Accounting for heterogeneous treatment effects in the FDA approval process.

The FDA employs an average-patient standard when reviewing drugs: it approves a drug only if is safe and effective for the average patient in a clinical trial. It is common, however, for patients to respond differently to a drug. Therefore, the average-patient standard can reject a drug that benefits certain patient subgroups (false negatives) and even approve a drug that harms other patient subgroups (false positives).

Influenza vaccination and mortality: differentiating vaccine effects from bias.

It is widely believed that influenza (flu) vaccination of the elderly reduces all-cause mortality, yet randomized trials for assessing vaccine effectiveness are not feasible and the observational research has been controversial. Efforts to differentiate vaccine effectiveness from selection bias have been problematic. The authors examined mortality before, during, and after 9 flu seasons in relation to time-varying vaccination status in an elderly California population in which 115,823 deaths occurred from 1996 to 2005, including 20,484 deaths during laboratory-defined flu seasons.

Leisure-time physical activity and all-cause mortality in an elderly cohort.

Background: Physical activity is one of the mainstays of secondary prevention in people with heart disease. It is not well understood, however, how the presence of heart disease or a history of habitual exercise prior to the study modify any mortality-sparing effects of leisure-time physical activity.

Methods: We analyzed data from a well-described cohort of subjects aged 54 years and older at intake (median age, 70 years) from Sonoma, CA, studied between 1993 and 2001 with mortality follow-up until 2003.

Delayed internal fixation of femoral shaft fracture reduces mortality among patients with multisystem trauma.

Background: Fractures of the femoral shaft are common and have potentially serious consequences in patients with multiple injuries. The appropriate timing of fracture repair is controversial. The purpose of the present study was to assess the effect of timing of internal fixation on mortality in patients with multisystem trauma.

Biomarker discovery using targeted maximum-likelihood estimation: application to the treatment of antiretroviral-resistant HIV infection.

Researchers in clinical science and bioinformatics frequently aim to learn which of a set of candidate biomarkers is important in determining a given outcome, and to rank the contributions of the candidates accordingly. This article introduces a new approach to research questions of this type, based on targeted maximum-likelihood estimation of variable importance measures.The methodology is illustrated using an example drawn from the treatment of HIV infection.

Analyzing sequentially randomized trials based on causal effect models for realistic individualized treatment rules.

In this paper, we argue that causal effect models for realistic individualized treatment rules represent an attractive tool for analyzing sequentially randomized trials. Unlike a number of methods proposed previously, this approach does not rely on the assumption that intermediate outcomes are discrete or that models for the distributions of these intermediate outcomes given the observed past are correctly specified. In addition, it generalizes the methodology for performing pairwise comparisons between individualized treatment rules by allowing the user to posit a marginal structural model for all candidate treatment rules simultaneously.

Statistical methods for analyzing sequentially randomized trials.

In this issue of the Journal, Thall et al. present the results of a clinical trial that makes use of sequential randomization, a novel trial design that allows the investigator to study adaptive treatment strategies. Our aim is to complement this groundbreaking work by reviewing the current state of the art of statistical methods available for such analyses.

Supervised detection of conserved motifs in DNA sequences with cosmo.

A number of computational methods have been proposed for identifying transcription factor binding sites from a set of unaligned sequences that are thought to share the motif in question. We here introduce an algorithm, called cosmo, that allows this search to be supervised by specifying a set of constraints that the position weight matrix of the unknown motif must satisfy. Such constraints may be formulated, for example, on the basis of prior knowledge about the structure of the transcription factor in question.

A practical illustration of the importance of realistic individualized treatment rules in causal inference.

The effect of vigorous physical activity on mortality in the elderly is difficult to estimate using conventional approaches to causal inference that define this effect by comparing the mortality risks corresponding to hypothetical scenarios in which all subjects in the target population engage in a given level of vigorous physical activity. A causal effect defined on the basis of such a static treatment intervention can only be identified from observed data if all subjects in the target population have a positive probability of selecting each of the candidate treatment options, an assumption that is highly unrealistic in this case since subjects with serious health problems will not be able to engage in higher levels of vigorous physical activity. This problem can be addressed by focusing instead on causal effects that are defined on the basis of realistic individualized treatment rules and intention-to-treat rules that explicitly take into account the set of treatment options that are available to each subject.