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 [Epub ahead of print]

  1. Contemp Clin Trials. 2012 Apr 20. [Epub ahead of print]
  2. A pilot ‘cohort multiple randomised controlled trial’ of treatment by a homeopath for women with menopausal hot flushes. Relton C, O’Cathain A, Nicholl J.

    INTRODUCTION: In order to address the limitations of the standard pragmatic RCT design, the innovative ‘cohort multiple RCT’ design was developed. The design was first piloted by addressing a clinical question ” What is the clinical and cost effectiveness of treatment by a homeopath for women with menopausal hot flushes?”. METHODS: A cohort with the condition of interest (hot flushes) was recruited through an observational study of women’s midlife health and consented  to provide observational data and have their data used comparatively. The ‘Hot Flush’ Cohort were then screened in order to identify patients eligible for a trial of the offer of treatment by a homeopath (Eligible Trial Group). A proportion of the Eligible Trial Group was then randomly selected to the Offer Group and offered treatment. A “patient centred” approach to information and consent was adopted. Patients were not (i) told about treatments that they would not be offered, and trial intervention information was only given to the Offer Group after random selection. Patients were not (ii) given prior information that their treatment would be decided by chance. RESULTS: The ‘cohort multiple RCT’ design was acceptable to the NHS Research Ethics Committee. The majority of patients completed multiple questionnaires. Acceptance of the offer was high (17/24). DISCUSSION: This pilot identified the feasibility of an innovative design in practice. Further research is required to test the concept of undertaking multiple trials within a cohort of patients and to assess the acceptability of the “patient centred” approach to information and consent. Copyright © 2012 Elsevier Inc. All rights reserved.

    PMID: 22551742  [PubMed – as supplied by publisher]

    LINK: http://www.sciencedirect.com/science/article/pii/S1551714412000973

  3. Am J Epidemiol. 2012 Apr 17. [Epub ahead of print]
  4. Comparison of Instrumental Variable Analysis Using a New Instrument With Risk Adjustment Methods to Reduce Confounding by Indication. Fang G, Brooks JM, Chrischilles EA.

    Confounding by indication is a vexing problem, especially in evaluating treatment effects using observational data, since treatment decisions are often related to disease severity, prognosis, and frailty. To compare the ability of the instrumental variable (IV) approach with a new instrument based on the local-area practice style and risk adjustment methods, including conventional multivariate regression and propensity score adjustment, to reduce confounding by indication, the authors investigated the effects of long-term control (LTC) therapy on the occurrence of acute asthma exacerbation events among children and young adults with incident and uncontrolled persistent asthma, using Iowa Medicaid claims files from 1997-1999. Established evidence from clinical trials has demonstrated the protective benefits of LTC therapy for persistent asthma. Among patients identified (n = 4,275), those with higher asthma severity at baseline were more likely to receive LTC therapy. The multivariate regression and propensity score adjustment methods suggested that LTC therapy had no effect on the occurrence of acute exacerbation events. Estimates from the new IV approach showed that LTC therapy significantly decreased the occurrence of acute exacerbation events, which is consistent with established clinical evidence. The authors discuss how to interpret estimates from the risk adjustment and IV methods when the treatment effect is heterogeneous.

    PMID: 22510277  [PubMed – as supplied by publisher]

    LINK: http://aje.oxfordjournals.org/cgi/pmidlookup?view=long&pmid=22510277

  5. Epidemiology. 2012 Apr 10. [Epub ahead of print]
  6. Estimating the Effects of Multiple Time-varying Exposures Using Joint Marginal Structural Models: Alcohol Consumption, Injection Drug Use, and HIV Acquisition. Howe CJ, Cole SR, Mehta SH, Kirk GD. Department of Epidemiology, Center for Population Health and Clinical Epidemiology, Brown University Program in Public Health, Providence, RI; Department of Epidemiology, University of North Carolina Gillings School of Global Public Health, Chapel Hill, NC; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; and Division of Infectious Diseases, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD.

