Evaluating Clinical and Public Health Interventions A Practical Guide to Study Design and Statistics
Book contents
- Frontmatter
- Contents
- Preface
- 1 Introduction
- 2 Interventions
- 3 Evaluating an intervention
- 4 Randomized designs
- 5 Nonrandomized studies
- 6 Statistical analysis of intervention trials
- 7 Methods for adjusting for baseline differences between treatment groups
- 8 Time series analysis
- 9 Special topics
- 10 Research to action
- 11 Conclusion
- Index
- References
7 - Methods for adjusting for baseline differences between treatment groups
Published online by Cambridge University Press: 10 May 2010
Book contents
- Frontmatter
- Contents
- Preface
- 1 Introduction
- 2 Interventions
- 3 Evaluating an intervention
- 4 Randomized designs
- 5 Nonrandomized studies
- 6 Statistical analysis of intervention trials
- 7 Methods for adjusting for baseline differences between treatment groups
- 8 Time series analysis
- 9 Special topics
- 10 Research to action
- 11 Conclusion
- Index
- References
- Type
- Chapter
- Information
- Evaluating Clinical and Public Health InterventionsA Practical Guide to Study Design and Statistics, pp. 101 - 127Publisher: Cambridge University PressPrint publication year: 2010
References
“Estimating causal effects from large data sets using propensity scores.” Ann. Intern. Med. 127 (1997): 757–63CrossRefGoogle ScholarPubMed
“Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group.” Statist. Med. 17 (1998): 2265–813.0.CO;2-B>CrossRefGoogle ScholarPubMed
, , , , and “Aspirin use and all-cause mortality among patients being evaluated for known or suspected coronary artery disease: A propensity analysis.” JAMA 286 (2001): 1187–94CrossRefGoogle ScholarPubMed
, , and “A comparison of the ability of different propensity score models to balance measured variables between treated and untreated subjects: A Monte Carlo study.” Statist. Med. 26 (2007): 734–53CrossRefGoogle ScholarPubMed
“Estimating causal effects from large data sets using propensity scores.” Ann. Intern. Med. 127 (1997): 757–63CrossRefGoogle ScholarPubMed
Multivariable Analysis: A Practical Guide for Clinicians (2nd edition). Cambridge: Cambridge University Press, 2006: p. 124CrossRefGoogle Scholar
and “Causal effect of ambulatory specialty care on mortality following myocardial infarction: A comparison of propensity score and instrumental variable analyses.” Health Serv. Outcomes Res. Methodol. 2 (2001): 221–45CrossRefGoogle Scholar
, , , , , and “Analysis of observational studies in the presence of treatment selection bias: Effects of invasive cardiac management on AMI survival using propensity score and instrument variable methods.” JAMA 297 (2007): 278–85CrossRefGoogle ScholarPubMed
“The performance of different propensity score methods for estimating marginal odds ratios.” Statist. Med. 26 (2007): 3078–94CrossRefGoogle ScholarPubMed
, , and “Risk of death in elderly users of conventional vs. atypical antipsychotic medications.” N. Engl. J. Med. 353 (2005): 2235–41CrossRefGoogle ScholarPubMed
“Estimating causal effects from large data sets using propensity scores.” Ann. Intern. Med. 127 (1997): 757–63CrossRefGoogle ScholarPubMed
and “Causal effect of ambulatory specialty care on mortality following myocardial infarction: A comparison of propensity score and instrumental variable analyses.” Health Serv. Outcomes Res. Methodol. 2 (2001): 221–45CrossRefGoogle Scholar
, , , , , and “Association between critical care physician management and patient mortality in the intensive care unit.” Ann. Intern. Med. 148 (2008): 801–9CrossRefGoogle ScholarPubMed
and “Effectiveness of a mental health court in reducing criminal recidivism and violence.” Am. J. Psychiatry 64 (2007): 1395–403CrossRefGoogle Scholar
and “Stratification and weighting via the propensity score in estimation of causal treatment effects: A comparative study.” Statist. Med. 23 (2004): 2937–60CrossRefGoogle ScholarPubMed
“Using propensity scores to help design observational studies: Application to the tobacco litigation.” Health Serv. Outcomes Res. Methodol. 2 (2001): 169–88CrossRefGoogle Scholar
, , and “Identification of causal effects using instrumental variables.” J. Am. Stat. Assoc. 91 (1996): 444–54CrossRefGoogle Scholar
and “Econometrics in outcomes research: The use of instrumental variables.” Annu. Rev. Public Health 19 (1998): 17–34CrossRefGoogle ScholarPubMed
and “Instruments for causal inference: An epidemiologist's dream?” Epidemiology 17 (2006): 360–72CrossRefGoogle ScholarPubMed
“An introduction to instrumental variables for epidemiologists.” Int. J. Epidemiol. 29 (2000): 722–9CrossRefGoogle ScholarPubMed
, , , , and “Comparing standard regression, propensity score matching, and instrumental variables for determining the influence of mammography on stage of diagnosis.” Health Serv. Outcomes Res. Methodol. 2 (2001): 279–90CrossRefGoogle Scholar
and “Instrumental variables regression with weak instruments.” Econometrica 63 (1997): 557–86CrossRefGoogle Scholar
, , , , and “Comparing standard regression, propensity score matching, and instrumental variables for determining the influence of mammography on stage of diagnosis.” Health Serv. Outcomes Res. Methodol. 2 (2001): 279–90CrossRefGoogle Scholar
, , and “Identification of causal effects using instrumental variables.” J. Am. Stat. Assoc. 91 (1996): 444–54CrossRefGoogle Scholar
, , , , and “Are primary care services a substitute or complement for specialty and inpatient services?” Health Serv. Res. 40 (2005): 1422–42CrossRefGoogle ScholarPubMed
and “Who is the marginal patient? Understanding instrumental variables estimates of treatment effects.” Health Serv. Res. 33 (1998): 1337–60Google ScholarPubMed
and “Identification and estimation of local average treatment effects.” Econometrica 62 (1994): 467–75CrossRefGoogle Scholar
Jr. and Sr. “Estimating treatment effects using observational data.” JAMA 297 (2007): 314–16CrossRefGoogle ScholarPubMed
Jr. and Sr. “Using observational data to estimate treatment effects – Reply.” JAMA 297 (2007): 2079Google Scholar
, , , , , and “Impact of housing on the survival of persons with AIDS.” BMC Public Health 9 (2009): 220CrossRefGoogle ScholarPubMed
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