Clinical Research Study Design and Analysis for Cardiologists

Nathan D. Wong, PhD, FACC

• Steps for protocol development• Advantages and disadvantages of different

research study designs - which is best for you?• Calculating sample size and power• Which statistical tests to use• Fallacies in presenting results• Recommendations for further assistance

Steps to Protocol Development

• Statement of Specific Aims and Hypotheses – may have primary and secondary

• Background – describe background of problem, prior studies, preliminary studies, rationale of proposed study and what it adds to prior research

• Methods, including as applicable: subject recruitment, eligibility criteria, screening procedures, laboratory procedures, treatment phase or follow-up procedures

Steps to Protocol Development (cont.)

• Description of Study Outcomes and how they are assessed/ measured

• Study power and sample size justification

• Data collection and management procedures

• Statistical methods of analysis

• Assessment of safety (clinical trials)

Types of Study Design: Strength of Studies to Prove Causation

• Weakest: Observational, cross-sectional

• Minimal: Observational, case-control

• Modest: Observational, prospective or nested case control

• Strongest: Randomized clinical trial

Features Affecting Strength and Generalizability of Study

• sample size• selection of comparison group (control or placebo)• selection of study sample (is it representative of

population the study results are intended to apply to?)• length of time of follow-up• outcome assessed (e.g., hard vs. soft or surrogate

endpoint)• Measurement and ability to control for potential

confounders

Observational, cross-sectional• Examines association between two factors (e.g, an

exposure and a disease state) assessed at a single point in time, or when temporal relation is unknown

• Example: Prevalence of a known condition, association of risk factors with prevalent disease.

• Conclusions: Associations found may suggest hypotheses to be further tested, but are far from conclusive in proving causation

Prevalence (%) of Combined HTN and HC in US Adults, by Gender and Disease Group

(Wong et al., AHA 2005)

8.8

36.740.7 44.4

69

9

42.5 43.850.5

72.7

0

10

20

30

40

50

60

70

80

Pre

vale

nce o

f H

TN

an

d H

C

(%)

Males Females

Neither MetS, DM, CVD MetS DM CVD CVD + DM/MetS

P<0.01 across disease groups for both males and females

Prevalence of CHD by the Metabolic Syndrome and Diabetes in the NHANES Population Age 50+

CH

D P

revale

nce

% of Population =

No MS/No No MS/No DMDM

54.2%54.2%

MS/No DMMS/No DM28.7%28.7%

DM/No MSDM/No MS2.3%2.3%

DM/MSDM/MS14.8%14.8%

8.7%

13.9%

7.5%

19.2%

0%

5%

10%

15%

20%

25%

Alexander CM et al. Diabetes 2003;52:1210-1214..

Odds of CVD Stratified by CRP Levels in U.S. Persons (Malik and Wong et al., Diabetes Care, March 2005)

–*p<.05, **p<.01, **** p<.0001 compared to no disease, low CRP

–CRP categories: >3 mg/l (High) and <3 mg/L (Low)

–age, gender, and risk-factor adjusted logistic regression (n=6497)age, gender, and risk-factor adjusted logistic regression (n=6497)

Nodisease Metabolic

Syndrome Diabetes

Low CRP

High CRP0

1

2

3

4

5

6

**

***

**

***Odds

Rat io

Metabolic Syndrome Independently Associated with Inducible Ischemia from SPECT

(Wong ND et al., Diabetes Care 2005; 28: 1445-50 )

Predictor OR 95% CI P value

Log coronary calcium (per SD)

4.11 2.60-6.51 <0.001

Chest Pain Symp 2.94 1.69-5.09 <0.001

1-2 MetS risk factors 2.99 0.70-12.8 0.14

3 MetS risk factors 4.80 1.01-22.9 0.049

4-5 MetS risk factors 10.93 2.09-57.2 0.005

Diabetes 4.55 0.98-21.1 0.053

*Estimates adjusted for age, gender, cholesterol and smoking. Odds of ischemia for metabolic abnormalities (yes vs. no) (separate model): 1.98 (1.20-3.98), p=0.008

