607 research outputs found
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Effect of smoking on comparative efficacy of antiplatelet agents: systematic review, meta-analysis, and indirect comparison
Objective: To evaluate whether smoking status is associated with the efficacy of antiplatelet treatment in the prevention of cardiovascular events. Design: Systematic review, meta-analysis, and indirect comparisons. Data sources Medline (1966 to present) and Embase (1974 to present), with supplementary searches in databases of abstracts from major cardiology conferences, the Cumulative Index to Nursing and Allied Health (CINAHL) and the CAB Abstracts databases, and Google Scholar. Study selection Randomized trials of clopidogrel, prasugrel, or ticagrelor that examined clinical outcomes among subgroups of smokers and nonsmokers. Data extraction Two authors independently extracted all data, including information on the patient populations included in the trials, treatment types and doses, definitions of clinical outcomes and duration of follow-up, definitions of smoking subgroups and number of patients in each group, and effect estimates and 95% confidence intervals for each smoking status subgroup. Results: Of nine eligible randomized trials, one investigated clopidogrel compared with aspirin, four investigated clopidogrel plus aspirin compared with aspirin alone, and one investigated double dose compared with standard dose clopidogrel; these trials include 74 489 patients, of whom 21 717 (29%) were smokers. Among smokers, patients randomized to clopidogrel experienced a 25% reduction in the primary composite clinical outcome of cardiovascular death, myocardial infarction, and stroke compared with patients in the control groups (relative risk 0.75, 95% confidence interval 0.67 to 0.83). In nonsmokers, however, clopidogrel produced just an 8% reduction in the composite outcome (0.92, 0.87 to 0.98). Two studies investigated prasugrel plus aspirin compared with clopidogrel plus aspirin, and one study investigated ticagrelor plus aspirin compared with clopidogrel plus aspirin. In smokers, the relative risk was 0.71 (0.61 to 0.82) for prasugrel compared with clopidogrel and 0.83 (0.68 to 1.00) for ticagrelor compared with clopidogrel. Corresponding relative risks were 0.92 (0.83 to 1.01) and 0.89 (0.79 to 1.00) among nonsmokers. Conclusions: In randomized clinical trials of antiplatelet drugs, the reported clinical benefit of clopidogrel in reducing cardiovascular death, myocardial infarction, and stroke was seen primarily in smokers, with little benefit in nonsmokers
Pharmacoepidemiology and "in silico" drug evaluation: is there common ground?
Shortcomings of randomized trials have long been recognized by medical researchers (1,2). These shortcomings include a reduced range of risk factors for the outcome among patients enrolled in pre-approval trials. The reduced range enhances the validity of the trials, but the patient populations are highly selected compared with the broad range of risk factors represented among patients who are eventually treated with the drug. This restriction may reduce the generalizability of the findings from those studied to those with different baseline risks
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Differential risk of death in older residents in nursing homes prescribed specific antipsychotic drugs: population based cohort study
Objective To assess risks of mortality associated with use of individual antipsychotic drugs in elderly residents in nursing homes
Adjusting effect estimates for unmeasured confounding with validation data using propensity score calibration
Often, data on important confounders are not available in cohort studies. Sensitivity analyses based on the relation of single, but not multiple, unmeasured confounders with an exposure of interest in a separate validation study have been proposed. In this paper, the authors controlled for measured confounding in the main cohort using propensity scores (PS's) and addressed unmeasured confounding by estimating two additional PS's in a validation study. The "error-prone" PS exclusively used information available in the main cohort. The "gold standard" PS additionally included data on covariates available only in the validation study. Based on these two PS's in the validation study, regression calibration was applied to adjust regression coefficients. This propensity score calibration (PSC) adjusts for unmeasured confounding in cohort studies with validation data under certain, usually untestable, assumptions. The authors used PSC to assess the relation between nonsteroidal antiinflammatory drugs (NSAIDs) and 1-year mortality in a large cohort of elderly persons. "Traditional" adjustment resulted in a hazard ratio for NSAID users of 0.80 (95% confidence interval (CI): 0.77, 0.83) as compared with an unadjusted hazard ratio of 0.68 (95% CI: 0.66, 0.71). Application of PSC resulted in a more plausible hazard ratio of 1.06 (95% CI: 1.00, 1.12). Until the validity and limitations of PSC have been assessed in different settings, the method should be seen as a sensitivity analysis
