Examining alternative core symptom inclusion criteria in randomized placebo-controlled trials for acute major depression

Analysis Plan OverviewThe analysis will be conducted using Stata and/or R. We are requesting .csv data files which can be easily imported into Stata or R.The analysis is estimated to require no more than 12 months to complete, during which time the data will be stored on the University of Saskatchewan encrypted network. After the analysis is complete, or at the end of the data access period (whichever is sooner), the data will be permanently deleted from this network. Only the statistical output (i.e., the analytic results) and the statistical syntax will be retained for a minimum of five years. We will not be using any kind of machine learning or artificial intelligence.The analysis will proceed in several steps:Step 1. Data PreparationData from the 10 trials (ITT populations only) will be pooled together resulting in one large dataset with two treatment groups: SSRI (n = 2653) and placebo (n = 1270).Missing HAMD-17 scores will be filled with LOCF (last observation carried forward) imputation, consistent with the original trials.A binary outcome variable reflecting response will be created. Response is defined as a HAMD-17 score decrease by 50% or more from baseline to the end of the trial (8 or 12 weeks, depending on the trial).Step 2. Treatment effects in the overall sampleDrug and placebo response rates with 95% confidence intervals and number-needed-to-treat statistics will be estimated to establish the pooled response rates in the entire study sample.Then, this will be repeated in only patients with screening/baseline HAMD Item 1 (depressed mood) scores >=2. Since it was not a requirement for most of these studies, we can gauge the utility of this now commonly used inclusion criterion.Step 3. Examination of alternative inclusion criteriaWe will then calculate the drug and placebo response rates with 95% confidence intervals and number-needed-to-treat statistics, stratified by the following criteria:a) Screening/Baseline HAMD Item 1 >=2 AND Item 7 >=2b) Screening/Baseline HAMD Item 1 >=3 OR Item 7 >=3c) Screening/Baseline HAMD Item 1 >=3d) Screening/Baseline HAMD Item 1 + 7 >=6e) Screening/Baseline HAMD Item 1 >=3 AND Item 7 >=3f) Screening/Baseline HAMD Item 1 ==4 OR Item 7 ==4These criteria allow us to examine the utility of requiring higher core depressive symptoms at study entry. We have made the assumption that the mean baseline score for item 1 (depressed mood) and item 7 (work/interests) in the pooled sample will likely be around 2.7-2.8 [10, 11, 19]. Thus, we expect the % of patients meeting each criterion to be around 50% (criteria a, b, and c) or less (criteria d, e, f). Based on a previous analysis [11], we anticipated very few patients would score 4 on both symptoms (<10%), thus we did not consider this to a feasible criterion to examine.In particular, these criteria were chosen to examine the utility of requiring higher scores on one particular core symptom (e.g., c), either symptom (e.g., b and f), or both (a, d, e).Our expectation is that, moving from criterion a through f, the % of patients meeting each criterion will be lower. This is expected to result in larger drug-placebo differences and smaller number needed-to-treat statistics, driven primarily by a reduction in placebo response rates.Non-overlapping confidence intervals for the drug-placebo response-rate difference between subjects meeting and not meeting a criterion will be considered evidence of treatment-effect heterogeneity. Only those with non-overlapping confidence intervals will be considered further in the analysis.We have not specified a method to select one “best” criterion because we have considered that different trials may benefit from different criteria. For example, trials that want to emphasize signal detection may opt for a criterion that is more restrictive (e.g., d, e, or f) but results in a larger drug-placebo difference compared to a less restrictive criterion. Conversely, a trial that wants to emphasize external validity may choose a criterion that results in a smaller treatment effect but excludes less patients.Instead, we are only looking to establish a list of possible alternative inclusion criteria by virtue of them selecting patient subgroups with significantly larger drug-placebo differences, as evidenced by non-overlapping confidence intervals. This can be confirmed using a logistic regression model, with a treatment-by-subgroup interaction, a significant interaction confirming an interpretation of treatment effect heterogeneity. If there are no non-overlapping confidence intervals, we would not proceed with further analysis in Step 4.Step 4. Heterogeneity and sensitivity analysisIn order to test for inter-study variability in the pooled estimate of treatment effect heterogeneity, we will use the individual patient data meta-analysis command developed by Fisher [20] for Stata. This allows us to re-estimate the effect size for each individual study (prior to pooling of data) then pool the study effects by using inverse-variance weights. A pooled estimate can be calculated that takes into account inter-study differences. This command can then calculate the Q statistic (an index of heterogeneity). Although we do not anticipate significant between-study heterogeneity given the similar trial methods and patient selection/sampling procedures, this will allow us to observe and test for between-study heterogeneity if it is present. The command allows the specification of treatment by factor interactions into the model (in this case, a treatment-by-subgroup interaction). Thus, we can test for inter-study heterogeneity in the measure of treatment-effect heterogeneity. The model to be used here is logistic regression, given the binary outcome. A significant interaction is interpreted as evidence of treatment-effect heterogeneity, similar to non-overlapping confidence intervals in Step 3.In addition to the above, we will re-estimate the drug and placebo response rates, stratified by each criterion with non-overlapping confidence intervals, using a leave-one-out-style sensitivity analyses. This allows us to observe whether significant interactions remain after sequentially excluding one of the 10 studies, in essence, ruling out that one outlier study resulted in the overall pooled estimate.In addition, we will calculate the drug and placebo response rates in the subgroup meeting a given inclusion criterion, but stratify this according to trial series: (GSK-29060 studies vs. AK- studies) and study duration (8 vs. 12 weeks) to see if the drug-placebo differences remain larger compared to the overall sample.In final logistic models, age and sex, then baseline HAMD-17 scores, can be included as covariates to see if treatment-subgroup interactions remain significant. Step 5. Comparison to HAMD-17 sum scoresWe will then use screening/baseline HAMD-17 sum scores to establish severity cut-offs that identify similarly sized subgroups compared to the criteria that appeared to result in larger treatment effects (e.g., if criterion c, d, and e resulted in significant treatment-effect heterogeneity, and isolated 50%, 40%, and 35% of the sample, we would calculate whatever HAMD-17 score cut-offs were needed to isolate 50%, 40% and 35% of the sample). We hypothesize that the drug-placebo response-rate differences will be smaller, with larger number-needed-to-treat statistics, with the criteria derived from the HAMD-17-based severity subgroups compared to their analogous Item 1/7-based subgroups.This is an indirect way of testing whether simply requiring higher HAMD-17 total score inclusion criteria would improve signal detection as efficiently as the criteria derived from the core symptom items. By efficiently, we mean excluding less patients from the sample overall to achieve the same drug-placebo difference. While it is possible that requiring a certain level of higher HAMD-17 sum scores would increase drug-placebo differences to a similar degree, we suspect that this would require excluding a larger % of patients from the sample. Thus, with this analysis, we can demonstrate the corollary: that comparably stringent criteria (i.e., criteria that exclude a similar number of patients) from the HAMD-17 do not improve signal detection as much as the core-symptom derived criteria. Another way to think of this analysis is that it is examining which index of severity is more proximally related to treatment outcomes.References[1] Furukawa, T. A., Cipriani, A., Leucht, S., Atkinson, L. Z., Ogawa, Y., Takeshima, N., ... & Salanti, G. (2018). Is placebo response in antidepressant trials rising or not? A reanalysis of datasets to conclude this long-lasting controversy. 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