Developments in Post‐marketing Comparative Effectiveness Research
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[1] Douglas G Altman,et al. Validity of indirect comparison for estimating efficacy of competing interventions: empirical evidence from published meta-analyses , 2003, BMJ : British Medical Journal.
[2] G. Lu,et al. Combination of direct and indirect evidence in mixed treatment comparisons , 2004, Statistics in medicine.
[3] F. Wolfe,et al. Increase in lifetime adverse drug reactions, service utilization, and disease severity among patients who will start COX-2 specific inhibitors: quantitative assessment of channeling bias and confounding by indication in 6689 patients with rheumatoid arthritis and osteoarthritis. , 2002, The Journal of rheumatology.
[4] R. Moore,et al. Tolerability and adverse events in clinical trials of celecoxib in osteoarthritis and rheumatoid arthritis: systematic review and meta-analysis of information from company clinical trial reports , 2005, Arthritis research & therapy.
[5] B. Davis,et al. Major outcomes in high-risk hypertensive patients randomized to angiotensin-converting enzyme inhibitor or calcium channel blocker vs diuretic: The Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT). , 2002, JAMA.
[6] S Shapiro,et al. Confounding by indication? , 1997, Epidemiology.
[7] S. Schneeweiss. Reference drug programs: effectiveness and policy implications. , 2007, Health policy.
[8] Jeffrey M. Woodbridge. Econometric Analysis of Cross Section and Panel Data , 2002 .
[9] A. McMahon,et al. Observation and experiment with the efficacy of drugs: a warning example from a cohort of nonsteroidal anti-inflammatory and ulcer-healing drug users. , 2001, American journal of epidemiology.
[10] J. Concato,et al. A simulation study of the number of events per variable in logistic regression analysis. , 1996, Journal of clinical epidemiology.
[11] S Greenland,et al. Analytic methods for two-stage case-control studies and other stratified designs. , 1991, Statistics in medicine.
[12] K. Rothman,et al. Insights into different results from different causal contrasts in the presence of effect‐measure modification , 2006, Pharmacoepidemiology and drug safety.
[13] Jan P Vandenbroucke,et al. When are observational studies as credible as randomised trials? , 2004, The Lancet.
[14] T. MacDonald,et al. Channelling bias and the incidence of gastrointestinal haemorrhage in users of meloxicam, coxibs, and older, non-specific non-steroidal anti-inflammatory drugs , 2003, Gut.
[15] S Suissa,et al. The Case‐Time‐Control Design: Further Assumptions and Conditions , 1998, Epidemiology.
[16] J. Boivin,et al. Controlling Confounding When Studying Large Pharmacoepidemiologic Databases: A Case Study of the Two‐Stage Sampling Design , 1998, Epidemiology.
[17] M Soledad Cepeda,et al. Comparison of logistic regression versus propensity score when the number of events is low and there are multiple confounders. , 2003, American journal of epidemiology.
[18] Douglas Pisano,et al. FDA regulatory affairs : a guide for prescription drugs, medical devices, and biologics , 2003 .
[19] J. Avorn,et al. Explained variation in a model of therapeutic decision making is partitioned across patient, physician, and clinic factors. , 2006, Journal of clinical epidemiology.
[20] R. Platt,et al. Effect of Increased Cost-Sharing on Oral Hypoglycemic Use in Five Managed Care Organizations: How Much Is Too Much? , 2005, Medical care.
[21] J. Avorn,et al. Zolpidem Use and Hip Fractures in Older People , 2001, Journal of the American Geriatrics Society.
[22] J. Selby,et al. Linking Automated Databases for Research in Managed Care Settings , 1997, Annals of Internal Medicine.
[23] A. M. Walker,et al. Anamorphic analysis: sampling and estimation for covariate effects when both exposure and disease are known. , 1982, Biometrics.
[24] D J Graham,et al. The role of databases in drug postmarketing surveillance , 2001, Pharmacoepidemiology and drug safety.
[25] A. Pablos-Mendez,et al. Run-in periods in randomized trials: implications for the application of results in clinical practice. , 1998, JAMA.
[26] M Alan Brookhart,et al. Evaluating Short-Term Drug Effects Using a Physician-Specific Prescribing Preference as an Instrumental Variable , 2006, Epidemiology.
[27] K. Rothman,et al. Beyond randomized controlled trials: A critical comparison of trials with nonrandomized studies , 2006, Hepatology.