    The joint effects of multiple exposures on an outcome are frequently of interest in epidemiologic research. In 2001, Hernán et al (J Am Stat Assoc. 2001;96:440-448) presented methods for estimating the joint effects of multiple time-varying exposures subject to time-varying confounding affected by prior exposure using joint marginal structural models. Nonetheless, the use of these joint models is rare in the applied literature. Minimal uptake of these joint models, in contrast to the now widely used standard marginal structural model, is due in part to a lack of examples demonstrating the method. In this paper, we review the assumptions necessary for unbiased estimation of joint effects as well as the distinction between interaction and effect measure modification. We demonstrate the use of marginal structural models for estimating the joint effects of alcohol consumption and injection drug use on HIV acquisition, using data from 1525 injection drug users in the AIDS Link to Intravenous Experience cohort study. In the joint model, the hazard ratio (HR) for heavy drinking in the absence of any drug injections was 1.58 (95% confidence interval = 0.67-3.73). The HR for any drug injections in the absence of heavy drinking was 1.78 (1.10-2.89). The HR for heavy drinking and any drug injections was 2.45 (1.45-4.12). The P values for multiplicative and additive interaction were 0.7620 and 0.9200, respectively, indicating a lack of departure from effects that multiply or add. We could not rule out interaction on either scale due to imprecision.

    PMID: 22495473  [PubMed – as supplied by publisher]

    LINK: http://journals.lww.com/epidem/Abstract/publishahead/Estimating_the_Effects_of_Multiple_Time_varying.99490.aspx

  7. Stat Methods Med Res. 2012 Apr 4. [Epub ahead of print]
  8. Sample size and power calculations for medical studies by simulation when closed form expressions are not available. Landau S, Stahl D. King’s College London, Institute of Psychiatry, Department of Biostatistics, London, UK.

    This paper shows how Monte Carlo simulation can be used for sample size, power or precision calculations when planning medical research studies. Standard study designs can lead to the use of analysis methods for which power formulae do not exist. This may be because complex modelling techniques with optimal statistical properties are used but power formulae have not yet been derived or because analysis models are employed that divert from the population model due to lack of availability of more appropriate analysis tools. Our presentation concentrates on the conceptual steps involved in carrying out power or precision calculations by simulation. We demonstrate these steps in three examples concerned with (i) drop out in longitudinal studies, (ii) measurement error in observational studies and (iii) causal effect estimation in randomised controlled trials with non-compliance. We conclude that the Monte Carlo simulation approach is an important general tool in the methodological arsenal for assessing power and precision.

    PMID: 22491174  [PubMed – as supplied by publisher]

    LINK: http://smm.sagepub.com/content/early/2012/04/04/0962280212439578.long

CER Scan [published within the last 30 days]

  1. BMC Med Res Methodol. 2012 Apr 10;12(1):46. [Epub ahead of print]
  2. Multiple imputation of missing covariates with non-linear effects and interactions: an evaluation of statistical methods. Seaman SR, Bartlett JW, White IR.

    BACKGROUND: Multiple imputation is often used for missing data. When a  model contains as covariates more than one function of a variable, it is not obvious how best to impute missing values in these covariates. Consider a regression with outcome Y and covariates X and X^2. In ‘passive imputation’ a value X* is imputed for X and then X^2 is imputed as (X*)^2. A recent proposal is to treat X^2 as ‘just another variable’ (JAV) and impute X and X^2 under multivariate normality. METHODS: We use simulation to investigate the performance of three methods that can easily be implemented in standard software: 1) linear regression of X on Y to impute X then passive imputation of X^2; 2) the same regression but with predictive mean matching (PMM); and 3) JAV. We also investigate the performance of analogous methods when the analysis involves an interaction, and study the theoretical properties of JAV. The application of the methods when complete or incomplete confounders are also present is illustrated using data from the EPIC Study. RESULTS: JAV gives consistent estimation when the analysis is linear regression with a quadratic or interaction term and X is missing completely at random. When X is missing at random, JAV may be biased, but this bias is generally less than for passive imputation and PMM. Coverage for JAV was usually good when bias was small. However, in some scenarios with a more pronounced quadratic effect, bias was large and coverage poor. When the analysis was logistic regression, JAV’s performance was sometimes very poor. PMM generally improved on passive imputation, in terms of bias and coverage, but did not eliminate the bias. CONCLUSIONS: Given the current state of available software, JAV is the best of a set of imperfect imputation methods for linear regression with a quadratic or interaction effect, but should not be used for logistic regression.