Treatment Effects in the Presence of Unmeasured Confounding: Dealing With Observations in the Tails of the Propensity Score Distribution--A Simulation Study
Frailty, a poorly measured confounder in older patients, can promote treatment in some situations and discourage it in others. This can create unmeasured confounding and lead to nonuniform treatment effects over the propensity score (PS). The authors compared bias and mean squared error for various PS implementations under PS trimming, thereby excluding persons treated contrary to prediction. Cohort studies were simulated with a binary treatment T as a function of 8 covariates X. Two of the covariates were assumed to be unmeasured strong risk factors for the outcome and present in persons treated contrary to prediction. The outcome Y was simulated as a Poisson function of T and all X’s. In analyses based on measured covariates only, the range of PS's was trimmed asymmetrically according to the percentile of PS in treated patients at the lower end and in untreated patients at the upper end. PS trimming reduced bias due to unmeasured confounders and mean squared error in most scenarios assessed. Treatment effect estimates based on PS range restrictions do not correspond to a causal parameter but may be less biased by such unmeasured confounding. Increasing validity based on PS trimming may be a unique advantage of PS's over conventional outcome models
Applying System Engineering to Pharmaceutical Safety
While engineering techniques are used in the development of medical devices and have been applied to individual healthcare processes, such as the use of checklists in surgery and ICUs, the application of system engineering techniques to larger healthcare systems is less common. System safety is the part of system engineering that uses modeling and analysis to identify hazards and to design the system to eliminate or control them. In this paper, we demonstrate how to apply a new, safety engineering static and dynamic modeling and analysis approach to healthcare systems. Pharmaceutical safety is used as the example in the paper, but the same approach is potentially applicable to other complex healthcare systems. System engineering techniques can be used in re-engineering the system as a whole to achieve the system goals, including both enhancing the safety of current drugs while, at the same time, encouraging the development of new drugs
Performance of propensity score calibration - A simulation study
Confounding can be a major source of bias in nonexperimental research. The authors recently introduced propensity score calibration (PSC), which combines propensity scores and regression calibration to address confounding by variables unobserved in the main study by using variables observed in a validation study. Here, the authors assess the performance of PSC using simulations in settings with and without violation of the key assumption of PSC: that the error-prone propensity score estimated in the main study is a surrogate for the gold-standard propensity score (i.e., it contains no additional information on the outcome). The assumption can be assessed if data on the outcome are available in the validation study. If data are simulated allowing for surrogacy to be violated, results depend largely on the extent of violation. If surrogacy holds, PSC leads to bias reduction between 32% and 106% (>100% representing overcorrection). If surrogacy is violated, PSC can lead to an increase in bias. Surrogacy is violated when the direction of confounding of the exposure-disease association caused by the unobserved variable(s) differs from that of the confounding due to observed variables. When surrogacy holds, PSC is a useful approach to adjust for unmeasured confounding using validation data
Propensity Score Calibration in the Absence of Surrogacy
