scholarly journals On the treatment effect heterogeneity of antidepressants in major depression: A Bayesian meta-analysis and simulation study

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0241497
Author(s):  
Constantin Volkmann ◽  
Alexander Volkmann ◽  
Christian A. Müller
Keyword(s):  
Major Depression ◽  
Random Effects ◽  
Treatment Effect ◽  
Clinical Relevance ◽  
Meta Analysis ◽  
Placebo Response ◽  
Average Treatment Effect ◽  
Average Treatment ◽  
Treatment Effect Heterogeneity ◽  
Effect Heterogeneity

Background The average treatment effect of antidepressants in major depression was found to be about 2 points on the 17-item Hamilton Depression Rating Scale, which lies below clinical relevance. Here, we searched for evidence of a relevant treatment effect heterogeneity that could justify the usage of antidepressants despite their low average treatment effect. Methods Bayesian meta-analysis of 169 randomized, controlled trials including 58,687 patients. We considered the effect sizes log variability ratio (lnVR) and log coefficient of variation ratio (lnCVR) to analyze the difference in variability of active and placebo response. We used Bayesian random-effects meta-analyses (REMA) for lnVR and lnCVR and fitted a random-effects meta-regression (REMR) model to estimate the treatment effect variability between antidepressants and placebo. Results The variability ratio was found to be very close to 1 in the best fitting models (REMR: 95% highest density interval (HDI) [0.98, 1.02], REMA: 95% HDI [1.00, 1.02]). The between-study standard deviation τ under the REMA with respect to lnVR was found to be low (95% HDI [0.00, 0.02]). Simulations showed that a large treatment effect heterogeneity is only compatible with the data if a strong correlation between placebo response and individual treatment effect is assumed. Conclusions The published data from RCTs on antidepressants for the treatment of major depression is compatible with a near-constant treatment effect. Although it is impossible to rule out a substantial treatment effect heterogeneity, its existence seems rather unlikely. Since the average treatment effect of antidepressants falls short of clinical relevance, the current prescribing practice should be re-evaluated.

scholarly journals On the treatment effect heterogeneity of antidepressants in major depression. A Bayesian meta-analysis

2020 ◽  
Author(s):  
Constantin Volkmann ◽  
Alexander Volkmann ◽  
Christian A. Müller
Keyword(s):  
Major Depression ◽  
Random Effects ◽  
Treatment Effect ◽  
Clinical Relevance ◽  
Meta Analysis ◽  
Placebo Response ◽  
Average Treatment Effect ◽  
Average Treatment ◽  
Treatment Effect Heterogeneity ◽  
Effect Heterogeneity

AbstractBackgroundThe average treatment effect of antidepressants in major depression was found to be about 2 points on the 17-item Hamilton Depression Rating Scale, which lies below clinical relevance. Here, we searched for evidence of a relevant treatment effect heterogeneity that could justify the usage of antidepressants despite their low average treatment effect.MethodsBayesian meta-analysis of 169 randomized, controlled trials including 58,687 patients. We considered the effect sizes log variability ratio (lnVR) and log coefficient of variation ratio (lnCVR) to analyze the difference in variability of active and placebo response. We used Bayesian random-effects meta-analyses (REMA) for lnVR and lnCVR and fitted a random-effects meta-regression (REMR) model to estimate the treatment effect variability between antidepressants and placebo.ResultsThe variability ratio was found to be very close to 1 in the best fitting models (REMR: 95% HPD [0.98, 1.02], REMA: 95% HPD [1.00, 1.02]). The between-study variance τ2 under the REMA was found to be low (95% HPD [0.00, 0.00]). Simulations showed that a large treatment effect heterogeneity is only compatible with the data if a strong correlation between placebo response and individual treatment effect is assumed.ConclusionsThe published data from RCTs on antidepressants for the treatment of major depression is compatible with a near-constant treatment effect. Although it is impossible to rule out a substantial treatment effect heterogeneity, its existence seems rather unlikely. Since the average treatment effect of antidepressants falls short of clinical relevance, the current prescribing practice should be re-evaluated.

scholarly journals Looking Inside the Black Box: The Importance of Causal Mechanism and Treatment Effect Heterogeneity in Experimentally Evaluated Criminal Justice Interventions

