INDIVIDUAL PARTICIPANT DATA META-ANALYSIS OF ANTIDEPRESSANT TRIALS FOR MAJOR DEPRESSION IN JAPAN

1   All arms within the licensed dose range (by FDA in USA, EMA in Europe, or PMDA in Japan) will be treated as effective dose. Arms using dosages outside these ranges will be excluded.2   The literature suggests many candidates for effect predictors (variables associated with response regardless of the treatment) and for effect modifiers (variables associated with differential response depending on the treatment) in the treatment of depression [2]. We have listed the possible candidate variables for effect predictors and effect modifiers based on the literature in the following. The variables to be actually examined will first be limited by their availability in the included original studies but when several variables that measure similar concepts are available, the research team will discuss which ones we believe are the most important predictors and which should be included in the model.Demographics1)   Age [3]Life and social history2)   Childhood maltreatment [4]3)   Education [5]4)   Employment [6, 7]5)   Marital status [6-8]6)   Recent life events and difficulties [6, 7]7)   Social adjustment/function [9]History of present illness8)   Age at onset [10]9)   Chronicity [3]10)   No of previous episodes [5, 11]11)   Prior treatments with antidepressants [7]Present illness: symptomatology12)   Baseline severity [12-14]13)   Baseline psychomotor symptoms [9, 15]14)   Baseline anxiety symptoms [15, 16]15)   Comorbid personality disorder [7]16)   Comorbid substance use/abuse [15]Therapeutic process17)   Early response [17]18)   Co-prescriptions other than antidepressants3   We will conduct IPD-MA to examine the relationship between each independent variable and the differences in change scores between the drugs and placebo using a mixed-effects model with maximum likelihood or restricted maximum likelihood estimation [18, 19]. The between-studies and between-drugs heterogeneities will be modelled by random effects. The effect modifications will be evaluated by involving treatment-by-covariates interaction terms in the mixed-effects model analysis. The strategy of modeling variables will be determined explanatorily because there have been only limited prior information what variables have predictive abilities and how these variables should be modeled in the statistical models. We expect to assemble 4000 participants' individual data from the eight trials which will provide enough power to examine the available variables. 4   In addition, we would attempt other data-driven subgroup identification methods which might detect predictive factors and responder subgroups more efficiently, if necessary. We will adopt the SIDES (subgroup identification based differential effect search) procedure and its companion methods [20-22] that effectively identify responder subgroups. The SIDES and its companion procedures are flexible searching algorithms that split the patient population for exploring the subpopulations that the treatment effects would be particularly different. These algorithms can also control the random errors effectively in the comprehensive explanatory analyses. The candidate variables and the analysis statistical models will be limited as above.5   The obtained model will then be used to build subgroups of those who are likely to respond on drug but not on placebo, those who are likely to respond both on drug and on placebo, and those who are likely not to respond to drug or placebo.1.   Carmody TJ, Rush AJ, Bernstein I, Warden D, Brannan S, Burnham D, et al. The Montgomery Asberg and the Hamilton ratings of depression: a comparison of measures. Eur Neuropsychopharmacol. 2006 Dec;16(8):601-11. PMID: 16769204. doi: 10.1016/j.euroneuro.2006.04.008.2.   Kessler RC, Bossarte R, Brenner L, Ebert D, de Junge P, Nierenberg A, et al. Using patient self-reports to study heterogeneity of treatment effects in major depressive disorder. Epidemiology and Psychiatric Sciences. in press.3.   Cuijpers P, Reynolds CF, 3rd, Donker T, Li J, Andersson G, Beekman A. Personalized treatment of adult depression: medication, psychotherapy, or both? A systematic review. Depress Anxiety. 2012 Oct;29(10):855-64. PMID: 22815247. doi: 10.1002/da.21985.4.   Nemeroff CB, Heim CM, Thase ME, Klein DN, Rush AJ, Schatzberg AF, et al. Differential responses to psychotherapy versus pharmacotherapy in patients with chronic forms of major depression and childhood trauma. Proc Natl Acad Sci U S A. 2003 Nov 25;100(24):14293-6. PMID: 14615578.5.   Perlis RH. A clinical risk stratification tool for predicting treatment resistance in major depressive disorder. Biol Psychiatry. 