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.

October 2011


 

CER Scan [Epub ahead of print]
     

     

    1. Pharmacoepidemiol Drug Saf. 2011 Sep 23. doi: 10.1002/pds.2251. [Epub ahead of print]

    Balance measures for propensity score methods: a clinical example on beta-agonist use and the risk of myocardial infarction. Groenwold RH, de Vries F, de Boer A, Pestman WR, Rutten FH, Hoes AW, Klungel OH. Department of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, The Netherlands; Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands. r.h.h.groenwold@umcutrecht.nl.

    PURPOSE: Propensity score (PS) methods aim to control for confounding by balancing confounders between exposed and unexposed subjects with the same PS. PS balance measures have been compared in simulated data but limited in empirical data. Our objective was to compare balance measures in clinical data and assessed the association between long-acting inhalation beta-agonist (LABA) use and myocardial infarction.

    METHODS: We estimated the relationship between LABA use and myocardial infarction in a cohort of adults with a diagnosis of asthma or chronic obstructive pulmonary disorder from the Utrecht General Practitioner Research Network database. More than two thousand PS models, including information on the observed confounders age, sex, diabetes, cardiovascular disease and chronic obstructive pulmonary disorder status, were applied. The balance of these confounders was assessed using the standardised difference (SD), Kolmogorov-Smirnov (KS) distance and overlapping coefficient. Correlations between these balance measures were calculated. In addition, simulation studies were performed to assess the correlation between balance measures and bias.

    RESULTS: LABA use was not related to myocardial infarction after conditioning on the PS (median heart rate=1.14, 95%CI=0.47-2.75). When using the different balance measures for selecting a PS model, similar associations were obtained. In our empirical data, SD and KS distance were highly correlated balance measures (r=0.92). In simulations, SD, KS distance and overlapping coefficient were similarly correlated to bias (e.g. r=0.55, r=0.52 and r=-0.57, respectively, when conditioning on the PS).

    CONCLUSIONS: We recommend using the SD or the KS distance to quantify the balance of confounder distributions when applying PS methods. Copyright © 2011 John Wiley & Sons, Ltd.

    PMID: 21953948 [PubMed – as supplied by publisher]

    2. Clin Trials. 2011 Sep 23. [Epub ahead of print]

    Beyond the intention-to-treat in comparative effectiveness research. Hernán MA, Hernández-Díaz S. Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA; Harvard-MIT Division of Health Sciences and Technology, Boston, MA, USA.

    BACKGROUND: The intention-to-treat comparison is the primary, if not the only, analytic approach of many randomized clinical trials.

    PURPOSE: To review the shortcomings of intention-to-treat analyses, and of ‘as treated’ and ‘per protocol’ analyses as commonly implemented, with an emphasis on problems that are especially relevant for comparative effectiveness research.

    METHODS and RESULTS: In placebo-controlled randomized clinical trials, intention-to-treat analyses underestimate the treatment effect and are therefore nonconservative for both safety trials and noninferiority trials. In randomized clinical trials with an active comparator, intention-to-treat estimates can overestimate a treatment’s effect in the presence of differential adherence. In either case, there is no guarantee that an intention-to-treat analysis estimates the clinical effectiveness of treatment. Inverse probability weighting, g-estimation, and instrumental variable estimation can reduce the bias introduced by nonadherence and loss to follow-up in ‘as treated’ and ‘per protocol’ analyses.

    LIMITATIONS: These analyse require untestable assumptions, a dose-response model, and time-varying data on confounders and adherence.

    CONCLUSIONS: We recommend that all randomized clinical trials with substantial lack of adherence or loss to follow-up are analyzed using different methods. These include an intention-to-treat analysis to estimate the effect of assigned treatment and ‘as treated’ and ‘per protocol’ analyses to estimate the effect of treatment after appropriate adjustment via inverse probability weighting or g-estimation.

    PMID: 21948059 [PubMed – as supplied by publisher]

    3. Am J Epidemiol. 2011 Sep 20. [Epub ahead of print]

    Comparison of Different Approaches to Confounding Adjustment in a Study on the Association of Antipsychotic Medication With Mortality in Older Nursing Home Patients. Huybrechts KF, Brookhart MA, Rothman KJ, Silliman RA, Gerhard T, Crystal S, Schneeweiss S.

