67

FIGURE 1. Statistical approach and method selection tree for longitudinal symptom cluster research. Note. CA, cluster analysis; CFA, confirmatory factor analysis; EFA, exploratory factor analysis; GEE, generalized estimating equations; LGC, latent growth curve modeling; IVC, integrated visualization and clustering; MANOVA, multivariate analysis of variance; PCA, principal component analysis; SEM, structural equation modeling. structure is not pre-determined. One involves identifying symptom clusters at each time point; the other involves identifying symptom clusters across time points.

Approach 1: Identifying symptom clusters at each time point. When a cluster structure is not predetermined, the most common approach is to identify symptom clusters at each symptom measurement time. In this approach, the same statistical technique is repeated at each time point. There are several techniques from which to choose, including exploratory factor analysis (EFA; Chen et al., 2011; Gleason et al., 2007; E. Kim et al., 2009; H. J. Kim, Barsevick, Tulman, & McDermott, 2008; Molassiotis, Wengstrom, & Kearney, 2010; Skerman, Yates, & Battistutta, 2012), principal component analysis (PCA; Chow, Fan, Hadi, & Filipczak, 2007; Chow et al., 2010a; Hadi et al., 2008; Hird et al., 2010), cluster analysis (Atay, Conk, & Bahar, 2012; Dodd et al., 2011; Reyes-Gibby et al., 2007), and integrated visualization and clustering approaches (Capp et al., 2009). Once the cluster results at each time point are obtained, differences among the cluster structures over time can be compared descriptively. For instance, researchers could evaluate alterations in the number and type of symptoms within each cluster. Identifying symptom clusters at each time point is helpful during the early stages of research when the structure and the stability of symptom clusters are less well-understood, which may explain why this approach was used by authors of more than half of the articles in which cancer symptoms were explored longitudinally. The work by Kim et al. (2008) illustrates findings when symptom clusters are identified at each time point. The authors conducted a secondary analysis of data from 282 patients with breast cancer. EFA was conducted for symptoms at three points in time: time 1 (before treatment),

Research in Nursing & Health

time 2 (48 hours after the second cycle of chemotherapy or the last week of radiotherapy), and time 3 (48 hours after the third cycle of chemotherapy or one month after completion of radiotherapy). The symptom clusters and the individual symptoms comprising these clusters varied slightly over time. For example, the authors found one cluster (psychoneurological cluster) at time 1, but two clusters (psychoneurological cluster and upper-gastrointestinal cluster) at times 2 and 3. In addition, symptoms within the psychoneurological cluster varied slightly at different time points: hot flashes were not in this cluster at time 1 or time 3 but were present at time 2. Others have similarly demonstrated that, regardless of statistical method of symptom cluster identification (i.e., EFA, PCA, or cluster analysis), the cluster structures may differ over time. There are several concerns related to this approach. First, there is less likelihood of producing a consistent cluster structure over time. The mathematical principles underpinning EFA, PCA, and cluster analysis involve grouping symptoms through correlations or distances among symptoms. The changes in these correlations and distances lead to variations in cluster structures. In longitudinal studies in which these relationships are measured anew at each time point, such changes are likely, reducing the likelihood that the cluster structure will remain consistent over time. A second concern with identifying symptom clusters at each time point is that the results generated under this approach are descriptive, with no inferential statistical capability to examine significant changes over time.

Approach 2: Identifying symptom clusters across time points. To overcome the limitations posed by Approach 1, investigators may choose instead to identify symptom clusters that persist across time based on a composite data set. Approach 2 allows for statistical

68

RESEARCH IN NURSING & HEALTH

comparisons among different cluster structures over time. The composite data set is created by using the average or sum of each symptom score across measurement points (Alterman et al., 1998). Then, EFA is conducted based on this composite data set, allowing the researcher to identify a composite symptom cluster structure. The stability of the composite cluster structures over time can be examined using inferential statistics, such as Wrigley–Neuhaus coefficients (Guadagnoli & Velicer, 1991) or k coefficients (Landis & Koch, 1977). Additionally, the composite cluster structure can be compared among various sample subgroups defined by characteristics such as age, gender, or race (Alterman et al., 1998). Approach 2 was used in one study in which investigators examined temporal changes in symptom clusters for patients with head and neck cancer (HNC) who had received combined chemoradiotherapy (Xiao et al., 2013). By using the average score of symptoms across three time points after treatment, two symptom clusters were identified in a randomly selected half sample: a HNC-specific cluster (including symptoms such as radiodermatitis, dysphagia, mucositis, and dry mouth) and a gastrointestinal cluster. This cluster structure was further verified by confirmatory factor analysis (CFA) in the remaining half sample. Then, Wrigley–Neulaus coefficients were used to compare the stability of the cluster structures over time and among various subgroups with different treatment modalities, primary cancer sites, disease stages, age, gender, race, education, and marital status. The stability of the HNC-specific cluster and the gastrointestinal cluster over time and within these subgroups was supported by high Wrigley–Neulaus coefficients. Like the approach of identifying symptom clusters at each time point, the approach of identifying symptom

