# Using the random forest method to detect a response shift in the quality of life of multiple sclerosis patients: a cohort study

Mohamed Boucekine, Anderson Loundou, Karine Baumstarck, Patricia Minaya-Flores, Jean Pelletier, Badih Ghattas, Pascal Auquier

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## ► To cite this version:

Mohamed Boucekine, Anderson Loundou, Karine Baumstarck, Patricia Minaya-Flores, Jean Pelletier, et al.. Using the random forest method to detect a response shift in the quality of life of multiple sclerosis patients: a cohort study. BMC Medical Research Methodology, 2013, 13 (1), pp.20. 10.1186/1471-2288-13-20. doi:10.1186/1471-2288-13-20

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# Using the random forest method to detect a response shift in the quality of life of multiple sclerosis patients: a cohort study

Mohamed Boucekine¹*, Anderson Loundou¹, Karine Baumstarck¹, Patricia Minaya-Flores¹, Jean Pelletier², Badih Ghattas³ and Pascal Auquier¹

**Abstract**

**Background:** Multiple sclerosis (MS), a common neurodegenerative disease, has well-described associations with quality of life (QoL) impairment. QoL changes found in longitudinal studies are difficult to interpret due to the potential response shift (RS) corresponding to respondents' changing standards, values, and conceptualization of QoL. This study proposes to test the capacity of Random Forest (RF) for detecting RS reprioritization as the relative importance of QoL domains' changes over time.

**Methods:** This was a longitudinal observational study. The main inclusion criteria were patients 18 years old or more with relapsing-remitting multiple sclerosis. Every 6 months up to month 24, QoL was recorded using generic and MS-specific questionnaires (MusiQoL and SF-36). At 24 months, individuals were divided into two 'disability change' groups: worsened and not-worsened patients. The RF method was performed based on Breiman's description. Analyses were performed to determine which QoL scores of SF-36 predicted the MusiQoL index. The average variable importance (AVI) was estimated.

**Results:** A total of 417 (79.6%) patients were defined as not-worsened and 107 (20.4%) as worsened. A clear RS was identified in worsened patients. While the mental score AVI was almost one third higher than the physical score AVI at 12 months, it was 1.5 times lower at 24 months.

**Conclusion:** This work confirms that the RF method offers a useful statistical approach for RS detection. How to integrate the RS in the interpretation of QoL scores remains a challenge for future research.

**Trial registration:** ClinicalTrials.gov identifier: NCT00702065

**Keywords:** Multiple sclerosis, Quality of life, Response shift, Random forest, MusiQoL, SF-36, Longitudinal studies, Variable importance

**Background**

Regulatory agencies such as the Food and Drug Adminis-
tration in the United States, the National Institute for
Health and Clinical Excellence in England, and the Na-
tional Authority for Health in France recommend
assessing the quality of life (QoL) in patients with chronic
disease. Particularly in the field of multiple sclerosis (MS),
QoL is recognized as a major outcome measure for

assessing health, evaluating treatment, and managing care
[1,2]. MS, the most common neurodegenerative disease in
young adults, has well-described associations with QoL
impairment [3,4].

QoL is a subjective measure of a patient's life satisfaction that is affected by many factors related to patients' intrinsic characteristics, such as mood, coping mechanisms, disease state/progression, and factors related to environmental characteristics, such as life experiences and emotional support. Evaluations of change in QoL are important for tracking the progression of the impact of the disease. QoL changes found in longitudinal studies are difficult to interpret. Are these changes due to a true change of the QoL

<footnote>* Correspondence: boucekine.m@gmail.com
¹EA3279, Self-perceived Health Assessment Research Unit, School of Medicine, Université de la Méditerranée, 27 bd Jean Moulin, Marseille cedex 05 F-13385, France
Full list of author information is available at the end of the article</footnote>
level or to respondents' changing standards, values, or conceptualizations [5,6]? This phenomenon is also well described and is referred to as a 'response shift' (RS). Classically, three types of RS have been distinguished: (a) changes in internal standards of measurement (recalibration), (b) changes in the priority (i.e., importance) of the component domains of the target construct (reprioritization), and (c) redefinition of the target construct (reconceptualization).

