Clustering based on adherence data
 Sylvia KiwuwaMuyingo^{1_71, 2_71}Email author,
 Hannu Oja^{1_71},
 Sarah A Walker^{3_71},
 Pauliina Ilmonen^{1_71},
 Jonathan Levin^{2_71} and
 Jim Todd^{4_71}
DOI: 10.1186/1742557383
© KiwuwaMuyingo et al; licensee BioMed Central Ltd. 2011
Received: 3 November 2009
Accepted: 8 March 2011
Published: 8 March 2011
Abstract
Adherence to a medical treatment means the extent to which a patient follows the instructions or recommendations by health professionals. There are direct and indirect ways to measure adherence which have been used for clinical management and research. Typically adherence measures are monitored over a long followup or treatment period, and some measurements may be missing due to death or other reasons. A natural question then is how to describe adherence behavior over the whole period in a simple way. In the literature, measurements over a period are usually combined just by using averages like percentages of compliant days or percentages of doses taken. In the paper we adapt an approach where patient adherence measures are seen as a stochastic process. Repeated measures are then analyzed as a Markov chain with finite number of states rather than as independent and identically distributed observations, and the transition probabilities between the states are assumed to fully describe the behavior of a patient. The patients can then be clustered or classified using their estimated transition probabilities. These natural clusters can be used to describe the adherence of the patients, to find predictors for adherence, and to predict the future events. The new approach is illustrated and shown to be useful with a simple analysis of a data set from the DART (Development of AntiRetroviral Therapy in Africa) trial in Uganda and Zimbabwe.
Introduction
Adherence is defined as the extent to which patients follow instructions for prescribed treatment necessary to achieve the full treatment benefits [1]. Treatment adherence is known to affect the outcome, but adherence behavior differs not only between patients, but also over time [2]. Also there is no standard measure of adherence, and different adherence measures (variables) are used in different settings and for different treatments [3]. For chronic diseases requiring continuous adherence to treatment a single measure is rarely useful, and one should use combined measures of adherence and consider both mean adherence and the variability in adherence over time [4].
To be clinically relevant, adherence measures should naturally be prominent predictors for future outcomes. With antiretroviral therapy (ART) for HIV infection, some patients maintain viral suppression and achieve good outcomes with moderate levels of adherence to the newer drugs (boosted protease inhibitor and nonnucleoside reverse transcriptase inhibitors) [5]. However, good adherence is needed to minimise the potential for the emergence of resistance strains of the virus, and to support maximal survival benefit in the longterm [6].
For example, in HIV infection several predictors of poor adherence to antiHIV drugs can be found, including low socioeconomic status of the patient [7], low education [8], regimen complexity [9], dosing frequency, cost of drugs and transport [10]. Nonadherence has also been associated with the drug regimen, personal factors, stigma, side effects, and travel away from home [11]. The impact of different patterns of adherence differs by drug class [5, 12, 13]. Various statistical methods have been used to predict adherence. Linear regression, logistic regression, or multinomial models have been used when adherence is expressed as a percentage, as a dichotomized variable (good vs poor adherence), or as a categorical or categorized ordinal variable (good, moderate, poor; 1,2,3+; etc.). Marginal models have been used for an analysis of repeated measures adherence data [14].
In the HIV studies, for example, adherence to ART is an important predictor of mortality, disease progression and virological failure [3, 5, 15]. Poor adherence to ART raises public health concerns of increased prevalence of disease, more potential for transmission of drug resistant virus to uninfected partners and minimizes the cost benefit of ART. However one difficulty with the analysis of adherence data is how to model the dynamic changes in adherence over time, and how to relate changes in adherence to patient characteristics, and to patient outcomes.
However since adherence is a dynamic and complex human behavior, the key is not so much the individual, observed values themselves, as whether we can characterize the underlying behavior of the patient outside the ART clinic from the observed pattern of reported adherence. In an alternative approach patient adherence measures are seen as a stochastic process, as described by Girard at al, Wong et al, and Sun et al [16], [17] and [18]. Stochastic models have the advantage of taking into account variability in adherence over time, being able to incorporate and distinguish missing data, and flexibility over the type of adherence measure used at each time point.