clusters across time points requires complete data sets (either without missing data or with missing data replaced based on imputation), as missing data may yield biased cluster results. In addition, the combination of data from different time points should make sense clinically. For instance, it might be reasonable to pool data separately across time based on whether they reflect acute side effects (e.g., symptoms that occur within 90 days after starting radiotherapy) or late side effects (e.g., symptoms that occur more than 90 days after the initiation of radiotherapy [Trotti et al., 2003]). The longer the time between acute and late side effect measurements, the more likely the two symptom profiles will show differences. Thus, combining acute and late symptom data sets may produce cluster structures that are not generalizable for different time points.

Pathway 2: When a Cluster Structure Is Pre-Determined Assumptions must guide the analysis of longitudinal cluster changes in a pre-determined cluster structure. The most common assumption is that the relationships among symptoms within an identified cluster are unchanging or stable over time. Under this assumption, researchers can explore change in the whole cluster over time. Alternately, researchers may assume that the relationships among symptoms within an identified cluster are changeable or unstable and examine the changes in symptom relationships within clusters over time. Advantages and disadvantages of the approaches discussed below are listed in Table 2.

Tactic 1: Assuming unchanging relationships among symptoms within a cluster. When symptom cluster structures are assumed to be unchanging or stable over time, the research design and/or data

Table 2. Statistical Approaches for Analysis of Pre-determined Symptom Clusters in Longitudinal Symptom Cluster Research Approach Multilevel model

Generalized estimating equations (GEE)

Latent growth curve model (LGC) in structural equation modeling (SEM) Multivariate repeated-measure analysis of variance Confirmatory factor analysis (CFA)

Path analysis in SEM

Research in Nursing & Health

Strengths Does not require complete data or equally spaced time intervals Tests individual differences over time Does not require complete data or equally spaced time interval Tests cluster changes over time for population Relief on assumption of data distribution Controls measurement errors No need for calculating cluster scores Analyzes complex relationships Tests individual differences over time No need to calculate cluster scores Compare cluster structures over time Have inferential statistics for the comparison over time Controls measurement errors Exam causal relationship among symptoms in a cluster

Weaknesses The estimates of the fixed effect of RRM are biased under departures from normality when missing data are presented Need to select an appropriate correlation matrix for the model

Requires complete data sets Requires equal time space between measurements Requires complete data sets Only for continuous dependent variables Not good for examining whole cluster severity or interference with daily life over time Requires complete data sets Requires complete data sets A causal model must be identified before data analysis

SYMPTOM CLUSTERS OVER TIME/ XIAO ET AL.

characteristics will guide the choice of statistical methods. Researchers may create and then track changes in a single composite score for a whole symptom cluster using a multilevel model or a generalized estimating equation (GEE) after calculating the single score at each point in time. If researchers are not interested in a single composite symptom cluster score, then latent growth curve modeling (LGC) via structural equation modeling (SEM) or multivariate analysis of variance (MANOVA) could be performed using original individual symptom scores.

Using a single composite symptom cluster score. Two statistical methods, the multilevel model and GEE, can be used if researchers are interested in using a single composite symptom cluster score. Multilevel models, also termed hierarchical general linear models, random regression models, nested models, mixed linear models, or covariance components models, are a recent extension of generalized linear models (GLM) for modeling repeated measurements (Goldstein, 2011) and can be used for longitudinal symptom cluster analyses (Xiao, 2011). The multilevel model is appropriate for research designs in which data are organized at more than one level. For example, if patients' data are collected from different hospitals, it is appropriate to use the multilevel model to adjust for covariates at both the patient level (lower level) and hospital level (higher level; Goldstein, 2011). Similarly, for symptom cluster studies, the lower level of data represents the separate symptoms measured in individuals at different times, and the higher level represents the individual who has several symptom measurements over time. The analysis using the multilevel model yields estimates of fixed and random effects (Hwu et al., 2002). Fixed effects refer to the coefficients that characterize the overall change of clusters over time. In contrast, random effects refer to parameters that describe individual variability around the fixed effects. In a simple linear model, there are two fixed effects, an intercept and a slope. The intercept is the average symptom cluster score at the initial observation/measurement. The slope represents the amount of change in the symptom cluster score per unit of time. The random effect associated with the slope describes the degree to which the rate of symptom cluster varies by person. The multilevel model may include an analysis of predictors, such as age, race, or sex, at each level of analysis. Xiao (2011) used a multilevel model in a study of patients with HNC receiving chemoradiotherapy to assess the change trajectories in previously identified symptom clusters over time and the effect of selected demographic and clinical variables (i.e., age, gender, race, radiation dose, and primary cancer site) on the changes of symptom cluster severity over time. Symptom cluster scores at each time point were the outcome variables, and the symptom assessment time points, along with selected demographic and clinical variables, were the predictors in this model. The severity of the two symptom clusters (the HNC-specific cluster and the gastrointestinal cluster) increased over the

