Effectiveness of Psychotherapies and Psychotropic Drugs against Mental Illness/Psychotherapies for psychosis

Effectiveness of Psychotherapies and Psychotropic Drugs against Mental Illness Psychotherapies for psychosis Psychotherapies for panic disorder Psychotherapies for eating disorders Loading libraries library(dplyr) library(meta) Part 1: Data At https://docs.metapsy.org/databases/psychosis-psyctr/ you can download documents in PDF format and data in ZIP format related to a meta-analysis conducted by researchers entitled "Psychosis: Psychotherapy vs. Control," which analyzed 206 clinical trials of psychotherapies for psychosis, comparing groups undergoing psychotherapy and control groups. The CSV dataset contains the Hedges' effect size "g" with its corresponding standard deviation for each study, the variable "symmpton_domain" which can take on total, positive, or negative values ​​depending on the type of schizophrenia symptoms, and the variable "condition_arm1" which contains the different types of intervention protocols used in the studies: ALL: Protocol with 4 combined psychotherapies. CBT/CBTp: Cognitive behavior therapy. CR: Cognitive remediation. CRSS: Cognitive remediation focused on social cognition. EMDR: Eye movement desensitization and reprocessing. FT: Family therapy. HIT: Integrative therapy focused on hallucinations. MCT: Metacognitive training. MPE: Mindfulness-based psychoeducation. PE: Psychoeducation. SST: Social skills training. WB: Wellbeing. Other specific variants present in the dataset include FSG (Family Support Group), SST_FPE (Social Skills Training + Family Psychoeducation), PST (Guided Self-Determination), and PMR (Progressive Muscle Relaxation). The variable "condition_arm2" contains the type of control group to which the psychological intervention group is compared: TAU0: Minimal contact or minimal intervention (e.g., medication only, no follow-up). TAU1: Medication with routine follow-up appointments or follow-up. TAU2: Case management or access to multidisciplinary services (social work, occupational therapy, psychosocial interventions). TAU3: TAU2 plus specific psychological interventions (e.g., CBT or motivational interviewing). BF: Befriending (intervention based on friendly social interaction). CCBF: Computerized control with befriending. CC: Computerized control. SC: Supportive counseling. OT: Occupational therapy. CAT: Cognitive adaptive therapy. Loading and displaying the first 6 studies: df <- read.csv("data.csv", sep = ";", dec = ",", stringsAsFactors = FALSE) df %>% select(study, .g, .g_se, condition_arm1, condition_arm2, symptom_domain) %>% head() study .g .g_se condition_arm1 condition_arm2 symptom_domain Agothor, 2010 -0.225459957905061 0.367183190233538 MCT BF total Agothor, 2010 -0.411854480940389 0.370020108549253 MCT BF positive Bark 2003 -0.099260181251593 0.28883778120896 CR TAU1 total Bark 2003 -0.615220380135897 0.294858883534907 CR TAU1 positive Bark 2003 -0.0607551606927302 0.288736079546077 CR TAU1 negative Barrowclough 2006 -0.251715758041738 0.202646896085208 CBT TAU2 total Part 2: Meta-analysis Calculating Hedges' g We calculate Hedges' g in meta-analysis by choosing total symptoms as the symptom and perform the Egger test to assess whether there is publication bias due to the