Introduction to Biostatistics for Clinical Researchers

University of Kansas Department of Biostatistics

& University of Kansas Medical Center

Department of Internal Medicine

ScheduleFriday, December 3 in 1023 Orr-Major

Friday, December 10 in 1023 Orr-MajorFriday, December 17 in B018 School of Nursing

Possibility of a 5th lecture, TBDAll lectures will be held from 8:30a - 10:30a

Materials PowerPoint files can be downloaded from the Department of

Biostatistics website at http://biostatistics.kumc.edu

A link to the recorded lectures will be posted in the same location

Sampling Variability and Confidence Intervals

Topics Sampling distribution of a sample mean

Variability in the sampling distribution

Standard error of the mean

Standard error versus standard deviation

Confidence intervals for the population mean μ

Sampling distribution of a sample proportion

Standard error and confidence intervals for a proportion

The Random Sampling Behavior of a Sample Mean Across Multiple Random Samples

Random Sample When a sample is randomly selected from a population, it is

called a random sample Technically speaking, values in a random sample are

representative of the distribution of the values in the population, regardless of size

In a simple random sample, each individual in the population has an equal chance of being chosen for the sample

Random sampling helps control systematic bias

Even with random sampling, there is still sampling variability or error

Sampling Variability of a Sample Statistic If we repeatedly choose samples from the same population,

a statistic will take different values in different samples

If the statistic does not change much from sample to sample, then it is fairly reliable (does not have a lot of variability)

Example: Blood Pressure of Males Recall, we had worked with data on blood pressures using a

random sample of 113 men taken from the population of all men

Assume the population distribution is given by the following:

Example: Blood Pressure of Males Suppose we had all the time in the world

We decide to do an experiment

We are going to take 500 separate random samples from this population of men, each with 20 subjects

For each of the 500 samples, we will plot a histogram of the sample BP values and record the sample mean and sample standard deviation

Random Samples Sample 1: n = 20 Sample 2: n = 20

Example: Blood Pressure of Males We did this 500 times—let’s look at a histogram of the 500

sample means

Example: Blood Pressure of Males We decide to do another experiment

Example: Blood Pressure of Males We did this 500 times—now let’s look at a histogram of the

500 sample means

Example: Blood Pressure of Males We decide to do one more experiment

Example: Blood Pressure of Males We did this 500 times—lets look at a histogram of the 500

sample means

Example: Blood Pressure of Males Let’s review the results

Population distribution of individual BP measurements for males is normal

μ = 125 mmHg; σ = 14 mmHg Results from 500 random samples:

Sample Size

Mean of 500

sample means

SD of 500 sample means

Shape of Distribution of 500 sample means

n = 20 125 mmHg 3.3 mmHg Approx. normal

Example: Blood Pressure of Males Let’s review the results

Example: Hospital Length of Stay Recall, we had worked with the data on length of stay (LOS)

using a random sample of 500 patients taken from all patients discharged in 2005

Assume the population distribution is given by the following:

Example: Hospital Length of Stay Boxplot

Example: Hospital Length of Stay Suppose we had all the time in the world, again

We decide to do another set of experiments

We are going to take 500 separate random samples from this population of patients, each with 20 subjects

For each of the 500 samples we will plot a histogram of the sample LOS values and record the sample mean and standard deviation

Example: Hospital Length of Stay We did this 500 times—let’s look at a histogram of the 500

sample means

We decide to do another experiment

Example: Hospital Length of Stay We did this 500 times—lets look at a histogram of the 500

sample means

We decide to do one more experiment

Example: Hospital Length of Stay We did this 500 times—lets look at a histogram of the 500

sample means

Example: Hospital Length of Stay Let’s review the results

Population distribution of individual LOS values for population of patients is right skewed

μ = 5.05 days; σ = 6.90 days Results from 500 random samples:

Sample Size

Mean of 500

sample means

SD of 500 sample means

Shape of Distribution of 500 sample means

n = 20 5.05 days 1.49 days Approx. normal

Example: Hospital Length of Stay Let’s review the results

Summary What did we see across the two examples?