Research in Nursing & Health

69

course of treatment and then was lower at the last measurement. Severity of the HNC-specific cluster varied by race and education, and severity of the gastrointestinal cluster varied based on gender and tobacco history. The multilevel model works effectively in the presence of missing data or unequal measurement time intervals, both likely in longitudinal symptom cluster research. Furthermore, the generalized multilevel model can be used for data with non-normal distributions (e.g., Poisson, binomial). In the generalized multilevel model, however, the estimates of fixed effects may be sensitive to model misspecification. The GEE provides asymptotically robust estimators against misspecification (Zeger, Liang, & Albert, 1988) and is another common way to analyze longitudinal data with composite symptom cluster scores (Liang & Zeger, 1986). Like the multilevel model, GEE is an extension of GLM for longitudinal data, given that GLM cannot be directly applied to the analysis of data that have dependence among repeated measures from the same individuals (McCullagh & Nelder, 1989). GEE assumes a certain correlation structure specified in advance of the analysis (Cui & Guoqi, 2007), but the GEE results remain robust if the correlation structure was misspecified. GEE may be used for data sets with unequally spaced time intervals, and covariates can be added to the models. GEE was used to explore the changing patterns of symptom clusters over time in a prospective, randomized control trial conducted by Jarden, Nelausen, Hovgaard, Boesen, and Adamsen (2009). The investigators tested the longitudinal effect of an intervention on treatment-related symptom clusters in patients receiving myeloablative allogeneic hematopoietic stem cell transplantation. Similar longitudinal change patterns of symptom clusters were revealed for both the intervention and control groups. The intervention group, however, showed a statistically significant reduction in severity over time for four out of five clusters. In Jarden et al.'s study (2009), GEE was used for continuous response variables; GEE is more powerful, however, when dealing with categorical response variables. The main difference between the multilevel model and GEE lies in the way inferences may be made. The multilevel model allows the investigator to make inferences at the individual response profile level; this is referred to as a subject-specific approach (Hilbe & Hardin, 2008). Conversely, when using GEE, the investigator makes inferences about the average response for all observations; this is referred to as a population-averaged approach (Hilbe & Hardin, 2008). With the multilevel model, inferences about changes in each participant's symptom cluster score, after adjusting for treatment group differences are easily estimated. Alternately, GEE may be more appropriate when the purpose is to make inferences about treatment group differences in symptom clusters over time. In addition, GEE provides valid inferences assuming missing data completely at random (MCAR), while the multilevel model does so

70

RESEARCH IN NURSING & HEALTH

under MCAR and data missing at random (MAR), two conditions general enough to accommodate most missing data mechanisms arising in practice (Gibbons, Hedeker, & DuToit, 2010; Hedecker & Gibbons, 2006). Using original individual symptom scores. If researchers are not interested in composite symptom cluster scores, they may use separate symptom scores to determine the longitudinal changes in a cluster with either LGC via SEM or with MANOVA. Determining the longitudinal changes in symptom clusters using LGC usually involves three steps (Wu, Liu, Gadermann, & Zumbo, 2009). The first step is to define a measurement model, or cluster structure, and to establish measurement invariance. The second step is to analyze a latent growth model to explain the changes in each cluster over time. An intercept and a slope are specified in which the intercept is the mean of the symptom cluster scores at the first assessment and the slope is the rate of change in the clusters during a period of time. The latent trajectory of the observed cluster scores over time is described by the intercept and the slope. The third step involves adding covariates that affect the change in symptom clusters over time, such as age or sex. Use of LGC modeling is exemplified in work by Steel et al. (2010), who explored the relationships among cancerrelated symptom clusters, immunity, and survival in 206 patients with hepatobiliary cancer. Symptoms and immunity were assessed at diagnosis and at 3 and 6 months. Multivariate latent growth curve modeling (MLGM), in combination with SEM, was used to observe the association between the rate of change of symptom clusters and immunity over time. Three symptoms—pain, fatigue, and depression—comprised the cluster. Contrary to the study hypothesis, there were no significant correlations between the rate of change in symptom clusters and immune system parameters. LGC enabled investigators to explore longitudinal changes in symptom clusters, between symptom clusters over time, and between symptom clusters and other outcome variables of interest. There are both advantages and limitations of LGC in SEM. One advantage is that the impact of measurement error on symptom cluster results is attenuated because parameter estimation in SEM controls measurement errors (Duncan, Duncan, Strycker, Li, & Alpert, 1999). The capacity to examine relationships between clusters over time is another advantage. A third advantage is that complex relationships (the relationship between predictors and symptom clusters or mediation effects) can be examined directly in SEM (Burgess et al., 2002; Duncan et al., 1999). The major limitation of this method is the requirement of continuous or ordered categorical symptom scores measured on at least three different occasions (Kline, 2005; Muthen, 2001). Researchers may choose other methods (e.g., GEE) for more flexibility in handling data with categorical variables or for symptom measurement at only two points in time.

