Methods
EOS
The propensity to respond to placebo was defined as a clinically relevant percent change from baseline in the MADRS or HAMD-17 total score, and therefore in the absence of any active treatment intervention. The relevant improvement was estimated by connecting the MADRS change scores to the clinician global impression-improvement (CGI-I) scale scores, using the equipercentile linking method. A CGI-I score of 3 (‘minimally improved’) corresponded to an average reduction from baseline in the total MADRS score of 24.5%, a CGI-I score of 2 (‘much improved’) corresponded to an average reduction of 52.5%; and a CGI-I score of 1 (‘very much improved’) to an average reduction of 82% [A case study is presented using the data of the study 29060/810. Details on this study have been previously reported [The propensity weighted analysis was conducted using a 5-step approach:Step 1: The pre-randomization (i.e., screening and baseline) and end of study data (EOS) (i.e., visit at 8 weeks) in subjects randomized to placebo were selected.Step 2: A predictive model was developed to estimate the probability to be placebo responder after 8 weeks of treatment using ANN and data collected in the pre-randomization period.Step 3: The model developed in step 2 was validated by comparing the model predicted probability to the observed placebo response.Step 4: The ANN model developed in step 2 was used to predict the individual probability to be placebo responder using the pre-randomization data of all subjects randomized in the study (i.e., subjects in the different treatment arms).Step 5: The inverse individual probability was used as a weighting factor in the MMRM analysis conducted on the longitudinal clinical scores to estimate the TE.The procedure used for model development and validation was based on a generally accepted procedure. This procedure consists of the random split of the original dataset into three datasets:The training set, applied for the ANN model development (in our case this dataset included 75% of the data in the placebo arm randomly selected).The validation set, applied for an unbiased model evaluation. The evaluation was conducted by comparing the model predictions based on the model developed in point 1 with the data observed in the validation dataset (in our case this dataset included the 25% data in the placebo arm not used for model development).The working dataset, with the full data set including all the subject data in the 3-arms. This dataset was used to provide the individual estimate of the propensity probability applying the ANN model validated in step 2 to the pre-randomization data of each subject in the 3-arms.A binary score was associated to each subjects: 0 or 1 for the absence or presence of response after 8 weeks of treatment (i.e., HAMD-17 ≥ 38% or greater change). The ability of the early collected (between two pre-randomization time points at screening and baseline) HAMD-17 individual item to predict the response to placebo at week 8 was investigated using artificial intelligence (AI) methodology. The AI approach was selected as this methodology provides the most performing predictive tool today available [The inverse of the estimated probability was included as weight in MMRM model used to analyze the longitudinal HAMD-17 total score and to assess the TE. The MMRM model was implemented in SAS (PROC MIXED, Version 9.4, SAS Institute, Carry, NC, USA), using the change from baseline of the HAMD-17 total score. In the MMRM analysis a random effect model was used on the change from baseline value, using an unstructured covariance matrix, time as a classification variable, and baseline measurement as a covariate, baseline × time interaction, and treatment × time interaction. A significance level of α = 0.05 was used to establish the significance of the TE.Drug-placebo TE sizes were calculated as the least squares means (LS means) difference divided by the pooled standard deviation, obtained as the standard error of the LS mean difference divided by the square root of the sum of inverse treatment group sample sizes.