Pitfalls of pharmacoepidemiology.
Ontology highlight
ABSTRACT:
SUBMITTER: Skegg DC
PROVIDER: S-EPMC1118945 | biostudies-literature | 2000 Nov
REPOSITORIES: biostudies-literature
Publications
Similar Datasets
Project description:Narrative electronic prescribing instructions (NEPIs) are text that convey information on the administration or co-administration of a drug as directed by a prescriber. For researchers, NEPIs have the potential to advance our understanding of the risks and benefits of medications in populations; however, due to their unstructured nature, they are not often utilized. The goal of this scoping review was to evaluate how NEPIs are currently employed in research, identify opportunities and challenges for their broader application, and provide recommendations on their future use. The scoping review comprised a comprehensive literature review and a survey of key stakeholders. From the literature review, we identified 33 primary articles that described the use of NEPIs. The majority of articles (n = 19) identified issues with the quality of information in NEPIs compared with structured prescribing information; nine articles described the development of novel algorithms that performed well in extracting information from NEPIs, and five described the used of manual or simpler algorithms to extract prescribing information from NEPIs. A survey of 19 stakeholders indicated concerns for the quality of information in NEPIs and called for standardization of NEPIs to reduce data variability/errors. Nevertheless, stakeholders believed NEPIs present an opportunity to identify prescriber's intent for the prescription and to study temporal treatment patterns. In summary, NEPIs hold much promise for advancing the field of pharmacoepidemiology. Researchers should take advantage of addressing important questions that can be uniquely answered with NEPIs, but exercise caution when using this information and carefully consider the quality of the data.
| S-EPMC8419095 | biostudies-literature
Project description:Risk is an important parameter to describe the occurrence of health outcomes over time. However, many outcomes of interest in healthcare settings, such as disease incidence, treatment initiation, and cause-specific mortality, may be precluded from occurring by other events, often referred to as competing events. Here, we review straightforward approaches to estimate risk in the presence of competing events. We illustrate the application of these methods using timely examples in pharmacoepidemiologic research and compare results to those obtained using analytic simplifications commonly used to handle competing events. These examples demonstrate how the analytic methods used to account for competing events affect the interpretation of results from pharmacoepidemiologic studies.
| S-EPMC5557056 | biostudies-literature
Project description:Background:Instrumental variable analysis, for example with physicians' prescribing preferences as an instrument for medications issued in primary care, is an increasingly popular method in the field of pharmacoepidemiology. Existing power calculators for studies using instrumental variable analysis, such as Mendelian randomization power calculators, do not allow for the structure of research questions in this field. This is because the analysis in pharmacoepidemiology will typically have stronger instruments and detect larger causal effects than in other fields. Consequently, there is a need for dedicated power calculators for pharmacoepidemiological research. Methods and Results:The formula for calculating the power of a study using instrumental variable analysis in the context of pharmacoepidemiology is derived before being validated by a simulation study. The formula is applicable for studies using a single binary instrument to analyse the causal effect of a binary exposure on a continuous outcome. An online calculator, as well as packages in both R and Stata, are provided for the implementation of the formula by others. Conclusions:The statistical power of instrumental variable analysis in pharmacoepidemiological studies to detect a clinically meaningful treatment effect is an important consideration. Research questions in this field have distinct structures that must be accounted for when calculating power. The formula presented differs from existing instrumental variable power formulae due to its parametrization, which is designed specifically for ease of use by pharmacoepidemiologists.
| S-EPMC5837396 | biostudies-literature
Project description:BackgroundCase-only designs have been used since late 1980's. In these, as opposed to case-control or cohort studies for instance, only cases are required and are self-controlled, eliminating selection biases and confounding related to control subjects, and time-invariant characteristics. The objectives of this systematic review were to analyze how the two main case-only designs - case-crossover (CC) and self-controlled case series (SCCS) - have been applied and reported in pharmacoepidemiology literature, in terms of applicability assumptions and specificities of these designs.Methodology/principal findingsWe systematically selected all reports in this field involving case-only designs from MEDLINE and EMBASE up to September 15, 2010. Data were extracted using a standardized form. The analysis included 93 reports 50 (54%) of CC and 45 (48%) SCCS, 2 reports combined both designs. In 12 (24%) CC and 18 (40%) SCCS articles, all applicable validity assumptions of the designs were fulfilled, respectively. Fifty (54%) articles (15 CC (30%) and 35 (78%) SCCS) adequately addressed the specificities of the case-only analyses in the way they reported results.Conclusions/significanceOur systematic review underlines that implementation of CC and SCCS designs needs to be more rigorous with regard to validity assumptions, as well as improvement in results reporting.
| S-EPMC3500300 | biostudies-literature
Project description:Transparency is increasingly promoted to instill trust in nonrandomized studies using real-world data. Graphics and data visualizations support transparency by aiding communication and understanding, and can inform study design and analysis decisions. However, other than graphical representation of a study design and flow diagrams (e.g., a Consolidated Standards of Reporting Trials [CONSORT] like diagram), specific standards on how to maximize validity and transparency with visualization are needed. This paper provides guidance on how to use visualizations throughout the life cycle of a pharmacoepidemiology study-from initial study design to final report-to facilitate rationalized and transparent decision-making about study design and implementation, and clear communication of study findings. Our intent is to help researchers align their practices with current consensus statements on transparency.