A few trends: Distribution of sample means tended to be

approximately normal, even with the original individual level data was not (LOS)

Variability in the sample mean values decreased as the size of the sample of each mean was based upon increased

Distribution of sample means was centered at true population mean

Clarification Variation in the sample mean values is tied to the size of

each sample selected in our exercise (i.e., 20, 50, or 100), not to the number of samples (i.e., 500)

The Theoretical Sampling Distribution of the Sample Mean and Its Estimate Based on a Single Sample

Sampling Distribution of the Sample Mean In the previous section we reviewed the results of

simulations that resulted in estimates of what’s formally called the sampling distribution of the sample mean

The sampling distribution of the sample mean is a theoretical probability distribution

It describes the distribution of sample means from all possible random samples of the same size taken from a population

Sampling Distribution of the Sample Mean For example, the histogram below is an estimate of the

sampling distribution of sample BP means based on random samples of n = 50 from the population of all men

Sampling Distribution of the Sample Mean In research, it is impossible to estimate the sampling

distribution of a sample mean by actually taking many random samples from the same population

No research would ever happen if a study needed to be repeated multiple times to understand this sampling behavior

Simulations are useful to illustrate a concept, but not to highlight a practical approach

Luckily, there is some mathematical machinery that generalizes some of the patterns we saw in the previous simulation results

The Central Limit Theorem (CLT) The Central Limit Theorem is a powerful mathematical tool

that gives several useful results The sampling distribution of sample means based on all

samples of size n is approximately normal, regardless of the distribution of the original, individual-level data in the population/sample

The mean of all sample means in the sampling distribution is the true mean of the population from which the samples were taken (μ)

The standard deviation of the sample means taken from samples of size n is equal to : this is often called the standard error of the mean,

Example: Blood Pressure of Males The population distribution of individual BP measurements

for males is normal with μ = 125 mmHg and σ = 14 mmHg

Sample Size

Mean of 500

Sample Means

Mean of 5000

Sample Means

SD of 500

Sample Means

SD of 5000

Sample Means

SD of Sample Means by CLT (SE)

n = 20 124.98 mmHg

125.05 mmHg 3.31 mmHg 3.11 mmHg 3.13 mmHg

n = 50 125.03 mmHg

n = 100 124.99 mmHg

Sample Size

Mean of 500

Sample Means

Mean of 5000

Sample Means

SD of 500

Sample Means

SD of 5000

Sample Means

n = 20 124.98 mmHg

n = 50 125.03 mmHg

n = 100 124.99 mmHg

Sample Size

Mean of 500

Sample Means

Mean of 5000

Sample Means

SD of 500

Sample Means

SD of 5000

Sample Means

n = 20 124.98 mmHg

n = 50 125.03 mmHg

n = 100 124.99 mmHg

Recap: CLT The CLT tells us:

When taking a random sample of continuous measures of size n from a population with true mean μ and true standard deviation σ, the theoretical sampling distribution of sample means from all possible random samples of size n is as follows:

x SE xn

CLT: So What? So what good is this information?

Using the properties of the normal curve, this shows that for most random samples we can take (i.e., 95% of them), the sample mean will fall within 2 SE of the true mean (actually, 1.96 SE)

CLT: So What? AGAIN, what good is this information?

We are going to take a single sample of size n and get one

We won’t know μ, and if we did know μ why would we care about the distribution of estimates of μ from imperfect subsets of the population?

CLT: So What? We are going to take a single sample of size n and get one

But for most (i.e., 95%) of the random samples we can get, our will fall within ± 1.96 SE of μ

So if we start at and go 1.96 SE in either direction, the interval created will contain μ most (i.e., 95%) of the time

Estimating a Confidence Interval Such an interval is called a 95% confidence interval for the

population mean μ

The interval is given by the formula:

Problem: we don’t know σ, either! We can estimate it with s, and will detail this in the next

section

What is the interpretation of a confidence interval?