Several statistical methods have been proposed to detect an RS [5], specifically in MS populations: the then-test, structural equation modeling (SEM) [7], latent trajectory analysis of residuals [8], and more recently, recursive partitioning tree analysis as a data mining method [9]. Each method has its own specific advantages and limitations that have been clearly discussed [10]. It would be premature to conclude which method is best for detecting an RS. The variety of methods developed illustrates the complexity and difficulty in detecting and measuring an RS.

The Random Forest (RF) method developed by Breiman [11,12] is mainly used as a predictive approach. It has become a popular technique because the RF classification and regression models are versatile. The RF method has high prediction accuracy compared to other classification and regression algorithms [13]. There are numerous examples of the application of the RF in a variety of fields [14], specifically in genomics research [15] and genetic association studies [16]. The method provides an original variable's importance index for classification and regression that can be applied in other fields [14]—for example, to assess the RS in the reprioritization component of QoL assessments.

This study proposes to test the capacity of the RF approach for detecting RS reprioritization as the relative importance of QoL domains change over time.

The manuscript is organized as follows:

- the methods section, including the study design and setting, a brief description of RF specifications and how we use the RF method to detect RS,

- the results section containing the main findings of the analysis,

- and the discussion section, including the strengths and limitations of the RF method and opportunities for further research.

# Methods

## Study design and setting

This was a multicenter, multiregional, longitudinal observational study carried out at 32 centers in 12 countries: Argentina, Australia, Austria, Germany, Spain, France, Israel, Italy, Norway, Turkey, the United Kingdom, and the United States [17] (Additional file 1: Table S1).

The inclusion criteria were as follows: patients 18 years old or more with relapsing-remitting multiple sclerosis (RR-MS) according to the McDonald criteria [18,19] with an Expanded Disability Status Scale (EDSS) score lower than 7.0, with or without treatment, followed up as per the local standard of care practices and with a signed informed consent form. Patients suffering from dementia were excluded. All therapeutic decisions during the study were made at the discretion of the treating physician.

## Ethics committee and regulatory requirements

This study (ClinicalTrials.gov identifier: NCT00702065) was performed in accordance with the Declaration of Helsinki and all applicable regulatory authority requirements and national laws (Institutional Review Board or Independent Ethics Committee in accordance with the local requirements of each of the 12 countries). Written informed consent from patients was obtained prior to any study procedures.

## Evaluation times and data collection

The follow-up measurements took place over 24 months after inclusion. At baseline, sociodemographic (age at inclusion, gender, education level, marital status, employment status) and clinical (disease duration) data were obtained. Neurological disability status was assessed using a neurologist-rated EDSS score [20]. QoL was determined using the MusiQoL and SF-36 questionnaires when patients attended their local neurological clinic. The MusiQoL questionnaire is a self-administered, multidimensional, patient-based QoL instrument comprising 31 items that describe nine dimensions (activity of daily living, psychological well-being, relationships with friends, symptoms, relationships with family, relationship with the healthcare system, sentimental and sexual life, coping, and rejection) [21]. MusiQoL provides a global index score, which is calculated as the mean of the individual dimension scores. The SF-36 is composed of 36 items that are used to calculate the following eight scale scores: physical functioning (PF), social functioning (SF), role-physical (RP), role-emotional (RE), mental health (MH), vitality (Vi), bodily pain (BP), and general health (GH) [22]. Two composite summary measures are also calculated: the Physical Component Summary (PCS) and the Mental Component Summary (MCS) scores. The PCS and MCS scores are norm-based, using a linear T-score transformation with a mean (standard deviation [SD]) of 50 [10]). Both the MusiQoL and SF-36 yield scores on a 0–100 scale, in which 0 represents the lowest and 100 the highest QoL.