| S-EPMC9826437 | biostudies-literature
Project description:Clinical trials have several important limitations for evaluating the safety of new medications, leading to many adverse events not being identified until the postmarketing period. Descriptive studies, including case reports, case series, cross-sectional, and ecologic studies, help identify potential safety signals and generate hypotheses. Further research using analytic study methods, including case-control studies and cohort studies, are necessary to determine if an association truly exists and to better understand the potential for causation. Pharmacoepidemiology research examines the use and effects of drugs when used in large populations of patients, using a variety of study designs and biostatistical techniques to reduce the confounding and systematic error associated with observational research. Understanding the strengths and limitations of pharmacoepidemiology research techniques is necessary to interpret the validity of drug safety studies, guiding both individual patient decisions and broader public health decisions.
| S-EPMC5798480 | biostudies-literature
Project description:The definition of a new case is a vital step in incidence studies in both epidemiology and pharmacoepidemiology, although with significant differences in methodology between the fields. We define and apply a framework for two different types of new cases of drug use, first-ever and recurrent, and show how the associated misclassifications related to length of run-in period can be expressed by the positive predictive value (PPV). In the study, we consider individual-level dispensations of statins 2006-2019 for 1,017,058 individuals with at least one dispensation in 2019 in Sweden. The incidence proportion for statins for both sexes of all ages in Sweden 2019 varied from 17.4/1000 with a run-in of 8 months, 9.45/1000 with 5 years and 8.4/1000 with 10 years. The PPV was 49% with 8 months and 89% for 5 years using 10 years as gold standard. We conclude that the interpretation of incidence and thus the selection of an appropriate run-in period, in pharmacoepidemiology, depends on whether first-ever use, recurrent treatment or both together (new cases) is the focus of the research question studied. At least five different misclassifications can be introduced depending on how incidence is defined.
| S-EPMC9320840 | biostudies-literature
Project description:BackgroundDrug exposure assessment based on dispensing data can be misclassified when patients do not adhere to their therapy or when information about over-the-counter drugs is not captured in the study database. Previous research has considered hypothetical sensitivity and specificity values, whereas this study aims to assess the impact of literature-based real values of exposure misclassification.MethodsA synthetic cohort study was constructed based on the proportion of exposure theoretically captured in a database (range 0.5-1.0) and the level of adherence (0.5-1.0). Three scenarios were explored: nondifferential misclassification, differential misclassification (misclassifications dependent on an unmeasured risk factor doubling the outcome risk), and nondifferential misclassification in a comparative effectiveness study (RRA and RRB both 2.0 compared to nonuse, RRA-B 1.0).ResultsFor the scenarios with nondifferential misclassification, 25% nonadherence or 25% uncaptured exposure changed the RR from 2.0 to 1.75, and 1.95, respectively. Applying different proportions of nonadherence or uncaptured use (20% vs. 40%) for subgroups with and without the risk factor, an RR of 0.95 was observed in the absence of a true effect (i.e., true RR = 1). In the comparative effectiveness study, no effect on RR was seen for different proportions of uncaptured exposure; however, different levels of nonadherence for the drugs (20% vs. 40%) led to an underestimation of RRA-B (0.89).DiscussionAll scenarios led to biased estimates, but the magnitude of the bias differed across scenarios. When testing the robustness of findings of pharmacoepidemiologic studies, we recommend using realistic values of nonadherence and uncaptured exposure based on real-world data.
| S-EPMC9292927 | biostudies-literature
Project description:While state sequence analysis (SSA) has been long used in social sciences, its use in pharmacoepidemiology is still in its infancy. Indeed, this technique is relatively easy to use, and its intrinsic visual nature may help investigators to untangle the latent information within prescription data, facilitating the individuation of specific patterns and possible inappropriate use of medications. In this paper, we provide an educational primer of the most important learning concepts and methods of SSA, including measurement of dissimilarities between sequences, the application of clustering methods to identify sequence patterns, the use of complexity measures for sequence patterns, the graphical visualization of sequences, and the use of SSA in predictive models. As a worked example, we present an application of SSA to opioid prescription patterns in patients with non-cancer pain, using real-world data from Italy. We show how SSA allows the identification of patterns in prescriptions in these data that might not be evident using standard statistical approaches and how these patterns are associated with future discontinuation of opioid therapy.
| S-EPMC8705850 | biostudies-literature
Project description:Recent work has demonstrated that propensity score matching may lead to increased covariate imbalance, even with the corresponding decrease in propensity score distance between matched units. The extent to which this paradoxical phenomenon might harm causal inference in real epidemiologic studies has not been explored. We evaluated the effect of this phenomenon using insurance claims data from the Pharmaceutical Assistance Contract for the Elderly (1999-2002) and Medicaid Analytic eXtract (2000-2007) databases in the United States. For each data set, we created several 1:1 propensity-score-matched data sets by manipulating the size of the covariate set used to generate propensity scores, the index exposure prevalence in the prematched data set, and the matching algorithm. We matched all index units, then progressively pruned matched sets in order of decreasing propensity score distance, calculating covariate imbalance after each pruning. Although covariate imbalance sometimes increased after progressive pruning of matched sets, the application of commonly used propensity score calipers for defining an acceptable match stopped pruning near the lowest region of the imbalance trend and resulted in an improvement over the imbalance in the prematched data set. Thus, propensity score matching does not appear to induce increased covariate imbalance when standard propensity score calipers are applied in these types of pharmacoepidemiologic studies.
| S-EPMC6118075 | biostudies-literature