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

BMC Med Res Methodol. 2011 Apr 1;11(1):36. [Epub ahead of print]Design of cohort studies in chronic diseases using routinely collected databases when a prescription is used as surrogate outcome.  Lodi S, Carpenter J, Egger P, Evans S.
BACKGROUND: There has been little research on design of studies based on routinely collected data when the clinical endpoint of interest is not recorded, but can be inferred from a prescription. This often happens when exploring the effect of a drug on chronic diseases. Using the LifeLink claims database in studying the possible anti-inflammatory effects of statins in rheumatoid arthritis (RA), oral steroids (OS) were treated as surrogate of inflammatory flare-ups. We compared two cohort study designs, the first using time to event outcomes and the second using quantitative amount of the surrogate.

METHODS: RA patients were extracted from the LifeLink database. In the first study, patients were split into two sub-cohorts based on whether they were using  OS within a specified time window of the RA index date (first record of RA). Using Cox models we evaluated the association between time-varying exposure to statins and (i) initiation of OS therapy in the non-users of OS at RA index date  and (ii) cessation of OS therapy in the users of OS at RA index date. In the second study, we matched new statin users to non users on age and sex. Zero inflated negative binomial models were used to contrast the number of days’ prescriptions of OS in the year following date of statin initiation for the two exposure groups.

RESULTS: In the unmatched study, the statin exposure hazard ratio (HR) of initiating OS in the 31451 non-users of OS at RA index date was 0.96(95% CI 0.9,1.1) and the statin exposure HR of cessation of OS therapy in the 6026 users of OS therapy at RA index date was 0.95 (0.87,1.05). In the matched cohort of 6288 RA patients the statin exposure rate ratio for duration on OS therapy was 0.88(0.76,1.02). There was digit preference for outcomes in multiples of 7 and 30 days.

CONCLUSIONS: The `time to event’ study design was preferable because it better exploits information on all available patients and provides a degree of robustness toward confounding. We found no convincing evidence that statins reduce inflammation in RA patients.PMID: 21457565 [PubMed – as supplied by publisher]Free Full text (PDF) available:

CER Scan – Epub Ahead of Print

Am J Epidemiol. 2011 Mar 23. [Epub ahead of print] Invited Commentary: Causation or “noitasuaC”? Schisterman E, Whitcomb B, Bowers K.
Longitudinal studies are often viewed as the “gold standard” of observational epidemiologic research. Establishing a temporal association is a necessary criterion to identify causal relations. However, when covariates in the causal system vary over time, a temporal association is not straightforward. Appropriate analytical methods may be necessary to avoid confounding and reverse causality. These issues come to light in 2 studies of breastfeeding described in the articles by Al-Sahab et al. (Am J Epidemiol. 2011;173(00):0000-0000) and Kramer et al. (Am J Epidemiol. 2011;173(00):0000-0000) in this issue of the Journal. Breastfeeding has multiple time points and is a behavior that is affected by multiple factors, many of which themselves vary over time. This creates a complex causal system that requires careful scrutiny. The methods presented here may be applicable to a wide range of studies that involve time-varying exposures and time-varying confounders.

PMID: 21430191 [PubMed – as supplied by publisher]Free Full text (HTML) available:
2. Pharmacoepidemiol Drug Saf. 2011 Mar 10. doi: 10.1002/pds.2098. [Epub ahead of print] The implications of propensity score variable s election strategies in pharmacoepidemiology: an empirical illustration. Patrick AR, Schneeweiss S, Brookhart MA, Glynn RJ, Rothman KJ, Avorn J, Stürmer T.  Division of Pharmacoepidemiology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA.
PURPOSE: To examine the effect of variable selection strategies on the performance of propensity score (PS) methods in a study of statin initiation, mortality, and hip fracture assuming a true mortality reduction of < 15% and no effect on hip fracture.

METHODS: We compared seniors initiating statins with seniors initiating glaucoma medications. Out of 202 covariates with a prevalence > 5%, PS variable selection strategies included none, a priori, factors predicting exposure, and factors predicting outcome. We estimated hazard ratios (HRs) for statin initiation on mortality and hip fracture from Cox models controlling for various PSs.

