Comparative Effectiveness of Prostate Cancer Treatments: Evaluating Statistical Adjustments for Confounding in Observational Data
Open Access
- 13 October 2010
- journal article
- research article
- Published by Oxford University Press (OUP) in JNCI Journal of the National Cancer Institute
- Vol. 102 (23) , 1780-1793
- https://doi.org/10.1093/jnci/djq393
Abstract
Using observational data to assess the relative effectiveness of alternative cancer treatments is limited by patient selection into treatment, which often biases interpretation of outcomes. We evaluated methods for addressing confounding in treatment and survival of patients with early-stage prostate cancer in observational data and compared findings with those from a benchmark randomized clinical trial. We selected 14 302 early-stage prostate cancer patients who were aged 66–74 years and had been treated with radical prostatectomy or conservative management from linked Surveillance, Epidemiology, and End Results–Medicare data from January 1, 1995, through December 31, 2003. Eligibility criteria were similar to those from a clinical trial used to benchmark our analyses. Survival was measured through December 31, 2007, by use of Cox proportional hazards models. We compared results from the benchmark trial with results from models with observational data by use of traditional multivariable survival analysis, propensity score adjustment, and instrumental variable analysis. Prostate cancer patients receiving conservative management were more likely to be older, nonwhite, and single and to have more advanced disease than patients receiving radical prostatectomy. In a multivariable survival analysis, conservative management was associated with greater risk of prostate cancer–specific mortality (hazard ratio [HR] = 1.59, 95% confidence interval [CI] = 1.27 to 2.00) and all-cause mortality (HR = 1.47, 95% CI = 1.35 to 1.59) than radical prostatectomy. Propensity score adjustments resulted in similar patient characteristics across treatment groups, although survival results were similar to traditional multivariable survival analyses. Results for the same comparison from the instrumental variable approach, which theoretically equalizes both observed and unobserved patient characteristics across treatment groups, differed from the traditional multivariable and propensity score results but were consistent with findings from the subset of elderly patient with early-stage disease in the trial (ie, conservative management vs radical prostatectomy: for prostate cancer–specific mortality, HR = 0.73, 95% CI = 0.08 to 6.73; for all-cause mortality, HR = 1.09, 95% CI = 0.46 to 2.59). Instrumental variable analysis may be a useful technique in comparative effectiveness studies of cancer treatments if an acceptable instrument can be identified.Keywords
This publication has 46 references indexed in Scilit:
- Outcomes of Localized Prostate Cancer Following Conservative ManagementJAMA, 2009
- Instrumental variables I: instrumental variables exploit natural variation in nonexperimental data to estimate causal relationshipsPublished by Elsevier ,2009
- Instrumental variables II: instrumental variable application—in 25 variations, the physician prescribing preference generally was strong and reduced covariate imbalanceJournal of Clinical Epidemiology, 2009
- Radical Prostatectomy Versus Watchful Waiting in Localized Prostate Cancer: the Scandinavian Prostate Cancer Group-4 Randomized TrialJNCI Journal of the National Cancer Institute, 2008
- Survival Following Primary Androgen Deprivation Therapy Among Men With Localized Prostate CancerJAMA, 2008
- Two-stage residual inclusion estimation: Addressing endogeneity in health econometric modelingJournal of Health Economics, 2008
- Analysis of Observational Studies in the Presence of Treatment Selection BiasJAMA, 2007
- 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 methodsJournal of Clinical Epidemiology, 2005
- Reducing Bias in Observational Studies Using Subclassification on the Propensity ScoreJournal of the American Statistical Association, 1984
- The central role of the propensity score in observational studies for causal effectsBiometrika, 1983