Points to consider for analyzing efficacy outcomes in long-term extension clinical trials.

This article focuses on 2 objectives in the analysis of efficacy in long-term extension studies of chronic diseases: (1) defining and discussing estimands of interest in such studies and (2) evaluating the performance of several multiple imputation methods that may be useful in estimating some of these estimands. Specifically, 4 estimands are defined and their clinical utility and inferential ramifications discussed. The performance of several multiple imputation methods and approaches were evaluated using simulated data. Results suggested that when interest is in a binary outcome derived from an underlying continuous measurement, it is preferable to impute the underlying continuous value that is subsequently dichotomized rather than to directly impute the binary outcome. Results also demonstrated that multivariate Gaussian models with Markov chain Monte Carlo imputation and sequential regression have minimal bias and the anticipated confidence interval coverage, even in settings with ordinal data where departures from normality are a concern. These approaches are further illustrated using a long-term extension study in psoriasis.

Improvements in psoriasis within different body regions vary over time following treatment with ixekizumab.

BACKGROUND: Ixekizumab is a high-affinity monoclonal antibody that selectively targets interleukin-17 A. OBJECTIVE: Examine the efficacy of ixekizumab in clearing psoriasis within different body regions. METHODS: Data from 3 placebo- (PBO) or PBO- and etanercept (ETN)-controlled trials were integrated. Patients with moderate-to-severe psoriasis were randomized to 12 weeks of PBO (UNCOVER-1, -2, -3, N = 792; UNCOVER-2, -3, N = 361), 50 mg ETN twice weekly (N = 740), or 80 mg ixekizumab every 2 (IXE Q2W; N = 1169; N = 736) or 4 weeks (IXE Q4W; N = 1165; N = 733) after a 160-mg starting dose. RESULTS: Mean percent improvements in regional Psoriasis Area and Severity Index (PASI) were noted at Week 1 and increased through Week 12 in the IXE Q2W (approved dosing regimen) group for each body region. Week 12 improvements were 91.4% (head/neck); 92.8% (trunk); 89.9% (arms); and 88.7% (legs) (all regions p < .001 vs. PBO). Mean regional PASI improvements at Week 12 were ≥84.2% for ixekizumab versus ≤70.9% for ETN in all regions (p < .001). Scaling and thickness reduced faster than erythema. CONCLUSIONS: Within 12 weeks of ixekizumab treatment, all signs of psoriasis across all body regions reached clinically meaningful improvements, with the head/neck and trunk responding quicker than psoriasis of the arms and legs, especially with reduced scaling and thickness.

Nonparametric covariate adjustment in estimating hazard ratios.

In randomized clinical trials with time-to-event outcomes, the hazard ratio is commonly used to quantify the treatment effect relative to a control. The Cox regression model is commonly used to adjust for relevant covariates to obtain more accurate estimates of the hazard ratio between treatment groups. However, it is well known that the treatment hazard ratio based on a covariate-adjusted Cox regression model is conditional on the specific covariates and differs from the unconditional hazard ratio that is an average across the population. Therefore, covariate-adjusted Cox models cannot be used when the unconditional inference is desired. In addition, the covariate-adjusted Cox model requires the relatively strong assumption of proportional hazards for each covariate. To overcome these challenges, a nonparametric randomization-based analysis of covariance method was proposed to estimate the covariate-adjusted hazard ratios for multivariate time-to-event outcomes. However, empirical evaluations of the performance (power and type I error rate) of the method have not been studied. Although the method is derived for multivariate situations, for most registration trials, the primary endpoint is a univariate outcome. Therefore, this approach is applied to univariate outcomes, and performance is evaluated through a simulation study in this paper. Stratified analysis is also investigated. As an illustration of the method, we also apply the covariate-adjusted and unadjusted analyses to an oncology trial.

To adjust or not to adjust for baseline when analyzing repeated binary responses? The case of complete data when treatment comparison at study end is of interest.

