Study Design

Study Design A study design is a careful advance plan of the

analytic approach needed to answer the research question under investigation in a scientific way.

The basics of study design: A carefully formed research question and a clearly stated

outcome measure Assessing the feasibility of study objectives and

considering alternative research designs Defining the study population and key concepts in

operational terms Selecting methods of sampling, data collection, and

analysis appropriate to the study's objectives Developing realistic budgets and time schedules for each

stage of the research.

Types of Study Experimental/ Interventional: Investigator controls

the assignment of the exposure or of the treatment e.g. randomized controlled trial.

Non-experimental/Observational: The allocation or assignment of factors is not under control of investigator. For example, in a study to see the effect of smoking, it is impossible for an investigator to assign smoking to the subjects. Instead, investigator can study the effect by choosing a control group and find the cause and relation effect. Some examples are-

Cross-sectional study Cohort study Case-control study

Study Design Randomized controlled Trial: Random allocation of different

interventions (or treatments) to subjects in which one treatment group is for the purpose of determining the efficacy of the other treatment (s). E.g. placebo or standard medication can be used as a controlled to compare the efficacy of the other (s) treatment (s)

Types of control groups: Placebo control group: Receive treatment Active control group: For example a cancer patient can’t be given

placebo. Need to use a standard medication in the market. Types of randomized controlled trials:

Open trial: Investigator and subject know the full details of the treatment.

Single-blind trial: Investigator knows about the treatment but subject does not.

Double-blind: Both investigator and subject do not know about the treatment

Study Design Cross-sectional study: A descriptive study of the relationship

between diseases and other factors at one point of time (usually) in a defined population. This is also known as prevalence study or survey study.

Cohort study: Subjects who presently have a certain condition and/or receive a particular treatment are followed over time and compared with another group who are not affected by the condition under investigation. Cohort analysis attempts to identify cohorts effects. E.g. recruit a group of smokers and a group of non-smokers and follow them for a set period of time and note differences in the incidence of lung cancer between the groups at the end of this time.

Case-control study: A study that compares two groups of people: those with the disease or condition under study (cases) and a very similar group of people who do not have the disease or condition (controls) and look back to see if they had the exposure of interest. E.g. two groups of people (lung cancer group and non-lung cancer group) are selected and compare for an exposure (smoke).

Study Design

A Protocol is a document that describes the background, objective(s), design, methodology, data collection and management, variable assessment, statistical considerations, and organization of the study.

Basically a protocol is a manuscript that describes every step from proposal to completion of the research study.

Basic concepts of clinical trials A clinical trial is a research study to answer specific questions about

vaccines or new therapies or new ways of using known treatments. Institutional Review Board (IRB): A committee of physicians,

statisticians, researchers, and others that ensures that a clinical trial is ethical and that the rights of study participants are protected.

Efficacy is the maximum ability of a drug or treatment to produce a result Baseline measurement is the measurement taken just before a

participant starts to receive the experimental treatment which is being tested

Change from baseline measurement is the difference between baseline and post-baseline measurements.

Percent change from baseline = (Change from baseline / baseline measurement) x 100

Pharmacokinetics (PK) analysis explores what the body does to the drug. That is, the processes (in a living organism) of absorption, distribution, metabolism, and excretion of a drug or vaccine. It helps to decide the duration of doses.

Pharmacodynamic (PD) analysis detects the effect of drug on the body or microorganisms of the body.

Basic concepts of clinical trials Classification of clinical trials by their purposes

Treatment trials: Test experimental treatments, new combinations of drugs, or new approaches to surgery or radiation therapy.

Prevention trials: Look for better ways to prevent disease in people who have never had the disease or to prevent a disease from returning. These approaches may include medicines, vitamins, vaccines, minerals, or lifestyle changes.

Diagnostic trials: Conducted to find better tests or procedures for diagnosing a particular disease or condition.

Screening trials: Test the best way to detect certain diseases or health conditions.

Quality of Life: Trials (or Supportive Care trials) explore ways to improve comfort and the quality of life for individuals with a chronic illness.

Study design of a Clinical Trial Title: Reflects the main research interest. Background/Rational of study: Importance of the study

and previous study results will be explained. Study Objectives:

Primary objective (s): Focuses on the core research question (s)

Secondary Objectives: Focuses on the secondary/ optional research questions

Investigational Plan: Variable (parameter) selections to achieve the research

objectives. Avoid selection of unnecessary variables Overall study design and plan description: Brief

description of design and assessments.