In our approach to develop new statistical tools to characterize and understand the adherence behavior of the HIV patients treated with ART, and to illustrate the use of these tool with real data. To do this the adherence measures at each time point are first categorized to a variable with finite number of values or "states". Repeated measures over time are then analyzed as a Markov chain of order 1, and the transition probabilities between the states are thought to describe the behavior of a patient. The patients are then clustered using their estimated transition probabilities between the various adherence states. These natural clusters can be used to describe variation in adherence, to find predictors for adherence, and to predict future disease progression or other outcomes. The new approach is illustrated with a simple analysis of a data set from the Development of Antiretroviral Therapy in Africa (DART) trial in Uganda and Zimbabwe (see http://www.ctu.mrc.ac.uk/dart). We compare the predictive powers of different models for mortality with adherence as a continuous and categorical explanatory variable under different Markov chain model assumptions. The comparisons are made using the ROC curves and areas under the ROC curves.
The paper is organized as follows. In Section 2 we describe the repeated adherence measurements on each individual as a Markov chain, and assume that the population consists of a finite number of clusters of patients with the same transition probabilities. In Section 3 the hierarchical clustering procedure based on the transition probabilities is described. Section 4 provides an example of DART trial data. We use three different models (repeated measurements are (i) independent and identically distributed, or distributed according to a (ii) homogeneous or (iii) nonhomogeneous Markov chain model) to analyse the data, we describe and interpret the clusters and compare their ability to predict mortality. A discussion of the relative merits of this new approach is given in section 5.
Adherence seen as a Markov chain
We assume that the adherence is measured by a discrete variable with finite number of possible values 1, ..., S. The values are here called states. For each individual the states are recorded at T time points 1, ..., T . The observed states are then denoted by X _{1}, ..., X _{ T } , and the whole process can be seen as one classificatory variable with S ^{ T } classes or profiles. Note that if the adherence measurements are continuous or multivariate, they must first be categorized for the analysis. Note also that missing data at some time point can be treated as one of the states.
Clustering based on Markov chain approach
In the paper we assume that the adherence behavior of each individual is a Markov chain with unknown transition probabilities. We also assume that the population of the patients can be divided into subpopulations or clusters such that within a cluster the transition probabilities are the same. The cluster memberships can then be used as a categorical variable in further analysis. The unknown cluster memberships must naturally be estimated from the data.
The vector Z is thus obtained by stacking the columns of Q on top of each other. Note that the estimates of the transition probabilities in P can be obtained by Q just by dividing each row of Q by its row sum. To avoid the possible divisions by zero we use Q instead of P in our analysis. The observed vectors Z are then used instead of the original X _{1}, ..., X _{ T }to cluster the data.
where , and are the sample mean vectors over the subsets with indices in I ∪ J , I and J, respectively. See Chapter 7 in [20]. R software was used in the practical analysis of data.
If X _{1}, ..., X _{ T } are identically and independently distributed then the clustering should be based on a Svector Z = (P _{1} , ..., P _{ S } ) only where , s = 1, ..., S. In case of the nonhomogeneous Markov chain, one may consider two matrices of estimated probabilities, Q _{1} and Q _{2}, which correspond to measurements at time points 1, ..., t_{1} and t_{1} + 1, ..., T, respectively. The clustering algorithm is then based on the 2S ^{2}vector Z = vec(Q_{1}, Q_{2}). The interpretations for the clusters can then made using two matrices of transition probabilities, P _{1} and P _{2} . In our application, the change point t _{1} is assumed to be fixed and known.
An example: The DART trial in Uganda and Zimbabwe
The data and the problem
We illustrate the clustering procedure and its use with a cohort data set of 2960 participants in the DART trial in Uganda and Zimbabwe. The trial started in January 2003, and the patients were followed until the end of December 2008. Participants' adherence to the treatment was assessed by pill counts and a structured questionnaire administered at each scheduled 4weekly clinic visit. Participants were asked questions on whether they had missed any dose in the last month, were late for the visit, had forgotten to take any dose at the weekend or missed any ART in the four days prior to the clinic visit. Drug possession ratio (DPR) previously defined as the days'supply of drugs delivered minus the days'supply of drugs returned divided by the number of days between clinic visits, assuming that ART was used continuously throughout the period between the clinic visits was obtained from clinic based pill counts [21]. Also missed clinic visits, death and loss to followup or withdrawals from the study were recorded.
The objective of this analysis was thus to find groups of DART patients with a similar adherence behavior (same proportions of being in each state, or same transition probabilities in homogeneous or nonhomogeneous Markov chains) during the first year of the followup. We then considered whether the cluster membership could be explained by age or gender (chisquare tests for independence) and whether the risk of death during the second and third year of the followup was different in different clusters (chisquare goodnessoffit tests, logistic analysis).