2016 ◽  
Vol 31 (1) ◽  
pp. 89-112
Author(s):  
Na Chong Min
Keyword(s):  
Treatment Effect ◽  
Empirical Studies ◽  
Evaluation Research ◽  
Black Box ◽  
Average Treatment Effect ◽  
Causal Mechanism ◽  
Population Average ◽  
Treatment Effect Heterogeneity ◽  
Policies And Programs ◽  
Effect Heterogeneity

This paper discusses limitations of the ???black-box??? experimental archetype by highlighting the narrowness of outcome-focused approaches. For a more complete understanding of the nuanced implications of policies and programs, this study calls for an investigation of causal mechanism and treatment effect heterogeneity in experimentally evaluated interventions. This study draws on two distinct but closely related empirical studies, one undertaken by Na and Paternoster (2012) and the other by Na, Loughran, and Paternoster (2015), that go beyond the estimation of a population average treatment effect by adopting more recent methodological advancements that are still underappreciated and underutilized in evaluation research.

scholarly journals Extending the I-squared statistic to describe treatment effect heterogeneity in cluster, multi-centre randomized trials and individual patient data meta-analysis

2020 ◽  
pp. 096228022094855
Author(s):  
Karla Hemming ◽  
James P Hughes ◽  
Joanne E McKenzie ◽  
Andrew B Forbes
Keyword(s):  
Randomized Trial ◽  
Treatment Effect ◽  
Treatment Effects ◽  
Randomized Trials ◽  
Individual Patient Data ◽  
Meta Analysis ◽  
Cluster Randomized Trial ◽  
Patient Data ◽  
Treatment Effect Heterogeneity ◽  
Effect Heterogeneity

Treatment effect heterogeneity is commonly investigated in meta-analyses to identify if treatment effects vary across studies. When conducting an aggregate level data meta-analysis it is common to describe the magnitude of any treatment effect heterogeneity using the I-squared statistic, which is an intuitive and easily understood concept. The effect of a treatment might also vary across clusters in a cluster randomized trial, or across centres in multi-centre randomized trial, and it can be of interest to explore this at the analysis stage. In cross-over trials and other randomized designs, in which clusters or centres are exposed to both treatment and control conditions, this treatment effect heterogeneity can be identified. Here we derive and evaluate a comparable I-squared measure to describe the magnitude of heterogeneity in treatment effects across clusters or centres in randomized trials. We further show how this methodology can be used to estimate treatment effect heterogeneity in an individual patient data meta-analysis.

scholarly journals Generalizability of heterogeneous treatment effect estimates across samples

2018 ◽  
Vol 115 (49) ◽  
pp. 12441-12446 ◽  
Author(s):  
Alexander Coppock ◽  
Thomas J. Leeper ◽  
Kevin J. Mullinix
Keyword(s):  
Treatment Effect ◽  
Treatment Effects ◽  
Average Treatment Effect ◽  
Partial Explanation ◽  
Convenience Sample ◽  
Conditional Average ◽  
Replication Studies ◽  
Average Treatment ◽  
Average Treatment Effects ◽  
Effect Heterogeneity

The extent to which survey experiments conducted with nonrepresentative convenience samples are generalizable to target populations depends critically on the degree of treatment effect heterogeneity. Recent inquiries have found a strong correspondence between sample average treatment effects estimated in nationally representative experiments and in replication studies conducted with convenience samples. We consider here two possible explanations: low levels of effect heterogeneity or high levels of effect heterogeneity that are unrelated to selection into the convenience sample. We analyze subgroup conditional average treatment effects using 27 original–replication study pairs (encompassing 101,745 individual survey responses) to assess the extent to which subgroup effect estimates generalize. While there are exceptions, the overwhelming pattern that emerges is one of treatment effect homogeneity, providing a partial explanation for strong correspondence across both unconditional and conditional average treatment effect estimates.

scholarly journals Combining parenthood and work: transmission channels and heterogeneous returns to early public childcare

2021 ◽  
Author(s):  
Eric Schuss ◽  
Mohammed Azaouagh
Keyword(s):  
Treatment Effect ◽  
Average Treatment Effect ◽  
Individual Treatment ◽  
Full Time ◽  
Maternal Labor Supply ◽  
Transmission Channels ◽  
Average Treatment ◽  
Part Time ◽  
Effect Heterogeneity ◽  
Marginal Returns