1.96 1.96x SE x xn

Interpretation of a 95% Confidence Interval (CI) Laypersons’ range of plausible values for the true mean

Researchers never can observe the true mean μ is the best estimate based on a single sample The 95% CI starts with this best estimate and

additionally recognizes uncertainty in this quantity

Technical interpretation: Were 100 random samples of size n taken from the same

population and 95% confidence interval limits computed from each of these 100 samples, 95 of the 100 intervals would contain the value of the true mean μ

Technical Interpretation One hundred 95% confidence intervals from 100 random

samples of size n = 50 BPs

Notes on Confidence Intervals Random sampling error

A confidence interval only accounts for random sampling error, not any other systematic sources of error (or bias)

Examples of Systematic Bias BP measurement is always +5 too high (broken instrument)

Only those with high BP agree to participate (non-response bias)

Notes on Confidence Intervals Are all CIs 95%?

No It is the most commonly used level of confidence A 99% CI is wider A 90% CI is narrower

To chance the level of confidence, adjust the number of SE added to and subtracted from the sample mean: For a 99% CI, you need ± 2.58SE For a 98% CI, you need ± 2.33SE For a 95% CI, you need ± 1.96SE For a 90% CI, you need ± 1.645SE

Standard Deviation versus Standard Error The term standard deviation refers to the variability in

individual observations in a single sample (s) or population (σ)

The standard error of the mean is also a measure of standard deviation, but not of individual values—rather, it is a measure of the variation in sample means computed from multiple random samples of the same size taken from the same population

Estimating Confidence Intervals for the Mean of a Population Based on a Single Sample of Size n

Estimating a 95% Confidence Interval In the previous section, we defined a 95% confidence

interval for the population mean μ

Interval is given by:

Problem: we don’t know σ We can estimate it with s, such that our estimated SE is

given by

Estimated 95% confidence interval for μ based on a single sample of size n is

1.96 1.96x SE x xn

sSE xn

1.96 sxn

Example 1 Suppose we had blood pressure measurements collected

from a random sample of 100 KUMC students collected in September 2010

We wish to use the results of the sample to estimate a 95% CI for the mean blood pressure of all KUMC students

Results:

A 95% CI for the true mean BP of all KUMC students:

123.4 mmHg13.7 mmHg

SE 13 100 1.3 mmHg

123.4 1.96 1.3 123.4 2.548120.9,125.9

Example 2 Data from the National Medical Expenditures Survey

(1987): U.S. Based Survey administered by the Centers for

Disease Control (CDC)

Some results: Smoking History

No Smoking History

Mean 1987 Expenditures

(US$)2260 2080

SD (US$) 4850 4600N 6564 5016

Example 2 95% CIs for 1987 medical expenditures by smoking history

Smoking history

No smoking history

48502260 1.96 2260 1176564

$2143,$2377

46002080 1.96 2080 1275016

$1953,$2207

Example 3 Effect of lower targets for blood pressure and LDL

cholesterol on atherosclerosis in diabetes: the SANDS Randomized Trial1 “Objective: To compare progression of subclinical

atherosclerosis in adults with type 2 diabetes treated to reach aggressive targets of low-density lipoprotein cholesterol (LDL-C) of 70 mg/dL or lower and systolic blood pressure (SBP) of 115 mmHg or lower versus standard targets of LDL-C of 100 mg/dL or lower and SBP of 130 mmHg or lower.”

1Howard, B., et al. (2008). Effect of lower targets for blood pressure and LDL cholesterol on atherosclerosis in diabetes: The SANDS Randomized Trial. JAMA 299, no. 14.

Example 3 “Design, setting, and participants: A randomized,

open-label, blinded-to-end point, three-year trial from April 2003 – July 2007 at four clinical centers in Oklahoma, Arizona, and South Dakota. Participants were 499 American Indian men and women aged 40 years or older with type 2 diabetes and no prior CVD events.”

“Interventions: Participants were randomized to aggressive (n = 252) versus standard (n = 247) treatment groups with stepped treatment algorithms defined for both.”

Example 3 Results: target LDL-C and SBP levels for both groups were

reached and maintained Mean (95% confidence interval) levels for LDL-C in the

last 12 months were 72 (69-75) and 104 (101-106) mg/dL and SBP levels were 117 (115-118) and 129 (128-130) mmHg in the aggressive versus standard groups, respectively

Example 3 Lots of 95% CIs!