RESULTS: During 1 year follow-up, 2693 of 55 610 study subjects died and 496 suffered a hip fracture. The crude HR for statin initiators was 0.64 for mortality and 0.46 for hip fracture. Adjusting for the non-parsimonious PS yielded effect estimates of 0.83 (95%CI:0.75-0.93) and 0.72 (95%CI:0.56-0.93). Including in the PS only covariates associated with a greater than 20% increase or reduction in outcome rates yielded effect estimates of 0.84 (95%CI:0.75-0.94) and 0.76 (95%CI:0.61-0.95), which were closest to the effects predicted from randomized trials.

CONCLUSION: Due to the difficulty of pre-specifying all potential confounders of an exposure-outcome association, data-driven approaches to PS variable selection may be useful. Selecting covariates strongly associated with exposure but unrelated to outcome should be avoided, because this may increase bias. Selecting variables for PS based on their association with the outcome may help to reduce such bias.

Copyright © 2011 John Wiley & Sons, Ltd.PMID: 21394812 [PubMed – as supplied by publisher]
3. Am J Epidemiol. 2011 Mar 8. [Epub ahead of print] Doubly Robust Estimation of Causal Effects. Funk MJ, Westreich D, Wiesen C, Stürmer T, Brookhart MA, Davidian M.
Doubly robust estimation combines a form of outcome regression with a model for the exposure (i.e., the propensity score) to estimate the causal effect of an exposure on an outcome. When used individually to estimate a causal effect, both outcome regression and propensity score methods are unbiased only if the statistical model is correctly specified. The doubly robust estimator combines these 2 approaches such that only 1 of the 2 models need be correctly specified to obtain an unbiased effect estimator. In this introduction to doubly robust estimators, the authors present a conceptual overview of doubly robust estimation, a simple worked example, results from a simulation study examining performance of estimated and bootstrapped standard errors, and a discussion of the potential advantages and limitations of this method. The supplementary material for this paper, which is posted on the Journal’s Web site (, includes a demonstration of the doubly robust property (Web Appendix 1) and a description of a SAS macro (SAS Institute, Inc., Cary, North Carolina) for doubly robust estimation, available for download at∼mfunk/dr/.

PMID: 21385832 [PubMed – as supplied by publisher]Free Full text (HTML) available:
4. Am J Epidemiol. 2011 Feb 28. [Epub ahead of print] Methods for Estimating Remission Rates From Cross -Sectional Survey Data: Application and Validation Using Data From a National Migraine Study. Roy J, Stewart WF.
Knowledge about remission rates can affect treatment decisions and facilitate etiologic discoveries. However, little is known about remission of many chronic episodic disorders, including migraine. This is partly due to the fact that medical records do not fully capture the history of these conditions, since patients might stop seeking care once they no longer have symptoms. For these disorders, remission rates would typically be obtained from prospective observational studies. Prospective studies of remission for chronic episodic conditions are rarely conducted, however, and suffer from many analytical challenges, such as outcome-dependent dropout. Here the authors propose an alternative approach that is appropriate for use with cross-sectional survey data in which reported age of onset was recorded. The authors estimated migraine remission rates using data from a 2004 national survey. They took a Bayesian approach and modeled sex- and age-specific remission rates as a function of incidence and prevalence. The authors found that remission rates were an increasing function of age and were similar for men and women. Follow-up survey data from migraine cases (2005) were used to validate the methods. The remission curves estimated from the validation data were very similar to the ones from the cross-sectional data.

PMID: 21357656 [PubMed – as supplied by publisher]
5. Int J Epidemiol. 2011 Mar 30. [Epub ahead of print]The Simpson’s paradox unraveled. Hernán MA, Clayton D, Keiding N. Department of Epidemiology, Harvard School of Public Health, Harvard-MIT Division of Health Sciences and Technology, Boston, MA 02115, USA, Department of Medical Genetics, Cambridge University, Addenbrooke’s Hospital, Cambridge, UK and Department of Biostatistics, University of Copenhagen, Copenhagen, Denmark.
BACKGROUND: In a famous article, Simpson described a hypothetical data example that led to apparently paradoxical results.

