outcome measures, known as predictive validity Accuracy Once a measure is deemed to be valid, its accuracy and precision should be assessed Accuracy is a reflection of validity in that it assesses how close a measure comes to the truth, but it also includes any systematic error or bias in making the measurement Systematic error refers to variations in the measurements that might always occur predominately in one direction In other words, the deviation of a measurement from the truth tends to be consistent Regarding aortic valve regurgitation, this might reflect technical differences in echocardiographic assessment, as in the settings of gain or frequency of the probe that was used This may also occur at the level of the observer, whereby the observer has a consistent bias in making the interpretation of aortic valve regurgitation, such as grading all physiologic aortic valve regurgitation as mild instead of trace Alternatively, some observers may place more weight on a specific aspect when assigning a specific grade that tends to shift their grade assignment in one direction Reliability Reliability or precision refers to the reproducibility of the measurement under a variety of circumstances and relates to random error or bias It is the degree to which the same value is obtained when the measurement is made under the same conditions Some of the random variation in measurements may be attributed to the instruments, such as obtaining the echocardiogram using two different machines Some of the random variation may also relate to the subject, such as variations in physiologic state when the echocardiograms were obtained The reliability and accuracy of a measurement can be optimized via measurement standardization Training sessions for observers on assessment and interpretation of a measure can be designed so that criteria for judgment are applied in a uniform manner Limiting the number of observers, having independent adjudications, and defining and standardizing all aspects of assessment also improve reliability In our case, this could be achieved by having the same readers assess aortic valve regurgitation using the same echocardiography machine with the same settings in patients of similar fluid status under similar resting conditions Analysis of Data Analysis is the method by which data or measurements are used to answer questions, and then to assess the confidence in inferring those findings beyond the subjects that were studied The plan for analysis of the data is an integral part of the study design and protocol The appropriate planning, strategy, execution, and interpretation are essential elements to the critical appraisal of any research report Research Question Every study must begin with a well-defined question, and the drafting of this question is the first step toward creating a research protocol The research question often suggests the design of the study, the population to be studied, the measurements to be made, and the plan for analysis of the data It also determines whether the study is descriptive or comparative The process of constructing a research question is often iterative For example, in considering the topic of hypertrophic cardiomyopathy, a descriptive research question might be “What are the outcomes of hypertrophic cardiomyopathy?” This question is nonspecific, but steps can subsequently be taken to refine and focus the question The first step would be to determine what answers are already known regarding this question and what areas of controversy warrant further study After a background review, an investigator may further clarify the question by asking the following: “What outcomes do I wish to study?”, “How will I define hypertrophic cardiomyopathy and in what subjects?”, and “At what time point or over what time do I wish to examine these outcomes?” In answering these questions, the research question is revised and further specified to “What is the subsequent risk of sudden death for children with familial hypertrophic cardiomyopathy presenting to a specialized clinic?” This refined question now defines the cohort to be studied—children with familial hypertrophic cardiomyopathy in a specialized clinic and the outcome of interest, sudden death —and it suggests that the study will have some type of observational design Thus a well-defined and focused research question is essential to considering other aspects of the proposed study or report Using Variables to Answer Questions Once the research question is established, the next step in generating an analysis plan is to select and define variables Specifically, the researcher must establish the information needed to answer the question This process should include setting definitions, determining the source(s) of data, and considering issues of measurement validity and reliability Types of Variables Variables can be classified for statistical purposes as either dependent or independent variables Dependent variables are generally the outcomes of interest, and either change in response to an intervention or are influenced by associated factors Independent variables are those that may affect the dependent variable The research question should define the primary independent variable, which is commonly a specific treatment or a key subject characteristic A detailed consideration of the question should clearly identify the key or primary dependent and independent variables In any study there are usually one or two primary outcomes of interest, but there are often additional secondary outcomes Analysis of secondary outcomes is used for supporting the primary outcome or exploring or generating additional hypotheses It should be recognized that the greater the number of outcomes examined in a study, called multiple comparisons, the more likely it is that one of them will be statistically significant purely by chance When assessing multiple comparisons, the level of certainty required to reach significance must increase Composite outcomes are a different but also important concept A composite outcome results when several different outcomes are grouped together into one catchall outcome As an example, a study of the effect of digoxin on adolescent patients with advanced heart failure might have a composite outcome of admission to the intensive care unit, listing for transplantation, and death Having a composite outcome raises the likelihood that the study has a high enough number of outcomes to support an analysis However, the appropriateness of composite outcomes is questionable, and issues have been raised about their validity.45 First, not all possible outcomes that might be included in a composite outcome have the same importance for subjects In our example, admission to the intensive care unit and death, while both serious,