    PMID: 22489953  [PubMed – as supplied by publisher]

    Available Open-Access: http://www.biomedcentral.com/content/pdf/1471-2288-12-46.pdf

  3. Value Health. 2012 Mar-Apr;15(2):217-30.
  4. Prospective observational studies to assess comparative effectiveness: the ISPOR  good research practices task force report. Berger ML, Dreyer N, Anderson F, Towse A, Sedrakyan A, Normand SL.  OptumInsight, Life Sciences, New York, NY 10026, USA. Marc.Berger@Optum.com

    OBJECTIVE: In both the United States and Europe there has been an increased interest in using comparative effectiveness research of interventions to inform health policy decisions. Prospective observational studies will undoubtedly be conducted with increased frequency to assess the comparative effectiveness of different treatments, including as a tool for “coverage with evidence development,” “risk-sharing contracting,” or key element in a “learning health-care system.” The principle alternatives for comparative effectiveness research include retrospective observational studies, prospective observational studies, randomized clinical trials, and naturalistic (“pragmatic”) randomized clinical trials.

    METHODS: This report details the recommendations of a Good Research Practice Task Force on Prospective Observational Studies for comparative effectiveness research. Key issues discussed include how to decide when to do a prospective observational study in light of its advantages and disadvantages with respect to alternatives, and the report summarizes the challenges and approaches to the appropriate design, analysis, and execution of prospective observational studies to make them most valuable and relevant to health-care decision makers.

    RECOMMENDATIONS: The task force emphasizes the need for precision and clarity in specifying the key policy questions to be addressed and that studies should be designed with a goal of drawing causal inferences whenever possible. If a study is being performed to support a policy decision, then it should be designed as hypothesis testing-this requires drafting a protocol as if subjects were to be randomized and that investigators clearly state the purpose or main hypotheses, define the treatment groups and outcomes, identify all measured and unmeasured confounders, and specify the primary analyses and required sample size. Separate from analytic and statistical approaches, study design choices may strengthen the ability to address potential biases and confounding in prospective observational studies. The use of inception cohorts, new user designs, multiple comparator groups, matching designs, and assessment of outcomes thought not to be impacted by the therapies being compared are several strategies that should be given strong consideration recognizing that there may be feasibility constraints. The reasoning behind all study design and analytic choices should be transparent and explained in study protocol. Execution of prospective observational studies is as important as their design and analysis in ensuring that results are valuable and relevant, especially capturing the target population of interest, having reasonably complete and nondifferential follow-up. Similar to the concept of the importance of declaring a prespecified hypothesis, we believe that the credibility of many prospective observational studies would be enhanced by their registration on appropriate publicly accessible sites (e.g., clinicaltrials.gov and encepp.eu) in advance of their execution. Copyright © 2012 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

    PMID: 22433752  [PubMed – in process]

    LINK: http://www.valueinhealthjournal.com/article/S1098-3015(12)00007-1/abstract

  5. Pharmacoepidemiol Drug Saf. 2012 May 2. doi: 10.1002/pds.3284. [Epub ahead of print]
  6. Algorithms to estimate the beginning of pregnancy in administrative databases. Margulis AV, Setoguchi S, Mittleman MA, Glynn RJ, Dormuth CR, Hernández-Díaz S. Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA; Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women’s Hospital, Boston, MA, USA. andreamargulis@post.harvard.edu.