METHODS: We make the causal structure of Simpson’s example explicit.

RESULTS: We show how the paradox disappears when the statistical analysis is appropriately guided by subject-matter knowledge. We  also review previous explanations of Simpson’s paradox that attributed it to two  distinct phenomena: confounding and non-collapsibility.

CONCLUSION: Analytical errors may occur when the problem is stripped of its causal context and analyzed  merely in statistical terms.PMID: 21454324 [PubMed – as supplied by publisher]Free Full text (PDF) available:

6. Clin Trials. 2011 Jan 31. [Epub ahead of print]Bayesian models for subgroup analysis in clinical trials. Jones HE, Ohlssen DI, Neuenschwander B, Racine A, Branson M. School of Social and Community Medicine, University of Bristol, UK.
BACKGROUND: In a pharmaceutical drug development setting, possible interactions between the treatment and particular baseline clinical or demographic factors are often of interest. However, the subgroup analysis required to investigate such associations remains controversial. Concerns with classical hypothesis testing approaches to the problem include low power, multiple testing, and the possibility of data dredging.

PURPOSE: As an alternative to hypothesis testing, the use of shrinkage estimation techniques is investigated in the context of an exploratory post hoc subgroup analysis. A range of models that have been suggested in the literature are reviewed. Building on this, we explore a general modeling strategy, considering various options for shrinkage of effect estimates. This is applied to a case-study, in which evidence was available from seven-phase II-III clinical trials examining a novel therapy, and also to two artificial datasets with the same structure.

METHODS: Emphasis is placed on hierarchical modeling techniques, adopted within a Bayesian framework using freely available software. A range of possible subgroup model structures are applied, each incorporating shrinkage estimation techniques.

RESULTS: The investigation of the case-study showed little evidence of subgroup effects. Because inferences appeared to be consistent across a range of well-supported models, and model diagnostic checks showed no obvious problems, it seemed this conclusion was robust. It is reassuring that the structured shrinkage techniques appeared to work well in a situation where deeper inspection of the data suggested little evidence of subgroup effects.

LIMITATIONS: The post hoc examination of subgroups should be seen as an exploratory analysis, used to help make better informed decisions regarding potential future studies examining specific subgroups. To a certain extent, the degree of understanding provided by such assessments will be limited by the quality and quantity of available data.

CONCLUSIONS: In light of recent interest by health authorities into the use of subgroup analysis in the context of drug development, it appears that Bayesian approaches involving shrinkage techniques could play an important role in this area. Hopefully, the developments outlined here provide useful methodology for tackling such a problem, in-turn leading to better informed decisions regarding subgroups.PMID: 21282293 [PubMed – as supplied by publisher]
7. Clin Trials. 2011 Jan 26. [Epub ahead of print]Challenges in the design and conduct of controlled clinical effectiveness trials in schizophrenia. Rosenheck RA, Krystal JH, Lew R, Barnett PG, Thwin SS, Fiore L, Valley D, Huang GD, Neal C, Vertrees JE, Liang MH. Veterans Affairs (VA) Connecticut Healthcare System, West Haven, CT, USA, Yale School of Medicine, New Haven, CT, USA.
BACKGROUND: The introduction of antipsychotic medication has been a major advance in the treatment of schizophrenia and allows millions of people to live outside of institutions. It is generally believed that long-acting intramuscular antipsychotic medication is the most effective approach to increasing medication adherence and thereby reduce relapse in high-risk patients with schizophrenia, but the data are scant.

PURPOSE: To report the design of a study to assess the  effect of long-acting injectable risperidone in unstable patients and under more realistic conditions than previously studied and to evaluate the effect of this medication on psychiatric inpatient hospitalization, schizophrenia symptoms, quality of life, medication adherence, side effects, and health care costs.

METHODS: The trial was an open randomized clinical comparative effectiveness trial in patients with schizophrenia or schizo-affective disorders in which parenteral risperidone was compared to an oral antipsychotic regimen selected by each control patient’s psychiatrist. Participants had unstable psychiatric disease defined by recent hospitalization or exhibition of unusual need for psychiatric services. The primary endpoint was hospitalization for psychiatric indications; the secondary endpoint was psychiatric symptoms.