    Selective prescribing of conventional antipsychotic medication (APM) to frailer patients is thought to have led to overestimation of the association with mortality in pharmacoepidemiologic studies relying on claims data. The authors assessed the validity of different analytic techniques to address such confounding. The cohort included 82,012 persons initiating APM use after admission to a nursing home in 45 states with 2001-2005 Medicaid/Medicare data, linked to clinical data (Minimum Data Set) and institutional characteristics. The authors compared the association between APM class and 180-day mortality with multivariate outcome modeling, propensity score (PS) adjustment, and instrumental variables. The unadjusted risk difference (per 100 patients) of 10.6 (95% confidence interval (CI): 9.4, 11.7) comparing use of conventional medication with atypical APM was reduced to 7.8 (95% CI: 6.6, 9.0) and 7.0 (95% CI: 5.8, 8.2) after PS adjustment and high-dimensional PS (hdPS) adjustment, respectively. Results were similar in analyses limited to claims-based Medicaid/Medicare variables (risk difference = 8.2 for PS, 7.1 for hdPS). Instrumental-variable estimates were imprecise (risk difference = 8.8, 95% CI: -1.3, 19.0) because of the weak instrument. These results suggest that residual confounding has a relatively small impact on the effect estimate and that hdPS methods based on claims alone provide estimates at least as good as those from conventional analyses using claims enriched with clinical information.

    PMID: 21934095 [PubMed – as supplied by publisher]

    4. Pharmacoepidemiol Drug Saf. 2011 Sep 15. doi: 10.1002/pds.2196. [Epub ahead of print]

    Study design for a comprehensive assessment of biologic safety using multiple healthcare data systems. Herrinton LJ, Curtis JR, Chen L, Liu L, Delzell E, Lewis JD, Solomon DH, Griffin MR, Ouellet-Hellstom R, Beukelman T, Grijalva CG, Haynes K, Kuriya B, Lii J, Mitchel E, Patkar N, Rassen J, Winthrop KL, Nourjah P, Saag KG. Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA. lisa.herrinton@kp.org.

    BACKGROUND: Although biologic treatments have excellent efficacy for many autoimmune diseases, safety concerns persist. Understanding the absolute and comparative risks of adverse events in patient and disease subpopulations is critical for optimal prescribing of biologics.

    PURPOSE: The Safety Assessment of Biologic Therapy collaborative was federally funded to provide robust estimates of rates and relative risks of adverse events among biologics users using data from national Medicaid and Medicare plus Medicaid dual-eligible programs, Tennessee Medicaid, Kaiser Permanente, and state pharmaceutical assistance programs supplementing New Jersey and Pennsylvania Medicare programs. This report describes the organizational structure of the collaborative and the study population and methods.

    METHODS: This retrospective cohort study (1998-2007) examined risks of seven classes of adverse events in relation to biologic treatments prescribed for seven autoimmune diseases. Propensity scores were used to control for confounding and enabled pooling of individual-level data across data systems while concealing personal health information. Cox proportional hazard modeling was used to analyze study hypotheses.

    RESULTS: The cohort was composed of 159,000 subjects with rheumatic diseases, 33,000 with psoriasis, and 46,000 with inflammatory bowel disease. This report summarizes demographic characteristics and drug exposures. Separate reports will provide outcome definitions and estimated hazard ratios for adverse events.

    CONCLUSION: This comprehensive research will improve understanding of the safety of these treatments. The methods described may be useful to others planning similar evaluations. Copyright © 2011 John Wiley & Sons, Ltd.

    PMID: 21919113 [PubMed – as supplied by publisher]

    5. Contemp Clin Trials. 2011 Sep 6. [Epub ahead of print]

    comparison of statistical approaches for physician-randomized trials with survival outcomes. Stedman MR, Lew RA, Losina E, Gagnon DR, Solomon DH, Brookhart MA. Orthopedics and Arthritis Center for Outcomes Research, Department of Orthopedics, Brigham and Women’s Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.

    This study compares methods for analyzing correlated survival data from physician-randomized trials of health care quality improvement interventions. Several proposed methods adjust for correlated survival data; however the most suitable method is unknown. Applying the characteristics of our study example, we performed three simulation studies to compare conditional, marginal, and non-parametric methods for analyzing clustered survival data. We simulated 1000 datasets using a shared frailty model with (1) fixed cluster size, (2) variable cluster size, and (3) non-lognormal random effects. Methods of analyses included: the nonlinear mixed model (conditional), the marginal proportional hazards model with robust standard errors, the clustered logrank test, and the clustered permutation test (non-parametric). For each method considered we estimated Type I error, power, mean squared error, and the coverage probability of the treatment effect estimator. We observed underestimated Type I error for the clustered logrank test. The marginal proportional hazards method performed well even when model assumptions were violated. Nonlinear mixed models were only advantageous when the distribution was correctly specified.