Using Excel to Create 95% CI for a Mean Use the “CONFIDENCE” function in Excel to obtain the limits

of the interval

For Example 1: = 123.4 mmHg; s = 13.7 mmHg; n = 100

of the interval

For Example 1: = 123.4 mmHg; s = 13.7 mmHg; n = 100

of the interval

With alpha = .05, CONFIDENCE(.05, 13.7, 100) returns 2.685151.

of the interval

The corresponding confidence interval is then 123.4 ± 2.685151 = approximately [120.7, 126.1].

What We Mean by Approximately Normal and What Happens to the Sampling Distribution of the Sample Mean with Small n

Recap: CLT The CLT tells us:

When taking a random sample of continuous measures of size n from a population with true mean μ and true standard deviation σ, the theoretical sampling distribution of sample means from all possible random samples of size n is as follows:

x SE xn

Recap: CLT Technically, this is true for “large n”

When n is “small,” the sampling distribution is not quite normal—it follows a Student’s t distribution

-3 -2 -1 0 1 2 3

Student T distributions

df=10Gaussian distribution

Student’s t The distribution of t is “flatter and fatter” than its cousin,

the normal distribution

The t-distribution is uniquely defined by its degrees of freedom

-3 -2 -1 0 1 2 3

Student T distributions

df=10Gaussian distribution

Why t? Basic idea: remember, the true is given by the

formula

But of course we don’t know σ, and replace with s to estimate

In small samples, there is a lot of sampling variability in s as well, so this estimate is less precise

To account for this additional uncertainty, we have to go slightly more than ±1.96 to get 95% coverage under the sampling distribution How much bigger than 1.96 depends on the sample size

x SE x n

ˆx SE x s n

The t distribution If we have a smaller sample size, we will have to go out

more than 1.96 SEs to achieve 95% confidence

How many standard errors we need to go depends on the degrees of freedom—this is linked to sample size

The appropriate degrees of freedom are n – 1

0.95, 1 0.95, 1n nsx t SE x x tn

Notes on the t-Correction The particular t-table gives the number of SEs needed to

cut off 95% under the sampling distribution

Notes on the t-Correction You can easily find a t-table for other cutoffs (90%, 99%) in

any stats text or by searching the internet

Also, using the TINV function in Excel will return cutoffs (use alpha/2 for “probability”)

The point is not to spend a lot of time looking up t-values: more important is a basic understanding of why slightly more needs to be added to the sample mean in smaller samples to get a valid 95% CI

The interpretation of the 95% CI (or any other level) is the same as discussed before

Example Small study on response to treatment among 12 patients

with hyperlipidemia (high LDL cholesterol) given a treatment

Change in cholesterol post–pre treatment computed for each of the 12 patients

Results:1.4 mmol/ L

0.55 mmol/ Lxs

Example 95% confidence interval for true mean change

0.95,11

0.551.4 2.2 121.75, 1.05

x t SE x

Using Excel to Create Other CIs for a Mean The TINV function

The Sample Proportion as a Summary Measure for Binary Outcomes and the CLT

Proportion (p) Proportion of individuals with health insurance

Proportion of patients who became infected

Proportion of patients who are cured

Proportion of individuals who are hypertensive

Proportion of individuals positive on a blood test

Proportion of adverse drug reactions

Proportion (p) For each individual in the study, we record a binary

outcome (Yes/No; Success/Failure) rather than a continuous measurement

Proportion (p) Compute a sample proportion, (pronounced “p-hat”), by

taking observed number of “yes” responses divided by total sample size This is the key summary measure for binary data,

analogous to a mean for continuous data There is a formula for the standard deviation of a

proportion, but the quantity lacks the “physical interpretability” that it has for continuous data

Example Proportion of dialysis patients with national insurance in 12

countries (only six shown..)1

Example: Canada1Hirth, R., et al. (2008). Out-of-pocket spending and medication adherence among dialysis patients in twelve countries, Health Affairs, 27 (1).

400ˆ 0.796503p

Example Maternal/infant transmission of HIV1

HIV-infection status was known for 363 births (180 in the zidovudine [AZT] group and 183 in the placebo group) 13 infants in the AZT group and 40 in the placebo group

were infected

13ˆ 0.07 7%18040ˆ 0.22 22%183

1Spector, S., et al. (1994). A controlled trial of intravenous immune globulin for the prevention of serious bacterial infections in children receiving zidovudine for advanced human immunodeficiency virus infection, NEJM 331 (18).