Every 6 months up to month 24, the EDSS and QoL were recorded: at baseline (M0), 6 months (M6), 12
months (M12), 18 months (M18), and 24 months post-inclusion (M24).

## Definition of disability deterioration

At 24 months, individuals were divided into two 'disability change' groups according to the following neurological standards [23,24]: 1. worsened patients experienced clinically meaningful worsening in the EDSS is defined as an increase of one point if the EDSS was less than 5.5, or by half a point if the EDSS was between 5.5 and 7.0, between the baseline and 24-month EDSS scores; 2. not-worsened patients comprised all other cases.

The not-worsened group was used as a control group in the analysis under the assumption that they were not prone to response shifts in perceived QoL.

## Data analysis

### Classification and regression trees

The Classification and Regression Trees (CART) method [25] is a binary splitting method that recursively partitions the data set into disjoint subgroups, called the leafs. It uses two algorithms. The first algorithm iteratively splits the data set into two sub-samples according to a binary rule such as "PCS < 50". The splitting rule is based on one of the explanatory variables and on a threshold for this variable. It is chosen in such a way as to minimize the heterogeneity of the obtained subsamples for a continuous outcome. Regression trees are constructed using the "deviance" criterion.

The two obtained sub-samples are then recursively partitioned in the same way until there are too few observations (usually five) in the obtained samples (other stopping rules are available). This procedure yields a tree that may have too many terminal nodes. The mean value of the output variable is assigned to each leaf, computed over the observations within the corresponding region.

To avoid overfitting the data when using this tree, a pruning algorithm is used to select an optimal sub-tree.

### The random forest method

Random Forests [11] is an ensemble method that aggregates K trees similar to the ones constructed with CART, each one grown using a bootstrap sample of the original data set. Each tree in the forest uses only a subset of the explanatory variables at each node. The trees are not pruned. The prediction given by an RF is the mean of the predictions given by the K trees in the forest when using regression trees.

### Variable importance

As the trees in the forest are developed using bootstrap samples of the original data set, the Out-of-Bag (OOB) samples are used as test samples. The performance of each tree is computed over the corresponding OOB

sample. The observations of each variable in the OOB sample are randomly permuted, and the trees' performance is computed over the perturbed OOB samples. A variable's importance (VI) is defined as the mean relative decrease in the trees' performance when the observations of this variable in the OOB sample are randomly permuted. To obtain more stable assessments of each VI, we run the RF K=300 times and use the average VI over the K runs.

### Detecting response shift reprioritization with random forest

We investigated the importance of different explanatory variables in the global MusiQoL index forecast. To do this, we calculated the VI by the RF method based on two models.

$$M^{(1)} Global_{Index} = f(PCS, MCS, X)$$

$$M^{(2)} Global_{Index} = f(PF, RP, VI, BP, SF, RE, MH, GH, X)$$

Where

$$X = (Age, Gender, Education Level, Marital Status, Employment Status, Disease Duration).$$

Model $M^{(2)}$ is more refined than $M^{(1)}$. We adjusted these two models separately for the worsened group and the not-worsened group at each moment t=0,...,4. In this way, we obtained the average of VI (AVI) that evolved with time for each explanatory variable $\tilde{X} AVI_t(\tilde{X})$. We compared the evolution of AVI for each variable in the two groups. Crossing curves were considered an effect of reprioritization.

To control the difference in baseline EDSS scores between the worsened and not-worsened groups, supplementary analyses were performed on baseline EDSS score-matched groups (100 worsened patients and 100 not-worsened patients).

## Results

### Sample characteristics

The sample included 580 patients enrolled from 12 countries between November 2007 and October 2010. The 24-month EDSS was available for 524 of 536 patients. A total of 417 (79.6%) patients were defined as not-worsened and 107 (20.4%) patients were defined as worsened. Table 1 shows the baseline demographic and clinical characteristics of the worsened and not-worsened subjects.

