CER Scan [Published within the past 30 days]

1. BMC Med Res Methodol. 2011 Apr 25;11(1):57. [Epub ahead of print]Exploratory trials, confirmatory observations: a new reasoning model in the era of patient-centered medicine. Sacristan JA.

BACKGROUND: The prevailing view in therapeutic clinical research today is that observational studies are useful for generating new hypotheses and that controlled experiments (i.e., randomized clinical trials, RCTs) are the most appropriate method for assessing and confirming the efficacy of interventions.

DISCUSSION: The current trend towards patient-centered medicine calls for alternative ways of reasoning, and in particular for a shift towards hypothetico-deductive logic, in which theory is adjusted in light of individual facts. A new model of this kind should change our approach to drug research and development, and regulation. The assessment of new therapeutic agents would be viewed as a continuous process, and regulatory approval would no longer be regarded as the final step in the testing of a hypothesis, but rather, as the hypothesis-generating step. The main role of RCTs in this patient-centered research paradigm would be to generate hypotheses, while observations would serve primarily to test their validity for different types of patients. Under hypothetico-deductive logic, RCTs are considered “exploratory” and observations, “confirmatory”.

SUMMARY: In this era of tailored therapeutics, the answers to therapeutic questions cannot come exclusively from methods that rely on data aggregation, the analysis of similarities, controlled experiments, and a search for the best outcome for the average patient; they must also come from methods based on data disaggregation, analysis of subgroups and individuals, an integration of research and clinical practice, systematic observations, and a search for the best outcome for the individual patient. We must look not only to evidence-based medicine, but also to medicine-based evidence, in seeking the knowledge that we need.

Free Article: http://www.biomedcentral.com/1471-2288/11/57PMID: 21518440
2. JAMA. 2011 Apr 13;305(14):1482-3.Infection prevention and comparative effectiveness research. Perencevich EN, Lautenbach E.Division of General Internal Medicine, University of Iowa, Carver College of Medicine, Iowa City, USA. eli-perencevich@uiowa.eduPMID: 21486981

CER Scan [Epub Ahead of Print]

1. Stat Med. 2011 May 3. doi: 10.1002/sim.4262. [Epub ahead of print]Alternative methods for testing treatment effects on the basis of multiple outcomes: Simulation and case study. Yoon FB, Fitzmaurice GM, Lipsitz SR, Horton NJ, Laird NM, Normand SL. Harvard Medical School, Boston, MA, U.S.A.. yoon@hcp.med.harvard.edu.
In clinical trials multiple outcomes are often used to assess treatment interventions. This paper presents an evaluation of likelihood-based methods for jointly testing treatment effects in clinical trials with multiple continuous outcomes. Specifically, we compare the power of joint tests of treatment effects obtained from joint models for the multiple outcomes with univariate tests based on modeling the outcomes separately. We also consider the power and bias of tests when data are missing, a common feature of many trials, especially in psychiatry. Our results suggest that joint tests capitalize on the correlation of multiple outcomes and are more powerful than standard univariate methods, especially when outcomes are missing completely at random. When outcomes are missing at random, test procedures based on correctly specified joint models are unbiased, while standard univariate procedures are not. Results of a simulation study are reported, and the methods are illustrated in an example from the ClinicalAntipsychotic Trials of Intervention Effectiveness for schizophrenia.

Copyright ©2011 John Wiley & Sons, Ltd.PMID: 21538986  [PubMed – as supplied by publisher]

