This approach may also allow to conduct a placebo-controlled trial in major depression in an open fashion [ 6 , 7 ]. Another type of study that may be useful in avoiding at least some of the problems discussed above is non-inferiority trials which compare a new therapy against standard care. However, in order to demonstrate the non-inferiority or superiority of the new therapy versus the established active comparator, a large sample size may be required.
However, this type of study does not address the clinically relevant question of whether any treatment is more effective than no treatment, for example compared to a watchful waiting strategy as used in clinical practice and recommended in international treatment guidelines.
Another interesting strategy is to conduct double-blind placebo-controlled trials in which the new drug or placebo is given simultaneously on top of standard antidepressant or anti-manic treatment. The feasibility of this approach in terms of enrolling real treatment-seeking severely ill patients some of them in-patients has been demonstrated recently.
In addition, it proved successful in terms of drug-placebo separation at endpoint [ 8 ]. Such an approach would also allow for the inclusion of treatment-resistant patients or patients with suicidal ideation who are typically excluded from placebo-controlled trials but represent a substantial portion of treatment-seeking patients in routine clinical care.
While some of the approaches outlined above may be useful in addressing typical problems of standard double-blind randomized placebo-controlled trials in the treatment of affective disorders, other problems remain unsolved. These are, among others, how to assess the efficacy of watchful waiting often practiced with mild major depressive episodes in routine clinical care as well as how to personalize treatment to patients with affective disorders. In the following, we introduce two statistical procedures that will allow us to address, among others, these questions: marginal structural models and Q -learning.
Furthermore, a treatment regime estimated from data can be used to generate new clinical questions for future investigation, e.
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Recent surveys include Nahum-Shani et al. Extensions of these methods exist to handle missing data [ 13 ], censoring [ 14 , 15 ], multiple outcomes [ 16 ], and continuous treatments e. However, all of these estimators share the same basic structure. Our goal is to describe the statistical thinking that underpins these estimators and to describe the implementation details of one such estimator, Q -learning.
Thus, S1 is generally informed by subject matter knowledge. Implementation of S2 depends on the study design used to generate the available data; special care must be taken with observational studies due to potential confounding [ 12 ]. We describe an implementation of S2 using Q -learning below. The complexity of S3 depends on the class chosen in S1 and the estimator used in S2. As we show below, S3 is trivial when using Q -learning with set to be all possible regimes. However, in other settings, this step can be extremely computationally intensive requiring specialized optimization algorithms or heuristics [ 16 , 21 ].
To define an optimal treatment regime, we use the language of potential outcomes. We have stated these assumptions informally, precise mathematical versions of these assumptions can be found in Zhang et al. Assumptions A1 — A2 are satisfied by design in a randomized clinical trial but are not applicable to an observational study.
Because Q -learning requires only a least squares fit, it can be implemented readily using essentially any existing statistical computing environment. Marginal structural models MSMs [ 22 , 23 , 24 , 25 ] aim at evaluating potential causal effects associated with different fixed treatment regimes that were realized in actual clinical practice by using the framework of potential outcomes and accounting for time-dependent confounding.
When comparing outcomes associated with various observed treatment regimes, confounding may occur because a decision to assign patients to different treatments over time may be driven by patients intermediate outcomes observed during the course of the treatment. Depending on previous experience, they agree that the best option is just to take sleeping medication for a short period, take several days off work, and resume exercising on a regular basis. The second-line treatment, denoted B , would be treatment A plus an antidepressant—which was introduced after failure of treatment A.
Therefore, a direct comparison of observed outcomes associated with different treatment plans would be inappropriate as it may lead to biased estimate of relative efficacy of the two plans. A direct comparison of observed outcomes for plans AABB and BBBB for these two patients or, more generally, for a sample of such patients may be biased in favor of AABB and therefore would mask potential efficacy associated with psychopharmacological treatment B.
Specifically, let us assume that a patient who is current receiving treatment A has a probability 0. A similar treatment assignment mechanism was used in a simulation experiment described in Severus et al. Note that had we known these probabilities we could have utilized them to estimate for each patient the probability of being assigned to the treatment sequence that they actually received during the entire treatment period simply as a product of associated probabilities of realized treatment assignment at every time point.
These probabilities could then be used to adjust the naive head-to-head comparison via inverse probability weighting when comparing the mean outcomes for patients who received different treatment sequences. Therefore, MSM proceeds in two stages. At the first stage, probability of observed treatment regimes is modeled based on various observed intermediate outcomes and patient characteristics as predictor variables.
Note that it is essential to have non-trivial probabilities of assignment to either treatment groups A or B, whether patients are experiencing higher or lower levels of depression severity so that the conditional probabilities of treatment assignment, given patients current severity status are well-defined and bounded away from 0 or 1 , otherwise the weights would be unstable or impossible to estimate.
Such an adverse situation may occur, for example, if physicians are deterministically assigning patients whose severity score at some time point exceeds a pre-defined cutoff to treatment B , leaving no chance of observing patients with the same severity scores but receiving A. This is sometimes called the experimental treatment assumption ETA [ 27 ].
