Long-Acting Injectable vs Oral Antipsychotics for Relapse Prevention in Schizophrenia: A Meta-Analysis of Randomized Trials

Background: While long-acting injectable antipsychotics (LAIs) are hoped to reduce high relapse rates in schizophrenia, recent randomized controlled trials (RCTs) challenged the benefits of LAIs over oral antipsychotics (OAPs). Methods: Systematic review/meta-analysis of RCTs that lasted = 6 months comparing LAIs and OAPs. Primary outcome was study-defined relapse at the longest time point; secondary outcomes included relapse at 3, 6, 12, 18, and 24 months, all-cause discontinuation, discontinuation due to adverse events, drug inefficacy (ie, relapse + discontinuation due to inefficacy), hospitalization, and nonadherence. Results: Across 21 RCTs (n = 5176), LAIs were similar to OAPs for relapse prevention at the longest time point (studies = 21, n = 4950, relative risk [RR] = 0.93, 95% confidence interval [CI]: 0.80-1.08, P =.35). The finding was confirmed restricting the analysis to outpatient studies lasting \u3e= 1 year (studies = 12, RR = 0.93, 95% CI: 0.71-1.07, P =.31). However, studies using first-generation antipsychotic (FGA)-LAIs (studies = 10, RR = 0.82, 95% CI: 0.69-0.97, P =.02) and those publishe

Limit theorems for von Mises statistics of a measure preserving transformation

For a measure preserving transformation $T$ of a probability space $(X,\mathcal F,\mu)$ we investigate almost sure and distributional convergence of random variables of the form $$x \to \frac{1}{C_n} \sum_{i_1<n,...,i_d<n} f(T^{i_1}x,...,T^{i_d}x),\, n=1,2,...,$$ where $f$ (called the \emph{kernel}) is a function from $X^d$ to $\R$ and $C_1, C_2,...$ are appropriate normalizing constants. We observe that the above random variables are well defined and belong to $L_r(\mu)$ provided that the kernel is chosen from the projective tensor product $$L_p(X_1,\mathcal F_1, \mu_1) \otimes_{\pi}...\otimes_{\pi} L_p(X_d,\mathcal F_d, \mu_d)\subset L_p(\mu^d)$$ with $p=d\,r,\, r\ \in [1, \infty).$ We establish a form of the individual ergodic theorem for such sequences. Next, we give a martingale approximation argument to derive a central limit theorem in the non-degenerate case (in the sense of the classical Hoeffding's decomposition). Furthermore, for $d=2$ and a wide class of canonical kernels $f$ we also show that the convergence holds in distribution towards a quadratic form $\sum_{m=1}^{\infty} \lambda_m\eta^2_m$ in independent standard Gaussian variables $\eta_1, \eta_2,...$. Our results on the distributional convergence use a $T$--\,invariant filtration as a prerequisite and are derived from uni- and multivariate martingale approximations

How effective are common medications: a perspective based on meta-analyses of major drugs

Study protocol. (PDF 161 kb

Proton Magnetic Resonance Spectroscopy and Illness Stage in Schizophrenia-A Systematic Review and Meta-Analysis

Background It is not known whether regional brain N-acetyl aspartate (NAA) changes in the progression from prodrome to chronic schizophrenia. We used effect size meta-analysis to determine which brain regions show the most robust reductions in NAA first episode and chronic schizophrenia as measured by proton magnetic resonance spectroscopy and to determine whether these changes are present in individuals at high risk of developing schizophrenia. Methods We identified 131 articles, of which 97 met inclusion criteria. Data were separated by stage of illness (at risk, first episode schizophrenia, chronic schizophrenia) and by brain region. For each region, mean and SD of the NAA measure was extracted. Results Significant reductions in NAA levels were found in frontal lobe, temporal lobe, and thalamus in both patient groups (effect size > .3; p < .01). In individuals at high risk of schizophrenia (of whom approximately 20% would be expected to undergo transition to psychosis), significant NAA reductions were present in thalamus (effect size = .78; p < .05), with reductions at trend level only in temporal lobe (effect size = .32; p < .1), and no reductions in frontal lobe (effect size = .05; p = .5). Conclusions These data suggest that schizophrenia is associated with loss of neuronal integrity in frontal and temporal cortices and in the thalamus and suggest that these changes in the frontal and temporal lobe might occur in the transition between the at-risk phase and the first episode

Should We Treat Depression with drugs or psychological interventions? A Reply to Ioannidis

We reply to the Ioannidis's paper "Effectiveness of antidepressants; an evidence based myth constructed from a thousand controlled trials." We disagree that antidepressants have no greater efficacy than placebo. We present the efficacy from hundreds of trials in terms of the percentage of patients with a substantial clinical response (a 50% improvement or more symptomatic reduction). This meta-analysis finds that 42-70% of depressed patients improve with drug and 21%-39% improve with placebo. The response benefit of antidepressant treatment is 33%-11% greater than placebo. Ioannidis argues that it would be vanishingly smaller because systematic biasing in these clinical trials would reduce the drug-placebo difference to zero. Ioannidis' argument that antidepressants have no benefit is eroded by his failures of logic because he does not present any evidence that there are a large number of studies where placebo is substantially more effective than drug. (To reduce to zero, one would also have to show that some of the unpublished studies find placebo better than drug and have substantial systematic or methodological bias). We also present the empirical evidence showing that these methodological concerns generally have the opposite effect of what Ioannidis argues, supporting our contention that the measured efficacy of antidepressants likely underestimates true efficacy

Sharing information across patient subgroups to draw conclusions from sparse treatment networks

Network meta-analysis (NMA) usually provides estimates of the relative effects with the highest possible precision. However, sparse networks with few available studies and limited direct evidence can arise, threatening the robustness and reliability of NMA estimates. In these cases, the limited amount of available information can hamper the formal evaluation of the underlying NMA assumptions of transitivity and consistency. In addition, NMA estimates from sparse networks are expected to be imprecise and possibly biased as they rely on large sample approximations which are invalid in the absence of sufficient data. We propose a Bayesian framework that allows sharing of information between two networks that pertain to different population subgroups. Specifically, we use the results from a subgroup with a lot of direct evidence (a dense network) to construct informative priors for the relative effects in the target subgroup (a sparse network). This is a two-stage approach where at the first stage we extrapolate the results of the dense network to those expected from the sparse network. This takes place by using a modified hierarchical NMA model where we add a location parameter that shifts the distribution of the relative effects to make them applicable to the target population. At the second stage, these extrapolated results are used as prior information for the sparse network. We illustrate our approach through a motivating example of psychiatric patients. Our approach results in more precise and robust estimates of the relative effects and can adequately inform clinical practice in presence of sparse networks