Predicting Potential Placebo Effect in Drug Treated Subjects

Petkova, Eva; Tarpey, Thaddeus; Govindarajulu, Usha

doi:10.2202/1557-4679.1152

Cited by 5 publications

(6 citation statements)

References 14 publications

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“…The fifth category (E) ('subjects who have an initial improvement due to nonspecific effects and then experience a drug effect') represents a more fine-grained distinction than used here. More recently, [29] used infinite mixtures in an attempt at isolating drug effects in the presence of placebo effects.…”

Section: Joint Gmm Analysis Of Drug and Placebo Groupsmentioning

confidence: 99%

Estimating drug effects in the presence of placebo response: Causal inference using growth mixture modeling

Muthén

Brown

2009

Statistics in Medicine

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Placebo-controlled randomized trials for antidepressants and other drugs often show a response for a sizeable percentage of the subjects in the placebo group. Potential placebo responders can be assumed to exist also in the drug treatment group, making it difficult to assess the drug effect. A key drug research focus should be to estimate the percentage of individuals among those who responded to the drug who would not have responded to the placebo (”Drug Only Responders”). This paper investigates a finite mixture model approach to uncover percentages of up to four potential mixture components: Never Responders, Drug Only Responders, Placebo Only Responders, and Always responders. Two examples are used to illustrate the modeling, a twelve-week antidepressant trial with a continuous outcome (Hamilton D score) and a 7-week schizophrenia trial with a binary outcome (illness level). The approach is formulated in casual modeling terms using potential outcomes and principal stratification. Growth mixture modeling (GMM) with maximum-likelihood estimation is used to uncover the different mixture components. The results point to the limitations of the conventional approach of comparing marginal response rates for drug and placebo groups. It is useful to augment such reporting with the GMM estimated prevalences for the four classes of subjects and the Drug Only Responder drug effect estimate.

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Section: Joint Gmm Analysis Of Drug and Placebo Groupsmentioning

confidence: 99%

Estimating drug effects in the presence of placebo response: Causal inference using growth mixture modeling

Muthén

Brown

2009

Statistics in Medicine

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“…Petkova et al. 72 provide a statistical framework for predicting potential placebo responses in drug-treated participants for classical RCTs. In this paper, mixed effects models were applied to the active treatment and placebo groups separately.…”

Section: Quantitative Analysis Methods For Addressing Placebo Effectsmentioning

confidence: 99%

“…One group of researchers has developed several methods for analyzing longitudinal depression data with the purpose to effectively subtract out any placebo responses from responses observed for drug-treated participants. [71][72][73][74] In these papers, depression symptom severity measured at evenly spaced time intervals (including baseline measures) was modeled by fitting a mixed-random effects model with a polynomial quadratic curve for each participant. The linear component of these functions describes the overall trend in outcomes while the quadratic term describes upwards or downwards concavity.…”

Section: Deriving the Placebo Response In Drug-treated Participantsmentioning

confidence: 99%

Methods for assessing and controlling placebo effects

Kessels

Mozer

Bloemers

2017

Stat Methods Med Res

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The placebo serves as an indispensable control in many randomized trials. When analyzing the benefit of a new treatment, researchers are often confronted with large placebo effects that diminish the treatment effect. Various alternative methods have been proposed for analyzing placebo and treatment effects in studies where large placebo effects are expected or have already occurred. This paper presents an overview of methodological work that has been proposed for assessing and/or controlling for placebo effects in randomized trials. Throughout this paper, two main approaches are discussed. The first approach considers designs that represent alternatives to the classical placebo-controlled randomized trial design. Separately, the second approach considers adopting new methods for the statistical analysis of placebo and treatment effects to be implemented after the data have been collected using a classical randomized trial design.

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“…Much research has focused on the identification of placebo responders and discovery of patients’ characteristics that could be related to placebo response (e.g., Joyce and Paykel 1989; Tarpey, Petkova and Ogden 2003; Elliott et al 2005; Muthén and Brown 2009; Petkova, Tarpey and Govindarajulu 2009; Tarpey and Petkova 2010). However, the typically measured clinical phenotypes, such as symptom severity and treatment history, have shown low predictive power (Leuchter et al 2002; Phillips et al 2015).…”

Section: Introductionmentioning

confidence: 99%

Latent class modeling using matrix covariates with application to identifying early placebo responders based on EEG signals

et al. 2017

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Latent class models are widely used to identify unobserved subgroups (i.e., latent classes) based upon one or more manifest variables. The probability of belonging to each subgroup is typically modeled as a function of a set of measured covariates. In this paper, we extend existing latent class models to incorporate matrix covariates. This research is motivated by a randomized placebo-controlled depression clinical trial. One study goal is to identify a subgroup of subjects who experience symptoms improvement early on during antidepressant treatment, which is considered to be an indication of a placebo rather than a true pharmacological response. We want to relate the likelihood of belonging to this subgroup of early responders to baseline electroencephalography (EEG) measurement that takes the form of a matrix. The proposed method is built upon a low rank Candecomp/Parafac (CP) decomposition of the target coefficient matrix through low-dimensional latent variables, which effectively reduces the model dimensionality. We adopt a Bayesian hierarchical modeling approach to estimate the latent variables, which allows a flexible way to incorporate prior knowledge about covariate effect heterogeneity and offers a data-driven method of regularization. Simulation studies suggest that the proposed method is robust against potentially misspecified rank in the CP decomposition. With the motivating example we show how the proposed method can be applied to extract valuable information from baseline EEG measurements that explains the likelihood of belonging to the early responder subgroup, helping to identify placebo responders and suggesting new targets for the study of placebo response.

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Predicting Potential Placebo Effect in Drug Treated Subjects

Cited by 5 publications

References 14 publications

Estimating drug effects in the presence of placebo response: Causal inference using growth mixture modeling

Estimating drug effects in the presence of placebo response: Causal inference using growth mixture modeling

Methods for assessing and controlling placebo effects

Latent class modeling using matrix covariates with application to identifying early placebo responders based on EEG signals

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