Adherence variables in this study
Note that the adherence values (X _{1}, ..., X _{ T } ) may be seen as one classificatory variable with S ^{ T } = 3^{12} = 531, 441 classes (profiles). Clustering is a way to reduce the number of classes in a rational way.
The rational and efficient use of the observed values X _{1}, ..., X _{ T } in the clustering naturally depends on the true statistical model. In the following we consider and compare three different models, namely,
(M1) the i.i.d. model, that is, X _{1}, ..., X _{ T } identically and independently distributed,
(M2) the homogeneous Markov chain model, and
(M3) the nonhomogeneous Markov chain model.
In the model (M3) we assume that the change point is at six months time. Note that the models are nested so that (M1) ⇒ (M2) ⇒ (M3). Likelihood ratio tests can be used to discriminate between the models.
and the three individuals are treated identically in the analysis ( ).
in the analysis. The variable is then 3, 9 or 18variate, respectively.
Clustering based on the models (M1), (M2), and (M3)
The probabilities of being in state 0, 1, and 9 in six clusters based the i.i.d. model (M1).
State  

0  1  9  Σ  
Cluster 1 (n = 469)  .065  .829  .107  1.000 
Cluster 2 (n = 426)  .280  .688  .032  1.000 
Cluster 3 (n = 360)  .167  .833  .000  1.000 
Cluster 4 (n = 618)  .083  .917  .000  1.000 
Cluster 5 (n = 196)  .489  .426  .085  1.000 
Cluster 6 (n = 891)  0  1  0  1.000 
Conditional transition probabilities in six clusters based on homogenous Markov chain model (M2)
Cluster 1 (n = 301)  Cluster 2 (n = 281)  

0  1  9  Σ  0  1  9  Σ  
0  0  1  0  1.000  0  0.425  0.523  0.052  1.000 
1  0.008  0.950  0.042  1.000  1  0.436  0.516  0.049  1.000 
9  0  1  0  1.000  9  0.235  0.307  0.458  1.000 
Cluster 3 ( n = 463)  Cluster 4 ( n = 596)  
0  1  9  Σ  0  1  9  Σ  
0  0.177  0.765  0.057  1.000  0  0.253  0.723  0.024  1.000 
1  0.073  0.871  0.056  1.000  1  0.207  0.770  0.023  1.000 
9  0.131  0.652  0.217  1.000  9  0.222  0.684  0.094  1.000 
Cluster 5 ( n = 469)  Cluster 6 ( n = 850)  
0  1  9  Σ  0  1  9  Σ  
0  0  1.000  0  1.000  0  .  .  .  . 
1  0.091  0.909  0  1.000  1  0  1.000  0  1.000 
9  .  .  .  .  9  .  .  .  . 
Cluster 2 is clearly the poorest one, as the proportion maintaining good adherence from one month to the next is the lowest. Patients in cluster 6 behave in an optimal way.
Conditional transition probabilities in six clusters based on heterogeneous Markov chain model (M3)
Cluster 1 (n = 519)  

Period 1  Period 2  
0  1  9  Σ  0  1  9  Σ  
0  0.027  0.960  0.133  1.000  0  0.095  0.866  0.039  1.000 
1  0.034  0.953  0.013  1.000  1  0.115  0.824  0.061  1.000 
9  0.117  0.860  0.023  1.000  9  0.051  0.800  0.149  1.000 
Cluster 2 ( n = 309)  
Period 1  Period 2  
0  1  9  Σ  0  1  9  Σ  
0  0.431  0.526  0.043  1.000  0  0.403  0.549  0.048  1.000 
1  0.497  0.458  0.045  1.000  1  0.279  0.672  0.049  1.000 
9  0.264  0.373  0.364  1.000  9  0.123  0.352  0.519  1.000 
Cluster 3 ( n = 408)  
Period 1  Period 2  
0  1  9  Σ  0  1  9  Σ  
0  0.275  0.684  0.041  1.000  0  0.000  1.000  0.000  1.000 
1  0.246  0.690  0.063  1.000  1  0.015  0.978  0.007  1.000 
9  0.226  0.598  0.177  1.000  9  0.067  0.933  0.000  1.000 
Cluster 4 ( n = 441)  
Period 1  Period 2  
0  1  9  Σ  0  1  9  Σ  
0  0.285  0.681  0.035  1.000  0  0.191  0.781  0.028  1.000 
1  0.163  0.799  0.039  1.000  1  0.273  0.697  0.030  1.000 
9  0.202  0.556  0.242  1.000  9  0.261  0.620  0.120  1.000 
Cluster 5 ( n = 433)  
Period 1  Period 2  
0  1  9  Σ  0  1  9  Σ  
0  0.000  1.000  0.000  1.000  0  .  .  .  . 
