The overall trajectory-based analyses of the treatment of 2515 patients successfully classified them into response trajectories and confirmed that duloxetine and SSRI treatment increased the likelihood that most patients treated with these medications would be classified in the responders trajectory. The magnitude of the effects seemed as large as the magnitude of the effects from end-point or mixed-model analyses.12,13
Therefore, trajectory analyses allowed for strong signal detection, although treatment effects were not more significant than in end-point and mixed-model analyses. The added improvements of trajectory analyses were that patients were classified into response trajectories and that the trajectories were different for active drug and for placebo. As noted earlier, “responder” in this study refers to a favorable clinical trajectory rather than achievement of a priori criteria based on symptom thresholds, an approach that is commonly used in clinical trials.34
Separate analyses of the active and placebo groups revealed that distinct trajectories were identified in the groups treated with duloxetine or SSRI but not in the placebo group. Most patients (about three-quarters) receiving active drug were classified as trajectory responders, and fewer (about one-quarter) were classified as trajectory nonresponders. Patients receiving placebo could not be separated into distinct trajectories and on average showed gradual improvement over time. The failure to identify more than 1 trajectory classes over time in the placebo group is remarkable because growth mixture models are more prone to overestimate rather than underestimate the number of trajectory classes.35,36
The finding of a single trajectory for patients assigned to placebo may reflect limited statistical power to resolve subtle differences in response trajectories in this group. However, this reasoning assumes that there are categorically different outcomes in each group, and this may not be the case. The present data suggest that widely divergent trajectories of individual patients treated with placebo are best explained as variations within a single class (ie, placebo response differences may be a dimensional rather than categorical characteristic).
The present findings challenge the prevailing view37,38
that placebo response is associated predominantly with rapid and transient clinical improvement. In some investigations, researchers classified patients showing this response pattern as placebo responders, although the validity of this assumption was never demonstrated. The problem with this conclusion is that growth mixture models provide valid results only under the assumption that placebo responders and nonresponders are groups differentiated by a categorical distinction. If categorically different classes do not exist, spurious latent classes are likely to be identified.35,36
Therefore, whether one finds categorically different trajectories depends directly on whether one is willing to assume a priori that they exist. This problem is exacerbated in studies with small sample sizes. This study did not assume a priori that placebo response was constituted by multiple trajectory classes. Despite the fact that this study analyzed a much larger sample than prior studies of its kind, its analyses do not support the notion that there is a specific placebo response profile. Rather, we observed an average gradual improvement over time among patients taking placebo, with noticeable dimensional but not categorical heterogeneity. Our results are consistent with the view that placebo response is a continuous measure that is manifested to varying degrees across patients. This view is consistent with the approach by Tarpey and Petkova,39
who used continuous rather than categorical latent variables to model patient responses to antidepressant medications.
The findings that patients receiving active drug were classified as trajectory responders and trajectory nonresponders, while there was only 1 trajectory for patients receiving placebo, may be partially explained by the effect of the active compound. Active compounds generally have the potential to produce adverse responses that could contribute to categorically different responses. For example, medications with inverted U-shaped dose-response relationships may be beneficial for patients with low baseline values but may be harmful to patients at the opposite end of the spectrum.
Another possible explanation for the single trajectory class among patients receiving placebo is that all clinical trials in this analysis had a placebo treatment phase before treatment with randomized medication. This placebo lead-in may have acted as a filter that reduced response heterogeneity during subsequent placebo treatment because it eliminated patients with immediate placebo response from the sample. Placebo lead-in is a practice that is intended to improve signal detection by selecting out patients who are likely to respond to placebo40
and by selecting in patients whose poor placebo response portends superior medication responses.41
However, the published data42–46
overwhelmingly indicate that the rate of clinical response during placebo lead-in is very low and that exclusion of placebo responders does not improve signal detection in clinical trials. Therefore, it is unlikely that the presence of placebo lead-in periods in the contributing studies influenced the present findings or compromised their generalizability.
Our analyses do not support the view that antidepressant treatment response was slower and less transient than placebo response. Rather, consistent with a recent meta-analysis47
confirming early signs of SSRI efficacy, the change in HAM-D scores as a result of antidepressant treatment among responders seemed to be faster and more sustained than the change in HAM-D scores among patients on placebo. Our analyses also support the notion that early improvement during the first 2 weeks of treatment is predictive of treatment outcome48
and that different antidepressant medications have similar treatment response profiles.49,50
In the present analysis, where there are 2 trajectories for patients treated with antidepressants and 1 trajectory for patients treated with placebo, some patients would seem to be more effectively treated with placebo than with a serotonergic antidepressant. The trajectory of nonresponse to antidepressants was not consistent with the timing of a transient increase in suicidality among antidepressant-treated patients with depression.6
Instead, the trajectories of responders and nonresponders diverged increasingly over time, suggesting that patients affected adversely by serotonin reuptake inhibitors might be better off if these medications were discontinued. At a minimum, they highlight the potential clinical importance of careful ongoing monitoring of the effect of prescribed antidepressants.
