Risk of Depression Associated With Finasteride Treatment

BACKGROUND

Finasteride is one of several inhibitors of the 5α-reductase that converts testosterone to dihydrotestosterone used to treat hair loss and benign prostatic enlargement. Emerging clinical observations indicate that such treatment may be associated with depression, anxiety, and possibly increased suicidal risks, in addition to sexual dysfunction, even after its discontinuation.

METHODS

We carried out a systematic review of reports pertaining to association of finasteride treatment with clinical depression or other adverse psychiatric effects. We analyzed reported risks of depression by pooling of rates and by meta-analysis of comparisons of subjects treated with finasteride or not.

FINDINGS

Crude pooled rates of depressive symptoms with versus without finasteride were 3.33% (confidence interval, 3.22%-3.44%) versus 2.54% (2.44%-2.64%); random-effects meta-analysis yielded an odds ratio of 2.14 (1.40-3.27) (both P < 0.0001). In addition, risk of suicidal ideation or behavior was greater with versus without finasteride (21.2% [21.0%-21.5%] vs 14.0% [13.8%-14.2%], P < 0.0001), and risk of sustained sexual dysfunction was high (60.1% [37.3%-82.9%]).

CONCLUSIONS

The findings support a growing impression that finasteride is associated with adverse psychiatric effects that can persist in association with sexual dysfunction after discontinuing finasteride treatment.

Formulating causal questions and principled statistical answers

Although review papers on causal inference methods are now available, there is a lack of introductory overviews on what they can render and on the guiding criteria for choosing one particular method. This tutorial gives an overview in situations where an exposure of interest is set at a chosen baseline ("point exposure") and the target outcome arises at a later time point. We first phrase relevant causal questions and make a case for being specific about the possible exposure levels involved and the populations for which the question is relevant. Using the potential outcomes framework, we describe principled definitions of causal effects and of estimation approaches classified according to whether they invoke the no unmeasured confounding assumption (including outcome regression and propensity score-based methods) or an instrumental variable with added assumptions. We mainly focus on continuous outcomes and causal average treatment effects. We discuss interpretation, challenges, and potential pitfalls and illustrate application using a "simulation learner," that mimics the effect of various breastfeeding interventions on a child's later development. This involves a typical simulation component with generated exposure, covariate, and outcome data inspired by a randomized intervention study. The simulation learner further generates various (linked) exposure types with a set of possible values per observation unit, from which observed as well as potential outcome data are generated. It thus provides true values of several causal effects. R code for data generation and analysis is available on www.ofcaus.org, where SAS and Stata code for analysis is also provided.

Assessing the comparative effectiveness of newly marketed medications: methodological challenges and implications for drug development

Comparative-effectiveness research (CER) aims to produce actionable evidence regarding the effectiveness and safety of medical products and interventions as they are used outside of controlled research settings. Although CER evidence regarding medications is particularly needed shortly after market approval, key methodological challenges include (i) potential bias due to channeling of patients to the newly marketed medication because of various patient-, physician-, and system-related factors; (ii) rapid changes in the characteristics of the user population during the early phase of marketing; and (iii) lack of timely data and the often small number of users in the first few months of marketing. We propose a mix of approaches to generate comparative-effectiveness data in the early marketing period, including sequential cohort monitoring with secondary health-care data and propensity score (PS) balancing, as well as extended follow-up of phase III and phase IV trials, indirect comparisons of placebo-controlled trials, and modeling and simulation of virtual trials.

Strategies for quantifying the relationship between medications and suicidal behaviour: what has been learned?

In recent years there has been considerable concern that certain classes of drugs, for example antidepressants, may increase the risk of suicide. In this current opinion article, we examine the literature on methodological and statistical approaches to the design and analysis of suicidal event studies. Experimental, ecological and observational studies of the relationship between drugs and suicidal events (thoughts, attempts and completion) are discussed. Areas considered include analysis of spontaneous reporting system data, ecological trends in national and/or small area (e.g. county) suicide rates, meta-analyses of randomized clinical trials, and large-scale medical claims data. New statistical and experimental strategies for investigating possible associations between drugs and suicide are highlighted, and we suggest directions for future statistical/methodological research. To put this into context, we then review the most recent literature on the relationship between drugs (antidepressants, antiepileptics, varenicline, montelukast and antipsychotics) and suicidal events. Overall, there appears to be little evidence that drugs increase the risk of suicide and related behaviour. Numerous lines of evidence in adults clearly demonstrate that inadequate treatment of depression (pharmacotherapy and/or psychotherapy) is associated with increased risk of suicidal behaviour. In children, the results are less clear and further study is required to better delineate which children benefit from treatment and who may be at increased risk as a consequence of treatment. From a statistical and methodological perspective, the field of pharmacoepidemiology is a fertile area for statistical research, both in theory and in application. In general, methods have been adopted from other areas such as general epidemiology, despite the singular nature of many of the problems that are unique to drug safety in general, in particular the study of rare events. Finally, there is considerable debate concerning the communication of risk. For suicide, regulatory action has been taken largely on the basis of evidence suggesting increased risk of suicidal thoughts. However, suicidal thoughts are quite common, particularly among patients with depression, and may have little relationship to suicidal behaviour and/or completion.

Post-approval drug safety surveillance

Following the drug-approval process, concerns remain regarding the safety of new drugs that are introduced into the marketplace. In the case of rare adverse events, the number of subjects that are treated in randomized controlled trials is invariably inadequate to determine the safety of the new pharmaceutical. Identifying safety signals for new and/or existing drugs is a major priority in the protection of public health. Unfortunately, design, analysis, and available data are often quite limited for detecting in a timely fashion any potentially harmful effects of drugs. In this review, we examine a variety of approaches for determining the possibility of adverse drug reactions. Our review includes spontaneous reports, meta-analysis of randomized controlled clinical trials, ecological studies, and analysis of medical claims data. We consider both experimental design and analytic problems as well as potential solutions. Many of these methodologies are then illustrated through application to data on the possible relationship between taking antidepressants and increased risk of suicidality.

ERROR-FREE MILESTONES IN ERROR PRONE MEASUREMENTS

A predictor variable or dose that is measured with substantial error may possess an error-free milestone, such that it is known with negligible error whether the value of the variable is to the left or right of the milestone. Such a milestone provides a basis for estimating a linear relationship between the true but unknown value of the error-free predictor and an outcome, because the milestone creates a strong and valid instrumental variable. The inferences are nonparametric and robust, and in the simplest cases, they are exact and distribution free. We also consider multiple milestones for a single predictor and milestones for several predictors whose partial slopes are estimated simultaneously. Examples are drawn from the Wisconsin Longitudinal Study, in which a BA degree acts as a milestone for sixteen years of education, and the binary indicator of military service acts as a milestone for years of service.