The benefits of adjusting for baseline covariates are not as straightforward with repeated binary responses as with continuous response variables. Therefore, in this study, we compared different methods for analyzing repeated binary data through simulations when the outcome at the study endpoint is of interest. Methods compared included chi-square, Fisher's exact test, covariate adjusted/unadjusted logistic regression (Adj.logit/Unadj.logit), covariate adjusted/unadjusted generalized estimating equations (Adj.GEE/Unadj.GEE), covariate adjusted/unadjusted generalized linear mixed model (Adj.GLMM/Unadj.GLMM). All these methods preserved the type I error close to the nominal level. Covariate adjusted methods improved power compared with the unadjusted methods because of the increased treatment effect estimates, especially when the correlation between the baseline and outcome was strong, even though there was an apparent increase in standard errors. Results of the Chi-squared test were identical to those for the unadjusted logistic regression. Fisher's exact test was the most conservative test regarding the type I error rate and also with the lowest power. Without missing data, there was no gain in using a repeated measures approach over a simple logistic regression at the final time point. Analysis of results from five phase III diabetes trials of the same compound was consistent with the simulation findings. Therefore, covariate adjusted analysis is recommended for repeated binary data when the study endpoint is of interest.

Comparison of several multiple imputation strategies for repeated measures analysis of clinical scales: to truncate or not to?

We evaluated via a simulation study several strategies for imputing missing ordinal outcomes in a longitudinal clinical trial, contrasting methods that involve truncation of imputed values outside plausible ranges with those that do not. Our aim was to identify a preferred imputation strategy for estimating treatment difference at study endpoint. Plausible data were simulated via resampling of existing placebo data sets and adding treatment effect; then different imputation strategies were evaluated under missingness at random (MAR) and varying dropout rates. Our conclusion is that imputation methods based on rounding and truncation lead to larger bias than strategies based on simple methods based on (nontruncated) multivariate normal distribution.

A multiple-imputation-based approach to sensitivity analyses and effectiveness assessments in longitudinal clinical trials.

It is important to understand the effects of a drug as actually taken (effectiveness) and when taken as directed (efficacy). The primary objective of this investigation was to assess the statistical performance of a method referred to as placebo multiple imputation (pMI) as an estimator of effectiveness and as a worst reasonable case sensitivity analysis in assessing efficacy. The pMI method assumes the statistical behavior of placebo- and drug-treated patients after dropout is the statistical behavior of placebo-treated patients. Thus, in the effectiveness context, pMI assumes no pharmacological benefit of the drug after dropout. In the efficacy context, pMI is a specific form of a missing not at random analysis expected to yield a conservative estimate of efficacy. In a simulation study with 18 scenarios, the pMI approach generally provided unbiased estimates of effectiveness and conservative estimates of efficacy. However, the confidence interval coverage was consistently greater than the nominal coverage rate. In contrast, last and baseline observation carried forward (LOCF and BOCF) were conservative in some scenarios and anti-conservative in others with respect to efficacy and effectiveness. As expected, direct likelihood (DL) and standard multiple imputation (MI) yielded unbiased estimates of efficacy and tended to overestimate effectiveness in those scenarios where a drug effect existed. However, in scenarios with no drug effect, and therefore where the true values for both efficacy and effectiveness were zero, DL and MI yielded unbiased estimates of efficacy and effectiveness.

The challenges of evaluating dose response in flexible-dose trials using marginal structural models.

Assessing dose response from flexible-dose clinical trials is problematic. The true dose effect may be obscured and even reversed in observed data because dose is related to both previous and subsequent outcomes. To remove selection bias, we propose marginal structural models, inverse probability of treatment-weighting (IPTW) methodology. Potential clinical outcomes are compared across dose groups using a marginal structural model (MSM) based on a weighted pooled repeated measures analysis (generalized estimating equations with robust estimates of standard errors), with dose effect represented by current dose and recent dose history, and weights estimated from the data (via logistic regression) and determined as products of (i) inverse probability of receiving dose assignments that were actually received and (ii) inverse probability of remaining on treatment by this time. In simulations, this method led to almost unbiased estimates of true dose effect under various scenarios. Results were compared with those obtained by unweighted analyses and by weighted analyses under various model specifications. The simulation showed that the IPTW MSM methodology is highly sensitive to model misspecification even when weights are known. Practitioners applying MSM should be cautious about the challenges of implementing MSM with real clinical data. Clinical trial data are used to illustrate the methodology.

Recent developments in improving signal detection and reducing placebo response in psychiatric clinical trials.