2. Stat Med. 2011 Apr 15. doi: 10.1002/sim.4241. [Epub ahead of print]Two-stage instrumental variable methods for estimating the causal o dds ratio: Analysis of bias. Cai B, Small DS, TenHave TR.Merck Research Laboratories, UG1D-60, P.O. Box 1000, North Wales, PA 19454-1099, U.S.A.; Department of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine, Blockley Hall, 6th FLR 423 Guardian Dr., Philadelphia, PA 19104-6021, U.S.A.. bcai@mail.med.upenn.edu.
We present closed-form expressions of asymptotic bias for the causal odds ratio from two estimation approaches of instrumental variable logistic regression: (i) the two-stage predictor substitution (2SPS) method and (ii) the two-stage residual inclusion (2SRI) approach. Under the 2SPS approach, the first stage model yields the predicted value of treatment as a function of an instrument and covariates, and in the second stage model for the outcome, this predicted value replaces the observed value of treatment as a covariate. Under the 2SRI approach, the first stage is the same, but the residual term of the first stage regression is included in the second stage regression, retaining the observed treatment as a covariate. Our bias assessment is for a different context from that of Terza (J. Health Econ. 2008; 27(3):531-543), who focused on the causal odds ratio conditional on the unmeasured confounder, whereas we focus on the causal odds ratio among compliers under the principal stratification framework. Our closed-form bias results show that the 2SPS logistic regression generates asymptotically biased estimates of this causal odds ratio when there is no unmeasured confounding and that this bias increases with increasing unmeasured confounding. The 2SRI logistic regression is asymptotically unbiased when there is no unmeasured confounding, but when there is unmeasured confounding, there is bias and it increases with increasing unmeasured confounding. The closed-form bias results provide guidance for using these IV logistic regression methods. Our simulation results are consistent with our closed-form analytic results under different combinations of parameter settings.

Copyright © 2011 John Wiley & Sons, Ltd.Copyright © 2011 John Wiley & Sons, Ltd.PMID: 21495062

3. Stat Med. 2011 Apr 15. doi: 10.1002/sim.4240. [Epub ahead of print]Semiparametric regression models for detecting effect mo dification in matched case-crossover studies. Kim I, Cheong HK, Kim H. Department of Statistics, Virginia Polytechnic Institute and State University, Blacksburg, VA, U.S.A.

In matched case-crossover studies, it is generally accepted that covariates on which a case and associated controls are matched cannot exert a confounding effect on independent predictors included in the conditional logistic regression model because any stratum effect is removed by the conditioning on the fixed number of sets of a case and controls in the stratum. Hence, the conditional logistic regression model is not able to detect any effects associated with the matching covariates by stratum. In addition, the matching covariates may be effect modification and the methods for assessing and characterizing effect modification by matching covariates are quite limited. In this article, we propose a unified approach in its ability to detect both parametric and nonparametric relationships between the predictor and the relative risk of disease or binary outcome, as well as potential effect modifications by matching covariates. Two methods are developed using two semiparametric models: (1) the regression spline varying coefficients model and (2) the regression spline interaction model. Simulation results show that the two approaches are comparable. These methods can be used in any matched case-control study and extend to multilevel effect modification studies. We demonstrate the advantage of our approach using an epidemiological example of a 1-4 bi-directional case-crossover study of childhood aseptic meningitis associated with drinking water turbidity.

Copyright © 2011 John Wiley & Sons, Ltd.PMID: 21495061

4. Pharmacoepidemiol Drug Saf. 2011 Apr 29. doi: 10.1002/pds.2133. [Epub ahead ofprint]Near real-time vaccine safety surveillance with partially accrued data. Greene SK, Kulldorff M, Yin R, Yih WK, Lieu TA, Weintraub ES, Lee GM. Department of Population Medicine, Harvard Medical School and Harvard PilgrimHealth Care Institute, Boston, MA, USA. Sharon_Greene@harvardpilgrim.org.

PURPOSE: The Vaccine Safety Datalink (VSD) Project conducts near real-time vaccine safety surveillance using sequential analytic methods. Timely surveillance is critical in identifying potential safety problems and preventing additional exposure before most vaccines are administered. For vaccines that are administered during a short period, such as influenza vaccines, timeliness can be improved by undertaking analyses while risk windows following vaccination are ongoing and by accommodating predictable and unpredictable data accrual delays. We describe practical solutions to these challenges, which were adopted by theVSD Project during pandemic and seasonal influenza vaccine safety surveillance in 2009/2010.