### Response shift detection on MusiQoL index

The results are provided in Figure 1 and Figure 2. The proportion of total variance was higher than 55% for each global index model using MCS and PCS variables
<table><caption>Table 1 Baseline sociodemographic and clinical patient characteristics</caption><thead><tr><th></th><th></th><th>Total sample°</th><th>Worsened patients*</th><th>Not-worsened patients*</th><th>p</th></tr><tr><th></th><th></th><th>N=580</th><th>N=107</th><th>N=417</th><th></th></tr></thead><tbody><tr><td>Female, n (%)</td><td></td><td>419 (72.2)</td><td>75 (70.1)</td><td>300 (71.9)</td><td>0.71</td></tr><tr><td rowspan="2">Age (years)</td><td>M (SD)</td><td>41.3 (10.2)</td><td>43.2 (10.2)</td><td>40.9 (10.1)</td><td rowspan="2"><strong>0.03</strong></td></tr><tr><td>Min, max</td><td>18, 71</td><td>19, 64</td><td>18, 69</td></tr><tr><td rowspan="2">Marital status, n (%)</td><td>Cohabiting/married</td><td>393 (67.8)</td><td>73 (68.2)</td><td>282 (67.6)</td><td rowspan="2">0.91</td></tr><tr><td>Divorced/separated/single/widowed</td><td>187 (32.2)</td><td>34 (31.8)</td><td>73 (68.2)</td></tr><tr><td rowspan="2">Employment status, n (%)</td><td>Employed</td><td>335 (57.8)</td><td>59 (55.1)</td><td>241 (57.8)</td><td rowspan="2">0.62</td></tr><tr><td>Unemployed/homemaker/retired/student</td><td>245 (42.2)</td><td>48 (44.9)</td><td>176 (42.2)</td></tr><tr><td rowspan="3">Educational level, n (%)</td><td>Elementary school</td><td>113 (19.5)</td><td>31 (28.4)</td><td>76 (18.2)</td><td rowspan="3"><strong>0.03</strong></td></tr><tr><td>College</td><td>81 (14.0)</td><td>9 (8.3)</td><td>61 (14.6)</td></tr><tr><td>High school/university</td><td>386 (66.6)</td><td>67 (63.3)</td><td>280 (67.1)</td></tr><tr><td rowspan="3">EDSS score</td><td>M (SD)</td><td>2.9 (1.9)</td><td>3.5 (2.2)</td><td>2.8 (1.8)</td><td rowspan="3"><strong>&lt;10<sup>-3</sup></strong></td></tr><tr><td>Median</td><td>2.5</td><td>4.0</td><td>2.0</td></tr><tr><td>Min, max</td><td>0.0, 7.5</td><td>0.0, 7.0</td><td>0.0, 7.0</td></tr><tr><td rowspan="4">MS course, n (%)</td><td>Relapsing-remitting</td><td>510 (87.9)</td><td>79 (73.8)</td><td>381 (91.4)</td><td rowspan="4"><strong>&lt;10<sup>-3</sup></strong></td></tr><tr><td>Secondary progressive</td><td>53 (9.1)</td><td>18 (16.8)</td><td>30 (7.2)</td></tr><tr><td>Primary progressive</td><td>12 (2.1)</td><td>8 (7.5)</td><td>2 (0.5)</td></tr><tr><td>Primary relapsing</td><td>5 (0.9)</td><td>2 (1.9)</td><td>4 (1.0)</td></tr><tr><td rowspan="2">Time since first MS symptom (years)</td><td>M (SD)</td><td>10.0 (7.5)</td><td>10.2 (8.1)</td><td>9.9 (7.2)</td><td rowspan="2">0.73</td></tr><tr><td>Min, max</td><td>0, 45</td><td>0, 45</td><td>0.0, 40</td></tr></tbody></table>

M (SD), mean (standard deviation);

EDSS, Expanded Disability Status Scale; MS, multiple sclerosis;

° the 24-month EDSS was not available for 56 individuals.