For a more technical treatment of this and other MSM assumptions, see Robins et al. Time-varying weights can be incorporated in analyses where the model is repeated measures or time to event. While traditional double-blind randomized placebo-controlled trials, in which a new approval-seeking drug is compared to placebo, are characterized by high internal validity, this type of study has substantial limitations regarding the extent to which the study results can be transferred into routine clinical care in the treatment of affective disorders.
Therefore, new methodological approaches which may overcome these problems are clearly needed, with marginal structural models and Q-learning representing two of the most promising approaches in this field. However, we will not pursue such technicalities further see Zhao et al. Emanuel Severus reports personal fees and other from Lundbeck, personal fees from Servier, outside the submitted work. Eric Laber and Ilya A. Lipkovich declare that they have no conflict of interest. Human and Animal Rights and Informed Consent. This article does not contain any studies with human or animal subjects performed by any of the authors.
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Laber I. Part of the following topical collections: Topical Collection on Mood Disorders. Introduction Double-blind randomized placebo-controlled trials are generally considered to be the gold standard for evaluating the efficacy of a psychopharmacological intervention in the treatment of affective disorders. The Q -learning algorithm is based on the point-of-view that the best possible clinical care requires treatment recommendations that are tailored to individual patient characteristics. This notion is formalized as a decision rule that maps patient characteristics to a recommended treatment.
Q -learning consists of the following two-steps. Compliance with Ethics Guidelines Conflict of Interest Emanuel Severus reports personal fees and other from Lundbeck, personal fees from Servier, outside the submitted work.
Human and Animal Rights and Informed Consent This article does not contain any studies with human or animal subjects performed by any of the authors. Can phase III trial results of antidepressant medications be generalized to clinical practice? Am J Psychiatry. Antidepressant clinical trials and subject recruitment: just who are symptomatic volunteers?
Does study design influence outcome? The effects of placebo control and treatment duration in antidepressant trials. Psychother Psychosom. Does inclusion of a placebo arm influence response to active antidepressant treatment in randomized controlled trials? Results from pooled and meta-analyses. J Clin Psychiatry.
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Papakostas GI, Fava M. Does the probability of receiving placebo influence clinical trial outcome? A meta-regression of double-blind, randomized clinical trials in MDD. Eur Neuropsychopharmacol. Mirroring everyday clinical practice in clinical trial design: a new concept to improve the external validity of randomized double-blind placebo-controlled trials in the pharmacological treatment of major depression. Neuroimaging has dramatically improved our understanding of the neurobehavioral systems that support moral cognition, emotion and interpersonal decisions.
Evidence on the neural architecture of moral feelings e. He is currently a visiting professor at Stanford University. The institute currently has over employees.
Obsessive compulsive disorder OCD is an anxiety-related mental health condition characterized by obsessions recurrent, unwanted and distressing thoughts, images, or impulses and compulsions repetitive mental or behavioural acts engaged in, in order to decrease the distress associated with the obsessions , and high levels of distress and impairment.
The risk appears greatest during the early postpartum. Women were recruited in pregnancy and were administered a semi-structured diagnostic interview to assess OCD in the third trimester of pregnancy, and at approximately 8-weeks and 5-months postpartum. In pregnancy, 4. In the first 5-months postpartum, The incidence of OCD in the postpartum period was 9.
Findings from this study add important information to our understanding of the prevalence and incidence of OCD in the perinatal period.
travcolnitogg.tk She received her Ph. Her current research projects include a large-scale study of maternal postpartum thoughts of infant-related harm and their relation to postpartum obsessive compulsive disorder ppOCD and harsh parenting, and a study of a new measure of fear of childbirth. She is currently in the planning stages for two randomized controlled trials of online CBT for perinatal anxiety - specific phobia, fear of childbirth, and ppOCD.
In , Prof. Tony Barker brought a new machine he had made from Sheffield to London and demonstrated at Queen Square that one could non-invasively and rather painlessly cause the thumb to move by using brief yet powerful magnetic fields to induce neuronal depolarizations in the motor cortex transcranial magnetic stimulation TMS.
In researchers began exploring whether repeated daily subconvulsive stimulation of the prefrontal cortex for several weeks with TMS might treat depression. This lecture will review some of the more interesting new advances with TMS, focused largely on using it to treat depression. Topics to be covered will include but not be limited to — EEG phase synchronization of pulses, advanced brain imaging studies to determine the best location for stimulation, accelerated protocols, putative biomarkers of response, and when to give up on a patient and declare them a TMS non-responder.
He has continued this interest throughout his career with a focus on using brain imaging and brain stimulation to understand mood regulating circuits and how they go awry in depression and then using this knowledge to devise new brain stimulation treatments. He received his medical degree from the Medical University of South Carolina in Charleston in , where he continued with dual residencies in both neurology and psychiatry. He is board certified in both areas. He then moved to Washington, DC, working with Dr.