    PMID: 21924382 [PubMed – as supplied by publisher]

    CER Scan [published within the last 30 days]

    1. BMC Med Res Methodol. 2011 Sep 21;11(1):132. [Epub ahead of print]

    Benefits of ICU admission in critically ill patients: Whether instrumental variable methods or propensity scores should be used. Pirracchio R, Sprung C, Payen D, Chevret S.

    BACKGROUND: The assessment of the causal effect of Intensive Care Unit (ICU) admission generally involves usual observational designs and thus requires controlling for confounding variables. Instrumental variable analysis is an econometric technique that allows causal inferences of the effectiveness of some treatments during situations to be made when a randomized trial has not been or cannot be conducted. This technique relies on the existence of one variable or “instrument” that is supposed to achieve similar observations with a different treatment for “arbitrary” reasons, thus inducing substantial variation in the treatment decision with no direct effect on the outcome. The objective of the study was to assess the benefit in terms of hospital mortality of ICU admission in a cohort of patients proposed for ICU admission (ELDICUS cohort).

    METHODS: Using this cohort of 8,201 patients triaged for ICU (including 6,752 (82.3%) patients admitted), the benefit of ICU admission was evaluated using 3 different approaches: instrumental variables, standard regression and propensity score matched analyses. We further evaluated the results obtained using different instrumental variable methods that have been proposed for dichotomous outcomes.

    RESULTS: The physician’s main specialization was found to be the best instrument. All instrumental variable models adequately reduced baseline imbalances, but failed to show a significant effect of ICU admission on hospital mortality, with confidence intervals far higher than those obtained in standard or propensity-based analyses.

    CONCLUSIONS: Instrumental variable methods offer an appealing alternative to handle the selection bias related to nonrandomized designs, especially when the presence of significant unmeasured confounding is suspected. Applied to the ELDICUS database, this analysis failed to show any significant beneficial effect of ICU admission on hospital mortality. This result could be due to the lack of statistical power of these methods.

    PMID: 21936926 [PubMed – as supplied by publisher]

    Free Full Text: http://www.biomedcentral.com/content/pdf/1471-2288-11-132.pdf

    2. BMC Med Res Methodol. 2011 Sep 19;11(1):129.

    Imputation of missing values of tumour stage in population-based cancer registration. Eisemann N, Waldmann A, Katalinic A. Institute of Cancer Epidemiology, University Luebeck, Ratzeburger Allee 160 (Haus 50), 23562 Luebeck, Germany. nora.eisemann@uksh.de.

    BACKGROUND: Missing data on tumour stage information is a common problem in population-based cancer registries. Statistical analyses on the level of tumour stage may be biased, if no adequate method for handling of missing data is applied. In order to determine a useful way to treat missing data on tumour stage, we examined different imputation models for multiple imputation with chained equations for analysing the stage-specific numbers of cases of malignant melanoma and female breast cancer.

    METHODS: This analysis was based on the malignant melanoma data set and the female breast cancer data set of the cancer registry Schleswig-Holstein, Germany. The cases with complete tumour stage information were extracted and their stage information partly removed according to a MAR missingness-pattern, resulting in five simulated data sets for each cancer entity. The missing tumour stage values were then treated with multiple imputation with chained equations, using polytomous regression, predictive mean matching, random forests and proportional sampling as imputation models. The estimated tumour stages, stage-specific numbers of cases and survival curves after multiple imputation were compared to the observed ones.

    RESULTS: The amount of missing values for malignant melanoma was too high to estimate a reasonable number of cases for each UICC stage. However, multiple imputation of missing stage values led to stage-specific numbers of cases of T-stage for malignant melanoma as well as T- and UICC-stage for breast cancer close to the observed numbers of cases. The observed tumour stages on the individual level, the stage-specific numbers of cases and the observed survival curves were best met with polytomous regression or predictive mean matching but not with random forest or proportional sampling as imputation models.

    CONCLUSIONS: This limited simulation study indicates that multiple imputation with chained equations is an appropriate technique for dealing with missing information on tumour stage in population-based cancer registries, if the amount of unstaged cases is on a reasonable level.