Case-control Studies• Most frequent type of epidemiologic study, can be

carried out in a shorter time and require a smaller sample size, so are less expensive

• Only practical approach for identifying risk factors for rare diseases (where follow-up of a large sample for occurrence of the condition would be impractical)

• Selection of appropriately matched control group (e.g., hospital vs. healthy community controls) and consideration of possible confounders crucial

• Relies on historical information to obtain exposure status (and information on confounders)

Example of case-control study: Folate and B6 intake and risk of MI (Tavani et al. Eur J Clin Nutr 2004)

• Cases were 507 patients with a first episode of nonfatal AMI, and controls were 478 patients admitted to hospital for acute conditions

• Information was collected by interviewer-administered questionnaires

• Compared to patients in the lowest tertile of intake, the ORs for those in the highest tertile were 0.56 (95% CI 0.35-0.88) for folate and 0.34 (95% CI 0.19-0.60) for vitamin B6.

• Author conclusion: A high intake of folates, vitamin B6 and their combination is inversely associated with AMI risk

Potential sources of bias and error in case control studies

• Information on the potential risk factor or confounding variables may not be available from records or subjects’ memories

• Cases may search for a cause of their disease and be more likely to report an exposure than controls (recall bias)

• Uncertainty as to whether agent caused disease or whether occurrence of the disease caused the person to be exposed to the agent

• Difficulty in assembling a case group representative of all cases, and/or assembling an appropriate control group

Prospective cohort studies• Examples:

– Framingham Heart Study– Cardiovascular Health Study (CHS)– Multiethnic Study of Atherosclerosis (MESA)– Nurses Health Study

• Advantages: – large sample size– ability to follow persons from healthy to diseased states– temporal relation between risk factor measures and

development of disease

Prospective Studies (cont.)

• Disadvantages: – expensive due to large sample size often needed

to accrue enough events– many years to development of disease– possible attrition– causal inference not definitive as difficult to

consider all potential confounders

Low HDL-C Levels Increase CHD Risk Even When Total-C Is Normal (Framingham)

Risk of CHD by HDL-C and Total-C levels; aged 48–83 yCastelli WP et al. JAMA 1986;256:2835–2838

02468

101214

< 40 40–49 50–59 60< 200

230–259200–229

260

HDL-C (mg/dL) Tota

l-C (m

g/dL

)

14

-y in

cid

en

ce

rate

s (%

) fo

r C

HD

11.24

11.91

12.50

11.91

6.56

4.67

9.05

5.53

4.85

4.15

3.77

2.782.0

6

3.83

10.7

6.6

4-Year Progression To Hypertension: The Framingham Heart Study

5

18

37

0

10

20

30

40

50

Optimal Normal High-Normal

Pat

ien

ts (

%)

(<120/80 mm Hg)

(130/85 mm Hg) (130-139/85-89 mm

Hg)Vasan, et al. Lancet 2001;358:1682-86

Participants age 36 and older

CHD, CVD, and Total Mortality: US Men and Women Ages 30-74

(age, gender, and risk-factor adjusted Cox regression) NHANES II Follow-Up (n=6255)(Malik and Wong, et al., Circulation 2004; 110: 1245-1250)

0

1

2

3

4

5

6

7

CHD Mortality CVD Mortality Total Mortality

None

MetS

Diabetes

CVD

CVD+Diabetes

* p<.05, ** p<.01, **** p<.0001 compared to none

*

***

***

***

**

***

***

***

******

***

1.00

0.99

0.98

0.97

0.96

0.000 2 4 6 8

Years of Follow-up

Low CRP-low LDL

Low CRP-high LDL

High CRP-low LDL

High CRP-high LDL

CV Event-Free 8-year Survival Using Combined hs-CRP and LDL-C

Measurements (n=27,939)

Ridker et al, N Engl J Med. 2002;347:1157-1165.