Research in Nursing & Health

MANOVA, an extension of repeated-measures ANOVA, is another way of examining the longitudinal changes in symptom clusters without calculating a composite cluster score. This method also allows examination of the main effects and interaction (moderator) effects of independent variables of interest. In this statistical technique, a new dependent variable is created that is a linear combination of the individual dependent variables. In cancer symptom cluster research, MANOVA was used by Chan, Richardson, and Richardson (2011) to test the effect of an intervention on symptom clusters over time. The dependent variables were three symptoms within a cluster: breathlessness, fatigue, and anxiety. The three symptom scores were then combined into a composite of the vector of means on the transformed scores for the three symptoms over time. Time and intervention were the independent variables in the model. The authors found significant differences in the individual symptoms and the composite outcome over time and by treatment group. The major limitation of MANOVA is that, compared to LGC in SEM, it does not allow for flexibility in modeling complex relationships among or within clusters, and measurement errors are not controlled. Additionally, like LGC in SEM, missing data need to be handled in MANOVA, whereas the multilevel model and GEE are more powerful in the presence of missing data (Raudenbush & Bryk, 2002).

Tactic 2: Assuming changeable relationships among symptoms within a cluster. When symptom cluster structures are assumed to be changeable or unstable over time, CFA or path analysis in SEM can be used. When the purpose of the research is to explore changes within cluster structure over time for previously identified symptom clusters, CFA is a viable choice. When the purpose of the research is to examine the causal structure changes among symptoms within a cluster, path analysis in SEM is the better option.

Exploring cluster structure changes over time. Given that the symptom cluster structure may not be stable over time, CFA can be used to test the stability of pre-determined clusters. CFA is commonly used in social or health research to validate a proposed factor structure of items in an instrument (Fournier, Derubeis, & Beck, 2012; Hahn et al., 2010). When combined with SEM, CFA can also test whether the factor structure of a questionnaire is invariant over time or over samples with different characteristics (Small, Hertzog, Hultsch, & Dixon, 2003). Chow et al. (2010b) conducted a study of patients treated with palliative radiotherapy for either breast or prostate cancer to determine whether the symptom cluster structure identified at baseline varied following radiotherapy. Symptom cluster data were collected at baseline and at 4, 8, and 12 weeks after radiation therapy. The best-fit baseline model had two clusters, a physical interference cluster and a psychosocial interference cluster, which was tested at follow-up time points using CFA. Several fit indices, the

SYMPTOM CLUSTERS OVER TIME/ XIAO ET AL.

adjusted goodness of fit index (AGFI), Bentler's comparative fit index (CFI), the standardized root mean square residual (SRMR), and the root mean square error of approximation (RMSEA), were used to compare models of symptom cluster shift over time. The poor scores on these fit indices demonstrated that the two symptom clusters present at baseline were not as well-defined following radiation treatment. Using CFA allows investigators to use inferential statistics to compare the stability of symptom cluster structures over time. As shown in the study by Chow et al. (2010b), fit indices are useful to inferentially compare cluster structures at each time point and identify the clusters with greater or lesser variance across time. The commonly used indices include CFI, SRMR, or RMSEA, and the cut-point levels of these indices are important for the comparison of different models. According to Hu and Bentler (1999), cut-point values close to 0.95, 0.08, and 0.06 for CFI, SRMR, and RMSEA, respectively, are necessary before a relatively good fit between the hypothesized model and the observed data can be concluded.

Examining causal relationship changes over time. Path analysis in SEM can be used to test for change in the causal relationship among symptoms within a cluster over time. An essential prerequisite must be met: researchers must have derived a causal framework from previous clinical experience or research. When using SEM for this purpose, researchers need to identify two types of variables: exogenous variables and endogenous variables. Exogenous variables are not caused by an influence measured in the system or study; endogenous variables are caused by other variables in the system or study (Kline, 2011). In SEM, a variable can serve as both an exogenous and an endogenous variable, which makes SEM more flexible than common regression models. In cancer symptom cluster research, once the researchers identify the symptoms caused by other symptoms in a cluster, the SEM can be used to test that causal relationship. A research group has published two articles in which they used the path analysis method in SEM to explore the causes of change in symptom clusters over time (Hayduk, Olson, Quan, Cree, & Cui, 2010; Olson et al., 2008). In both analyses, the investigators used a sample of 82 patients with cancer-related symptoms. In both studies, the exogenous variables, symptoms causing other symptoms, included pain, anxiety, nausea, shortness of breath, and drowsiness. The endogenous variables, symptoms caused by other symptoms, included decreased appetite, tiredness, depression, and lower well-being. In tests of two separate SEMs for data at two points in time (Olson et al., 2008) and of one SEM for the data at the same two time-points (Hayduk et al., 2010), causal relationships among symptoms changed over the final month of life. Path analysis in SEM is a powerful way to explore the causal relationship changes among symptoms within a