Recent (2007-2010) empirical and theoretical literature on associations of trial design features with signal detection and placebo response were investigated, along with data and analytic considerations. Trials with greater percentages of patients randomized to placebo had larger average drug-placebo differences in two comprehensive meta-analyses (MDD and Schizophrenia). Excluding patients with large responses during double-blind placebo lead-ins resulted in small increases in drug-placebo differences. Core factor subscales of the HAMD yielded larger drug-placebo differences than the HAMD total score. Direct likelihood-based (MMRM) and similar analyses provided better control of false positive and false negative results than LOCF and BOCF. Theoretical considerations suggested that the number of sites and number of countries can influence power, depending on the correlation structure in the data and on how sites and countries are chosen. Use of centralized ratings reduced placebo response and improved drug-placebo differences. However, the number of comparisons was too small to draw conclusions. Use of patient ratings and reducing the number of study visits reduced placebo response, but their effects on signal detection were unclear. Practical experience with novel designs such as the sequential parallel approach hold promise for improvements in signal detection. Given the complexities of signal detection and placebo response, no single strategy is likely to fully solve the problem and combinations of approaches may be most useful. Utilizing appropriate analytic techniques and randomizing an adequate fraction of patients to placebo are perhaps the most broadly applicable approaches.

Signal detection and placebo response in schizophrenia: parallels with depression.

OBJECTIVE: Placebo response and the rate of failed clinical trials are increasing in schizophrenia, resembling previous experience with antidepressant clinical trials. In depression, the percent of patients randomized to placebo was shown to be strongly associated with drug-placebo differences (signal detection).We hypothesized that this factor would also be important in recent schizophrenia clinical trials. To test this hypothesis a database of acute schizophrenia placebo-controlled studies conducted between 1997 and 2008 was constructed. The database contained 27 studies, with 79 active treatment arms. As percentage of patients randomized to placebo increased, mean placebo improvement decreased (p = 0.047) and mean drug-placebo differences tended to increase (p = 0.166). The frequency of significant contrasts from studies with ≥ 25% randomized to placebo was 83.3%, compared with 58.3% in studies with <25% randomized to placebo. Caveats to these findings include limited data and confounding of potentially influential factors. These limitations prevent definitive conclusions. However, results are consistent with previous findings in depression where having a higher percent of patients randomized to placebo increased drug-placebo differences.

Utility of positive controls in assessing assay sensitivity in ICH S7B and ICH E14 guidance for evaluation of QT/QTc interval prolongation.

Positive controls have often been used in nonclinical and clinical cardiovascular safety studies to evaluate the study's assay sensitivity with respect to drug-induced prolongation of the QT interval of the electrocardiogram (ECG). If the study is able to detect such QT prolongation by the control, then a finding of negative QT effect of comparable size for the test drug will constitute evidence that the test drug does not in fact prolong the QT interval by the amount of regulatory concern. Current regulatory guidance regarding QT interval prolongation includes ICH S7B (nonclinical) and ICH E14 (clinical). However, the underlying null hypothesis settings of the two documents are quite different. This paper quantitatively evaluates the utility of positive controls in nonclinical and clinical studies in which a test drug and a positive control are simultaneously assessed in a study. The results show that positive controls, when powered at 80%, can be beneficial in 58% of nonclinical QT studies with Prob(S)=0.8; when powered at 90%, positive controls can be beneficial 73% of clinical QT studies with Prob(S)=0.9, where Prob(S) represents the probability of success with no QT risk at the stage of development.

Assessing onset of treatment benefit in depression and anxiety: conceptual considerations.

OBJECTIVE: Methods for characterizing the onset of treatment benefit in major depressive disorder and generalized anxiety disorder have been studied for some time, yet there is no universal agreement as to the best approaches. Our purpose is to summarize the conceptual framework underlying modern methods for characterizing onset and detailed approaches for which there is consensus from the perspective of a clinician, clinical researcher, and statistician. Possible alternatives to unresolved issues are discussed. PARTICIPANTS: There were 17 experts from academia, the pharmaceutical industry, and the US Food and Drug Administration who met on April 19, 2007, to consider the issues. Many others from sponsoring firms observed the proceedings. EVIDENCE: A series of papers was presented at a consensus meeting and, after discussions, a sense of the participants was obtained. A small group subsequently reviewed the material and articles from the literature and prepared this article, which was reviewed by all of the participants. CONCLUSIONS: The elements that form the basis for describing onset of treatment benefit include defining a clinical event or measurable threshold that validly signals that a treatment has begun to provide clinically meaningful and sustained improvement and utilizing methods for estimating the probability of crossing the onset threshold, the distribution of time to onset for those who do cross, and when to alter or change interventions if the treatment is unsuccessful.

