The DEcIDE Methods Center publishes a monthly literature scan of current articles of interest to the field of comparative effectiveness research.
You can find them all here.
CER Scan [Published within the past 30 days]
1. Clin Trials. 2011 May 24. [Epub ahead of print]
Confounding due to changing background risk in adaptively randomized trials. Lipsky AM, Greenland S. Gertner Institute for Epidemiology and Health Policy Research, Chaim Sheba Medical Center, Tel Hashomer, Israel.
BACKGROUND: While adaptive trials tend to improve efficiency, they are also subject to some unique biases. PURPOSE: We address a bias that arises from adaptive randomization in the setting of a time trend in disease incidence.
METHODS: We use a potential-outcome model and directed acyclic graphs to illustrate the bias that arises from a changing subject allocation ratio with a concurrent change in background risk.
RESULTS: In a trial that uses adaptive randomization, time trends in risk can bias the crude effect estimate obtained by naively combining the data from the different stages of the trial. We illustrate how the bias arises from an interplay of departures from exchangeability among groups and the changing randomization proportions.
LIMITATIONS: We focus on risk-ratio and risk-difference analysis.
CONCLUSIONS: Analysis of trials using adaptive randomization should involve attention to or adjustment for possible trends in background risk. Numerous modeling strategies are available for that purpose, including stratification, trend modeling, inverse-probability-of-treatment weighting, and hierarchical regression.
PMID: 21610005 [PubMed – as supplied by publisher]
2. Pharmacoepidemiol Drug Saf. 2011 May 30. doi: 10.1002/pds.2121. [Epub ahead of print] Simultaneously assessing intended and unintended treatment effects of multiple treatment options: a pragmatic “matrix design.” Rassen JA, Solomon DH, Glynn RJ, Schneeweiss S. Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA. email@example.com.
PURPOSE: A key aspect of comparative effectiveness research is the assessment of competing treatment options and multiple outcomes rather than a single treatment option and a single benefit or harm. In this commentary, we describe a methodological framework that supports the simultaneous examination of a “matrix” of treatments and outcomes in non-randomized data.
METHODS: We outline the methodological challenges to a matrix-type study (matrix design). We consider propensity score matching with multiple treatment groups, statistical analysis, and choice of association measure when evaluating multiple outcomes. We also discuss multiple testing, use of high-dimensional propensity scores for covariate balancing in light of multiple outcomes, and suitability of available software.
CONCLUSION: The matrix design study methods facilitate examination of the comparative benefits and harms of competing treatment choices, and also provides the input required for calculating the numbers needed to treat and for a broader benefit/harm assessment that weighs endpoints of varying severity. Copyright © 2011 John Wiley & Sons, Ltd.
PMID: 21626604 [PubMed – as supplied by publisher]
3. Stat Med. 2011 May 20;30(11):1199-217. doi: 10.1002/sim.4156. Epub 2010 Dec 29.
Exploring the benefits of adaptive sequential designs in time-to-event endpoint settings. Emerson SC, Rudser KD, Emerson SS. Department of Statistics, Oregon State University, U.S.A.. firstname.lastname@example.org.
Sequential analysis is frequently employed to address ethical and financial issues in clinical trials. Sequential analysis may be performed using standard group sequential designs, or, more recently, with adaptive designs that use estimates of treatment effect to modify the maximal statistical information to be collected. In the general setting in which statistical information and clinical trial costs are functions of the number of subjects used, it has yet to be established whether there is any major efficiency advantage to adaptive designs over traditional group sequential designs. In survival analysis, however, statistical information (and hence efficiency) is most closely related to the observed number of events, while trial costs still depend on the number of patients accrued. As the number of subjects may dominate the cost of a trial, an adaptive design that specifies a reduced maximal possible sample size when an extreme treatment effect has been observed may allow early termination of accrual and therefore a more cost-efficient trial. We investigate and compare the tradeoffs between efficiency (as measured by average number of observed events required), power, and cost (a function of the number of subjects accrued and length of observation) for standard group sequential methods and an adaptive design that allows for early termination of accrual. We find that when certain trial design parameters are constrained, an adaptive approach to terminating subject accrual may improve upon the cost efficiency of a group sequential clinical trial investigating time-to-event endpoints. However, when the spectrum of group sequential designs considered is broadened, the advantage of the adaptive designs is less clear. Copyright © 2010 John Wiley & Sons, Ltd.
PMID: 21538450 [PubMed – in process]
4. Stat Methods Med Res. 2011 Jun;20(3):191-215. Epub 2008 Nov 26.
Estimating dose-response effects in psychological treatment trials: the role of instrumental variables. Maracy M, Dunn G. Biostatistics, Health Methodology Research Group, School of Community Based Medicine, University of Manchester, UK.
We present a relatively non-technical and practically orientated review of statistical methods that can be used to estimate dose-response relationships in randomised controlled psychotherapy trials in which participants fail to attend all of the planned sessions of therapy. Here we are investigating the effects on treatment outcome of the number of sessions attended when the latter is possibly subject to hidden selection effects (hidden confounding). The aim is to estimate the parameters of a structural mean model (SMM) using randomisation, and possibly randomisation by covariate interactions, as instrumental variables. We describe, compare and illustrate the equivalence of the use of a simple G-estimation algorithm and two two-stage least squares procedures that are traditionally used in economics.
PMID: 19036909 [PubMed – in process]