[28] Til Stürmer,et al. A review of the application of propensity score methods yielded increasing use, advantages in specific settings, but not substantially different estimates compared with conventional multivariable methods. , 2006, Journal of clinical epidemiology.
[29] D. Asch,et al. Physicians’ preferences for active-controlled versus placebo-controlled trials of new antihypertensive drugs , 2002, Journal of General Internal Medicine.
[30] J Urquhart,et al. Channeling bias in the interpretation of drug effects. , 1991, Statistics in medicine.
[31] S. Schneeweiss,et al. Claims Data Studies of Sedative‐Hypnotics and Hip Fractures in Older People: Exploring Residual Confounding Using Survey Information , 2005, Journal of the American Geriatrics Society.
[32] M. Gail,et al. Indirect corrections for confounding under multiplicative and additive risk models. , 1988, American journal of industrial medicine.
[33] M. Maclure. The case-crossover design: a method for studying transient effects on the risk of acute events. , 1991, American journal of epidemiology.
[34] S. Schneeweiss. Sensitivity analysis and external adjustment for unmeasured confounders in epidemiologic database studies of therapeutics , 2006, Pharmacoepidemiology and drug safety.
[35] J. Avorn,et al. A review of uses of health care utilization databases for epidemiologic research on therapeutics. , 2005, Journal of clinical epidemiology.
[36] M. Weinstein,et al. Long-term persistence in use of statin therapy in elderly patients. , 2002, JAMA.
[37] W. Ray,et al. Evaluating medication effects outside of clinical trials: new-user designs. , 2003, American journal of epidemiology.
[38] T. Lumley. Network meta‐analysis for indirect treatment comparisons , 2002, Statistics in medicine.
[39] D. Solomon. Selective cyclooxygenase 2 inhibitors and cardiovascular events. , 2005, Arthritis and rheumatism.
[40] J. Robins,et al. Results of multivariable logistic regression, propensity matching, propensity adjustment, and propensity-based weighting under conditions of nonuniform effect. , 2006, American journal of epidemiology.
[41] Daniel Weintraub,et al. Effectiveness of atypical antipsychotic drugs in patients with Alzheimer's disease. , 2006, The New England journal of medicine.
[42] M Alan Brookhart,et al. Simultaneous assessment of short-term gastrointestinal benefits and cardiovascular risks of selective cyclooxygenase 2 inhibitors and nonselective nonsteroidal antiinflammatory drugs: an instrumental variable analysis. , 2006, Arthritis and rheumatism.
[43] Joshua D. Angrist,et al. Identification of Causal Effects Using Instrumental Variables , 1993 .
[44] B. Strom,et al. Use of automated databases for pharmacoepidemiology research. , 1990, Epidemiologic reviews.
[45] H. Leufkens,et al. Availability of comparative trials for the assessment of new medicines in the European Union at the moment of market authorization. , 2007, British journal of clinical pharmacology.
[46] Til Stürmer,et al. Performance of propensity score calibration--a simulation study. , 2007, American journal of epidemiology.
[47] J. Angrist,et al. Journal of Economic Perspectives—Volume 15, Number 4—Fall 2001—Pages 69–85 Instrumental Variables and the Search for Identification: From Supply and Demand to Natural Experiments , 2022 .
[48] O S Miettinen,et al. Stratification by a multivariate confounder score. , 1976, American journal of epidemiology.
[49] S D Walter,et al. The results of direct and indirect treatment comparisons in meta-analysis of randomized controlled trials. , 1997, Journal of clinical epidemiology.
[50] D. Rubin. Estimating causal effects of treatments in randomized and nonrandomized studies. , 1974 .
[51] Til Stürmer,et al. Adjusting effect estimates for unmeasured confounding with validation data using propensity score calibration. , 2005, American journal of epidemiology.
[52] J. Hallas. Evidence of Depression Provoked by Cardiovascular Medication: A Prescription Sequence Symmetry Analysis , 1996, Epidemiology.
[53] P. Rothwell. Subgroup analysis in randomised controlled trials: importance, indications, and interpretation , 2005, The Lancet.
[54] R. Leone,et al. Upper Gastrointestinal Bleeding Associated with the Use of NSAIDs Newer Versus Older Agents , 2004 .
[55] Liam Smeeth,et al. Exposure to tricyclic and selective serotonin reuptake inhibitor antidepressants and the risk of hip fracture. , 2003, American journal of epidemiology.