Duloxetine for the treatment of recurrent major depressive disorder in elderly patients: treatment outcomes in patients with comorbid arthritis.

BACKGROUND: Evaluation and treatment of major depression (MDD) in elderly patients is frequently complicated by the presence of comorbid medical conditions, which can reduce the effect of depression treatment, leading to lower rates of depressive-symptom improvement and higher rates of relapse. OBJECTIVE: The authors investigated results of antidepressant concurrent with arthritis pain treatment in elderly patients. METHOD: Patients age 65 and over with recurrent MDD were stratified by arthritis status and randomized to duloxetine (a dual reuptake-inhibitor of serotonin and norepinephrine) or placebo treatment for 8 weeks (duloxetine, N=117; placebo, N=55). RESULTS: Duloxetine significantly reduced MDD symptom severity in elderly patients with and without arthritis, and produced significant reduction in several pain measures in those patients with comorbid arthritis. DISCUSSION: The magnitude and time-course of depressive symptom improvement did not differ significantly between patients with and without arthritis. Some studies have suggested that the severity of pain in arthritis patients may be linked to depression severity.

Using longitudinal data from a clinical trial in depression to assess the reliability of its outcome scales.

Longitudinal studies are permeating clinical trials in psychiatry. Additionally, in the same field, rating scales are frequently used to evaluate the status of the patients and the efficacy of new therapeutic procedures. Therefore, it is of utmost importance to study the psychometric properties of these instruments within a longitudinal framework. In the area of depression, the Hamilton depression rating scale (HAMD) is regularly used for antidepressant treatment evaluation. However, the use of HAMD has not been exempted from criticism what has lead to the development of new scales that are expected to be more sensitive for change, such as the Montgomery-Asberg depression rating scale (MADRS). In general, the reliability of these scales has been extensively studied by using classical methods for reliability estimation, developed for specifically designed reliability studies. Unfortunately, the settings customarily considered in these reliability studies are usually far from the practical conditions in which these scales are applied in clinical trials and practice. In the present paper, we assess the reliability of these instruments in a more realistic scenario thereby using longitudinal data coming from clinical studies. Nowadays, newly developed methodology based on an extended concept of reliability, allows us to use longitudinal data for reliability estimation. This new approach not only enables to avoid bias by offering a better control of disturbing factors but it also produces more precise estimates by taking advantage of the large sample taking sizes available in clinical trials. Further, it offers practical guidelines for an optimal use of a rating scale in order to achieve a particular level of reliability. The merits of this new approach are illustrated by applying it on two clinical trials in depression to assess the reliability of the three outcome scales, HAMD, MADRS, and the Hamilton anxiety rating scale (HAMA).

The significance of treating somatic symptoms on functional outcome improvement in patients with major depressive disorder: a post hoc analysis of 2 trials.

BACKGROUND: Functional impairment is associated with major depressive disorder (MDD), and patients with MDD often present with somatic symptoms. OBJECTIVE: To examine the relationships between improved global functioning and core depressive symptoms as well as painful and nonpainful somatic symptoms in patients with MDD. METHOD: This post hoc analysis of 2 identical trials compared the efficacy of duloxetine with that of paroxetine or placebo as treatment of MDD. In the trials, patients with DSM-IV-defined MDD received duloxetine 80 mg/day (N = 188), duloxetine 120 mg/day (N = 196), paroxetine 20 mg/day (N = 183), or placebo (N = 192) for 8 weeks. The Sheehan Disability Scale (SDS), Maier subscale of the 17-item Hamilton Rating Scale for Depression, 21-item Somatic Symptom Inventory, and Visual Analog Scale for overall pain were used to measure functional impairment, core symptoms of depression, and nonpainful and painful somatic symptoms, respectively. Baseline-to-endpoint mean changes in SDS total and subdomains were measured using analysis of variance with last-observation-carried-forward Pearson partial correlations, and path analysis was used to assess the significance of associations and relative contributions of improvement in global functional impairment, depression, and somatic symptoms. The trials were conducted from November 2000 to July 2002. RESULTS: The difference between antidepressant treatment and placebo in SDS total and subdomains was significant (p < .001). At baseline and in change from baseline to endpoint, associations between global functional impairment and core depressive and somatic symptoms were all significant (p < .05). Path analysis demonstrated improvement of functional impairment attributed to treatment effect as 37.0% (core depressive symptoms), 13.0% (nonpainful somatic symptoms), and 11.0% (painful somatic symptoms). CONCLUSION: In patients with MDD, over a third of functional improvement associated with antidepressant therapy was mediated through improvement in core depressive symptoms. In addition, a significant proportion of functional improvement, although to a lesser degree, was associated with the treatment of both nonpainful and painful somatic symptoms.