AbstractWe exploit local and temporal variation in the availability of public childcare for children under the age of three that induces exogenous variation in childcare attendance. We find a weak, positive average treatment effect (ATE) on maternal labor supply. The estimation of the average treatment effect is interesting – however, possibly masking important effect heterogeneity. Examining selection behavior and estimating marginal treatment effects along the distribution of observables and unobservables that drive individual treatment decisions reveal transmission channels and uncover substantial heterogeneity in marginal returns from public childcare reforms. By estimating marginal returns, we detect reverse selection on gains at the intensive margin, whereas a substantial share (40 percent) of mothers with median desire to public childcare react with increased probability to work full time. Thus, if the supply of public childcare is expanded from a modest to a more generous level of coverage, those with average resistance towards early public childcare do gain. At the extensive margin, positive selection on gains is found; however, only a small fraction of mothers with the lowest distaste for early public childcare shift from non-employment to part-time jobs.

scholarly journals Reappraising Treatment Effect Heterogeneity in Schizophrenia: A Meta-analysis

2021 ◽  
Author(s):  
Robert Ali McCutcheon ◽  
Toby Pillinger ◽  
Orestis Efthimiou ◽  
Marta Maslej ◽  
Benoit Mulsant ◽  
...  
Keyword(s):  
Treatment Effect ◽  
Treatment Effects ◽  
Individual Patient Data ◽  
Placebo Response ◽  
Patient Data ◽  
Antipsychotic Treatment ◽  
Level Data ◽  
Treatment Effect Heterogeneity ◽  
Meta Analyses ◽  
Effect Heterogeneity

Objective Determining whether individual patients differ in response to treatment ('treatment effect heterogeneity') is important as it is a prerequisite to developing personalised treatment approaches. Previous variability meta-analyses of response to antipsychotics in schizophrenia found no evidence for treatment effect heterogeneity. Conversely, individual patient data meta-analyses suggest treatment effect heterogeneity does exist. In the current paper we combine individual patient data with study level data to resolve this apparent contradiction and quantitively characterise antipsychotic treatment effect heterogeneity in schizophrenia. Method Individual patient data (IPD) was obtained from the Yale University Open Data Access (YODA) project. Clinical trials were identified in EMBASE, PsycInfo, and PubMed. Treatment effect heterogeneity was estimated from variability ratios derived from study-level data from 66 RCTs of antipsychotics in schizophrenia (N=17,202). This estimation required a correlation coefficient between placebo response and treatment effects to be estimated. We estimated this from both study level estimates of the 66 trials, and individual patient data (N=560). Results Both individual patient (r=-0.32, p=0.002) and study level (r=-0.38, p<0.001) analyses yielded a negative correlation between placebo response and treatment effect. Using these estimates we found evidence of clinically significant treatment effect heterogeneity for total symptoms (our most conservative estimate was SD = 13.5 Positive and Negative Syndrome Scale (PANSS) points). Mean treatment effects were 8.6 points which, given the estimated SD, suggests the top quartile of patients experienced beneficial treatment effects of at least 17.7 PANSS points, while the bottom quartile received no benefit as compared to placebo. Conclusions We found evidence of clinically meaningful treatment effect heterogeneity for antipsychotic treatment of schizophrenia. This suggests efforts to personalise treatment have potential for success, and demonstrates that variability meta-analyses of RCTs need to account for relationships between placebo response and treatment effects.

Broken or Fixed Effects?

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Charles E. Gibbons ◽  
Juan Carlos Suárez Serrato ◽  
Michael B. Urbancic
Keyword(s):  
Empirical Evidence ◽  
Treatment Effect ◽  
Fixed Effects ◽  
Treatment Effects ◽  
Average Treatment Effect ◽  
Significant Treatment Effect ◽  
Significant Treatment ◽  
Heterogeneous Treatment Effects ◽  
Treatment Effect Heterogeneity ◽  
Effect Heterogeneity

Abstract We replicate eight influential papers to provide empirical evidence that, in the presence of heterogeneous treatment effects, OLS with fixed effects (FE) is generally not a consistent estimator of the average treatment effect (ATE). We propose two alternative estimators that recover the ATE in the presence of group-specific heterogeneity. We document that heterogeneous treatment effects are common and the ATE is often statistically and economically different from the FE estimate. In all but one of our replications, there is statistically significant treatment effect heterogeneity and, in six, the ATEs are either economically or statistically different from the FE estimates.

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