Andersons pediatric cardiology 633

Often the relationship between two variables can be represented by a line graphed on a set of axes In these cases, regression analyses are powerful and useful tools For continuous dependent variables, that line can be straight, in which case the appropriate analysis would be linear regression Sometimes the relationship is more complex over the range of values of the dependent variable and may not be linear In this case, transformations of dependent and independent variables may be used, or nonlinear regression techniques can be applied If the dependent variable is dichotomous, then the relationship is between the probability of a value of the dependent variable as a function of the independent variable In this case a logistic regression is used These different regression techniques can be applied to incorporate the relationship between the dependent variable and multiple independent variables Regression equations are very useful in determining the independent effect of specific variables of interest on a given dependent variable by allowing the investigator to account for bias resulting from potential confounding factors It should be noted that although this reduces bias, the adjustments are always incomplete, as they account only for factors that have been included or measured Bias or confounding can (and usually are) always present from unmeasured factors Statistical testing can be applied to the whole regression equation and to the individual variables that were included Interaction can also be explored within a regression analysis Survival or Time-to-Event Analysis In a survival analysis, the dependent variable is time-to-event, and we assess how independent variables influence this time Since times-to-events are usually not normally distributed and there are incomplete data regarding individuals who do not reach an observed event, standard multivariable regression models that ignore the time element are not ideal Additionally, we must make sure that all of the surviving subjects at risk are accounted for One of the challenges of following cohorts over time is that subjects may be lost to follow-up or be lost to further observation before they achieve the event of interest, which is known as censoring They may also have other events, called competing events, that preclude the event of interest, as in an analysis of time to heart transplantation, where some subjects die To depict these phenomena accurately over time, we must continuously account for these changes to the cohort both for people who reach the outcome and for those who are no longer available to study or at risk for the event of interest The most common form of time-to-event analysis is the Kaplan-Meier approach Here the proportion of individuals surviving out of all individuals still available to be measured (at risk) is plotted in series of time intervals KaplanMeier takes into account that subjects are dropping out of both the numerator (having events) and the denominator (ending their period of observation) over time Typically, the proportion surviving (or not reaching the specified event) is plotted on the y-axis and time is plotted on the x-axis, which gives a visual representation of temporal survival trends Further, after creating the plot, we can use statistical tests to determine if independent variables have an association with time-to-event using Wilcoxon and log rank tests We can also use a particular type of multivariable regression analysis that handles time-related events as the dependent variable by using Cox's proportional hazard regression modeling This allows us to explore the relationship and independence of multiple variables with time to event Longitudinal or Serial/Repeated Measures Analysis The value of some variables can change over time if we measure them repeatedly, and trends can be noted An example might be left ventricular ejection fraction in an individual If we measure something repeatedly in a subject, then the measures for that individual are not independent—they are clearly related to one another and more related to each other than to the measurement of left ventricular ejection fraction in a different individual We need to account for this in an analysis We may also wish to determine if independent variables are associated with the measurements’ change over time Specific types of regression analyses have been developed and applied to handle this type of complex data If the serial measurements are of a continuous variable, then mixed linear or nonlinear regression analysis can be used If the variable is categorical or ordinal, a general estimating equation type of regression analysis can be used Research Study Design Studies can be classified into several different designs (Table 24.2) Each design has its own strengths and limitations, and proper selection of a study design is key to completing successful and meaningful research, which means obtaining findings that are valid and reliable The type of research approach and study design chosen should be appropriate, feasible, and likely to provide valid and reliable results that can help answer the original research question Various study designs are discussed here Table 24.2 Advantages and Disadvantages of Different Study Designs Study Type Design Advantages OBSERVATIONAL STUDIES Case reports Reports that describe a patient or Provide significant and case group of patients detail and experience series Helpful in very rare or unique occurrences Inexpensive CrossA defined set of individuals Useful for determining sectional is assessed for specific prevalence of risk factors study factors or measures in the and outcomes at a chosen present point Commonly done as a survey Cohort A defined set of individuals Multiple potential study is followed over time for patient factors may be development of an outcome assessed Patient factors at baseline or Factors may be assessed the develop over time are dynamically as they assessed for association with develop outcomes Useful for determining May be prospective or incidence of outcomes retrospective May help to assess causal relationships Casecontrol study A group of subjects in the population with a given condition or outcome is assembled (the cases) A comparison group is assembled from the same population who are free of the condition (the controls) The two groups are assessed for differences in baseline Efficient for rare risk factors or outcomes Cost effective compared to cohort studies Can control some confounding by matching groups on multiple factors Disadvantages/Limitations No control group Limited ability to apply findings to larger population Difficult to establish any causal relationship between patient factors and outcomes Inefficient when the prevalence of risk factors or outcomes is rare Prospective studies are inefficient when the duration of time to outcome is long Retrospective studies are subject to bias in assessing risk factors and are more likely to have incomplete data Only the outcome of interest that differentiates the groups can be assessed Sampling bias may be present in group selection May be difficult to recall historical risk factors/recall bias