Early Improvement in Individual Symptoms and Response to Antidepressants in Patients With Major Depressive Disorder
This trial is active, not recruiting.
|Condition||major depressive disorder|
|Sponsor||University Medical Center Groningen|
|Start date||September 2016|
|End date||September 2017|
|Trial size||10000 participants|
|Trial identifier||NCT02934035, 1575|
Major depressive disorder (MDD) affects around 7% of the population yearly. Although effective treatments are available, only around half of all patients participating in clinical trials respond to 6 to 12 weeks of antidepressant treatment. Given these high failure rates, the ability to predict as early as possible whether a patient is (un)likely to respond would be of great value, as it would enable physicians to change treatment strategies faster.
Early improvement has consistently been found to be a strong predictor of later response. However, misclassification is still quite common, with perhaps a third of those who do not show early improvement going on to respond. Conversely, a substantial proportion of those who do show early improvement do not go on to respond.
One possibility for improving the predictive power of early improvement is to examine individual symptoms, rather than the total score on a depression rating scale. Some items, for example, could reflect antidepressant side effects (e.g. gastrointestinal symptoms) and may not be very predictive.
In the proposed project, we aim to examine the relationship between early improvement in individual symptoms and response to antidepressants in a very large patient sample. This large sample size will enable us to use more rigorous methods than previous studies, such as the use of cross-validation to confirm our findings. It will also allow us to examine a large set of predictors, including possible interactions among early-improving symptoms and between symptoms and demographic factors like age and gender. We will also examine the added value of individual symptoms over and above using the total symptom score alone and possible differences between different antidepressant classes.
We will use penalized (lasso) regression, which is well-suited to analyzing data with a large number of (potentially highly correlated) predictors. In our primary analysis, we will predict response after 6 weeks of treatment. In secondary analyses, we will also predict remission at week 6 and response and remission at week 12.
time frame: Week 6
time frame: Week 6
time frame: Week 12
time frame: Week 12
Male or female participants at least 18 years old.
Inclusion Criteria: - Minimum duration of double-blind treatment of 6 weeks - Participants must have a valid baseline, 2-week and 6 (±1)- or 12 (±1)-week Hamilton Depression Rating Scale score - Aged 18 years or older - Participants must have been assigned to either placebo or to an FDA-approved antidepressant Exclusion Criteria: - Inclusion criteria of the trial specify a specific subtype of MDD (e.g. MDD and anxiety; MDD and pain)
|Description||Study design The proposed project is an individual patient data meta-analysis. Data will be collated from 31 trials of second-generation antidepressants for the treatment of major depressive disorder, using data from trial arms treated with placebo or Food and Drug Administration (FDA)-approved antidepressants. These trials include a total of approximately 7,800 antidepressant-treated and 3,000 placebo-treated participants. We will use penalized regression methods (specifically least absolute shrinkage and selection operator, 'lasso') to investigate the relationship between early improvement in specific depressive symptoms and response to treatment. Furthermore, we will investigate whether interactions among early-improving symptoms and between early-improving symptoms and demographic variables such as age and gender improve the prediction of treatment outcome. Finally, we will examine whether the prediction of response to treatment by early improvement in specific symptoms is dependent upon the type of treatment provided (placebo, selective serotonin reuptake inhibitors [SSRIs] or serotonin-norepinephrine reuptake inhibitors [SNRIs]), which would suggest a drug-specific mechanism. Statistical Analysis Plan Missing data: We will take a complete cases approach, as we are interested in predicting response and remission in participants who have actually taken an antidepressant for the specified period of time. Therefore, we will select only participants who have valid baseline, week 2 and week 6 (±1) HDRS scores for our main analyses (or valid week 12 data for our secondary analyses of week 12 outcome). Training and validation sample: The data will be randomly divided into an 80% training sample and a 20% validation sample (stratified by treatment group). Model discovery will be done in the training sample, while the predictive performance of the models will be assessed in the validation sample. Predictors: Improvement in individual symptoms will be derived from the HDRS items at baseline and week 2. The answer choices for these items range from 0 - 2 for 7 items and 0 - 4 for 10 items. Early improvement will be dichotomized into "no improvement" and "improvement". "No improvement" is indicated by worsening of the item score (e.g. from 1 at baseline to 2 at week 2) or no change in the item score. "Improvement" is indicated by an improvement in the item score of ≥1. We will use cross-tables to check whether any variables are very highly correlated and if so, we will remove one of the items. Baseline scores on the HDRS items will also be included in the model in order to investigate the added value of improvement of individual items over and above the baseline item scores. For the total HDRS-17 score, early improvement will also be dichotomized into no/minor improvement (<20% improvement) or improvement (≥20% improvement). The baseline HDRS-17 score will be standardized and included in the model as a covariate. With regard to the demographic factors, gender is already a dichotomous variable and age will be standardized. Lasso regression: We will apply lasso regression to the following models: Primary analysis (in the antidepressant-treated group only) 1. A model containing variables for early improvement at week 2 in all 17 HDRS items, age, gender, and all two-way interactions between these variables; baseline HDRS item scores; and additionally total HDRS score at baseline and early improvement (≥20% improvement in score) in total HDRS score at week 2. Exploratory analysis (in all participants, including those treated with placebo) 2. As model 1 above, but including treatment group (placebo, SSRI, SNRI) and all two- and three-way interactions with treatment group. The tuning parameter (lambda) resulting in minimal prediction error (based on deviance) will be selected with the help of 10-fold cross-validation. We will use the GLMMLasso package for multilevel data and subsequently refit the model with mixed-effects logistic regression using only the variables selected by lasso regression. Model performance: Prediction accuracy will be assessed by applying the mixed-effects logistic regression model to the independent validation sample. The area under the curve (AUC) for the receiver-operating characteristic (ROC) curve in predicting response/remission at 6 or 12 weeks will be used to assess prediction accuracy. We will also determine sensitivity, specificity, and accuracy (percentage of correct predictions). Secondary analyses: Secondary analyses will examine 12-week outcomes within the subgroup of trials with a double-blind treatment duration of at least 12 weeks.|
Call for more information