Addressing methodological issues in studying antidepressant onset efficacy using prespecified sensitivity analyses.

OBJECTIVES: Assessing antidepressant onset efficacy presents substantial methodological challenges. Most assessments of onset efficacy are based on post hoc analyses. This article presents a case study of a prospectively designed trial to compare antidepressant onset efficacy. EXPERIMENTAL DESIGN: The current study design was compared with previously published criteria for an ideal onset of action study. Prospectively defined sensitivity analyses were used to assess whether results of the present study were influenced by the assumptions and decisions necessary to implement this study. PRINCIPAL OBSERVATIONS: The study achieved its primary objective of establishing noninferiority between SNRI and SSRI. Sensitivity analyses suggested that results from the primary analysis were not influenced by patient population, outcome measure, cut-off for defining clinically meaningful sustained improvement, or analytical method. Although not all limitations could be addressed, appropriate conclusions could be drawn. For example, the study tested only one fixed dose of each drug; hence, conclusions are limited to those dosages and cannot be extrapolated across the entire dose ranges, as would be possible in the ideal study. CONCLUSION: This article illustrates that prospectively designed studies (as opposed to retrospective comparisons) can be implemented and sensitivity analyses can be used to address concerns regarding assumptions and arbitrary decisions.

The relationship between functional outcomes and the treatment of anxious and painful somatic symptoms in patients with generalized anxiety disorder.

OBJECTIVE: To examine the relationship between global functional impairment and the treatment of anxious symptoms and painful somatic symptoms (PSS) in patients with generalized anxiety disorder (GAD). RESEARCH DESIGN AND METHODS: Data from two double-blind, placebo-controlled trials in adult outpatients meeting DSM-IV criteria for GAD were pooled. In the first trial (9-week, fixed-dose treatment period), patients were randomized to duloxetine 60 mg QD (n=168), duloxetine 120 mg QD (n=170), or placebo (n=175). In the second trial (10-week, flexible-dose treatment period), patients were randomized to 60-120 mg QD of duloxetine (n=168) or placebo (n=159). Path analysis was used to assess the relative contributions of changes in psychic and somatic anxiety and PSS on improved functional outcomes. CLINICAL TRIAL REGISTRATION INFORMATION: Study 1: NCT00122824; Study 2: study completed before registration required. MAIN OUTCOME MEASURES: Sheehan Disability Scale (SDS), Hamilton Anxiety Rating Scale (HAMA), and Visual Analog Scale for overall pain (VAS). RESULTS: There was a moderate correlation (0.45, p<0.05) at endpoint between changes in global functional impairment and changes in psychic anxiety (controlling for somatic anxiety and PSS); whereas the correlation between changes in global functional impairment and changes in somatic anxiety (controlling for psychic anxiety and PSS) was weak (0.09, p<0.05). The correlation between changes in global functional impairment and changes in PSS (controlling for psychic and somatic anxiety) was weak (0.27, p<0.05). Path analysis revealed that 37% of the total improvement in functional impairment (Sheehan Disability Scale total score) due to duloxetine treatment was independent of improvement in the Hamilton Anxiety Rating Scale (HAMA) psychic and somatic anxiety subscale scores or Visual Analog Scale for overall pain (VAS) score. Improvement in psychic anxiety accounted for 47% of the total treatment effect on improvement of functional impairment, whereas 7% and 9% of the improvement in functional impairment was accounted for by improvements in somatic anxiety and overall pain, respectively.