Research in Nursing & Health

71

cluster. Using this method in symptom cluster research also has all the benefits of SEM, such as controlling for measurement error. However, researchers must have a clear understanding of the causal relationships among symptoms within a cluster before using SEM, because SEM can test but not create a causal model. A best-fit model, as shown by good fit indices, does not necessarily warrant causal claims unless evidence of causality, such as time precedence and a robust relationship in the absence or presence of other variables (Pearl, 2000), is well-established. In addition, like all analyses in SEM, missing data have to be handled in the usual ways, and large samples are required.

Summary and Recommendations Longitudinal research designs for cancer symptom cluster studies are better able to capture the dynamic aspects of patients' symptom profiles, as compared with cross-sectional designs. Several statistical techniques that may advance our understanding of temporal changes in symptom clusters were reviewed in this article. The selection of an appropriate statistical analysis for identifying symptom cluster changes over time depends on the framework for positing the existence of symptom clusters, that is, whether or not the cluster structure is pre-determined. Understanding the strengths and weaknesses of the various statistical techniques and having familiarity with these statistical techniques provides a critical foundation for longitudinal symptom cluster research. It is important to note that there are two distinct conceptual approaches in cancer symptom cluster research (Miaskowski, Aouizerat, Dodd, & Cooper, 2007). One is to group associated symptoms together for the purpose of identifying symptom clusters. The other is to group patients with similar symptom profiles within established symptom clusters. The statistical methods discussed here are applicable to the first conceptual approach: grouping associated symptoms together. The second conceptual approach, grouping patients with similar symptom profiles within an established symptom cluster to examine group membership changes over time, was used in only one longitudinal study (Dodd et al., 2010), and challenges were encountered due to sample size issues and unstable membership. Researchers may select different techniques based on study purposes and designs. Because these statistical techniques require advanced mathematical understanding, often it is extremely useful to involve statisticians in the study design, data analysis, and data interpretation. As more investigators conduct longitudinal symptom cluster studies, and the nature of symptom cluster changes over time is better understood, findings can be used to better target interventions to decrease symptom burden following cancer therapies.

72

RESEARCH IN NURSING & HEALTH

References Alterman, A. I., McDermott, P. A., Cook, T. G., Metzger, D., Rutherford, M. J., Cacciola, J. S., & Brown, L. S. J. (1998). New scales to assess change in the Addiction Severity Index for the opioid, cocaine, and alcohol dependent. Psychology of Addictive Behaviors, 12, 233–246. Atay, S., Conk, Z., & Bahar, Z. (2012). Identifying symptom clusters in paediatric cancer patients using the Memorial Symptom Assessment Scale. European Journal of Cancer Care (English), 21, 460–468. doi: 10.1111/j.1365-2354.2012.01324.x Barsevick, A. M., Whitmer, K., Nail, L. M., Beck, S. L., & Dudley, W. N. (2006). Symptom cluster research: Conceptual, design, measurement, and analysis issues. Journal of Pain and Symptom Management, 31, 85–95. doi: 10.1016/j.jpainsymman.2005. 05.015 Burgess, E. S., Brown, R. A., Kahler, C. W., Niaura, R., Abrams, D. B., Goldstein, M. G., & Miller, I. W. (2002). Patterns of change in depressive symptoms during smoking cessation: Who’s at risk for relapse? Journal of Consulting and Clinical Psychology, 70, 356–361. doi: 10.1037/0022-006X.70.2.356 Capp, A., Inostroza-Ponta, M., Bill, D., Moscato, P., Lai, C., Christie, D., … Denham, J. W. (2009). Is there more than one proctitis syndrome? A revisitation using data from the TROG 96.01 trial. Radiotherapy and Oncology, 90, 400–407. doi: S0167-8140(08) 00510-0[pii]10.1016/j.radonc.2008.09.019 [doi] Chan, C. W., Richardson, A., & Richardson, J. (2005). A study to assess the existence of the symptom cluster of breathlessness, fatigue and anxiety in patients with advanced lung cancer. European Journal of Oncology Nursing, 9, 325–333. doi: S1462-3889 (05)00033-5[pii]10.1016/j.ejon.2005.02.003 Chan, C. W., Richardson, A., & Richardson, J. (2011). Managing symptoms in patients with advanced lung cancer during radiotherapy: Results of a psychoeducational randomized controlled trial. Journal of Pain and Symptom Management, 41, 347–357. doi: 10.1016/j.jpainsymman.2010.04.024 Chen, E., Nguyen, J., Cramarossa, G., Khan, L., Zhang, L., Tsao, M., … Chow, E. (2011). Symptom clusters in patients with advanced cancer: Sub-analysis of patients reporting exclusively non-zero ESAS scores. Palliative Medicine, 26, 826–833. doi: 10.1177/0269216311420197 Chow, E., Fan, G., Hadi, S., & Filipczak, L. (2007). Symptom clusters in cancer patients with bone metastases. Supportive Care in Cancer, 15, 1035–1043. doi: 10.1007/s00520-0070241-z Chow, E., James, J., Barsevick, A., Hartsell, W., Ratcliffe, S., Scarantino, C., … Bruner, D. (2010a). Functional interference clusters in cancer patients with bone metastases: A secondary analysis of RTOG 9714. International Journal of Radiation Oncology Biology Physics, 76, 1507–1511. doi: 10.1016/j.ijrobp. 2009.04.024 Chow, E., James, J., Barsevick, A., Hartsell, W., Ratcliffe, S., Scarantino, C., … Bruner, D. (2010b). Confirmatory factor analysis of brief pain inventory (BPI) functional interference clusters in patients with bone metastases. Journal of Pain and Symptom Management, 3, 247–253. Cui, J., & Guoqi, Q. (2007). Selection of working correlation structure and best model in GEE analyses of longitudinal data. Communications in Statistics: Simulation & Computation, 36, 987–996. doi: 10.1080/03610910701539617

