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Non-inferiority designs for relapse prevention of schizophrenia. Gene Laska Ph.D. Department of Psychiatry NYU School of Medicine Nathan Kline Institute for Psychiatric Research. The problem of relapse in schizophrenia.
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Non-inferiority designs for relapse prevention of schizophrenia Gene Laska Ph.D. Department of Psychiatry NYU School of Medicine Nathan Kline Institute for Psychiatric Research
The problem of relapse in schizophrenia • Montero’s review of the literature estimated that 42 percent of patients with schizophrenia relapsed over the course of a year1 • For patients who discontinued antipsychotic therapy, relapse was an almost certain at one year2 1. Montero, I., Pérez, I. Ruiz, & Gómez-Beneyto, M. (1998). Social adjustment in schizophrenia: Factors predictive of short-term social adjustment in a sample of schizophrenic patients. Acta Psychiatrica Scandinavica, 97, 116-121 2. Weiden, P.J., and Olfson, M. (1995). Cost of relapse in schizophrenia. Schizophrenia Bulletin, 24, 419-429.
Framing the issue • Comparisons to placebo in a RCT provide the most persuasive evidence • It may be unethical to use placebo when better treatments exist • Are active controlled trials using non-inferiority designs a valid alternative?
Logic of non-inferiority trials • If a standard S is consistently superior to placebo, then • To show that a test treatment T is superior to placebo… • It suffices to show that the test treatment is as good as (not inferior) to the standard
The formal concept of non-inferiority: definition • If T’s effect is not worse than C’s effect by more than δ, it is said to be non-inferior • δ is a pre-specified non-inferiority margin
Equivalence, non-inferiority and superiority: graphically Test better Control better equivalent non-inferior test superior control superior uninformative Test - Control - d 0 d
Setting the non-inferiority margin • Subjective – often contentious • If too large: inferior treatments may be called non-inferior • If too small: huge sample sizes are required • Usually a fraction of the historical difference between control and placebo
Assay sensitivity • The ability of a RCT to find a difference between treatments if there truly is a difference- a property of one trial • Sensitivity is a property for a class of RCTs • Statisticians call this “power” – the probability of detecting a true difference of size d
Assay sensitivity in a three armed RCT with T,C and P • If T>P or S>P then the trial has demonstrated assay sensitivity • If T=S=P then the trial is a failure • It has no assay sensitivity because it is known that S>P • No inference regarding the equivalence of S and T is possible
Assay sensitivity in a two armed RCT with T and S • A trial that finds T>S or S>T has ipso facto demonstrated assay sensitivity • Problem: A two armed trial that does not distinguish treatments (S=T) has not demonstrated assay sensitivity • Differentiating a failed trial from a trial that correctly finds no difference is not possible
Therefore • The conclusion that a test drug is non inferior to a standard is only valid if the standard would have been superior to placebo had one been included in the trial • This possibility can only be appraised by comparing the results with historical RCTs
Randomized controlled relapse prevention studies in the literature • Leucht et al - AJP 2003 numerous studies* • Schooler et al - AJP 2005 risperidone/haloperidol • Pigott et alJ - Clin Psych 2003 aripiprazole /placebo *Trials needed to have estimated relapse rates using Kaplan Meier and have reasonable sample sizes
Probability of relapsing in 6 months based on KM Estimates-Aty vs Pl atypical placebo
Probability of relapsing in 6 months based on KM Estimates-Aty vs Conv atypical conventional
Six month relapse rates for eleven prevention studies atypical conventional atypical placebo
Implications of the historical RCTs Six-month relapse rates Range Mean vs P vs Con • Atypicals 3 - 39 19.1 21.6 17.0 • Conventionals 3 - 47 26.6 • Placebo 53 – 63 56.0 • There is no overlap of active vs placebo • Reasonable to set d = 10 -15% test vs a conventional 15 - 20% test vs an atypical
Powering the trial: example • Assume PC = PT is about .35 • Suppose d = .15 • Let the two groups have equal sample sizes • What sample size will reject H0 with 80% power? • Approximate sample size is 183 per group (based on Fisher’s exact test)
Conclusion • Regulatory Concern: Just as in an acute trial, the major concern is concluding that an ineffective drug works • The historical record suggests that in active controlled relapse preventions studies in schizophrenia this risk is small