A randomized, double-blind study of increasing or maintaining duloxetine dose in patients without remission of major depressive disorder after initial duloxetine therapy.

OBJECTIVE: To compare efficacy of remaining on duloxetine 60 mg to increasing to 120 mg q.d. in patients without remission of major depressive disorder (MDD) after 6 weeks at 60 mg. METHOD: This double-blind, parallel study was conducted in adults with MDD (DSM-IV-TR criteria). Patients initially randomly assigned to duloxetine 60 mg for 6 weeks with a 17-item Hamilton Rating Scale for Depression (HAM-D-17) score > 7 (nonremitters) were randomly reassigned to 60 mg or 120 mg duloxetine for 8 weeks. Patients with a HAM-D-17 score < or =7 (remitters) continued on duloxetine 60 mg. The primary objective was to compare time to remission (HAM-D-17 score < or =7) between rerandomized groups. Secondary objectives included evaluation of HAM-D-17 and Inventory of Depressive Symptomatology assessments and safety and tolerability evaluations in nonremitters and remitters. Patients were enrolled from November 2004 to January 2006. RESULTS: Nonremitters randomly reassigned to 60 mg and 120 mg achieved similar time to remission and similar improvements on efficacy measures. Remission was achieved in 30.0% and 30.5% in the 60-mg and 120-mg groups, respectively. Of the remitters, 85.5% continued to be in remission at study end. Other than a greater incidence of hyperhidrosis and chest pain in the 120-mg group, adverse events were similar between groups, as were discontinuations due to adverse events. CONCLUSION: Nonremitters to 60 mg of duloxetine for 6 weeks randomly reassigned to 60 mg or 120 mg of duloxetine demonstrated continued symptom improvement in the 8-week extension. Patients randomly reassigned to 120 mg showed no advantage over those who continued on 60 mg. Duloxetine was well tolerated at both doses and had similar safety profiles.

The impact of missing data and how it is handled on the rate of false-positive results in drug development.

In drug development, a common choice for the primary analysis is to assess mean changes via analysis of (co)variance with missing data imputed by carrying the last or baseline observations forward (LOCF, BOCF). These approaches assume that data are missing completely at random (MCAR). Multiple imputation (MI) and likelihood-based repeated measures (MMRM) are less restrictive as they assume data are missing at random (MAR). Nevertheless, LOCF and BOCF remain popular, perhaps because it is thought that the bias in these methods lead to protection against falsely concluding that a drug is more effective than the control. We conducted a simulation study that compared the rate of false positive results or regulatory risk error (RRE) from BOCF, LOCF, MI, and MMRM in 32 scenarios that were generated from a 2(5) full factorial arrangement with data missing due to a missing not at random (MNAR) mechanism. Both BOCF and LOCF inflated RRE were compared to MI and MMRM. In 12 of the 32 scenarios, BOCF yielded inflated RRE compared with eight scenarios for LOCF, three scenarios for MI and four scenarios for MMRM. In no situation did BOCF or LOCF provide adequate control of RRE when MI and MMRM did not. Both MI and MMRM are better choices than either BOCF or LOCF for the primary analysis.

A comparison of initial duloxetine dosing strategies in patients with major depressive disorder.

