The information about drugs and sex/gender are based on structured literature searches in PubMed. The selection of items for extensive review has been made according to the principles of evidence-based medicine. Thus, the knowledge compilation is based primarily on meta-analysis or well-done randomized clinical trials. When these are lacking, observational studies have been used.
Few studies are structured in order to directly study sex and gender differences in drug utilization. The information has been taken from many different sources. Thus, a fair grading of evidence becomes difficult to implement, so we opted not to enter evidence level of this knowledge bank Janusmed Sex & Gender. General levels of evidence grading that the Swedish agencies Swedish Medical Products Agency, Swedish Agency for Health Technology Assessment and Assessment of Social Services (SBU), and The National Board of Health and Welfare apply are available on their websites.
It is important to emphasize that the evidence grade not only depend on the study type, but also how well-done the study is. A well-designed observational study can provide a higher evidence grade that a bad (or too small) clinical trial. Generalizability to actual medical care is also higher in observational studies because patients participating in clinical trials often differ from those receiving the drug when it’s available on the market, due to clinical trials narrow inclusion and exclusion criteria.
In a clinical trial, an active intervention is taking place, where the principal investigator prospectively study the efficacy or safety of a particular treatment. Often a selected group of patients are studied (patients chosen based on various inclusion an exclusion criteria). Patients were randomized to one of several treatments. Patients are randomized to one of several treatments.
In an observational study, patients are included based on one or more selection criteria, for example, based on which disease they have, or what medicines they are taking. Subsequently, data are collected retrospectively or prospectively using forms or the data can be retrieved from existing databases or medical records. Cohort studies and case-control studies are two common types of studies, see Glossary below.
The lack of randomization and thus the risk of confounding give the observational studies a lower evidence level than clinical trials. However, they can be a complement to study issues that can’t be answered in a clinical trial or when, for ethical or practical reasons is not possible to perform a clinical trial.
The set-up of cohort studies are similar to a traditional clinical trial. A group of people (cohort) are classified based on how they were exposed to a particular drug and a relevant control group is settled. Variables of interest are specified and measured, and the whole cohort is followed, and the effects or side effects between the exposed and unexposed groups are compared. The effect is measured in terms of absolute or relative risk.
In case-controls studies, the analysis starts from the outcome. People who had some effect or side effect are identified and an appropriate control group consisting of people who are not affected by the outcome are selected. Cases and controls are compared in terms of the relationship treated vs. untreated with the drug being studies. The effect is measure in terms of odds ratios, i.e. the ratio of the odds of exposure in the case group and the odds of exposure in the control group.
A confounder is a factor that is independently associated with the exposure and outcome, that is to say something (disease, risk factor, another drug) that is more common (or rarer) in people who receive the drug but that may also be the explanation behind the effect being studied. It can lead to both overestimation and underestimation of the association between exposure to a drug and an effect or side effect. If the two groups compared in a study are not comparable, and any factor suspected to be a confounder, it can be handled in the analysis through standardization, stratification or multivariate analysis.