* defined as worsened or not-worsened based on change in EDSS score from baseline to month 24 according to the definition of Lublin [Lublin 1996] and Kappos [Kappos 2004];

Bold values: p-value < 0.05.

from M6 to M24, both for worsened and not-worsened individuals (at 24 months, 68% and 66%, respectively). Figure 1a identifies a clear RS in the worsened patients based on the crossing of the MCS and PCS curves over time. In the patients, the MCS and PCS AVI were close

at M0, and the MCS AVI was almost one-third higher than the PCS AVI at M12. However, the AVI of PCS was 1.5 times greater than the AVI of MCS at M24. In the worsened patients, the reprioritization RS related specifically to the 'physical-like' dimensions of the SF-36 RP

<figure><img src="image_3.png" /></figure>
<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /><figcaption>**Figure 2** Average of variable importance of dimensions of SF-36 to MusiQoL index prediction. Figure 2a. Worsened individuals (n=107). Figure 2b. Not-worsened individuals (n=417).</figcaption></figure>

and PF dimensions and not the ‘mental-like’ dimensions (Figure 2a). Figure 1b shows the absence of an RS in not-worsened patients with the curves that did not cross over time and with the MCS and PCS AVI progressing symmetrically. In the not-worsened patients, the order of AVI of the SF-36 dimensions (Figure 2b) did not obviously differ between M0 and M24. At M24, the proportion of total variance for the models using the SF-36 dimensions accounted for 71 and 67% for the worsened and not-worsened groups, respectively.

The results of the baseline EDSS-matched groups are detailed in additional figures (Additional file 2: Figure S1 and Additional file 3: Figure S2). The findings were globally similar. One discrepancy concerns the not-worsened patients. While the MCS and PCS AVI progressed symmetrically in the entire sample, the 2 curves were close at M12 in the matched groups.

## Discussion

In longitudinal studies, the fundamental assumption is that the measures are interpretable across time; however, when an RS occurs, this assumption is invalidated because an RS makes change difficult to assess. It is not uncommon for MS patients to report improved mental health status despite severe impairments in physical functioning [9]. When an RS is present, conventional

statistical analyses might not detect true change in the measures [26,27]. It is critical for researchers and clinicians to have access to methods for detecting the presence of RS in their data. While several methods were previously used for this purpose, to our knowledge, this is the first study that assesses RS detection using the RF method. The RF method identified patterns of an RS in a global QoL change score. The reprioritization aspect of the RS was recognized through the qualitative differences of the importance of QoL specific domains that were retained by RF analysis.

Using the RF method, the RS was well identified in our worsened population. In this group, we observed that the mental composite score became more important during the twelve months following inclusion, while the importance of mental and physical aspects was close at the initial evaluation. This reprioritization effect should reflect a reaction of psychological compensation highlighted by the specific increase of the importance of the mental health dimension over time. The natural evolution of the disease generally includes deterioration and disability. During the second year of follow-up, the order of prioritization was inverted, with the greatest importance given to the physical component. Among the ‘physical-like’ dimensions of the SF-36, we observed a greater importance of physical functioning and role
physical dimensions compared to both bodily pain and
general health dimensions, for which the scores were
relatively stable over time. This finding can be explained
by the fact that the disease is not particularly painful
and does not affect general health in the short term.

In the not-worsened population, no crossing of the
curves was observed during the 24-month follow-up.
The mental composite scores had a greater and, conse-
quently, more important impact on the global quality of
life index compared to the physical composite scores
from the initial evaluation. In this population, the spe-
cific analysis of the ‘mental-like’ dimensions indicated
that social functioning was clearly an important dimen-
sion, showing higher importance indices than the vitality
and role emotional dimensions. In contrast, in the
worsened population, the three dimensions showed simi-
lar importance, reflecting a lower priority for the social
domain of the QoL domain. In our study, the lower im-
portance of social life in this group is independent of
marital status, although a relationship between the two
parameters was previously reported elsewhere, specific-
ally in MS [28]. Considering marital status as an indirect
marker of global social interactions, we thus hypothesize
that an MS patient with a severe disease course would
anticipate a decrease in his/her social interactions. This
reaction would result from the patient’s behavior and
beliefs related to the disease. On the contrary, the
reprioritization phenomenon found for the social di-
mension in MS individuals presenting with less severe
disease may reflect a willingness to adapt to their
situation.