Pro

bab

ility

of

Eve

nt-

free

Su

rviv

al

Median LDL 124 mg/dlMedian CRP 1.5mg/l

Prospective, observational: nested case-control

• In this design, one takes incident cases (e.g., incident CVD) and a matched set of controls to examine the association of a risk factor measured sometime before development of the outcome of interest

• Less costly than a true prospective design where all subjects are included in analysis; may not provide equivalent estimates

Prospective study of CRP and risk of future CVD events among apparently healthy

women (Ridker et al., Circulation 1998) – a nested case control study

• 122 female pts who suffered a first CVD event and 244 age and smoking-matched controls free of CVD

• Logistic regression estimated relative risks and 95% CI’s, adjusted for BMI, diabetes, HTN, hypercholesterolemia, exercise, family hx, and trt

• Those who developed CVD events had higher baseline CRP than controls; those in the highest quartile of CRP had a 4.8-fold (4.1 adjusted) increased risk of any vascular event. For MI or stroke, RR=7.3 (5.5 adjusted)

hs-CRP Adds to Predictive Value of TC:HDL Ratio in Determining Risk of First MI

0.0

1.0

2.0

3.0

4.0

5.0

High Medium Low Low

Medium

High

Total Cholesterol:HDL RatioTotal Cholesterol:HDL Ratio

Ridker et al, Circulation. 1998;97:2007–2011.

hs-CRP

hs-CRP

Rel

ativ

e R

isk

Rel

ativ

e R

isk

Randomized Clinical Trial

• Considered the gold standard in proving causation– e.g., by “reducing” putative risk factor of interest

• Randomization “equalizes” known and unknown confounders/covariates so that results can be attributed to treatment with reasonable confidence

• Inclusion and exclusion criteria can often be strict (to maximize success of trial) and may require screening numerous patients for each patient randomized

Randomized Clinical Trials

• Expensive, labor intensive, attrition from loss to follow-up or poor compliance can jeopardize results, esp. if more than outcome difference between groups

• Conditions are highly controlled and may not reflect clinical practice or the real world

• Funding source of study and commercial interests of investigators can raise questions about conclusions of study

MRC/BHF Heart Protection Study (HPS): Eligibility

• Age 40–80 years

• Increased risk of CHD death due to prior disease

– Myocardial infarction or other coronary heart disease

– Occlusive disease of noncoronary arteries

– Diabetes mellitus or treated hypertension

• Total cholesterol > 3.5 mmol/L (> 135 mg/dL)

• Statin or vitamins not considered clearly indicated or contraindicated by patient’s own doctors

Heart Protection Study Group. Lancet. 2002;360:7-22.

HPS: First Major Coronary Event

0.4 0.6 0.8 1.0 1.2 1.4

Nonfatal MI

Coronary death

Subtotal: MCE

Coronary

Noncoronary

Subtotal: any RV

Any MVE

Coronary events

Revascularizations

Type of Major Vascular Event

Statin-Allocated

(n = 10269)

Placebo-Allocated

(n = 10267)

357 (3.5%) 574 (5.6%)

587 (5.7%) 707 (6.9%)

898 (8.7%) 1212 (11.8%)

513 (5.0%) 725 (7.1%)

450 (4.4%) 532 (5.2%)

939 (9.1%) 1205 (11.7%)

2033 (19.8%) 2585 (25.2%)

0.73 (0.670.79)P < 0.0001

0.76 (0.700.83)P < 0.0001

0.76 (0.720.81)P < 0.0001

Statin Better Placebo Better

Heart Protection Study Collaborative Group. Lancet. 2002;360:722.

Relative vs. Absolute Risk

• Data from clinical trials are often reported in terms of relative risk (or relative risk reduction); however, the actual effects in terms of absolute risk may be very different:

• For example, a risk factor may be associated with a 2-fold or relative risk of 2 for the outcome of interest, but this may be an increase in risk from 1% to 2% in the next 10 years.

• Likewise, a treatment may be reputed to effect a 25% reduction in risk of the outcome, but this may only be reducing the risk of the outcome from 4% to 3%.

