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Fundamentals of Biostatistics. Lecture 2 Clinical Trials Validity/Reliability Assessing Evidence. Randomized Clinical Trials (RCT). Randomization first used by RA Fisher in agriculture expts in 1920s

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Fundamentals of biostatistics

Fundamentals of Biostatistics

Lecture 2

Clinical Trials


Assessing Evidence

Randomized clinical trials rct

Randomized Clinical Trials (RCT)

Randomization first used by RA Fisher in agriculture expts in 1920s

First clinical trial using randomization in 1931 by Anderson on use of sanocrysin on TB patients. Also, first trial using blinding.

Placebo first used in RCT in 1938 in cold vaccine trial.

Randomized clinical trials rct1

Randomized Clinical Trials (RCT)

Fundamental Point:

A properly planned clinical trial is a powerful expt’l technique for assessing effectiveness of a drug or intervention.

Defn: A clinical trial is a prospective study comparing the effect of intervention(s) against a control in humans.

Randomized clinical trials rct2

Randomized Clinical Trials (RCT)

Phases of clinical trials:

Phase I: Determine tolerance of a compound in humans (i.e., how large a dose can be given until unacceptable toxicity?).

Phase II: Evaluation of biologic activity or effect, and estimate rate of adverse events.

Phase III: Definitive comparative trial. Designed to determine effectiveness and its role in clinical practice.

Randomized clinical trials rct3

Randomized Clinical Trials (RCT)


Efficacy: How well an intervention works in an ideal setting.

Effectiveness: How well an intervention works in actual practice.

Phase IV: Evaluation of long-term saftey of an intervention believed to be effective in phase III trials.

Randomized clinical trials rct4

Randomized Clinical Trials (RCT)

Why are clinical trials needed?

Because evaluating the effectiveness of a treatment using uncontrolled observations is very difficult, since other factors affecting treatment outcome may not be balanced in treatment groups.

Randomized clinical trials rct5

Randomized Clinical Trials (RCT)

Advantages of RCT:

Groups more comparable b/c confounding variables are balanced

Ability to detect small effects

Most stat tests based on assumption of random allocation of pts to trt groups (validity)

Randomized clinical trials rct6

Randomized Clinical Trials (RCT)

Disadvantages of RCT:

Expensive and time consuming

Subject pool may not be representative

Effective treatment may be withheld

Expose pts to dangerous drugs

Randomized clinical trials rct7

Randomized Clinical Trials (RCT)

What is the question?

Each clinical trial must have a primary question.

The primary, as well as secondary questions, must be carefully selected, clearly defined, clinically relevant, and stated in advance.

This includes the choice of response variable (ie., true clinical endpoint or surrogate endpoint).

Randomized clinical trials rct8

Randomized Clinical Trials (RCT)

Study population

The study population is a subset of the population with the condition or characteristics of interest defined by the eligibility criteria.

This population should be defined in advance, stating unambiguous inclusion (eligibility) criteria.

The impact that the inclusion criteria will have on study design, ability to generalize, and participant recruitment must be considered.

Randomized clinical trials rct9

Randomized Clinical Trials (RCT)

Randomized control studies

comparative studies with an intervention group and a control group

the assignment of the participant to a group is determined by the formal process of randomization.

Randomized clinical trials rct10

Randomized Clinical Trials (RCT)

Randomized control studies . . .

Sound scientific investigation almost always demands that a control group be used against which the new intervention can be compared.

Randomization is the preferred way of assigning participants to control and intervention groups.

Randomized clinical trials rct11

Randomized Clinical Trials (RCT)


Randomization tends to produce study groups that are:

comparable with respect to known and unknown risk factors

Removes investigator bias in the allocation and treatment of patients

Guarantees statistical tests will have valid significance levels.

Randomized clinical trials rct12

Randomized Clinical Trials (RCT)

Randomization . . .

Simple randomization is easiest to understand and use, but randomization can also be blocked, stratified, adaptive, etc.

Randomization is best accomplished by an independent central statistical unit.

Randomized clinical trials rct13

Randomized Clinical Trials (RCT)

Blinding (Masking)

Ideally, a clinical trial should use a double-blind design to avoid potential problems with bias during data collection and assessment.

If using a double-blind design is not feasible, a single-blind design and other measures to reduce bias should be used.

Randomized clinical trials rct14

Randomized Clinical Trials (RCT)

Baseline Assessment

Relevant baseline data should be measured in all study participants before the start of intervention.

These baseline measurements can be used to determine eligibility (if obtained prior to assignment to treatment group).

Can be also used to determine if the randomization produced identical groups (if not, can be used as covariates for adjustment of imbalance).

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Randomized Clinical Trials (RCT)

Data Analysis

Excluding randomized participants or observed outcomes from analysis and sub-grouping on the basis of outcome or response variables (including non-adherence) can lead to biased results of unknown magnitude or direction.