1  0.118  0.853  0.029  1.000  1  .  1.000  .  1.000 
9  0.000  1.000  0.000  1.000  9  .  .  .  . 
Cluster 6 ( n = 850)  
Period 1  Period 2  
0  1  9  Σ  0  1  9  Σ  
0  .  .  .  .  0  .  .  .  . 
1  .  1.000  .  1.000  1  .  1.000  .  1.000 
9  .  .  .  .  9  .  .  .  . 

Cluster 1: Good adherence  getting worse

Cluster 2: Poor adherence with missing data  getting slightly better

Cluster 3: First poor with some missing data  then very good

Cluster 4: Poor with some missing data  no big changes

Cluster 5: First good  then optimal

Cluster 6: Optimal in both periods
Contingency tables for cluster categories when clusters are based on (a) models (M1) and (M2), (b) models (M1) and (M3), and (c) (M2) and (M3).
(a) (M2)  

1  2  3  4  5  6  
(M1)  1  146  1  250  72  0  0 
2  0  11  6  309  0  0  
3  0  0  172  188  0  0  
4  114  0  35  0  469  0  
5  0  169  0  27  0  0  
6  41  0  0  0  0  850  
(b) (M3)  
1  2  3  4  5  6  
1  187  14  116  89  63  0  
2  5  127  82  212  0  0  
3  63  1  187  109  0  0  
4  223  0  19  6  370  0  
5  0  167  4  25  0  0  
6  41  0  0  0  0  850  
(c) (M3)  
1  2  3  4  5  6  
(M2)  1  124  0  0  0  177  0 
2  0  223  0  58  0  0  
3  158  2  252  51  0  0  
4  24  84  156  332  0  0  
5  213  0  0  0  256  0  
6  0  0  0  0  0  850 
Adherence clusters, predictors and explanatory variables
Clusterwise mortality in the second and third year on ART, proportion of women and proportion of patients in three age groups.
Age at ART initiation  

n  deaths  women  1835  3545  45+  
Cluster 1  (n = 519)  .033  .65  .41  .42  .17 
Cluster 2  (n = 309)  .061  .62  .39  .42  .19 
Cluster 3  (n = 408)  .034  .65  .41  .42  .16 
Cluster 4  (n = 441)  .048  .65  .41  .43  .16 
Cluster 5  (n = 433)  .020  .65  .37  .45  .15 
Cluster 6  (n = 850)  .024  .65  .40  .46  .15 
Estimated odds ratios (OR) with 95 percent confident intervals to compare the risk of deaths in different clusters, also adjusted for age and sex.
OR  95% CI  Adjusted OR  95% CI  

Cluster 1  1.4  (0.72, 2.71)  1.4  (0.71, 2.68) 
Cluster 2  2.7  (1.42, 5.18)  2.7  (1.41, 5.17) 
Cluster 3  1.5  (0.72, 2.93)  1.5  (0.71, 2.91) 
Cluster 4  2.1  (1.11, 3.89)  2.1  (1.10, 3.87) 
Cluster 5  0.9  (0.38, 1.90)  0.88  (0.38, 1.90) 
Cluster 6  1  1 
Discussion
The main motivation of this paper was to develop and illustrate new statistical tools to characterize and understand the adherence behavior of the HIV patients treated with ART, and to illustrate these tools with data from the DART study. The Markov chain model is perhaps the simplest model for dependent categorical repeated measurements. In this work, estimated proportions and transition probabilities in a threestate Markov chain model were used to cluster the individuals into groups with different adherence patterns. Transition probabilities represent the dynamics of adherence to ART, and the nonhomogeneous Markov chain model allows the patients to change their adherence behaviour over time. Relating patient characteristics to transition probabilities may enable a better understanding of adherence patterns compared to the traditional methods of just averaging the raw adherence data.