Study design of a Clinical Trial Selection of Study Population:

Inclusion criteria: A set of conditions to include a subject in the study. E.g. a adult study will include subjects only of age 18 or more.

Exclusion criteria: A set of conditions under which a subject (met inclusion criteria) will be excluded from the study. E.g. protocol violations, Non-compliance of the treatment etc.

Sample size calculation: Based on the effect size and the statistical power needed to test of main research question. Here are some useful websites for power and sample size calculation- compare means, compare proportions, population survey

http://department.obg.cuhk.edu.hk/researchsupport/Sample_size_EstMean.asp

Experimental Design, survival analysishttp://hedwig.mgh.harvard.edu/sample_size/size.html

Regression/multiple regressionhttp://www.danielsoper.com/statcalc/calc01.aspx

Study design of a Clinical Trial Description of the treatment groups/ treatment

administration/ treatment period Randomization of the treatment to the subjects Detailed descriptions of assessment/collection of all

parameters/variables including sign and symptoms (adverse events)

Statistical Methods: Detailed descriptions of the statistical analyses of all variables in the study. A typical clinical trial may include- Hypothesis and Decision rules Rules for handling missing values Interim/Final analysis Subjects Disposition/summaries including the summary of

the reasons of early termination

Study design of a Clinical Trial Statistical Methods (continued)

Disease Diagnosis/History: Usually descriptive statistics is enough

Summary of Medical/Surgical history Demographics (age, sex, race, BMI, height, weight,

etc.) and baseline characteristics : Usually descriptive statistics are enough but these variables are often used as covariates in efficacy analysis.

Efficacy analysis: Needs some reasonable statistical analysis to justify the research goal. Researchers sometimes perform analysis on the change from baseline and percent change from baseline values instead of the observed values.

Study design of a Clinical Trial Statistical Methods (continued)

Safety analysis (if subjects receives medication): Vital signs (temperature, blood pressures, respiration, pulse etc.), ECG/MRI results, Laboratory parameters, Physical exams, Adverse Events, pregnancy tests etc.)- Usually summary of the observed values and change from baseline values are provided.

Prior/concomitant medications: Summary of the all medications taken during the study or just immediate prior to study ( usually not more than one month) are provided.

Quality of life measurements: Both summary statistics and reasonable statistical analysis are required.

Pharmacokinetic (PK) & pharmacodynamic (PD) parameters: Summary statistics is enough for most cases.

Study design of a Clinical Trial Ethics:

Independent Ethics committee (IEC) or Institutional Review Board (IRB)

Ethical conduct of the study: Guidelines of Food and Drug Administration (FDA) and International Conference on Harmonization (ICH) for good clinical practices and maintaining the quality of research.

Patient information and consent: A document that describes the rights and risks of the study participants, and includes details about the study, such as its purpose, duration, required procedures, and key contacts. The participant then decides whether or not to sign the document.

Data collection and management Storage security Protection from data loss Checking inconsistency of the data

Experimental Design for Microarray Experiments

Suzanne McCahan, Ph.D.Molecular Biologist

Microarray Research

Should be Hypothesis Driven Test a specific statement Ask a specific question

Involves data mining Often generates new hypotheses

Microarrays can measure…

Gene Expression Chromatin Structure

Methylation of Cytosine Histone Binding

Array Comparative Genomic Hybridization (aCGH) Amplification of Chromosomal Regions Deletion of Chromosomal Regions

General Background

The application and type of array to be used determine what should be considered when designing microarray experiments.

A general understanding of microarrays is needed.

Image courtesy of Affymetrix.

General Characteristics of Microarrays

Microarrays are small. This is an picture of an

Affymetrix GeneChip.

Image courtesy of Affymetrix.

Microarrays are comprised of DNA probes

Probes are attached to (or synthesized on) a surface. Oligos - 25 bp Oligos – 50-70 bp Cloned or Amplified DNA

PCR – 500 bp BAC (Bacterial Artificial

Chromosome) – 300kb

CTAAGAGC

GATTCTCG

C : GT : AA : TA : TG : CA : TG : CC : G

Strands represent A probe on an array Labeled DNA or RNA

from a sample (This is also referred to as the ‘target’.)

Image courtesy of Affymetrix.

Hybridization

Hybridization

Images courtesy of Affymetrix.

Fluorescence where labeled DNA (or RNA) hybridizes to probe.

No fluorescence where labeled DNA (or RNA) does NOT hybridize to probe.

Image courtesy of Affymetrix.

DNA Microarrays

There are many probes on a single microarray.

Amount of target is relative to the intensity of fluorescent signal.