Other methods to detect reprioritization RS have
already been developed, specifically in MS populations.

The design-based approaches, specifically the then-test
approach [5], assess the self-reported patient quality of
life at two different times and calculate the difference
between the first time (pre-test) and the last time (then-
test). Such methods are sensitive and biased and tend to
be restricted to retrospective studies [29].

Structural equation modeling (a model-based approach)
tests for a change in the magnitude of factor loadings on a
common latent variable over time [30,31]. This approach
cannot always be implemented in studies with small sam-
ple sizes because the larger number of parameters to esti-
mate may result in a lack of model convergence. The
order in which parameters are tested can affect the con-
clusion. If a substantial portion of the sample has not
undergone an RS, the method is more likely to conclude
that the RS did not occur.

More recently, the RS was tested using a recursive
partitioning tree analysis that is based on the disease tra-
jectory [9,32]. A tree is created for each disease trajec-
tory group. The order of the disability domain indicates
reprioritization. This relatively recent data mining

method shows promise for identifying small changes in
patient-reported outcomes scores over time.

The method based on latent trajectory analysis was
centered and used to create trajectories [33]. An RS was
hypothesized to be present when an individual's centered
residuals showed a pattern of fluctuation over time. This
method does not determine the type of RS that oc-
curred, but it is used to identify subgroups of the popu-
lation who present an RS.

Finally, methods based on the item response theory
should be tested.

The RF method presents several advantages. First, the
combination of several trees in a forest results in a
stronger classification predictor compared to a single
tree. Cross-validation procedures to assess the classifica-
tion performance of the model are unnecessary because
they are already built in, as each tree in the forest has its
own training and test (OOB) data. Third, RF are non-
parametric, non-linear stable models; no assumptions
about the form of underlying relationships between the
predictor variables and the response are made [34].
Fourth, variable importance may be assessed. Finally, the
RF algorithm is available in many different open source
software packages. Our choice of the RandomForest
package [35], available as an R implementation of the
original RF code [36], relied on its wide distribution,
ease of use, and the benefit from R data processing
functionalities.

The main drawback of our approach is that it only
detects the reprioritization component of RS. The role
of reprioritization in the score is not quantified. The
random forest variable importance measures may be
biased in situations where potential predictor variables
vary in their scale of measurement or their number of
categories [10]. The method does not provide a statis-
tical test for evaluating the assumption of differences
between two importance variable scores, making it diffi-
cult to give a clear interpretation when the importance
measures are close. A test comparing the score curves
should provide an objective decision tool for this
purpose.

Strengths and limitations

This study has several strengths and limitations.

The RS phenomenon should not be restricted to RS
detection. Future research should be developed to
address the remaining essential question: Does the RS
need to be integrated into the interpretation of QoL
score changes, and how can the weight of the RS in the
QoL measure be determined when an RS is detected?
The need to restore the usefulness and credibility of the
QoL assessment has been recently discussed [37,38];
answering this question will contribute to the reintegra-
tion of QoL data into clinical practice.
The nature of the use of the QoL questionnaire should be investigated. Some authors expected that disease-specific measures would be less susceptible to a response shift because they query specific symptoms or functional limitations more than generic measures [39]. We do not accept this assumption because we do not consider MusiQoL to be a symptom-function measure. MusiQoL is a well-validated multidimensional instrument assessing physical, mental, and social domains. Nevertheless, our analyses were performed on an index that is not expected to provide the most sensitive score of changes in the MusiQoL [21]. This restriction illustrates the results more clearly. Future works should provide data from MusiQoL dimension scores that more accurately demonstrate the RS.

