Task 3: Key Concepts about Using Logistic Regression In NHANES

Logistic regression is used to assess the association between independent variable(s) (X_j) -- sometimes called exposure or predictor variables — and a dichotomous dependent variable (Y) — sometimes called the outcome or response variable. Logistic regression analysis tells you how much an increment in a given exposure variable affects the odds of the outcome.

Simple logistic regression is used to explore associations between one (dichotomous) outcome and one (continuous, ordinal, or categorical) exposure variable.  For example, you answer questions like, "how does calcium supplement use affect the probability of receiving treatment for osteoporosis?,” similar to using a chi-square test.

Multiple logistic regression is used to explore associations between one (dichotomous) outcome variable and two or more exposure variables (which may be continuous, ordinal or categorical).  The purpose of multiple logistic regression is to isolate the relationship between the exposure variable and the outcome variable from the effects of one or more other variables (called covariates or confounders).   For example, you can answer questions like, "How does calcium supplement use affect the probability of receiving treatment for osteoporosis, after accounting for (or unconfounded by, or independent of, or holding constant) age, income, etc.?" This process of accounting for covariates or confounders is called adjustment. For example, say that the prevalence of osteoporosis treatment tends to be lower in younger people; and younger people are less likely to take calcium supplements. In this case, inferences about osteoporosis treatment and calcium supplement use get confused by the effect of age on supplement use and osteoporosis treatment. This kind of "confusion" is called confounding (and these covariates are sometimes called confounders).  Confounders are variables which are associated with both the exposure and outcome of interest.  This relationship is shown in the following figure.

Diagram of the Relationship between Exposure, Outcome, and the Confounder

You can use multiple logistic regression to adjust for confounding and isolate the relationship of interest. The process of accounting for covariates is also called adjustment.

Comparing the results of simple and multiple logistic regression can help to answer the question, "How much did the covariates in the model alter the relationship between exposure and outcome (i.e., How much confounding was there)?" 

Research Question

In this module, you will assess the association between calcium supplement use (the exposure variable) and the likelihood of receiving treatment for osteoporosis (the outcome).  You will look at both simple logistic regression and then multiple logistic regression.  The multiple logistic regression model will include age, race/ethnicity, and body mass index (BMI) as covariates. This analysis will answer the question, “What is the effect of calcium supplement use on the likelihood of receiving treatment for osteoporosis – after controlling for age, race/ethnicity, and body mass index (BMI)?”

Dependent Variable and Independent Variables

As noted, the dependent variable Y for a Logistic Regression is dichotomous, which means that it can take on one of two possible values. NHANES includes many questions where people must answer either “yes” or “no”; these include questions like “Has the doctor ever told you that you have congestive heart failure?”.  Alternatively, you can create dichotomous variables by setting a threshold (e.g., “diabetes” = 1 if fasting blood sugar > 126 and “diabetes”=0 otherwise); or by combining information from several variables.  In this module, we will use a dichotomous variable called “treatosteo” indicating osteoporosis treatment. In SUDAAN, and SAS Survey Procedures the dependent variable is coded as 1 (for having the outcome) and 0 (for not having the outcome).   A participant was coded as having had treatment for osteoporosis if he or she responded “yes” to OSQ.070 (“{Were you/Was SP} treated for osteoporosis?”) from the osteoporosis questionnaire, and was set to “no” if he or she responded “no” to OSQ.070 or to OSQ.060 (“Has a doctor ever told {you/SP} that {you/s/he} had osteoporosis, sometimes called thin or brittle bones?”) from the osteoporosis questionnaire. (The SAS code to create this variable is found in the “Supplement Program” sample SAS code.) In this example, for people who receive treatment for osteoporosis, the treatosteo variable would have a value of 1, while the variable would have a value of 0 for people who were are not receiving treatment. 

In logistic regression, the independent variables X_j can be categorical (e.g., gender, race), ordinal (e.g., supplement use (y/n), age groups, BMI categories), or continuous (e.g., continuous BMI).  There are different ways that one can define categorical variables using indicator, or “dummy” variables.  One common way is to define a reference category, i.e., the category to which the other levels of the categorical variable are compared.

Note that getting statistical packages like SUDAAN and SAS Survey to run analyses is the easy part of regression.  What is not easy is knowing which variables to include in your analyses, how to represent them, and when to worry about confounding; determining if your models are any good; and knowing how to interpret them.  These tasks require thought, training, experience, and respect for the underlying assumptions of regression.  Remember, garbage in - garbage out. 