fact that studies with positive results and high g are more likely to be published than those with modest or negative results. Since the p-value is <0.05, there is indeed publication bias in the test, so we use Duval and Tweedie's "trim and fill" procedure, which estimates the number of missing studies and recalculates g, obtaining g = -0.07 with a 95% confidence interval: [ -0.17 , 0.04 ], so in this case the effect of psychological interventions on psychosis is small: # Filter for total symptoms data_clean <- df %>% filter(symptom_domain == "total") # Performing the Meta-Analysis # We use the inverse variance method (metagen) m_g <- metagen(TE = .g, seTE = .g_se, data = data_clean, studlab = study, sm = "SMD", random = TRUE, # Random-effects model (recommended) common = FALSE # Disables the fixed-effects model ) # Egger's test for publication bias metabias(m_g, method.stat = "linreg") Linear regression test of funnel plot asymmetry Test result: t = -4.65, df = 84, p-value < 0.0001Bias estimate: -2.0322 (SE = 0.4370) Details: - multiplicative residual heterogeneity variance (tau^2 = 2.4050) - predictor: standard error - weight: inverse variance - reference: Egger et al. (1997), BMJ m_g<- trimfill(m_g) print(paste("g =",round(m_g$TE.random,2),"; 95%CI: [",round(m_g$lower.random,2),",",round(m_g$upper.random,2),"]")) "g = -0.07; 95%CI: [ -0.17, 0.04]" Measuring g for the various psychological interventions, we find that CRSS: Social Cognition-Focused Cognitive Remediation has the highest g (g=-0.91), MPE: Mindfulness-Based Psychoeducation (g=-0.75), HIT: Hallucination-Focused Integrative Therapy (g=-0.64), and PE: Psychoeducation (g=-0.54) etc. , so the effect of some psychological interventions, excluding publication bias, is medium-high, while the others are low: for (p in unique(df$condition_arm1)) { df_post <- data_clean %>% filter(condition_arm1==p) m_pst<-metagen(TE = .g, seTE = .g_se, data = df_post, studlab = study, sm = "SMD", random = TRUE) print(paste(p,": g=",round(m_pst$TE.random,2),"; 95%CI: [",round(m_pst$lower.random,2),",",round(m_pst$upper.random,2),"]")) } [1] "MCT : g= -0.33 ; 95%CI: [ -0.92 , 0.25 ]" [1] "CR : g= -0.18 ; 95%CI: [ -0.36 , 0 ]" [1] "CBT : g= -0.28 ; 95%CI: [ -0.44 , -0.13 ]" [1] "FSG : g= -0.19 ; 95%CI: [ -0.75 , 0.36 ]" [1] "SST_FPE : g= -0.46 ; 95%CI: [ -1 , 0.08 ]" [1] "FPE : g= -0.39 ; 95%CI: [ -0.71 , -0.07 ]" [1] "MPE : g= -0.75 ; 95%CI: [ -1.17 , -0.33 ]" [1] "PE : g= -0.54 ; 95%CI: [ -0.86 , -0.22 ]" [1] "CRmeta : g= -0.41 ; 95%CI: [ -0.96 , 0.13 ]" [1] "CR_CRSS : g= 0.08 ; 95%CI: [ -0.54 , 0.71 ]" [1] "CRSS : g= -0.91 ; 95%CI: [ -1.25 , -0.57 ]" [1] "CBTp : g= -0.32 ; 95%CI: [ -0.54 , -0.1 ]" [1] "FT : g= -0.06 ; 95%CI: [ -0.63 , 0.51 ]" [1] "ALL : g= 0.29 ; 95%CI: [ 0.15 , 0.44 ]" [1] "SST : g= -0.44 ; 95%CI: [ -0.61 , -0.27 ]" [1] "HIT : g= -0.64 ; 95%CI: [ -1.12 , -0.15 ]" [1] "PST : g= -0.14 ; 95%CI: [ -0.54 , 0.27 ]" [1] "CR_SST : g= -0.07 ; 95%CI: [ -0.65 , 0.5 ]" [1] "EMDR : g= -0.3 ; 95%CI: [ -1.08 , 0.47 ]" [1] "PMR : g= -0.34 ; 95%CI: [ -1.1 , 0.43 ]" [1] "CPS : g= -0.18 ; 95%CI: [ -0.48 , 0.12 ]" [1] "WB : g= -0.45 ; 95%CI: [ -0.88 , -0.02 ]" [1] "PESC : g= -1.11 ; 95%CI: [ -1.72 , -0.5 ]" [1] "FSIT : g= -0.8 ; 95%CI: [ -1.42 , -0.19 ]" [1] "CBT_CAT : g= -0.05 ; 95%CI: [ -0.56 , 0.46 ]"