Proportion (p) What is the sampling behavior of a sample proportion?

In other words, how do sample proportions estimated from random samples of the same size from the same population behave?

Example: Health Insurance Coverage Suppose we have a population in which 80% of persons

have some form of health insurance:

Example: Health Insurance Coverage Suppose we had all the time in the world . . . Again

We decide to do another set of experiments

We are going to take 500 separate random samples from this population, each with 20 subjects

For each of the 500 samples, we will plot a histogram of the insured and uninsured numbers and record the sample proportion of insured subjects

Estimating the Sampling Distribution What does the histogram of the 500 sample proportions

look like?

Example: Health Insurance Coverage We decide to do another experiment

We are going to take 500 random samples from this population, each with 100 subjects

look like?

Example: Health Insurance Coverage We decide to do another experiment

We are going to take 500 random samples from this population, each with 1000 subjects

look like?

Example: Health Insurance Coverage Results review:

True proportion of insured: p = 0.80 Results from 500 random samples:

Sample Size

Means of 500 Sample Proportions

SD of 500 Sample

Proportions

Shape of Distribution

of 500 Sample

Proportions

n = 20 0.805 0.094 Approaching normal?

n = 100 0.801 0.041 Approximately normal

n = 1000 0.799 0.012 Approximately normal

Example: Health Insurance Coverage Results:

The Theoretical Sampling Distribution of the Sample Proportion and Its Estimate Based on a Single Sample

Sampling Distribution of the Sample Proportion In the previous section, we reviewed the results of

simulations that resulted in estimates of what was formally called the sampling distribution of the sample proportion

The sampling distribution of the sample proportion is a theoretical probability distribution It describes the distribution of sample proportions

calculated from all possible random samples of the same size taken from a population

Sampling Distribution of the Sample Proportion In research, it is impossible to estimate the sampling

distribution of a sample proportion by actually taking many random samples from the same population to understand this sampling behavior

Luckily, there exists some mathematical machinery that generalizes some of the patters we saw in the simulation results

The Central Limit Theorem (CLT) The Central Limit Theorem (CLT) is a powerful mathematical

tool that gives several useful results: The sampling distribution of the sample proportion

calculated from all samples of size n is approximately normal

The mean of all sample proportions is the true mean (p) of the population from which the samples were taken

The standard deviation of the sample proportions is equal to

This quantity is often called the standard error of the sample proportion,

ˆSE p

Example: Health Insurance Coverage Population distribution of individual insurance status

True proportion: p = 0.8

Sample SizeMean of 500

Sample Proportions

Mean of 5000

Sample Proportions

SD of 500 Sample

Proportions

SD of 5000 Sample

Proportions

SD of Sample

Proportions (SE) by CLT

n = 20 0.805 0.799 0.094 0.090 0.089n = 100 0.801 0.799 0.041 0.040 0.040n = 1000 0.799 0.80 0.012 0.012 0.012

Recap: CLT The CLT tells us the following:

When taking a random sample of binary measures of size n from a population with true proportion p, the theoretical sampling distribution of sample proportions from all possible random samples of size n is

p̂ ˆ

p pSE p

CLT: So What? What good is this information?

Using the properties of the normal curve, this shows that for most random samples (i.e., 95% of them), the sample proportion will fall within 1.96 SEs of the true proportion, p:

p 11.96 p pp

11.96 p p

CLT: So What? AGAIN, what good is this information?

We are going to take a single sample of size n and get one

We won’t know p, and if we did know p why would we care about the distribution of estimates of p from imperfect subsets of the population?

p 11.96 p pp

11.96 p p

But for most (i.e., 95%) of the random samples we can get, our will fall within ± 1.96 SE of p

So if we start at and go 1.96 SE in either direction, the interval created will contain p most (i.e., 95%) of the time

Estimating a Confidence Interval Such an interval is called a 95% confidence interval for the

population proportion p

Interval is given by:

Problem: we don’t know p Can estimate with , but we will detail this in the next

section

What is the interpretation?