Propensity score calibration (PSC) can be used to adjust for unmeasured confounders using a cross-sectional validation study that lacks information on the disease outcome (Y), under a strong surrogacy assumption. Using directed acyclic graphs and path analysis, the authors developed a formula to predict the presence and magnitude of the bias of PSC in the simplest setting of a binary exposure (T) and 1 confounder (X) that are observed in the main study and 1 confounder (C) that is observed in the validation study only. PSC bias is predicted on the basis of parameters that can be estimated from the data and a single unidentifiable parameter, the relative risk (RR) associated with C (RRCY). The authors simulated 1,000 cohort studies each with a Poisson-distributed outcome Y, varying parameter values over a wide range. When using the true parameter for RRCY, the formula predicts PSC bias almost perfectly in this simple setting (correlation with observed bias over 24 scenarios assessed: r = 0.998). The authors conclude that the bias from PSC observed in certain scenarios can be estimated from the imbalance in C between treated and untreated persons, after adjustment for X, in the validation study and assuming a range of plausible values for the unidentifiable RRCY
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Risk of Ischemic Cerebrovascular and Coronary Events in Adult Users of Anticonvulsant Medications in Routine Care Settings
Background: Older‐generation anticonvulsants that highly induce cytochrome P450 enzyme system activity produce metabolic abnormalities that may increase cardiovascular risk. The objective of this study was to evaluate the risk of ischemic cerebrovascular and coronary events in adult new users of anticonvulsants that highly induce cytochrome P450 activity compared with other anticonvulsant agents, as observed in a routine care setting. Methods and Results: This was a cohort study of patients 40 to 64 years old from the HealthCore Integrated Research Database who had initiated an anticonvulsant medication between 2001 and 2006 and had no recorded major coronary or cerebrovascular condition in the 6 months before treatment initiation. Propensity score (PS) matching was used to evaluate ischemic cerebrovascular and coronary risk among anticonvulsant new users. High‐dimensional propensity score (hdPS)–matched analyses were used to confirm adjusted findings. The study identified 913 events in 166 031 unmatched new treatment episodes with anticonvulsant drugs. In a PS‐matched population of 22 864 treatment episodes, the rate ratio (RR) for ischemic coronary or cerebrovascular events associated with highly inducing agents versus other agents was 1.22 (95% CI, 0.90‐1.65). The RR moved to 0.99 (95% CI, 0.73‐1.33) with adjustment for hdPS matching (RR, 1.47; 95% CI, 0.95‐2.28 for cerebrovascular events; RR, 0.70; 95% CI, 0.47‐1.05 for coronary events). Conclusions: In this exploratory analysis, there was no evidence of a consistent and statistically significant effect of initiating anticonvulsants that highly induce cytochrome P450 activity on ischemic coronary or cerebrovascular outcomes compared with other agents, given routine care utilization patterns
Analytic strategies to adjust confounding using exposure propensity scores and disease risk scores: Nonsteroidal antiinflammatory drugs and short-term mortality in the elderly
Little is known about optimal application and behavior of exposure propensity scores (EPS) in small studies. In a cohort of 103,133 elderly Medicaid beneficiaries in New Jersey, the effect of nonsteroidal antiinflammatory drug use on 1-year all-cause mortality was assessed (1995-1997) based on the assumption that there is no protective effect and that the preponderance of any observed effect would be confounded. To study the comparative behavior of EPS, disease risk scores, and "conventional" disease models, the authors randomly resampled 1,000 subcohorts of 10,000, 1,000, and 500 persons. The number of variables was limited in disease models, but not EPS and disease risk scores. Estimated EPS were used to adjust for confounding by matching, inverse probability of treatment weighting, stratification, and modeling. The crude rate ratio of death was 0.68 for users of nonsteroidal antiinflammatory drugs. "Conventional" adjustment resulted in a rate ratio of 0.80 (95% confidence interval: 0.77, 0.84). The rate ratio closest to 1 (0.85) was achieved by inverse probability of treatment weighting (95% confidence interval: 0.82, 0.88). With decreasing study size, estimates remained further from the null value, which was most pronounced for inverse probability of treatment weighting (n = 500: rate ratio = 0.72, 95% confidence interval: 0.26, 1.68). In this setting, analytic strategies using EPS or disease risk scores were not generally superior to "conventional" models. Various ways to use EPS and disease risk scores behaved differently with smaller study size
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