We illustrated the approach using one variable  "missed any dose of ART in the last month"  but the approach could be extended to a any number of adherence variables. Several variables can then be used together to construct the states needed for Markov chain model. Our approach can naturally be applied to different data sources (patients diaries, electronic event monitoring, drug possession ratio %), and to the adherence to several drugs simultaneously. Using different adherence variables (if not highly correlated) would produce different clusters with different predictive powers for mortality. The choice of adherence variables is therefore a crucial step, and depends on the data and application.
In our approach, the adherence observations X _{1}, ..., X _{ T } at time points 1, ..., T , are seen as a realization of a random process. Data analysts often implicitly assume that the observed values X _{1}, ..., X _{ T } are independent and identically distributed (iid). We think that such assumptions should be explicitly stated, and that it is unrealistic to assume that there is no dependence and no changes in distributions of X _{ i }. With our Markov chain model we have explicitly stated and assumed a certain simple dependency between the observations. In the paper, we compare three different models, namely the iid model (M1), the homogenous Markov chain model (M2), and the nonhomogenous Markov chain model (M3). Note that both (M1) and (M2) assume that there is no change in the adherence behavior over the followup period. In model (M3) this is allowed. If we assume constant transition probabilities over time we may lose information and important aspects of the phenomenon. It also important to note that the three models are nested within each other, so that the regular likelihood ratio tests can be used to distinguish between the models; this will be studied in our future work. In the paper we are interested in modeling adherence behavior in general rather than in modelling the changes in adherence behavior as in Lazo et al [22]. However, our model (M3) is flexible enough for modeling the changes as well. In model (M3), the transition probabilities can naturally be made to depend on explaining (modifiable) factors; this is however beyond the scope of this paper.
In our earlier paper [21] we showed that although adherence looked very high overall in the first year in DART, this masked an inconsistent adherence behavior at the individual level. For the illustration and comparison of different adherence profiles we gave an example of three individuals with very different profiles ((9, 9, 9, 0, 0, 0, 0, 0, 1, 1, 1, 1), (9, 0, 1, 1, 1, 9, 1, 0, 0, 0, 0, 9), and (9, 1, 0, 0, 0, 9, 0, 0, 1, 1, 1, 9)) but similar overall adherence (measured as proportions). The differences between the individuals cannot be explained with model (M1) and not even with model (M2). Only model (M3) can analyse the differences between these three individuals. For the DART data set, we found six clusters based on models (M1), (M2), and (M3). The clusters were genuinely different with different interpretations. Also clusters with changing behavior could be found (which supports the use of model (M3)). Our findings suggest that different approaches may be potentially useful in practical data analysis, and that overall (mean) adherence may not be enough when dealing with ART adherence. We compare the predictive powers of the procedures based on models (M1), (M2), and (M3) for mortality with ROC curves. We could not find any big differences between the procedures, but this may just be due to the short period for the measurements of adherence. Whilst viral load is of major importance to participants, this was not done in realtime, so not available for all participants. Death was also relatively infrequent in the second and third year (proportion of deaths = 3%) and so the power to distinguish different effects of M1 from M2 or M3, M2 from M3 was low. However the fact that M2 and M3 classified people differently illustrates potential of our approach.
The DART trial has collected data on virological failures and immunological failures as well. Those outcome variables will be considered in future analyses looking at the predictive value of these adherence clusters. We did not find any significant association between the adherence groups and age or gender. It is possible, however, that other sociodemographic variables are associated with the adherence groups. This approach could then be used in the future to identify individuals at risk of poor adherence. It is of course important to validate this new approach against outcomes that are associated with adherence to ART. In this work, the clustering variable based on the nonhomogeneous Markov model was seen to be associated with the risk of death in the second and third year of ART. Those reporting poor or less than adequate adherence had a significantly higher mortality in the second and third year than those that achieved optimal adherence, with the good and adequate users somewhere inbetween. The worst cluster has the highest mortality risk.
Our analysis was restricted to those who survived the first year of the trial [21]. The majority of deaths in the first year occurred in the first 3 months (50%). The patients with early deaths did not have the opportunity to fully demonstrate their adherence behavior. We also reasoned that poor patient outcome associated with adherence would likely manifest later in the course of treatment. We considered mortality during the second or third year as the outcome that adherence might predict. The causal pathway is that poor adherence leads to viral replication in the presence of low levels of drug which leads to drug resistance which leads to viral rebound which leads to CD4 decline, and finally leads to morbidity/mortality. It is precisely this process which takes several months, and motivates our prediction model where adherence in the first year predicts mortality in the second or third year.