RESULTS: Overall, 382 patients were randomized. Determination of a persons’ competency to understand the elements of informed consent was addressed. The use of a closed-circuit TV interview for psychosocial measures provided an economical, high quality, reliable means of collecting data. A unique method for insuring that usual care was optimal was incorporated in the follow-up of all subjects.

LIMITATIONS: Patients with schizophrenia or schizo-affective disorders and with the common co-morbid illnesses seen in the VA are a challenging group of subjects to study in long-term trials. Some techniques unique in the VA and found useful may not be generalizable or applicable in other research or treatment settings.

CONCLUSIONS: The trial tested a new antipsychotic medication early in its adoption in the Veterans Health Administration. The VA has a unique electronic medical record and database which can be used to identify the endpoint, that is, first hospitalization due to a psychiatric problem, with complete ascertainment. Several methodologic solutions addressed competency to understand elements of consent, the costs and reliability of collecting interview data gathering, and insuring usual care.

PMID: 21270143 [PubMed – as supplied by publisher]

Author Scan – Published within the past 30 days/ Epub Ahead of Print

1. PLoS One. 2011 Mar 22;6(3):e18062. Multicenter Evaluation of a Novel Surveillance Paradigm for Complications of Mechanical V entilation. Klompas M, Khan Y, Kleinman K, Evans RS, Lloyd JF, Stevenson K, Samore M, Platt R; for the CDC Prevention Epicenters Program. Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts, United States of America.
BACKGROUND: Ventilator-associated pneumonia (VAP) surveillance is time consuming, subjective, inaccurate, and inconsistently predicts outcomes. Shifting surveillance from pneumonia in particular to complications in general might circumvent the VAP definition’s subjectivity and inaccuracy, facilitate electronic assessment, make interfacility comparisons more meaningful, and encourage broader prevention strategies. We therefore evaluated a novel surveillance paradigm for ventilator-associated complications (VAC) defined by sustained increases in patients’ ventilator settings after a period of stable or decreasing support.

METHODS: We assessed 600 mechanically ventilated medical and surgical patients from three hospitals. Each hospital contributed 100 randomly selected patients ventilated 2-7 days and 100 patients ventilated >7 days. All patients were independently assessed for VAP and for VAC. We compared incidence-density, duration of mechanical ventilation, intensive care and hospital lengths of stay, hospital mortality, and time required for surveillance for VAP and for VAC. A subset of patients with VAP and VAC were independently reviewed by a physician to determine possible etiology.

RESULTS: Of 597 evaluable patients, 9.3% had VAP (8.8 per 1,000 ventilator days) and 23% had VAC (21.2 per 1,000 ventilator days). Compared to matched controls, both VAP and VAC prolonged days to extubation (5.8, 95% CI 4.2-8.0 and 6.0, 95% CI 5.1-7.1 respectively), days to intensive care discharge (5.7, 95% CI 4.2-7.7 and 5.0, 95% CI 4.1-5.9), and days to hospital discharge (4.7, 95% CI 2.6-7.5 and 3.0, 95% CI 2.1-4.0). VAC was associated with increased mortality (OR 2.0, 95% CI 1.3-3.2) but VAP was not (OR 1.1, 95% CI 0.5-2.4). VAC assessment was faster (mean 1.8 versus 39 minutes per patient). Both VAP and VAC events were predominantly attributable to pneumonia, pulmonary edema, ARDS, and atelectasis.

CONCLUSIONS: Screening ventilator settings for VAC captures a similar set of complications to traditional VAP surveillance but is faster, more objective, and a superior predictor of outcomes.