    PURPOSE: The role of administrative databases for research on drug safety during pregnancy can be limited by their inaccurate assessment of the timing of exposure, as the gestational age at birth is typically unavailable. Therefore, we sought to develop and validate algorithms to estimate the gestational age at birth using information available in these databases. METHODS: Using a population-based cohort of 286,432 mother-child pairs in British Columbia (1998-2007), we validated an ICD-9/10-based preterm-status indicator and developed algorithms to estimate the gestational age at birth on the basis of this indicator, maternal age, singleton/multiple status, and claims for routine prenatal care tests. We assessed the accuracy of the algorithm-based estimates relative to the gold standard of the clinical gestational age at birth recorded in the delivery discharge record. RESULTS: The preterm-status indicator had specificity and sensitivity of 98% and 91%, respectively. Estimates from an algorithm that assigned 35 weeks of gestational age at birth to deliveries with the preterm-status indicator and 39 weeks to those without them were within 2 weeks of the clinical gestational age at birth in 75% of preterm and 99% of term deliveries. CONCLUSIONS: Subtracting 35 weeks (245 days) from the date of birth in deliveries with codes for preterm birth and 39 weeks (273 days) in those without them provided the optimal estimate of the beginning of pregnancy among the algorithms studied. Copyright © 2012 John Wiley & Sons, Ltd.

    PMID: 22550030  [PubMed – as supplied by publisher]

    LINK: http://dx.doi.org/10.1002/pds.3284

  7. Cancer. 2012 Apr 19. doi: 10.1002/cncr.27552. [Epub ahead of print]
  8. Data for cancer comparative effectiveness research: Past, present, and future potential. Meyer AM, Carpenter WR, Abernethy AP, Stürmer T, Kosorok MR. Universisty of North Carolina-Lineberger Comprehensive Cancer Center, University  of North Carolina, Chapel Hill, North Carolina; Cecil G. Sheps Center for Health Services Research, University of North Carolina, Chapel Hill, North Carolina.

    Comparative effectiveness research (CER) can efficiently and rapidly generate new scientific evidence and address knowledge gaps, reduce clinical uncertainty, and guide health care choices. Much of the potential in CER is driven by the application of novel methods to analyze existing data. Despite its potential, several challenges must be identified and overcome so that CER may be improved, accelerated, and expeditiously implemented into the broad spectrum of cancer care and clinical practice. To identify and characterize the challenges to cancer CER, the authors reviewed the literature and conducted semistructured interviews with 41 cancer CER researchers at the Agency for Healthcare Research and Quality’s Developing Evidence to Inform Decisions about Effectiveness (DEcIDE) Cancer CER Consortium. Several data sets for cancer CER were identified and differentiated into an ontology of 8 categories and were characterized in terms of strengths, weaknesses, and utility. Several themes emerged during the development of this ontology and discussions with CER researchers. Dominant among them was accelerating cancer CER and promoting the acceptance of findings, which will necessitate transcending disciplinary silos to incorporate diverse perspectives and expertise. Multidisciplinary collaboration is required, including those with expertise in nonexperimental data, statistics, outcomes research, clinical trials, epidemiology, generalist and specialty medicine, survivorship, informatics, data, and methods, among others. Recommendations highlight the

    systematic, collaborative identification of critical measures; application of more rigorous study design and sampling methods; policy-level resolution of issues in data ownership, governance, access, and cost; and development and application of consistent standards for data security, privacy, and confidentiality. Cancer 2012. © 2012 American Cancer Society.

    PMID: 22517505  [PubMed – as supplied by publisher]

    LINK: http://onlinelibrary.wiley.com/doi/10.1002/cncr.27552/abstract

  9. Arch Intern Med. 2012 Apr 9;172(7):548-54.
  10. Influenza vaccine effectiveness in patients on hemodialysis: an analysis of a natural experiment. McGrath LJ, Kshirsagar AV, Cole SR, Wang L, Weber DJ, Stürmer T, Brookhart MA. Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, 2105F McGavran-Greenberg, Campus Box CB 7435, Chapel Hill, NC 27599-7435. mabrook@email.unc.edu.

    BACKGROUND: Although the influenza vaccine is recommended for patients with end-stage renal disease, little is known about its effectiveness. Observational studies of vaccine effectiveness (VE) are challenging because vaccinated subjects may be healthier than unvaccinated subjects.