[56] S. Schneeweiss,et al. Association Between SSRI Use and Hip Fractures and the Effect of Residual Confounding Bias in Claims Database Studies , 2004, Journal of clinical psychopharmacology.
[57] J. Avorn,et al. Risk of death in elderly users of conventional vs. atypical antipsychotic medications. , 2005, The New England journal of medicine.
[58] D. Rubin,et al. The central role of the propensity score in observational studies for causal effects , 1983 .
[59] Sebastian Schneeweiss,et al. A Medicare database review found that physician preferences increasingly outweighed patient characteristics as determinants of first-time prescriptions for COX-2 inhibitors. , 2005, Journal of clinical epidemiology.
[60] A. Walker,et al. Newer oral contraceptives and the risk of venous thromboembolism. , 1998, Contraception.
[61] Sebastian Schneeweiss,et al. Adjusting for Unmeasured Confounders in Pharmacoepidemiologic Claims Data Using External Information: The Example of COX2 Inhibitors and Myocardial Infarction , 2005, Epidemiology.
[62] Michael P. Murray. Avoiding Invalid Instruments and Coping with Weak Instruments , 2006 .
[63] M Alan Brookhart,et al. Analytic strategies to adjust confounding using exposure propensity scores and disease risk scores: nonsteroidal antiinflammatory drugs and short-term mortality in the elderly. , 2005, American journal of epidemiology.
[64] S Suissa,et al. THE CASE‐TIME-CONTROL DESIGN , 1995, Epidemiology.
[65] J. Bolognese,et al. Gastrointestinal tolerability of the selective cyclooxygenase-2 (COX-2) inhibitor rofecoxib compared with nonselective COX-1 and COX-2 inhibitors in osteoarthritis. , 2000, Archives of internal medicine.
[66] Sebastian Schneeweiss,et al. Selective prescribing led to overestimation of the benefits of lipid-lowering drugs. , 2006, Journal of clinical epidemiology.
[67] C. Goldsmith,et al. Appropriateness of NSAID and Coxib prescribing for patients with osteoarthritis by primary care physicians in Ontario: results from the CANOAR study. , 2004, The American journal of managed care.
[68] Sebastian Schneeweiss,et al. Use of the case-crossover design to study prolonged drug exposures and insidious outcomes. , 2004, Annals of epidemiology.
[69] J. Cabrita,et al. Using a pharmacoepidemiological approach to estimate diabetes type 2 prevalence in Portugal , 2006, Pharmacoepidemiology and drug safety.
[70] M Maclure,et al. Performance of comorbidity scores to control for confounding in epidemiologic studies using claims data. , 2001, American journal of epidemiology.
[71] K C Cain,et al. Logistic regression analysis and efficient design for two-stage studies. , 1988, American journal of epidemiology.
[72] O. Miettinen,et al. Postmarketing studies of drug efficacy: When must they be randomized? , 1983, Clinical pharmacology and therapeutics.
[73] G. Shaw,et al. Maternal pesticide exposure from multiple sources and selected congenital anomalies. , 1999 .
[74] M. Abrahamowicz,et al. Mortality Rates in Elderly Patients Who Take Different Angiotensin-Converting Enzyme Inhibitors after Acute Myocardial Infarction: A Class Effect? , 2004, Annals of Internal Medicine.
[75] J. Avorn,et al. Paradoxical Relations of Drug Treatment with Mortality in Older Persons , 2001, Epidemiology.
[76] C P Farrington,et al. Within‐subject exposure dependency in case‐crossover studies , 2001, Statistics in medicine.
[77] J. Schlesselman. Assessing effects of confounding variables. , 1978, American journal of epidemiology.
[78] G. Eisen,et al. Meta‐analysis: upper gastrointestinal tolerability of valdecoxib, a cyclooxygenase‐2‐specific inhibitor, compared with nonspecific nonsteroidal anti‐inflammatory drugs among patients with osteoarthritis and rheumatoid arthritis , 2005, Alimentary pharmacology & therapeutics.
[79] B. Psaty,et al. Assessment and Control for Confounding by Indication in Observational Studies , 1999, Journal of the American Geriatrics Society.
[80] J. Pearl,et al. Causal diagrams for epidemiologic research. , 1999, Epidemiology.
[81] C. Clancy. Comparative effectiveness research is a key component of, but tightly linked with, health care delivery in the Information Age. , 2006 .