Finally, remember that NHANES analyses can only establish associations and not causal relationships.  This is because the data are cross-sectional, so there is no way to establish temporal sequences (i.e., which came first the "exposure" or the "outcome"?).

Logit Function

Because you are trying to find associations between risk factors and a condition, you need a formula that will allow you to link these variables. The logit function that is used in logistic regression is also known as the link function because it connects, or links, the values of the independent variables to the probability of occurrence of the event defined by the dependent variable.

Logit Model

In the logit formula above, Pr(Y=1) means the “probability that Y=1”, or the probability that the event occurs. In this equation ‘log' indicates natural log.

Optional: Learn more about odds ratios, linear and logistic regression

 

Output of Logistic Regression

The statistics of primary interest in logistic regression are the beta coefficients (ß₁,ß₂, ß₃...), their standard errors, and their p-values.  Like other statistics, the standard errors are used to calculate confidence intervals around the beta coefficients.

It is easy to transform the beta coefficients into a more interpretable format, the odds ratio, as follows:

                        e^ß= odds ratio.

If X_j is a dichotomous variable with values of 1 or 0, then the beta coefficient represents the log odds that an individual will have the event for a person with X_j=1 versus a person with X_j=0. In a multivariate model, this beta coefficient is the independent effect of variable X_j on Y_i after adjusting for all other covariates in the model.   The odds ratio e^ß represents the odds that an individual will have the event for a person with X_j= 1 versus an individual with X_j=0.

If X_j is a continuous variable, then the e^ß represents the odds that an individual will have the event for a person with X_j=m+1 versus an individual with X_j=m. In other words, for every one unit increase in X_j, the odds of having the event Y_i changes by e^ß adjusting for all other covariates in a multivariate model.

A summary table about interpretation of beta coefficients is provided below:

Table: What does the Beta Coefficient Mean?

Independent Variable Type	Example Variables	The beta coefficient in simple logistic regression	The beta coefficient in multiple logistic regression
Continuous	height, weight, LDL	The change in the log odds of the dependent variable per 1 unit change in the independent variable.	The change in the log odds of dependent variable per 1 unit change in the independent variable after controlling for the confounding effects of the covariates in the model.
Categorical (also known as discrete)	Supplement use (two subgroups – yes and no. This example will use no as the reference group.)	The difference in the log odds of the dependent variable for one value of the categorical variable vs. the reference group (for example, between supplement users, and the reference group, non-users).	The difference in the log odds of the dependent variable for one value of the categorical variable vs. the reference group (for example, between supplement users, and the reference group, non-users), after controlling for the confounding effects of the covariates in the model.

 

In particular, odds and probability are two different ways to express the likelihood of an outcome. 

Here are their definitions and some examples. 

Table of Differences between Odds and Probability

	Definition	Example: Getting heads in 1 flip of a coin	Example: Getting a 1 in a single roll of a die
Odds	# of times something       happens       # of times it does NOT happen	= 1/1 = 1 (or 1:1)	= 1/5 = 0.2 (or 1:5)
Probability	# of times something         happens        # of times it could happen	= 1/2 = .5 (or 50%)	= 1/6 = .16 (or 16%)

The above example illustrates the difference between odds and probabilities.  In the example, the odds of getting a 1 in a single roll of a die is 20%, whereas the probability is 16%.  The same is true for odds ratios and relative risk.  Both are ratios of the odds or probability of the event for one group compared to another.  For example, if we wanted to know the odds ratio of rolling a 1 with a blue die compared to a red die.

Few people think in terms of odds. Many people equate odds with probability and thus equate odds ratios with risk ratios.  When the outcome of interest is uncommon (i.e., it occurs less than 10% of the time), such confusion makes little difference, since odds ratios and risk ratios are approximately equal.  When the outcome is more common, however, the odds ratio increasingly overstates the risk ratio. So, to avoid confusion, when event rates are high, odds ratios should be converted to risk ratios. (Schwartz LM, Woloshin S, Welch HG. Misunderstandings about the effects of race and sex on physicians’ referrals for cardiac catheterization. N Engl J Med 1999;341:279–83) There are simple methods of conversion for both crude and adjusted data.  (Zhang J, Yu KF. What's the relative risk? A method of correcting the odds ratio in cohort studies of common outcomes. JAMA 1998;280:1690-1691. Davies HT, Crombie IK, Tavakoli M. When can odds ratios mislead? BMJ 1998;316:989-991)

The following formulas demonstrate how to convert between probability and odds.