Research in Nursing & Health

Dodd, M. J., Cho, M. H., Cooper, B. A., & Miaskowski, C. (2010). The effect of symptom clusters on functional status and quality of life in women with breast cancer. European Journal of Oncology Nursing, 14, 101–110. doi: 10.1016/j.ejon.2009.09.005 Dodd, M. J., Cho, M. H., Cooper, B. A., Petersen, J., Bank, K. A., Lee, K. A., & Miaskowski, C. (2011). Identification of latent classes in patients who are receiving biotherapy based on symptom experience and its effect on functional status and quality of life. Oncology Nursing Forum, 38, 33–42. doi: 10.1188/11.onf.33-42 Dodd, M. J., Miaskowski, C., & Paul, S. M. (2001). Symptom clusters and their effect on the functional status of patients with cancer. Oncology Nursing Forum, 28, 465–470. Duncan, T. E., Duncan, S. C., Strycker, L. A., Li, F., & Alpert, A. (1999). An introduction to latent variable growth curve modeling: Concepts, issues, and applications. Mahwah, NJ: Lawrence Erlbaum. Fournier, J. C., Derubeis, R. J., & Beck, A. T. (2012). Dysfunctional cognitions in personality pathology: The structure and validity of the Personality Belief Questionnaire. Psychological Medicine, 42, 795–805. doi: 10.1017/s0033291711001711 Gibbons, R. D., Hedeker, D., & DuToit, S. (2010). Advances in analysis of longitudinal data. Annual Review of Clinical Psychology, 6, 79–107. doi: 10.1146/annurev.clinpsy.032408.153550 Gift, A. G., Jablonski, A., Stommel, M., & Given, C. W. (2004). Symptom clusters in elderly patients with lung cancer. Oncology Nursing Forum, 31, 202–212. doi: 10.1188/04.ONF.203212 Gift, A. G., Stommel, M., Jablonski, A., & Given, W. (2003). A cluster of symptoms over time in patients with lung cancer. Nursing Research, 52, 393–400. Gleason, J. F., Jr., Case, D., Rapp, S. R., Ip, E., Naughton, M., Butler, J. M., Jr., & Shaw, E. G. (2007). Symptom clusters in patients with newly-diagnosed brain tumors. Journal of Supportive Oncology, 5, 427–433, 436. Goldstein, H. (2011). Multilevel statistical models (4th ed.). West Sussex, UK: John Wiley & Sons. Guadagnoli, E., & Velicer, W. (1991). A comparison of pattern matching indices. Multivariate Behavioral Research, 26, 323–343. http://dx.doi.org/10.1207/s15327906mbr2602_7 Hadi, S., Fan, G., Hird, A. E., Kirou-Mauro, A., Filipczak, L. A., & Chow, E. (2008). Symptom clusters in patients with cancer with metastatic bone pain. Journal of Palliative Medicine, 11, 591– 600. doi: 10.1089/jpm.2007.0145 Hahn, E. A., Devellis, R. F., Bode, R. K., Garcia, S. F., Castel, L. D., Eisen, S. V., … Cella, D. (2010). Measuring social health in the patient-reported outcomes measurement information system (PROMIS): Item bank development and testing. Quality of Life Research, 19, 1035–1044. doi: 10.1007/s11136-010-9654-0 Hayduk, L., Olson, K., Quan, H., Cree, M., & Cui, Y. (2010). Temporal changes in the causal foundations of palliative care symptoms. Quality of Life Research, 19, 299–306. doi: 10.1007/ s11136-010-9603-y Hedecker, D., & Gibbons, R. D. (2006). Generalized estimating equation (GEE) models. In D. Hedecker & R. D. Gibbons (Eds.), Longitudinal data analysis (pp. 131–148). Hoboken, NJ: John Wiley & Sons. Hilbe, J. M., & Hardin, J. W. (2008). Generalized estimating equations for longitudinal panel analysis. In S. Menard (Ed.),