1ˆ ˆ ˆ1.96 1.96 p pp SE p p

Interpretation of a 95% Confidence Interval Laypersons’ range of “plausible” values for the true

proportion p Researchers can never observe p is the best estimate based on a single sample The 95% CI starts with this best estimate and

additionally recognizes uncertainty in this quantity

Technical interpretation Were 100 random samples of size n taken from the same

population and 95% confidence limits computed using each of these 100 samples, 95 of them would contain p

Summary Trends

Distribution of sample proportions tended to be approximately normal--even when original, individual-level data was not (e.g., a binary outcome)

Variability in sample proportion values decreased as the size of the sample each proportion was based upon increased

As with the sample mean, variation in proportions is tied to the size of each sample selected, NOT the number of samples

Estimating Confidence Intervals for the Proportion of a Population Based on a Single Sample of Size n

Estimating a 95% Confidence Interval for p In the last section, we defined a 95% confidence interval for

the population proportion p

Interval given by:

Problem: we don’t know p Can estimate with , such that our estimated SE is

Estimated 95% CI for p based on a single sample of size n

1ˆ ˆ ˆ1.96 1.96 p pp SE p p

ˆ ˆ1ˆ p pSE p

ˆ ˆ1ˆ ˆ ˆ1.96 1.96 p pp SE p p

Example 1 Proportion of dialysis patients with national insurance in 12

countries (only six are shown):

Example: France

219ˆ 0.46481p

Example 1 Estimated 95% confidence interval

ˆ ˆ1ˆ 1.96

0.46 1 0.460.46 1.96 4810.46 1.96 0.023

0.41,0.51

Example 2 Maternal/infant transmission of HIV

HIV-infection status was known for 363 births (180 in AZT group and 183 in placebo group)

Thirteen infants in AZT and 40 in placebo were infected

13ˆ 0.0718040ˆ 0.22183

Example 2 Estimated 95% confidence interval for transmission

percentage in the placebo group:

ˆ ˆ1ˆ 1.96

0.22 1 0.220.22 1.96 1830.22 1.96 0.031

0.16,0.28

Small Sample Considerations for Confidence Intervals for Population Proportions

The Central Limit Theorem (CLT) The Central Limit Theorem is a powerful mathematical tool

that gives us: The shape of the sampling distribution of --

approximately normal Mother/infant transmission example, placebo group:

CLT 95% CI: (0.16,0.28) Can be done by hand

Exact 95% CI: (0.160984, 0.2855248) Requires computer, always correct

Notes on 95% CI for p The CLT-based formula for a 95% CI is only approximate--it

works very well if you have enough data in your sample

The approximation works better for bigger values of

“Large sample” is indicative not only of total sample size, but of the balance between ‘yes’ and ‘no’ outcomes in the population

ˆ ˆ1np p

Notes on 95% CI for p HIV example, AZT group:

n = 180,

CLT 95% CI: (0.03,0.11)

Exact 95% CI: (0.0390137,0.1203358)

ˆ 13/ 180 0.07p

Notes on 95% CI for p In the placebo sample:

In the AZT sample:

ˆ ˆ1 183 0.22 0.78 31PLA PLAnp p

ˆ ˆ1 180 0.07 0.93 12AZT AZTnp p

Notes on 95% CI for p When we had sample size issues for population mean

estimation, we used the Student’s t to calculate 95% confidence intervals For population proportion estimation, we use exact

binomial confidence intervals

The interpretation of the confidence interval is exactly the same with either the large sample method or the exact method In real life, using the computer will always give a valid

result CLT only breaks down with “small” sample sizes

Example Random sample of 16 patients on drug A: two of sixteen

patients experience drug failure in first month

CLT 95% CI:

Exact 95% CI: (0.02, 0.38)

ˆ ˆ1ˆ 1.96

2 16 1 2 162 1.9616 160.05,0.28

Next Lecture Friday, December 10 in 1023 Orr-Major from 8:30a -

10:30a

Topics include―P-values―One- and Two-sample t-tests―ANOVA―Linear Regression―Chi-square test―Survival Analysis

References and CitationsLectures modified from notes provided by John McGready and Johns Hopkins Bloomberg School of Public Health accessible from the World Wide Web: http://ocw.jhsph.edu/courses/introbiostats/schedule.cfm

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