METHODS: Adjustments were made to two sequential analytic approaches. The Poisson-based approach compared the number of pre-defined adverse events observed following vaccination with the number expected using historical data.The expected number was adjusted for the proportion of the risk window elapsed and the proportion of inpatient data estimated to have accrued. The binomial-based approach used a self-controlled design, comparing the observed numbers of events in risk versus comparison windows. Events were included in analysis only if they occurred during a week that had already passed for both windows.

RESULTS: Analyzing data before risk windows fully elapsed improved the timeliness of safety surveillance. Adjustments for data accrual lags were tailored to each data source and avoided biasing analyses away from detecting a potential safety problem, particularly early during surveillance.

CONCLUSIONS: The timeliness of vaccine and drug safety surveillance can be improved by properly accounting for partially elapsed windows and data accrual delays.

Copyright © 2011 John Wiley & Sons, Ltd.PMID: 21538670  [PubMed – as supplied by publisher]

5. Epidemiology. 2011 Apr 11. [Epub ahead of print]Validation Data-based Adjustments for Outcome Misclassification in Logistic Regression: An Illustration. Lyles RH, Tang L, Superak HM, King CC, Celentano DD, Lo Y, Sobel JD.

From the Department of Biostatistics and Bioinformatics, The Rollins School of Public Health of Emory University, Atlanta, GA; Centers for Disease Control and Prevention, Atlanta, GA; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; Montefiore Medical Center and Albert Einstein College of Medicine, Bronx, NY; and Department of Medicine, Wayne State University School of Medicine, Detroit, MI.
Misclassification of binary outcome variables is a known source of potentially serious bias when estimating adjusted odds ratios. Although researchers have described frequentist and Bayesian methods for dealing with the problem, these methods have seldom fully bridged the gap between statistical research and epidemiologic practice. In particular, there have been few real-world applications of readily grasped and computationally accessible methods that make direct use of internal validation data to adjust for differential outcome misclassification in logistic regression. In this paper, we illustrate likelihood-based methods for this purpose that can be implemented using standard statistical software. Using main study and internal validation data from the HIV Epidemiology Research Study, we demonstrate how misclassification rates can depend on the values of subject-specific covariates, and we illustrate the importance of accounting for this dependence. Simulation studies confirm the effectiveness of the maximum likelihood approach. We emphasize clear exposition of the likelihood function itself, to permit the reader to easily assimilate appended computer code that facilitates sensitivity analyses as well as the efficient handling of main/external and main/internal validation-study data. These methods are readily applicable under random cross-sectional sampling, and we discuss the extent to which the main/internal analysis remains appropriate under outcome-dependent (case-control) sampling. PMID: 21487295  [PubMed – as supplied by publisher]

Theme: The Changing Face of Epidemiology

1. Epidemiology. 2011 May;22(3):295-7.Making observational studies count: shaping the future of comparative effectiveness research.Dreyer NA. Outcome Sciences Inc., Cambridge, MA.PMID: 21464648

2. Epidemiology. 2011 May;22(3):290-1.With great data comes great responsibility: publishing comparative effectiveness research in epidemiology. Hernán MA. From Harvard School of Public Heath, Boston, MA.PMID: 21464646  [PubMed – in process]

3. Epidemiology. 2011 May;22(3):302-4.Improving automated database studies. Ray WA. From the The Division of pharmacoepidemiology, Department of Preventive Medicine, Vanderbilt University School of Medicine, Nashville, TN; and bGeriatric Research, Education and Clinical Center, Nashville Veterans Administration Medical Center, Nashville, TN.PMID: 21464650  [PubMed – in process]

4. Epidemiology. 2011 May;22(3):298-301.Nonexperimental comparative effectiveness research using linked healthcare databases. Stürmer T, Jonsson Funk M, Poole C, Brookhart MA. From the Pharmacoepidemiology Program, Department of Epidemiology, UNC Gillings School of Global Public Health University of North Carolina at Chapel Hill, Chapel Hill, NC.PMID: 21464649  [PubMed – in process]

5. Epidemiology. 2011 May;22(3):292-4.The new world of data linkages in clinical epidemiology: are we being brave or foolhardy? Weiss NS. From the Department of Epidemiology, University of Washington, and the Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA. PMID: 21464647  [PubMed – in process]

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