OBJECTIVE: To compare the effects of starting doses of duloxetine taken with or without food on tolerability and efficacy in patients with major depressive disorder (MDD). METHOD: This double-blind, concurrent-dose-controlled, parallel-design trial contained a variable expected-duration placebo lead-in period and was conducted in adult outpatients with DSM-IV-TR-defined MDD at psychiatric outpatient sites between October 2004 and January 2006. In actuality, patients received placebo for 1 week and then were randomly assigned to duloxetine 30 mg once daily in the morning (q.a.m.) (N = 219), 30 mg twice daily (b.i.d.) (N = 213), or 60 mg q.a.m. (N = 215) for 1 week along with 1 of 2 instructions about food: take study drug with food or do not take within 1 hour of eating. For the remaining 5 weeks of acute treatment, all patients received 60 mg once daily. The primary objective was to compare incidence of treatment-emergent nausea at 30 mg q.a.m. versus 60 mg q.a.m. using item 112 (nausea) of the Association for Methodology and Documentation in Psychiatry adverse event scale (AMDP-5). Secondary outcome measures included mean change on AMDP-5 item 112, discontinuations due to adverse events, mean changes in AMDP-5 items and subscales, spontaneously reported treatment-emergent adverse events, and vital signs. Efficacy was evaluated by the 17-item Hamilton Rating Scale for Depression (HAM-D-17). RESULTS: The primary analysis, which combined data from both food groups, showed no significant difference in the incidence of nausea between starting doses of 30 mg q.a.m. and 60 mg q.a.m. (23% vs. 29%, respectively; p = .207). However, mean changes on the AMDP-5 nausea item revealed a significant main effect of food (p = .010) and a significant interaction between food and starting dose (p = .033). The food-by-dose interaction indicated that the benefit from taking drug with food was greatest in patients started at 60 mg q.a.m., and the benefit of starting at 30 mg q.a.m. was greatest in patients taking drug without food. In patients who took study drug without food, there was a significant difference across initial-dose groups for discontinuation due to adverse events (30 mg q.a.m. = 3.6%, 30 mg b.i.d. = 14.0%, 60 mg q.a.m. = 10.2%; 30 mg q.a.m. vs. 30 mg b.i.d., p = .008; 30 mg q.a.m. vs. 60 mg q.a.m., p = .066); however, in patients who took study drug with food, discontinuations due to adverse events did not significantly differ (30 mg q.a.m. = 5.4%, 30 mg b.i.d. = 7.5%, 60 mg q.a.m. = 7.4%; all p values > .50). Patients who started at 30 mg b.i.d. or 60 mg q.a.m. without food did not differ regarding mean changes (i.e., increases) in the common adverse events score after 1 week of treatment but had significantly greater mean changes than patients who started at 30 mg q.a.m. without food (0.87, 0.82, and 0, respectively; p < .05 vs. 30 mg b.i.d. and 60 mg q.a.m.). No significant differences were found between initial-dose groups in vital signs. CONCLUSIONS: These data imply that starting dulox-etine at 30 mg q.a.m. for 1 week with or without food or starting duloxetine at the therapeutic dose of 60 mg q.a.m. with food can improve the initial tolerability of the medication. Adding this information to existing knowledge of duloxetine will enable the clinician to tailor therapy most appropriately for the individual patient. CLINICAL TRIALS REGISTRATION: ClinicalTrials.gov identifier NCT 00191061.

Differential antidepressant symptom efficacy: placebo-controlled comparisons of duloxetine and SSRIs (fluoxetine, paroxetine, escitalopram).

OBJECTIVE: To test the hypothesis that in patients with major depressive disorder (MDD), the response for specific Hamilton Depression Rating Scale items will differ for duloxetine compared with selective serotonin reuptake inhibitors (SSRIs) and that patterns of response will differ based on symptom severity at baseline. METHOD: Data were pooled from all Lilly-sponsored clinical trials where duloxetine was compared with placebo and an SSRI in patients with MDD: 7 randomized, double-blind, fixed-dose, 8-week studies of duloxetine (n = 1,133) versus SSRI (n = 689) versus placebo (n = 641). Duloxetine doses were 40, 60, 80 and 120 mg/day. SSRI doses were 10 mg/day (escitalopram) and 20 mg/day (fluoxetine and paroxetine). RESULTS: Compared to SSRI-treated patients, duloxetine-treated patients had a significantly greater (p < or = 0.05) reduction in the 17-item Hamilton Depression Rating Scale (HAMD17) total score and HAMD17 items of work and activities, psychomotor retardation, genital symptoms and hypochondriasis. Differences favoring the SSRIs approached significance for middle insomnia (p = 0.057) and late insomnia (p = 0.06), with effect sizes at least twice the magnitude of the corresponding effect sizes for duloxetine. Similarly, the advantage for duloxetine versus the SSRIs approached significance for general somatic symptoms (p = 0.056), with an effect size twice that observed for the SSRIs. The HAMD17 total score difference was driven mostly by patients with lower baseline MDD severity (HAMD17 total score < or = 19), where the HAMD17 effect size advantage for duloxetine over combined SSRIs was statistically significant (p = 0.031). CONCLUSION: Potentially important differences in symptom response patterns were found between duloxetine and the combined SSRIs depending on symptom severity, and different HAMD17 items responded differently to duloxetine compared with SSRIs. Understanding these differences may be useful in tailoring antidepressant therapy for individual patients.