Proportional Mortality Ratio (PMR) Clinical Study
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A Proportional Mortality Ratio (PMR) Clinical Study is an Observational Clinical Study that utilizes death records and deceased patient medical records to find a disease exposure-outcome relationship.
- Context:
- It can (typically) be a retrospective study, but it can also be a prospective study, in which the disease risk measures are the proportional mortality ratio and the standardized mortality ratio.
- Strengths/Advantages:
- very inexpensive;
- fast
- outcome (death) well captured.
- Weaknesses/Disadvantages:
- utilize deaths only;
- inaccuracy of data (death certificates);
- inability to control for confounders.
- Example(s):
- Counter-Example(s):
- See: Disease Exposure Measure, Health Outcome Measure, Descriptive Clinical Trial, Diagnostic Clinical Trial, Interventional Clinical Trial, Uncontrolled Clinical Intervention Study.
References
2014
- (Thiese, 2014) ⇒ Matthew S. Thiese. (2014). “Observational and Interventional Study Design Types; An Overview.” In: Biochemia Medica (Zagreb). Journal, 24(2).
- QUOTE: Proportional mortality ratio studies (PMR) utilize the defined well recorded outcome of death and subsequent records that are maintained regarding the decedent (...). By using records, this study design is able to identify potential relationships between exposures, such as geographic location, occupation, or age and cause of death. The epidemiological outcomes of this study design are proportional mortality ratio and standardized mortality ratio. In general these are the ratio of the proportion of cause-specific deaths out of all deaths between exposure categories (...). As an example, these studies can address questions about higher proportion of cardiovascular deaths among different ethnic and racial groups (...). A significant drawback to the PMR study design is that these studies are limited to death as an outcome (...). Additionally, the reliance on death records makes it difficult to control for individual confounding factors, variables that either conceal or falsely demonstrate associations between the exposure and outcome. An example of a confounder is tobacco use confounding the relationship between coffee intake and cardiovascular disease. Historically people often smoked and drank coffee while on coffee breaks. If researchers ignore smoking they would inaccurately find a strong relationship between coffee use and cardiovascular disease, where some of the risk is actually due to smoking. There are also concerns regarding the accuracy of death certificate data. Strengths of the study design include the well-defined outcome of death, the relative ease and low cost of obtaining data, and the uniformity of collection of these data across different geographical areas.