SYMPTOM CLUSTERS OVER TIME/ XIAO ET AL.

Handbook of longitudinal research: Design, measurement, and analysis (pp. 668). Burlington, VT: Elsevier. Hird, A., Wong, J., Zhang, L., Tsao, M., Barnes, E., Danjoux, C., & Chow, E. (2010). Exploration of symptom clusters within cancer patients with brain metastases using the Spitzer Quality of Life Index. Supportive Care in Cancer, 18, 335–342. doi: 10.1007/ s00520-009-0657-8 Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6, 1–55. doi: 10.1080/10705519909540118 Hwu, H. G., Chen, C. H., Hwang, T. J., Liu, C. M., Cheng, J. J., Lin, S. K., … Chen, W. J. (2002). Symptom patterns and subgrouping of schizophrenic patients: Significance of negative symptoms assessed on admission. Schizophrenia Research, 56, 105–119. doi: S0920996401002511 [pii] Jarden, M., Nelausen, K., Hovgaard, D., Boesen, E., & Adamsen, L. (2009). The effect of a multimodal intervention on treatmentrelated symptoms in patients undergoing hematopoietic stem cell transplantation: A randomized controlled trial. Journal of Pain and Symptom Management, 38, 174–190. Kim, E., Jahan, T., Aouizerat, B. E., Dodd, M. J., Cooper, B. A., Paul, S. M., … Miaskowski, C. (2009). Changes in symptom clusters in patients undergoing radiation therapy. Supportive Care in Cancer, 17, 1383–1391. doi: 10.1007/s00520-009-0595-5 [doi] Kim, H. J., Barsevick, A. M., Tulman, L., & McDermott, P. A. (2008). Treatment-related symptom clusters in breast cancer: A secondary analysis. Journal of Pain and Symptom Management, 36, 468–479. doi: S0885-3924(08)00210-8[pii]10.1016/j.jpainsymman. 2007.11.011 [doi] Kim, H. J., McGuire, D. B., Tulman, L., & Barsevick, A. M. (2005). Symptom clusters: Concept analysis and clinical implications for cancer nursing. Cancer Nursing, 28, 270–282; quiz 283– 284. Kirkova, J., Aktas, A., Walsh, D., & Davis, M. P. (2011). Cancer symptom clusters: Clinical and research methodology. Journal of Palliative Medicine, 14, 1149–1166. doi: 10.1089/jpm.2010.0507 Kline, R. B. (2005). Mean structure and latent growth models. In R. B. Kline (Ed.), Principles and practice of structural equation modeling (2nd ed., pp. 263–288). New York, NY: Guilford Press. Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd ed.). New York, NY: Guilford Press. Landis, J. R., & Koch, G. G. (1977). An application of hierarchical Kappa-type statistics in the assessment of majority agreement multiple observers. Biometrics, 22, 363–374. Lengacher, C. A., Reich, R. R., Post-White, J., Moscoso, M., Shelton, M. M., Barta, M., … Budhrani, P. (2012). Mindfulness based stress reduction in post-treatment breast cancer patients: An examination of symptoms and symptom clusters. Journal of Behavioral Medicine, 35, 86–94. doi: 10.1007/s10865-011-9346-4 Liang, K., & Zeger, S. L. (1986). Longitudinal data analysis using generalized linear models. Biometrika, 73, 13–22.

