Use of disease risk scores in pharmacoepidemiologic studies
Department of Biostatistics, Vanderbilt University, Nashville, TN 37232-2158, USA. Statistical Methods in Medical Research
(Impact Factor: 4.47).
07/2008; 18(1):67-80. DOI: 10.1177/0962280208092347
Automated databases are increasingly used in pharmacoepidemiologic studies. These databases include records of prescribed medications and encounters with medical care providers from which one can construct very detailed surrogate measures for both drug exposure and covariates that are potential confounders. Often it is possible to track day-by-day changes in these variables. However, while this information is often critical for study success, its volume can pose challenges for statistical analysis. One common approach is the use of propensity scores. An alternative approach is to construct a disease risk score. This is analogous to the propensity score in that it calculates a summary measure from the covariates. However, the disease risk score estimates the probability or rate of disease occurrence conditional on being unexposed. The association between exposure and disease is then estimated adjusting for the disease risk score in place of the individual covariates. This review describes the use of disease risk scores in pharmacoepidemiologic studies, and includes a brief discussion of their history, a more detailed description of their construction and use, a summary of simulation studies comparing their performance vis-á-vis traditional models, a comparison of their utility with that of propensity scores, and some further topics for future research.
Available from: Brian K Lee
- "In this article, we propose a simple new balance measure based on prognostic scores, also known as ''disease risk scores'' when the outcome is binary  . Fundamentally, this new balance measure attempts to ensure that the groups Conflict of interest: The authors have no conflicts of interest related to the content of this article. "
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ABSTRACT: Examining covariate balance is the prescribed method for determining the degree to which propensity score methods should be successful at reducing bias. This study assessed the performance of various balance measures, including a proposed balance measure based on the prognostic score (similar to a disease risk score), to determine which balance measures best correlate with bias in the treatment effect estimate.
The correlations of multiple common balance measures with bias in the treatment effect estimate produced by weighting by the odds, subclassification on the propensity score, and full matching on the propensity score were calculated. Simulated data were used, based on realistic data settings. Settings included both continuous and binary covariates and continuous covariates only.
The absolute standardized mean difference (ASMD) in prognostic scores, the mean ASMD (in covariates), and the mean t-statistic all had high correlations with bias in the effect estimate. Overall, prognostic scores displayed the highest correlations with bias of all the balance measures considered. Prognostic score measure performance was generally not affected by model misspecification, and the prognostic score measure performed well under a variety of scenarios.
Researchers should consider using prognostic score-based balance measures for assessing the performance of propensity score methods for reducing bias in nonexperimental studies.
Journal of clinical epidemiology 08/2013; 66(8 Suppl):S84-S90.e1. DOI:10.1016/j.jclinepi.2013.01.013 · 3.42 Impact Factor
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ABSTRACT: Clinicians and payers require rapid comparative effectiveness (CE) evidence generation to inform decisions for new drugs. We empirically assessed treatment dynamics of newly marked drugs and their implications for conducting CE research.
We used claims data to evaluate five drug-outcome pairs: 1) raloxifene (vs. alendronate) and fracture; 2) risedronate (vs. alendronate) and fracture; 3) simvastatin plus ezetimibe fixed-dose combination (simvastatin + ezetimibe) (vs. simvastatin alone) and cardiovascular events; 4) rofecoxib (vs. nonselective nonsteroidal anti-inflammatory drugs [ns-NSAIDs]) and myocardial infarction; and 5) rofecoxib (vs. ns-NSAIDS) and gastrointestinal bleed. We examined utilization dynamics in the early marketing period, including evolving utilization patterns, outcome risk among those treated with new versus established drugs, and prior treatment patterns that may indicate treatment resistance or intolerance. We addressed these challenges by replicating active CE monitoring with sequential matched cohort analysis.
Patients initiating new drugs were more likely to have used other drugs for the same indication in the past, but the majority of patients in all new drug cohorts were treatment naive (82.0% overall). Patients initiating rofecoxib had higher predicted baseline risk of gastrointestinal bleed than did patients initiating ns-NSAIDs. Patients initiating risedronate and alendronate had similar predicted baseline risks of fracture, while those initiating raloxifene and simvastatin + ezetimibe had lower risks of outcomes of interest relative to their comparators. Prospective monitoring yielded results consistent with expectation for each example.
Many challenges to assessing the CE of new drugs are borne out in empirical data. Attention to these challenges can yield valid CE results.
Value in Health 09/2013; 16(6):1054-62. DOI:10.1016/j.jval.2013.05.008 · 3.28 Impact Factor
Available from: Anders Sundström
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