Our study investigated the RS phenomena in the global MusiQoL index. It would be of interest to analyze the RS in the SF36 scores in order to make comparisons of the RS among different diseases.

Another important aspect of this study concerns the appraisal process of the RS, which is not directly measured in the present work. In the absence of an external criterion for the RS (pleasure appraisal processes), an RS interpretation of results will remain disputable [10,40]. Future research should measure the RS with direct measures of appraisal.

Future explorations should be performed to compare the capacity of the RF method for detecting the RS with other usual methods and of the degree of convergence of the isolated phenomena.

## Conclusion

Investigation of the response shift in multiple sclerosis is required to establish a strong construct. This work suggests that the random forest method offers a useful statistical approach to response shift detection.

## Additional files

**Additional file 1: Table S1.** Investigators and centers.

**Additional file 2: Figure S1.** Average of variable importance of mental and physical composite scores of SF-36 to MusiQoL index prediction on baseline EDSS score matched groups. Additional Figure 1a. Worsened individuals (n=100). Additional Figure 1b. Not-worsened individuals (n=100).

**Additional file 3: Figure S2.** Average of variable importance of dimensions of SF-36 to MusiQoL index prediction on baseline EDSS score matched groups. Additional Figure 2a. Worsened individuals (n=100). Additional Figure 2b. Not-worsened individuals (n=100).

## Abbreviations

AVI: Average variable importance; CART: Classification and Regression Trees; EDSS: Expanded Disability Status Scale; MusiQoL: Multiple Sclerosis International Quality of Life; MS: Multiple sclerosis; OOB: Out-of-Bag; QoL: Quality of life; RF: Random Forest; RR-MS: Relapsing-remitting multiple sclerosis; RS: Response shift; SEM: Structural equation modeling; SF36: Short Form 36; VI: Variable's importance; ADL: Activity of daily living; PWB: Psychological well-being; RFr: Relationships with friends;

SPT: Symptoms; RFa: Relationships with family; RHCS: Relationships with health care system; SSL: Sentimental and sexual life; COP: Coping; REJ: Rejection; PF: Physical function; SF: Social function; RP: Role physical; RE: Role emotional; MH: Mental health; Vi: Vitality; BP: Bodily pain; GH: General health; PCS: Physical composite score; MCS: Mental composite score.

## Competing interest

The authors declare that they have no competing interests.

## Authors' contributions

Conception and design: MB, BG, PA. Study coordination: JP, BG, PA. Inclusion and clinical data collection: JP. Analysis of data: MB, AL, KB. Interpretation of data: MB, AL, KB, PMF, JP, PA. Drafting and writing of manuscript: MB, KB, BG, PA. Revision of manuscript: MB, AL, KB, PMF, JP, BG, PA. All authors read and approved the final manuscript.

## Acknowledgements

The authors are grateful to all the patients and investigators for their participation in the study.

This study was supported by institutional grants from the French 2009 Institut de Recherche en Santé Publique (CUD-QV, Concepts, Usages et Déterminants en Qualité de Vie), and by Merck Serono S.A. - Geneva, Switzerland (a branch of Merck Serono S.A., Coinsins, Switzerland, an affiliate of Merck KGaA, Darmstadt, Germany). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors retained full control over the content of the manuscript.

## Author details

¹EA3279, Self-perceived Health Assessment Research Unit, School of Medicine, Université de la Méditerranée, 27 bd Jean Moulin, Marseille cedex 05 F-13385, France. ²Departments of Neurology and CRMBM CNRS6612, Timone University Hospital, APHM, Marseille, France. ³Department of Mathematics, Faculté des Sciences de Luminy, Aix-Marseille University, Marseille, France.

Received: 5 November 2012 Accepted: 13 February 2013

Published: 15 February 2013

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doi:10.1186/1471-2288-13-20

Cite this article as: Boucekine et al: Using the random forest method to detect a response shift in the quality of life of multiple sclerosis patients: a cohort study. BMC Medical Research Methodology 2013 13:20.

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