Because we have used a relatively short time period for the assessment of adherence, the vector Z (with dimensions 3, 9, and 18 in different models) that we use for clustering seems to have a higher dimension than the vector of original observations (with dimension 12). However, the original adherence measurements yield in fact 3^{12} different profiles, and the idea here is to classify these profiles in a rational way. The transformed variables Z provide sufficient statistics in different models; if Z is known then X _{1}, ..., X _{ T } does not carry any additional information on the model. Our clustering procedures were based on Euclidean distances and Ward's minimum variance method as they seemed to work well in our case. Alternative linkage methods and distance measures should be used to generate the clusters for the comparison. In determining the distances between the individuals we could for example assign different weights to different transition probabilities. This will be a part of our future work on the use of stochastic adherence models and their use to predict future events.
It is of course not always clear what population quantities we are estimating when we report the odds ratio estimates for mortality for the six clusters we have obtained. The underlying assumption is that the data set used in the analysis is a random sample from a population with six subpopulations having different adherence behavior, then the cluster memberships (with six clusters) estimate the unknown subpopulation memberships, and the odds ratios using cluster memberships estimate the unknown odds ratios for the difference in mortality in the subpopulations. Under the above strict assumptions one may hope that the estimates are consistent to population values. However, if one does not believe in this assumption, one can still consider the predictive power of the whole procedure (area under the ROC curve) and use that for metaanalysis.
There are several extensions and possibilities to develop and deepen the analysis of DART trial data: Another extension to this approach would be to use two or more measures of reported adherence for the states in the Markov chain model. Continuous measurements such as the drug possession ratio could be categorized and used in the Markov chain model. One could look at trends over time and/or over a longer period of 3 years. One could use more states such as lost in the followup. In our analysis the problem of dropouts did not arise as all patients who died or were lost to followup in the first year of the trial had been excluded from the dataset [21]. Only 968(2.7%) clinic visits had missing data, 653 were due to forms not being completed by the adherence nurse and 315 were due to missed visits. These numbers were small and were not divided further in this application but could easily be divided if the analysis required it.
For example in clinical trials, nonresponse or dropout are important outcomes in their own right and should be distinguished from incomplete forms or poor documentation. Another extension of the model used here would be the use of the Markov chain model of order 2. Statistical tools are needed for the model selection: Statistical tests and estimates for the change point in a nonhomogeneous Markov chain model, and tests and estimates for the order of the model. We could easily build likelihood ratio tests for our nested parametric families of distributions. To show whether our Markov chain fits better than an independence model and more specifically test for homogeneity; if our nonhomogeneous Markov chain of order 2 fits better than the homogeneous one.
Our aim in this paper was to develop and illustrate some new ideas on how to classify patients based on adherence data using a stochastic model and to illustrate how this analysis could be carried out on real data. Further detailed analyses will be undertaken using the full DART dataset, in which we will explore the extensions to the basic model, develop ways of testing different models and evaluate factors that influence the transition probabilities. We believe this may develop a new way of looking at adherence and in better analysing adherence data.
Declarations
Acknowledgements
We thank the investigators of the DART trial for their permission to use the adherence data to develop the ideas and the models. We thank the reviewers for their helpful comments that have helped us to strengthen the paper in several ways.
We thank all the patients and staff from all the centres participating in the DART trial. MRC/UVRI Uganda Research Unit on AIDS, Entebbe, Uganda: H Grosskurth, P Munderi, G Kabuye, D Nsibambi, R Kasirye, E Zalwango, M Nakazibwe, B Kikaire, G Nassuna, R Massa, K Fadhiru, M Namyalo, A Zalwango, L Generous, P Khauka, N Rutikarayo, W Nakahima, A Mugisha, J Todd, J Levin, S Muyingo, A Ruberantwari, P Kaleebu, D Yirrell, N Ndembi, F Lyagoba, P Hughes, M Aber, A Medina Lara, S Foster, J Amurwon, B Nyanzi Wakholi, K Wangati, B Amuron, D Kajungu, J Nakiyingi, W Omony, K Fadhiru, D Nsibambi, P Khauka.