    PMID: 21929796 [PubMed – as supplied by publisher]

    Free Full Text: http://www.biomedcentral.com/content/pdf/1471-2288-11-129.pdf

    3. Med Care. 2011 Oct;49(10):940-7.

    The Mortality Risk Score and the ADG Score: Two Points-Based Scoring Systems for the Johns Hopkins Aggregated Diagnosis Groups to Predict Mortality in a General Adult Population Cohort in Ontario, Canada. Austin PC, Walraven C. *Institute for Clinical Evaluative Sciences, Toronto, Ontario †Department of Health Management, Policy and Evaluation ‡Dalla Lana School of Public Health, University of Toronto §Ottawa Hospital Research Institute Department of Medicine, University of Ottawa, Ottawa, Ontario, Canada.

    BACKGROUND: Logistic regression models that incorporated age, sex, and indicator variables for the Johns Hopkins’ Aggregated Diagnosis Groups (ADGs) categories have been shown to accurately predict all-cause mortality in adults.

    OBJECTIVES: To develop 2 different point-scoring systems using the ADGs. The Mortality Risk Score (MRS) collapses age, sex, and the ADGs to a single summary score that predicts the annual risk of all-cause death in adults. The ADG Score derives weights for the individual ADG diagnosis groups.

    RESEARCH DESIGN: Retrospective cohort constructed using population-based administrative data.

    PARTICIPANTS: All 10,498,413 residents of Ontario, Canada, between the age of 20 and 100 years who were alive on their birthday in 2007, participated in this study. Participants were randomly divided into derivation and validation samples.

    MEASURES: Death within 1 year.

    RESULTS: In the derivation cohort, the MRS ranged from -21 to 139 (median value 29, IQR 17 to 44). In the validation group, a logistic regression model with the MRS as the sole predictor significantly predicted the risk of 1-year mortality with a c-statistic of 0.917. A regression model with age, sex, and the ADG Score has similar performance. Both methods accurately predicted the risk of 1-year mortality across the 20 vigintiles of risk.

    CONCLUSIONS: The MRS combined values for a person’s age, sex, and the John Hopkins ADGs to accurately predict 1-year mortality in adults. The ADG Score is a weighted score representing the presence or absence of the 32 ADG diagnosis groups. These scores will facilitate health services researchers conducting risk adjustment using administrative health care databases.

    PMID: 21921849 [PubMed – in process]

    4. Ann Epidemiol. 2011 Oct;21(10):780-6.

    Mixture analysis of heterogeneous physical activity outcomes. Lee AH, Xiang L. Department of Epidemiology and Biostatistics, School of Public Health, Curtin University, Perth, WA, Australia.

    PURPOSE: The health benefits of physical activity (PA) are well established. PA outcomes, being semicontinuous in nature, often exhibit a large portion of zero values together with continuous positive values that are right-skewed. We propose a novel two-part mixture regression model with random effects to characterize heterogeneity of the clustered PA data.

    METHODS: In the binary part, the odds of PA participation are modeled with the use of a logistic mixed regression model. In the continuous part, the PA intensity conditional on those individuals engaging in PA is assessed by a gamma mixture regression model. Random effects are incorporated within the two parts to account for correlation of the observations.

    RESULTS: Model fitting and inference are performed through the Gaussian quadrature technique, which is implemented conveniently in the SAS PROC NLMIXED. The development of mixture methodology for analyzing PA is motivated by a study of PA in the daily life of patients with chronic obstructive pulmonary disease.

    CONCLUSIONS: The findings demonstrate the usefulness of the mixture analysis, which enables the separate identification of pertinent factors affecting PA participation and PA intensity for different patient subgroups.

    PMID: 21684174 [PubMed – in process]

    Additional Article of Interest [published within the last 90 days]:

    1. Am J Prev Med 2011;40(6):637–644.

    A Proposal to Speed Translation of Healthcare Research Into Practice: Dramatic Change Is Needed. Kessler R, Glasgow RE.

    Efficacy trials have generated interventions to improve health behaviors and biomarkers. However, these efforts have had limited impact on practice and policy. It is suggested that key methodologic and contextual issues have contributed to this state of affairs. Current research paradigms generally have not provided the answers needed for more probable and more rapid translation. A major shift is proposed to produce research with more rapid clinical, public health, and policy impact. Copyright © 2011 American Journal of Preventive Medicine. All rights reserved.

    PMID: 21565657 [PubMed – indexed for MEDLINE]

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