Including all randomized participants in the analysis, in the group they were assigned, is the intent-to-treat principle.

The intent-to-treat analysis is viewed as the most valid approach (least susceptible to bias).

Randomized clinical trials rct16

Randomized Clinical Trials (RCT)

Intent-to-Treat Principle

Once randomized . . .

. . . Always analyzed!

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Randomized Clinical Trials (RCT)

Covariate Adjustment

While randomization eliminates bias, it does not guarantee comparable baseline characteristics of patients in different treatment groups in a particular trial.

Baseline balance is not a requirement for obtaining valid variables.

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Randomized Clinical Trials (RCT)

Covariate Adjustment . . .

Imbalance will matter only if characteristic is related to patient outcome, ie., it is prognostic.

When randomization leads to chance baseline imbalance, estimates of treatment effects will be biased when using unadjusted analysis.

Validity and reliability

Validity and Reliability


Represents the degree to which a measurement represents a true value.


A measure of the reproducibility of a result or observation. ie., How closely do repeated measurements on the same object agree?

Validity and reliability1

Validity and Reliability

Errors can be caused by a lack of either validity or reliability

Validity and reliability are related. If a measure is unreliable it is not capable of producing valid results.

Better reliability is necessary, but not sufficient for validity.

Threats to study validity

Threats to Study Validity





Regression to the Mean

Threats to study validity1

Threats to Study Validity


Distortion of effect estimate because of

(i) manner subjects selected: biased sampling,

(ii) selective losses: loss to follow-up and non- responses.

(Case-control studies are particularly susceptible)

Threats to study validity2

Threats to Study Validity


Distortion of effect estimate when measurement of exposure or disease is systematically inaccurate.

(i) misclassification bias - incorrect classification of exposure or disease,

(ii) recall bias: accuracy of self-reported data varies across comparison groups.

Threats to study validity3

Threats to Study Validity

Other common types of bias:

(i) Investigator/Patientbias

(ii) Measurement error

Many forms of bias can be eliminated or reduced by using randomization and blinding (preferably double-blinding).

Threats to study validity4

Threats to Study Validity


Type of bias occurring when effect of exposure mixed-up with one or more extraneous variables.

can create appearance of exposure-disease relationship, when none exists.

can hide true nature of exposure-disease relationship.

Threats to study validity5

Threats to Study Validity

Confounding . . .

due to presence of key relationships between extraneous variable(s) and both exposure and disease.

confounding variables are risk factors for the disease, or a correlate of a causal factor.

confounding variables are associated with exposure of interest.

Threats to study validity6

Threats to Study Validity

Confounding . . .

Addressing the Problem:

At the design stage we can use:



At the analysis stage we can use:

Stratified analysis (Mantel-Haenszel Methods)

logistic regression

Threats to study validity7

Threats to Study Validity

Regression to the Mean (RTM)

tendency of extreme observations by chance to move closer to the mean when repeated

What is the danger of ignoring RTM?

Causality may erroneously be inferred

Threats to study validity8

Threats to Study Validity

Regression to the Mean (RTM) . . .


Patients having higher than average cholesterol levels at initial screening, have lower levels on repeat.

Acute pain patients seek help when symptoms severe, and any change is likely to be improvement => useless treatment may appear effective.

Threats to study validity9

Threats to Study Validity

Regression to the Mean (RTM) . . .

RTM is a function of:

Correlation between pre- and post-treatment values.

How extreme the pre-treatment values are.

Threats to study validity10

Threats to Study Validity

Regression to the Mean (RTM) . . .

RTM implies that if we select subjects because they appear abnormal on some test, AND

do nothing to them,

they will seem to improve when retested, thus

treatment effects become confounded with RTM.

Threats to study validity11

Threats to Study Validity

Regression to the Mean (RTM) . . .

Four ways to minimize RTM:

1. Increasing reliability of screening test

2. Testing each subject twice, and requiring all tests be extreme before entry into study.

3. Adjust for the correlation of pre/post measures

4. Use a randomized placebo-control trial

Types of reliability

Types of Reliability

Interobserver – agreement among observers

(Kappa, Intraclass correlation)

2. Test-Retest – stability, does the same measure give the same results repeatedly?

3. Parallel form – Two parallel test forms with different items are correlated.

4. Split-half- Split individual measure into two random parts.

Ten steps for evaluating evidence
Ten Steps forEvaluating Evidence

  • Be skeptical

  • Don’t rely on biological plausibility

  • Reliable info requires comparison

  • Ensure cause precedes the effect

  • Post-trial questions maybe unreliable

  • Pre-trial question should be specific and clinically relevant

Ten steps for evaluating evidence1
Ten Steps forEvaluating Evidence . . .

  • Discovering small effects requires randomization

  • Be wary of conflicts of interest

  • Non-specific exposure effects can be important

  • Unblinded examiners may introduce bias.