PMID: 21445364 [PubMed – as supplied by publisher]Free Full text (PDF) available:

2. Med Care. 2011 Mar 18. [Epub ahead of print] Placebo Adherence, Clinical Outcomes, and Mortality in the Women’s Health Initiative Randomized Hormone Therapy Trials. R Curtis J, Larson JC, Delzell E, Brookhart MA, Cadarette SM, Chlebowski R, Judd S, Safford M, Solomon DH, Lacroix AZ. * Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham, AL † Fred Hutchinson Cancer Research Center, Seattle, WA ‡ Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL § Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC ∥ Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON, Canada ¶ Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA ♯ Division of Preventive Medicine, University of Alabama at Birmingham, Birmingham, AL ** Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women’s Hospital, Boston, MA.
BACKGROUND: Medication adherence may be a proxy for healthy behaviors and other factors that affect outcomes. Prior studies of the association between placebo adherence and health outcomes have been limited primarily to men enrolled in clinical trials and cardiovascular disease outcomes. We examined associations between adherence to placebo and the risk of fracture, coronary heart disease, cancer, and all-cause mortality in the 2 Women’s Health Initiative hormone therapy randomized trials.

METHODS: Postmenopausal women randomized to placebo with adherence measured at least once were eligible for analysis. Time-varying adherence was assessed by dispensing history and pill counts. Outcome adjudication was based on physician review of medical records. Cox proportional hazards models evaluated the relation between high adherence (≥80%) to placebo and various outcomes, referent to low adherence (<80%).

RESULTS: A total of 13,444 postmenopausal women were under observation for 106,066 person-years. High placebo adherence was inversely associated with most outcomes including hip fracture [hazard ratio (HR), 0.50; 95% confidence interval (CI), 0.33-0.78], myocardial infarction (HR, 0.69; 95% CI, 0.50-0.95), cancer death (HR, 0.60; 95% CI, 0.43-0.82), and all-cause mortality (HR, 0.64; 95% CI, 0.51-0.80) after adjustment for potential confounders. Women with low adherence to placebo were 20% more likely to have low adherence to statins and osteoporosis medications.

CONCLUSIONS: In the Women’s Health Initiative clinical trials, high adherence to placebo was associated with favorable clinical outcomes and mortality. Until the healthy behaviors and/or other factors for which high adherence is a proxy can be better elucidated, caution is warranted when interpreting the magnitude of benefit of medication adherence.

PMID: 21422960 [PubMed – as supplied by publisher]

3. Health Serv Res. 2011 Mar 17. doi: 10.1111/j.1475-6773.2011.01253.x. [Epub ahead of print] Crowd-out and Exposure Effects of Physical Comorbidities on Mental Healt h Care Use: Implications for Racial-Ethnic Disparities in Access. Lê Cook B, McGuire TG, Alegría M, Normand SL.  Center for Multicultural Mental Health Research, 120 Beacon St., 4th Floor, Somerville, MA 02143 Department of Psychiatry, Harvard Medical School, Boston, MA Department of Health Care Policy, Harvard Medical School, Boston, MA Center for Multicultural Mental Health Research, Somerville, MA.
Objectives. In disparities models, researchers adjust for differences in “clinical need,” including indicators of comorbidities. We reconsider this practice, assessing (1) if and how having a comorbidity changes the likelihood of recognition and treatment of mental illness; and (2) differences in mental health care disparities estimates with and without adjustment for comorbidities.

Data. Longitudinal data from 2000 to 2007 Medical Expenditure Panel Survey (n=11,083) split into pre and postperiods for white, Latino, and black adults with probable need for mental health care.

Study Design. First, we tested a crowd-out effect (comorbidities decrease initiation of mental health care after a primary care provider [PCP] visit) using logistic regression models and an exposure effect (comorbidities cause more PCP visits, increasing initiation of mental health care) using instrumental variable methods. Second, we assessed the impact of adjustment for comorbidities on disparity estimates.

Principal Findings. We found no evidence of a crowd-out effect but strong evidence for an exposure effect. Number of postperiod visits positively predicted initiation of mental health care. Adjusting for racial/ethnic differences in comorbidities increased black-white disparities and decreased Latino-white disparities.

Conclusions. Positive exposure findings suggest that intensive follow-up programs shown to reduce disparities in chronic-care management may have additional indirect effects on reducing mental health care disparities.

© Health Research and Educational Trust.PMID: 21413984 [PubMed – as supplied by publisher]


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