Relative vs. Absolute Risk: The Example from The Women’s Health Initiative• Those randomized to estrogen/progestin compared

to placebo and statistically significant increased risks:– Breast cancer 26% (8/10,000 person years)– Total coronary heart disease 29% (7/10,000

person years)– Stroke 41% (8/10,000 person years)– Pulmonary embolism 2.1 X (8/10,000 person

years)– Protective for colorectal cancer (37% lower)

and hip fracture (34% lower): no effect endometrial cancer or total mortality

Defining Independent and Dependent Variables

• Dependent variable is the outcome of interest—e.g., prevalence of CHD or HTN status (cross-sectional), disease status (case-control), incidence of MI (prospective study or clinical trial).

• Independent variables include treatment status (e.g., treatment vs. placebo), baseline demographic and clinical characteristics (which could be covariates and confounders)

• Important to know how dependent and independent variables are classified—e.g., continuous, categorical (binary or 3+ categories).

Guidelines for Sample Size / Power Determination

• Necessary for any research grant application

• Need to estimate what “control group” rate of disease or outcome is

• Need to state what is minimum difference (effect size) you want to detect that is clinically significant--e.g., difference in rates, or risk ratio

• Either power can be estimated for a fixed sample size at fixed alpha (usually 0.05 two-tailed) for different effect, OR sample size can be estimated for a given power (usually 0.80) for different effect sizes

Statistical significance and power

• Statistical significance is based on the Type I or Alpha error– the probability of rejecting the null hypothesis when it

was true (saying there was a relationship when there isn’t one)

– usually we accept being wrong <5% of the time, or alpha=0.05

– Setting alpha depends on how important it is that we not make a mistake in our conclusion.

• The Type II or Beta error is the probability of accepting the null when it was false– saying there is no relationship when there is one– power is 1-B, and 80% or 90% (beta error of 10% or

20%) is conventional.

Statistics and Statistical Procedures for Different Study Designs

• Cross-sectional: Pearson correlation, Chi-square test of proportions- prevalence odds ratio for likelihood of factor Y in those with vs. w/o X

• Case-control: Odds ratio for likelihood of exposure in diseased vs. non-diseased-- Chi-square test of proportions / logistic regression

• Prospective: Relative risk (RR) for incidence of disease in those with vs. without risk factor of interest, adjusted for covariates and considering follow-up time to event--Cox PH regression. Correlations and linear/ transformed regression used for continuous outcomes.

Statistics and Statistical Procedures (continued)

• Randomized clinical trial: Relative risk (RR) of event occurring in intervention vs. control group - Cox PH regression– For continuously measured outcomes, such as

pre-post changes in risk factors (lipids, blood pressure, etc.) initial treatment vs. control differences examined by Student’s T-test, repeated measures ANOVA / ANCOVA used for multiple measures across a treatment period and covariates

Fallacies in Presenting Results: Statistically vs. Clinically Significant?

• Having a large sample size can virtually assure statistically significant results even if the correlation, odds ratio, or relative risk are low

• Conversely, an insufficient sample size can hide (not significant) clinically important differences (higher beta error or concluding no difference when there is one)

• Statistical significance directly related to sample size and magnitude of difference, and indirectly related to variance in measure

Data Collection / Management • Always have a clear plan on how to collect data-- design and

pilot questionnaires, case report forms.

• The medical record should only serve as source documentation to back up what you have coded on your forms

• Use acceptable error checking data entry screens or spreadsheet software (e.g., EXCEL) that is covertable into a statistical package (SAS highly recommended and avail via UCI site license)

• Carefully design the structure of your database (e.g, one subject/ record, study variables in columns) so convertible into an analyzable format

Where to Go for Help

• Epidemiology and statistics books

• Statistical Consulting Center

(Robert Newcomb, PhD)

• Dean’s Scientific Review Committee - considers appropriateness of research design, procedures, statistical considerations for UCI-COM investigator initiated studies

Now Available from McGraw Hill… for more information: www.heart.uci.edu