Numbers of Probes on Microarrays

Gene Expression Affymentrix Rat GeneChip has ~300,00 probes

representing ~15,000 genes Chromatin Structure

Agilent Mouse CpG Island Array has ~100,000 probes

aCGH (Amplification/Deletion) NimbleGen Human X Chromosome Tiling Array

~385,000 probes

Keep comparisons simple Two well defined groups is best, although more can be used.

Normal vs control Untreated vs treated (one drug)

Less complex samples are better than complex ones. Cell lines are the least complex Blood

RBC should be removed, hemoglobin mRNA and protein can interfere

Mononuclear cells are better than total white cells Solid tissue

Tumor only, no contaminating normal tissue Muscle only, no contaminating fat

Decrease Variability Samples should be as much the same as possible

If from patients Exact same tissue Strict criteria for diagnosis Only meds to be studied Same pubertal stage

Handled in a similar manner (immediately on ice) Same quality of starting material (RNA or DNA) Hybridization, washing and scanning should be done

by a single person at a single location.

How many samples should be included in a study?

Many ‘tests’ are done on a single sample. Each hybridization is expensive. This usually limits

the number of samples that can be included in an experiment.

With the usual budget, it is not feasible to use standard statistical tools to determine the number of samples to be included in a study and analyze the data.

The best way to determine sample size is to do a pilot study to obtain data from a particular experimental system and do a power analysis taking the challenges of microarrays into consideration.

A few publications assess sample size with public gene expression microarray data sets. The results vary with dataset. One estimation for sample size was 10-12 per group.

When a pilot study is not feasible, a general rule of thumb is 5 – 10 samples per group.

How many samples should be included in a study?

1-Color Hybridizations Common format for gene expression arrays RNA or DNA from each sample is hybridized

to a single array If there are two groups (control and

treated) with 10 samples each, then A total of 20 samples will be used A total of 20 arrays will be used

2-Color Hybridizations Format for some gene expression, some

aCGH, and all chromatin structure arrays RNA or DNA from two samples

simultaneously hybridized to a single array One sample is experimental Other sample is control

Each of the two samples is labeled with a different fluorochrome.

One array is needed for each pair of samples

aCGH Arrays – Detection of Amplification/Deletion Most platforms for aCGH require 2-color

hybridizations Tumor Studies

Hybridize labeled DNA from tumor and normal tissue from a single subject (patient/animal) together.

Genetic Studies Hybridize labeled DNA from control subject with

DNA from diseased subject The same control should be used with all

diseased subjects. The control could be DNA from a single

individual or from a single pool of individuals

Confirmation of results is necessary

Since there is such disparity in the number of samples examined and the number of tests performed (e.g. level of transcripts measured) microarray experiments results must be confirmed.

Methods for confirmation of results Additional samples that were not examined on

the microarray should be used. Quantitative PCR methods are often performed. Confirmation usually involves a small number

of genes (or chromosomal regions, depending on the type of array that was used).

The combination of a larger sample size and small number of tests allows standard statistical methods to be used.

Example of a microarry experiment

Modeled after: Insight into Pathogenesis of Antibiotic-

Resistant Lyme Arthritis through Gene Expression Profiling

AnneMare Brescia, MD Principal Investigator

Hypothesis

There are differences in gene expression of synovial fibroblasts from individuals with acute Lyme synovitis and chronic Lyme synovitis and these differences allow for the perpetuation of inflammation in chronic Lyme synovitis.

Biological material – Cell lines Collect synovial fluid

Site of disease activity Prospectively – don’t know whether the case is

acute or chronic Lyme disease at time of collection

Culture cells from fluid Primary cells Adherent cells are selected Consistent phenotype - no monocytes Harvest while cells are dividing

At same passage Before reaching confluence

Synovial fluid from 3 groups

Control Injured joints

Acute Lyme Disease Lyme synovitis resolved within 2 months

of initiation of antibiotic therapy Chronic Lyme Disease

Lyme synovitis persisted for six or more months despite antibiotic therapy

Experimental Details

Uniform samples Cell line probably representing single cell

type Affymetrix GeneChip

Single color hybridization Human gene expression arrays – 15 chips

Biological replicates 5 samples per group

Overview of Analysis Determine differential expression Confirm results with real-time RT-PCR Determine functional categories that are

represented by differentially expressed genes Replication? Recruitment and/or activation of immune system?

Unexpected results can generate additional hypotheses

The Future

New technologies may replace microarrays High through-put sequencing is on the

horizon Less expensive Faster Short sequences (25 nt – 400 nt) Presents new computing challenges

Experimental design will need to be adjusted