The effect of adherence to statin therapy on cardiovascular mortality: quantification of unmeasured bias using falsification end-points
Background: To determine the clinical effectiveness of statins on cardiovascular mortality in practice, observational studies are needed. Control for confounding is essential in any observational study. Falsification end-points may be useful to determine if bias is present after adjustment has taken place.
Methods: We followed starters on statin therapy in the Netherlands aged 46 to 100 years over the period 1996 to 2012, from initiation of statin therapy until cardiovascular mortality or censoring. Within this group (n = 49,688, up to 16 years of follow-up), we estimated the effect of adherence to statin therapy (0 = completely non-adherent, 1 = fully adherent) on ischemic heart diseases and cerebrovascular disease (ICD10-codes I20-I25 and I60-I69) as well as respiratory and endocrine disease mortality (ICD10-codes J00-J99 and E00-E90) as falsification end points, controlling for demographic factors, socio-economic factors, birth cohort, adherence to other cardiovascular medications, and diabetes using time-varying Cox regression models.
Results: Falsification end-points indicated that a simpler model was less biased than a model with more controls. Adherence to statins appeared to be protective against cardiovascular mortality (HR: 0.70, 95 % CI 0.61 to 0.81).
Conclusions: Falsification end-points helped detect overadjustment bias or bias due to competing risks, and thereby proved to be a useful technique in such a complex setting.
The effect of adherence to statin therapy on cardiovascular mortality: quantification of unmeasured bias using falsification end-points. Bijlsma, Maarten J.; Vansteelandt, Stijn; Janssen, Fanny; Hak, Eelko. BMC PUBLIC HEALTH, 16 10.1186/s12889-016-2986-0 APR 11 2016
The Importance of Integrating Clinical Relevance and Statistical Significance in the Assessment of Quality of Care-Illustrated Using the Swedish Stroke Register
When profiling hospital performance, quality inicators are commonly evaluated through hospital-specific adjusted means with confidence intervals. When identifying deviations from a norm, large hospitals can have statistically significant results even for clinically irrelevant deviations while important deviations in small hospitals can remain undiscovered. We have used data from the Swedish Stroke Register (Riksstroke) to illustrate the properties of a benchmarking method that integrates considerations of both clinical relevance and level of statistical significance.
The performance measure used was case-mix adjusted risk of death or dependency in activities of daily living within 3 months after stroke. A hospital was labeled as having outlying performance if its case-mix adjusted risk exceeded a benchmark value with a specified statistical confidence level. The benchmark was expressed relative to the population risk and should reflect the clinically relevant deviation that is to be detected. A simulation study based on Riksstroke patient data from 2008-2009 was performed to investigate the effect of the choice of the statistical confidence level and benchmark value on the diagnostic properties of the method.
Simulations were based on 18,309 patients in 76 hospitals. The widely used setting, comparing 95% confidence intervals to the national average, resulted in low sensitivity (0.252) and high specificity (0.991). There were large variations in sensitivity and specificity for different requirements of statistical confidence. Lowering statistical confidence improved sensitivity with a relatively smaller loss of specificity. Variations due to different benchmark values were smaller, especially for sensitivity. This allows the choice of a clinically relevant benchmark to be driven by clinical factors without major concerns about sufficiently reliable evidence.
The study emphasizes the importance of combining clinical relevance and level of statistical confidence when profiling hospital performance. To guide the decision process a web-based tool that gives ROC-curves for different scenarios is provided.
The Importance of Integrating Clinical Relevance and Statistical Significance in the Assessment of Quality of Care-Illustrated Using the Swedish Stroke Register. Lindmark, Anita; van Rompaye, Bart; Goetghebeur, Els; Glader, Eva-Lotta; Eriksson, Marie. PLOS ONE, 11 (4):10.1371/journal.pone.0153082 APR 7 2016