2013 Jul 1;74(1):7-14. PMID: 23380715. doi: 10.1016/j.biopsych.2012.12.007.6.   Fournier JC, DeRubeis RJ, Shelton RC, Hollon SD, Amsterdam JD, Gallop R. Prediction of response to medication and cognitive therapy in the treatment of moderate to severe depression. J Consult Clin Psychol. 2009 Aug;77(4):775-87. PMID: 19634969. doi: 10.1037/a0015401.7.   DeRubeis RJ, Cohen ZD, Forand NR, Fournier JC, Gelfand LA, Lorenzo-Luaces L. The Personalized Advantage Index: translating research on prediction into individualized treatment recommendations. A demonstration. PLoS ONE. 2014;9(1):e83875. PMID: 24416178. doi: 10.1371/journal.pone.0083875.8.   Barber JP, Muenz LR. The role of avoidance and obsessiveness in matching patients to cognitive and interpersonal psychotherapy: empirical findings from the treatment for depression collaborative research program. J Consult Clin Psychol. 1996 Oct;64(5):951-8. PMID: 8916624.9.   Frank E, Cassano GB, Rucci P, Thompson WK, Kraemer HC, Fagiolini A, et al. Predictors and moderators of time to remission of major depression with interpersonal psychotherapy and SSRI pharmacotherapy. Psychol Med. 2011 Jan;41(1):151-62. PMID: 20380782. doi: 10.1017/S0033291710000553.10.   Andreescu C, Mulsant BH, Houck PR, Whyte EM, Mazumdar S, Dombrovski AY, et al. Empirically derived decision trees for the treatment of late-life depression. Am J Psychiatry. 2008 Jul;165(7):855-62. PMID: 18450930. doi: 10.1176/appi.ajp.2008.07081340.11.   Jarrett RB, Minhajuddin A, Kangas JL, Friedman ES, Callan JA, Thase ME. Acute phase cognitive therapy for recurrent major depressive disorder: who drops out and how much do patient skills influence response? Behav Res Ther. 2013 May;51(4-5):221-30. PMID: 23485420. doi: 10.1016/j.brat.2013.01.006.12.   Fournier JC, DeRubeis RJ, Hollon SD, Dimidjian S, Amsterdam JD, Shelton RC, et al. Antidepressant drug effects and depression severity: a patient-level meta-analysis. JAMA. 2010 Jan 6;303(1):47-53. PMID: 20051569. doi: 303/1/47 [pii]10.1001/jama.2009.1943.13.   Driessen E, Cuijpers P, Hollon SD, Dekker JJ. Does pretreatment severity moderate the efficacy of psychological treatment of adult outpatient depression? A meta-analysis. J Consult Clin Psychol. 2010 Oct;78(5):668-80. PMID: 20873902. doi: 10.1037/a0020570.14.   Weitz ES, Hollon SD, Twisk J, van Straten A, Huibers MJ, David D, et al. Baseline depression severity as moderator of depression outcomes between cognitive behavioral therapy vs pharmacotherapy: An individual patient data meta-analysis. JAMA psychiatry. 2015 Nov 1;72(11):1102-9. PMID: 26397232. doi: 10.1001/jamapsychiatry.2015.1516.15.   Rush AJ, Wisniewski SR, Warden D, Luther JF, Davis LL, Fava M, et al. Selecting among second-step antidepressant medication monotherapies: predictive value of clinical, demographic, or first-step treatment features. Arch Gen Psychiatry. 2008 Aug;65(8):870-80. PMID: 18678792. doi: 10.1001/archpsyc.65.8.870.16.   Ninan PT, Rush AJ, Crits-Christoph P, Kornstein SG, Manber R, Thase ME, et al. Symptomatic and syndromal anxiety in chronic forms of major depression: effect of nefazodone, cognitive behavioral analysis system of psychotherapy, and their combination. J Clin Psychiatry. 2002 May;63(5):434-41. PMID: 12025827.17.   Steidtmann D, Manber R, Blasey C, Markowitz JC, Klein DN, Rothbaum BO, et al. Detecting critical decision points in psychotherapy and psychotherapy + medication for chronic depression. J Consult Clin Psychol. 2013 Oct;81(5):783-92. PMID: 23750462. doi: 10.1037/a0033250.18.   Hedeker D, Gibbons RD. Longitudinal Data Analysis. Hoboken, New Jersey: John Wiley & Sons, Inc.; 2006.19.   Gibbons RD, Hur K, Brown CH, Davis JM, Mann JJ. Benefits from antidepressants: Synthesis of 6-week patient-level outcomes from double-blind placebo-controlled randomized trials of fluoxetine and venlafaxine. Arch Gen Psychiatry. 2012 Mar 5. PMID: 22393205. doi: 10.1001/archgenpsychiatry.2011.2044.20.   Lipkovich I, Dmitrienko A, Denne J, Enas G. Subgroup identification based on differential effect search--a recursive partitioning method for establishing response to treatment in patient subpopulations. Stat Med. 2011 Sep 20;30(21):2601-21. PMID: 21786278. doi: 10.1002/sim.4289.21.   Lipkovich I, Dmitrienko A. Strategies for identifying predictive biomarkers and subgroups with enhanced treatment effect in clinical trials using SIDES. J Biopharm Stat. 2014;24(1):130-53. PMID: 24392982. doi: 10.1080/10543406.2013.856024.22.   Lipkovich I, Dmitrienko A, B. R. D' Agostino S. Tutorial in biostatistics: data-driven subgroup identification and analysis in clinical trials. Stat Med. 2017 Jan 15;36(1):136-96. PMID: 27488683. doi: 10.1002/sim.7064.