73

Miaskowski, C., Aouizerat, B. E., Dodd, M., & Cooper, B. (2007). Conceptual issues in symptom clusters research and their implications for quality-of-life assessment in patients with cancer. Journal of the National Cancer Institute Monographs, 2007(37), 39–46. doi: 10.1093/jncimonographs/lgm003 Miaskowski, C., Dodd, M., & Lee, K. (2004). Symptom clusters: The new frontier in symptom management research. Journal of the National Cancer Institute Monographs, 2004(32), 17–21. doi: 10.1093/jncimonographs/lgh023 Molassiotis, A., Lowe, M., Blackhall, F., & Lorigan, P. (2011). A qualitative exploration of a respiratory distress symptom cluster in lung cancer: Cough, breathlessness and fatigue. Lung Cancer, 71, 94–102. doi: 10.1016/j.lungcan.2010.04.002 Molassiotis, A., Wengstrom, Y., & Kearney, N. (2010). Symptom cluster patterns during the first year after diagnosis with cancer. Journal of Pain and Symptom Management, 39, 847–858. doi: 10.1016/j.jpainsymman.2009.09.012 Muthen, B. (2001). Second-generation structural equation modeling with a combination of categorical and continuous latent variables. In L. M. Collins & A. Sayer (Eds.), New opportunities for latent class/latent growth modeling (pp. 291–322). Washington, DC: American Psychological Association. Olson, K., Hayduk, L., Cree, M., Cui, Y., Quan, H., Hanson, J., … Strasser, F. (2008). The changing causal foundations of cancerrelated symptom clustering during the final month of palliative care: A longitudinal study. BMC Medical Research Methodology, 8, 36. doi: 1471-2288-8-36[pii]10.1186/1471-2288-8-36 [doi] Pearl, J. (2000). Causality: Models, reasoning, and inference. Cambridge, UK: Cambridge University Press. Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Thousand Oaks, CA: Sage. Reyes-Gibby, C. C., Chan, W., Abbruzzese, J. L., Xiong, H. Q., Ho, L., Evans, D. B., … Crane, C. (2007). Patterns of self-reported symptoms in pancreatic cancer patients receiving chemoradiation. Journal of Pain and Symptom Management, 34, 244–252. doi: 10.1016/j.jpainsymman.2006.11.007 Skerman, H. M., Yates, P. M., & Battistutta, D. (2012). Cancerrelated symptom clusters for symptom management in outpatients after commencing adjuvant chemotherapy, at 6 months, and 12 months. Supportive Care in Cancer, 20, 95–105. doi: 10.1007/s00520-010-1070-z Small, B. J., Hertzog, C., Hultsch, D. F., & Dixon, R. A. (2003). Stability and change in adult personality over 6 years: Findings from the Victoria Longitudinal Study. Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 58, 166– 176. Steel, J. L., Kim, K. H., Dew, M. A., Unruh, M. L., Antoni, M. H., Olek, M. C., … Gamblin, T. C. (2010). Cancer-related symptom clusters, eosinophils, and survival in hepatobiliary cancer: An exploratory study. Journal of Pain and Symptom Management, 39, 859– 871. doi: 10.1016/j.jpainsymman.2009.09.019

Lopez, V., Copp, G., Brunton, L., & Molassiotis, A. (2011). Symptom experience in patients with gynecological cancers: The development of symptom clusters through patient narratives. Journal of Supportive Oncology, 9, 64–71.

Trotti, A., Colevas, A. D., Setser, A., Rusch, V., Jaques, D., Budach, V., … Rubin, P. (2003). CTCAE v3.0: Development of a comprehensive grading system for the adverse effects of cancer treatment. Seminars in Radiation Oncology, 13, 176–181. doi: 10.1016/s1053-4296(03)00031-6

McCullagh, P., & Nelder, J. A. (1989). Generalized linear models (2nd ed.). London, UK: Chapman & Hall.

Wang, X. S., Fairclough, D. L., Liao, Z., Komaki, R., Chang, J. Y., Mobley, G. M., & Cleeland, C. S. (2006). Longitudinal study

Research in Nursing & Health

74

RESEARCH IN NURSING & HEALTH

of the relationship between chemoradiation therapy for nonsmall-cell lung cancer and patient symptoms. Journal of Clinical Oncology, 24, 4485–4491. doi: 10.1200/jco.2006.07. 1126 Wu, A., Liu, Y., Gadermann, A., & Zumbo, B. (2009). Multiple-Indicator Multilevel Growth Model: A solution to multiple methodological challenges in longitudinal studies. Social Indicators Research, 97, 123–142. Xiao, C. (2011). Identifying symptom clusters in patients with head and neck cancer post combined chemoradiation therapy.

Research in Nursing & Health

(Doctoral dissertation), Available from ProQuest Dissertations and Theses database (UMI No. 3475935). Xiao, C., Hanlon, A., Zhang, Q., Ang, K., Rosenthal, D. I., NguyenTan, P. F., … Bruner, D. W. (2013). Symptom clusters in patients with head and neck cancer receiving concurrent chemoradiotherapy. Oral Oncology, 49, 360–366. doi: 10.1016/j.oraloncology. 2012.10.004 Zeger, S. L., Liang, K. Y., & Albert, P. S. (1988). Models for longitudinal data: A generalized estimating equation approach. Biometrics, 44, 1049–1060.