    METHODS: Using US Renal Data System data, we estimated VE for influenza-like illness, influenza/pneumonia hospitalization, and mortality in adult patients undergoing hemodialysis by using a natural experiment created by the year-to-year variation in the match of the influenza vaccine to the circulating virus. We compared vaccinated patients in matched years (1998, 1999, and 2001) with a mismatched year (1997) using Cox proportional hazards models. Ratios of hazard ratios compared vaccinated patients between 2 years and unvaccinated patients between 2 years. We calculated VE as 1 – effect measure.

    RESULTS: Vaccination rates were less than 50% each year. Conventional analysis comparing vaccinated with unvaccinated patients produced average VE estimates of 13%, 16%, and 30% for influenza-like illness, influenza/pneumonia hospitalization, and mortality, respectively. When restricted to the preinfluenza period, results were even stronger, indicating bias. The pooled ratio of hazard ratios comparing matched seasons with a placebo season resulted in a VE of 0% (95% CI, -3% to 2%) for influenza-like illness, 2% (-2% to 5%) for hospitalization, and 0% (-3% to 3%) for death.

    CONCLUSIONS: Relative to a mismatched year, we found little evidence of increased VE in subsequent well-matched years, suggesting that the current influenza vaccine strategy may have a smaller effect on morbidity and mortality in the end-stage renal disease population than previously thought. Alternate strategies (eg, high-dose vaccine, adjuvanted vaccine, and multiple doses) should be investigated.

    PMID: 22493462  [PubMed – in process]

    LINK: http://archinte.ama-assn.org/cgi/content/full/172/7/548

  11. Epidemiology. 2012 Mar;23(2):223-32.
  12. Using marginal structural models to estimate the direct effect of adverse childhood social conditions on onset of heart disease, diabetes, and stroke. Nandi A, Glymour MM, Kawachi I, VanderWeele TJ. Institute for Health and Social Policy and Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, QC Canada. arijit.nandi@mcgill.ca

    Comment in Epidemiology. 2012 Mar;23(2):233-7.

    BACKGROUND: Early-life socioeconomic status (SES) is associated with adult chronic disease, but it is unclear whether this effect is mediated entirely via adult SES or whether there is a direct effect of adverse early-life SES on adult disease. Major challenges in evaluating these alternatives include imprecise measurement of early-life SES and bias in conventional regression methods to assess mediation. In particular, conventional regression approaches to direct effect estimation are biased when there is time-varying confounding of the association between adult SES and chronic disease by chronic disease risk factors.

    METHODS: First-reported heart disease, diabetes, and stroke diagnoses were assessed in a national sample of 9760 Health and Retirement Study participants followed biennially from 1992 through 2006. Early-life and adult SES measures were derived using exploratory and confirmatory factor analysis. Early-life SES was measured by parental education, father’s occupation, region of birth, and childhood rural residence. Adult SES was measured by respondent’s education, occupation, labor force status, household income, and household wealth. Using marginal structural models, we estimated the direct effect of early-life SES on chronic disease onset that was not mediated by adult SES. Marginal structural models were estimated with stabilized inverse probability-weighted log-linear models to adjust for risk factors that may have confounded associations between adult SES and chronic disease.

    RESULTS: During follow-up, 24%, 18%, and 9% of participants experienced first onset of heart disease, diabetes, and stroke, respectively. Comparing those in the most disadvantaged with the least disadvantaged quartile, early-life SES was associated with coronary heart disease (risk ratio = 1.30 [95% confidence interval = 1.12-1.51]) and diabetes (1.23 [1.02-1.48]) and marginally associated  with stroke via pathways not mediated by adult SES.

    CONCLUSIONS: Our results suggest that early-life socioeconomic experiences directly influence adult chronic disease outcomes.

    PMID: 22317806  [PubMed – in process]

    LINK: http://journals.lww.com/epidem/Abstract/2012/03000/Using_Marginal_Structural_Models_to_Estimate_the.8.aspx

    MAY THEME: PDS proceedings from the 2011 DEcIDE Methods Symposium on methods for developing and analyzing clinically rich data for patient-centered outcomes

    http://www.drugepi.org/recently-at-dope/journal-supplement-from-3rd-decide-now-available/

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