Joint Clinical Research Centre, Kampala, Uganda: P Mugyenyi, C Kityo, F Ssali, D Tumukunde, T Otim, J Kabanda, H Musana, J Akao, H Kyomugisha, A Byamukama, J Sabiiti, J Komugyena, P Wavamunno, S Mukiibi, A Drasiku, R Byaruhanga, O Labeja, P Katundu, S Tugume, P Awio, A Namazzi, GT Bakeinyaga, H Katabira, D Abaine, J Tukamushaba, W Anywar, W Ojiambo, E Angweng, S Murungi, W Haguma, S Atwiine, J Kigozi, L Namale. A Mukose, G Mulindwa, D Atwiine, A Muhwezi, E Nimwesiga, G Barungi, J Takubwa, S Murungi, D Mwebesa, G Kagina, M Mulindwa, F Ahimbisibwe, P Mwesigwa, S Akuma, C Zawedde, D Nyiraguhirwa, C Tumusiime, L Bagaya, W Namara, J Kigozi, J Karungi, R Kankunda, R Enzama.
University of Zimbabwe, Harare, Zimbabwe: A Latif, J Hakim, V Robertson, A Reid, E Chidziva, R BulayaTembo, G Musoro, F Taziwa, C Chimbetete, L Chakonza, A Mawora, C Muvirimi, G Tinago, P Svovanapasis, M Simango, O Chirema, J Machingura, S Mutsai, M Phiri, T Bafana, M Chirara, L Muchabaiwa, M Muzambi, E Chigwedere, M Pascoe, C Warambwa, E Zengeza, F Mapinge, S Makota, A Jamu, N Ngorima, H Chirairo, S Chitsungo, J Chimanzi, C Maweni, R Warara, M Matongo, S Mudzingwa, M Jangano, K Moyo, L Vere, I Machingura.
Infectious Diseases Institute (formerly the Academic Alliance) Makerere University, Mulago, Uganda: E Katabira, A Ronald, A Kambungu, F Lutwama, I Mambule, A Nanfuka, J Walusimbi, E Nabankema, R Nalumenya, T Namuli, R Kulume, I Namata, L Nyachwo, A Florence, A Kusiima, E Lubwama, R Nairuba, F Oketta, E Buluma, R Waita, H Ojiambo, F Sadik, J Wanyama, P Nabongo, J Oyugi, F Sematala, A Muganzi, C Twijukye, H Byakwaga.
The AIDS Support Organisation (TASO), Uganda: R Ochai, D Muhweezi, A Coutinho, B Etukoit.
Imperial College, London, UK: C Gilks, K Boocock, C Puddephatt, C Grundy, J Bohannon, D Winogron.
MRC Clinical Trials Unit, London, UK: J Darbyshire, DM Gibb, A Burke, D Bray, A Babiker, AS Walker, H Wilkes, M Rauchenberger, S Sheehan, C SpencerDrake, K Taylor, M Spyer, A Ferrier, B Naidoo, D Dunn, R Goodall.
Independent DART Trial Monitors: R Nanfuka, C MufukaKapuya.
DART Virology Group: P Kaleebu (CoChair), D Pillay (CoChair), V Robertson, D Yirrell, S Tugume, M Chirara, P Katundu, N Ndembi, F Lyagoba, D Dunn, R Goodall, A McCormick.
DART Health Economics Group: A Medina Lara (Chair), S Foster, J Amurwon, B Nyanzi Wakholi, J Kigozi, L Muchabaiwa, M Muzambi.
Trial Steering Committee: I Weller (Chair), A Babiker (Trial Statistician), S Bahendeka, M Bassett, A Chogo Wapakhabulo, J Darbyshire, B Gazzard, C Gilks, H Grosskurth, J Hakim, A Latif, C Mapuchere, O Mugurungi, P Mugyenyi; Observers: C Burke, M Distel, S Jones, E Loeliger, P Naidoo, C Newland, G Pearce, S Rahim, J Rooney, M Smith, W Snowden, JM Steens.
Data and Safety Monitoring Committee: A Breckenridge (Chair), A McLaren (Chairdeceased), C Hill, J Matenga, A Pozniak, D Serwadda.
Endpoint Review Committee: T Peto (Chair), A Palfreeman, M Borok, E Katabira.
Funding: DART is funded by the UK Medical Research Council, the UK Department for International Development (DFID), and the Rockefeller Foundation. GlaxoSmithKline, Gilead and BoehringerIngelheim donated firstline drugs for DART, and Abbott provided LPV/r (Kaletra/Aluvia) as part of the secondline regimen for DART.
Authors’ Affiliations
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