The clear separation of patients treated with antidepressants into responders and nonresponders and the observed homogeneity of treatment response on placebo, together with the significantly worse mean response of trajectory nonresponders taking active drug compared with the mean response of patients taking placebo, may help explain the failure of many clinical trials of antidepressant medication to demonstrate treatment efficacy. The main hypotheses in almost all clinical trials focus on demonstrating average treatment effects. However, when there is sizable heterogeneity in treatment response, with most patients benefiting from a treatment but with some patients demonstrating significantly worse outcomes than the average improvement while receiving placebo, it is likely that average treatment effects will be diminished and may lose statistical significance. Therefore, unless strategies are introduced to reduce this heterogeneity or study sample sizes are increased significantly to augment statistical power, it would seem that the status quo regarding failed trials of serotonin reuptake inhibitors is unlikely to change.
The fact that almost all trajectory nonresponders were clinical nonresponders but only about two-thirds of trajectory responders were clinical responders suggests that the clinical response definition is stricter than the trajectory response definition. It is possible that patients who do not meet the clinical response definition but are classified as trajectory responders may meet the clinical response definition with a longer follow-up period. This can be evaluated in follow-up analyses. Validation of our results in other studies is also necessary to assess how stable the trajectory class definitions are.
The inferences drawn from the present analyses are limited by several factors. First, we were unable to differentiate whether the poor responses to antidepressants arose from a negative effect of taking antidepressant medication or as a consequence of discontinuation of these medications. Although our limited sensitivity analysis under MNAR assumptions suggests that our results are not sensitive to the effects of missing data, further research is necessary to estimate the extent of the influence of missing data due to different model assumptions for missing data.
Second, although the reported studies included only patients with unipolar major depression determined using rigorous diagnostic methods, it is impossible to rule out the presence of latent bipolar disorder in the study population. Antidepressants are reported to have reduced efficacy in treating depression among patients with bipolar disorder, and they may increase mood cycling.51,52
Similarly, adverse effects of other forms of psychopathologic conditions, particularly personality disorders, or the social use of alcohol cannot be determined using the existing data set.
Third, the implications are limited by the brief duration (8 weeks) of the reported studies. In the present study, it was impossible to predict whether nonresponders may become responders with extended treatment or whether the negative consequences of antidepressant discontinuation in some nonresponders outweigh the apparent benefits.
Fourth, the present analysis is predicated on the assumption that heterogeneous classes of antidepressant medications exist. An alternative view is that the probability of nonresponse may vary continuously across the population, and if this is indeed the case, alternative models with latent trait rather than latent class variables might be more appropriate.39
Fifth, we considered only 1 type of linear parametric model. Alternative nonlinear models have been considered by other authors53,54
to predict treatment outcome from early treatment response. It is difficult to distinguish the fit of such models from the fit of simpler polynomial models with a limited number of time points. Such models have not been used in the context of growth mixture modeling and are unavailable in standard software, but they might provide an important tool for flexible modeling of treatment response for more frequently collected treatment outcome data.
Sixth, our analyses do not assess causal treatment effects because we consider treatment a predictor of class membership rather than a predictor of growth factors within a class, as suggested by Muthén and Brown.55
In the causal framework, latent classes are considered characteristics of the patients (eg, never responders, drug-only responders, placebo-only responders, and always responders), and a key assumption is that the “numbers of classes and the true, population proportions of patients in each class be the same across intervention conditions.”56(pS96)
In our analysis, classes are considered empirically derived distinct trajectories of longitudinal response, and we have direct evidence that the number of classes and the proportion of classes vary by treatment. Although our propensity scoring approach allowed us to control for observed predictors of trajectory membership, it is possible that unmeasured confounders (eg, genotypes and environmental factors) can affect our results.
Identifying predictors of clinical trajectories might advance the personalized treatment of major depressive disorder (ie, to assist in the better matching of patients and treatments). In this regard, the present study replicated the finding from the large multicenter antidepressant trial Sequenced Treatment Alternatives to Relieve Depression (STAR*D) that high levels of anxiety predicted poorer antidepressant response.57
Predictors of membership in both favorable and poor response trajectories might be used to inform the selection of medications for particular patients. Despite a long history of the study of clinical,31,58
predictors of subtypes of depression and treatment response, there are still no objective bases for the personalized treatment of depression that are sufficiently explanatory and specific to guide the treatment of individual patients. Future research will be needed to determine whether trajectory-based analyses will be useful in advancing this objective.