Biostatistics in Practice

Peter D. ChristensonBiostatistician

http://gcrc.LABioMed.org/Biostat

Session 1: Quantitative and Inferential Issues

Session 1 Objectives

General quantitative needs in biological research

Statistical software

Protocol examples, with statistical sections

Overview of statistical issues using a published paper

General Quantitative Needs

Descriptive: Appropriate summarization to meet scientific questions: e.g.,

• changes, or % changes, or reaching threshold?

• mean, or minimum, or range of response?

• average time to death, or chances of dying by a fixed time?

General Quantitative Needs, Cont’d

• Inferential: Could results be spurious, a fluke, due to “natural” variations or chance?

• Sensitivity/Power: How many subjects are needed?

• Validity: Issues such as bias and valid inference are general scientific ones, but can be addressed statistically.

Statistical Software

Professional Statistics Software Package

Output

Enter code; syntax.

Stored data; access-ible.

Typical Statistics Software PackageSelect Methods from Menus

Output after menu selection

Data in spreadsheet

www.ncss.com

www.minitab.com

Microsoft Excel for Statistics

• Primarily for descriptive statistics.

• Limited output.

• No analyses for %s.

Almost Free On-Line Statistics Software

• Run from browser, not local.

• Can store data, results on statcrunch server.

• $5/ 6 months usage.

www.statcrunch.com

Free Study Size Softwarewww.stat.uiowa.edu/~rlenth/Power

Protocols

Typical Statistics Section of Protocol

11.1 Overview of study design and goals

11.2 Randomization/treatment assignment11.3 Study size11.4 Missing data / subject withdrawal or

incompletion11.5 Definitions / outcomes11.6 Analysis populations11.7 Data analysis methods11.8 Interim analyses

Public Protocol Registration

www.clincialtrials.gov

www.controlled-trials.com

Attempt to allow the public to be aware of studies that may be negative.

Many journals now require registration in order to consider future publication.

Public Protocol Registration

Example of Protocol

--- Displayed in Class ---

Statistical Issues

Subject selectionRandomization Efficiency from study design Summarizing study resultsMaking comparisonsStudy sizeAttributability of resultsEfficacy vs. effectiveness

Exploring vs. proving

Paper with Common Statistical Issues

Case Study:

Selecting Study Subjects

Representative or Random Samples

How were the children to be studied selected (second column on the first page)? The authors purposely selected "representative" social classes.

Is this better than a "randomly" chosen sample that ignores social class?

Often hear: Non-random = Non-scientific.

Case Study: Participant Selection

No mention of random samples.

Case Study: Participant Selection

It may be that only a few schools are needed to get sufficient individuals. If the % among all possible schools that are lower SES is small, none of these schools may be chosen.

So, a random sample of schools is chosen from the lower SES schools, and another random sample from the higher SES schools.

Selection by Over-Sampling

It is not necessary that the % lower SES in the study is the same as in the population. There may still be too few subjects in a rare subgroup to get reliable data.

Can “over-sample” a rare subgroup, and then weight overall results by proportions of subgroups in the population. The CDC NHANES studies do this.

Randomization

Random Samples vs. Randomization

We have been discussing the selection of subjects to study, often a random sample.

An observational study would, well, just observe them. An interventional study assigns each subject to one or more treatments in order to compare treatments. Randomization refers to making these assignments in a random way.

Basic Study Designs

1. Observational

2. Interventional

3. Cross sectional vs. longitudinal

4. Cohort vs. Case-Control

Why Randomize?

A B A B A B

B A B A B A

A B A B A B

B A B A B A

A B A B A B

B A B A B A

Plant breeding example: Compare yields of varieties A and B, planting each to 18 plots.Which design is better?

B A B A B B

A A B A B A

B A B B A A

B B A A B B

A B A B B A

A A B A B A

Systematic Randomized

Why Randomize?

So that groups will be similar except for the intervention (treatment or order of treatment). Case-control studies may not provide this similarity.

When enrolling, we will not unconsciously choose an “appropriate” treatment.

Minimizes the chances of introducing bias when attempting to systematically remove it, as in plant yield example.

Efficiency from Study Design

Case Study: Crossover Design

Each child is studied on 3 occasions under different diets.

Is this better than three separate groups of children?

Why, intuitively?

How could you scientifically prove your intuition?

Case Study: Diet Assignments

Week: 1 2 3 4 5 6

Order 1

Pla Pla Wash

A Wash

B

Order 2

Pla Pla Wash

B Wash

A

Order 3

Pla A Wash

Pla Wash

B

Order 4

Pla A Wash

B Wash

Pla

Order 5

Pla B Wash

Pla Wash

A

Order 6

Pla B Wash

A Wash

Pla

At each school, randomize equal #s of subjects to each order of 3 diets:

Pla = Placebo Wash = Washout (placebo here)

Blocked vs. Unblocked Studies

AKA matched vs. unmatched.AKA paired vs. unpaired.

Block = Pair = Set receiving all treatments. Set could be an individual at multiple times (pre and post), or left and right arms for sunscreen comparison; twins or family; centers in multi-center study, etc.

Blocking is efficient because treatment differences are usually more consistent among subjects than each treatment is.

Potential Efficiency Due to Pairing

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A B A B Δ=B-A

…….… ….

…….… ….

Δ3 3

3

Unpaired

A and B Separate Groups

Paired

A and B in a Paired Set

Summarizing Study Results and

Making Comparisons

Outcome Measures

Generally, how were the outcome measures defined (third page)?

They are more complicated here than for most studies.

What are the units (e.g., kg, mmol, $, years)?

Outcome measures are specific and pre-defined. Aims and goals may be more general.

Summarization / Data Reduction

How are the outcome measures summarized? e.g., Table 2:

Case Study: Statistical Comparisons

How might you intuitively decide from the summarized results whether the additives have an effect?

Different Enough?Clinically? Statistically?

Statistical Comparisons: Figure 3

Statistical Comparisons and Tests of Hypotheses

Engineering analogy: Signal and Noise

Signal = Diet effectNoise = Degree of precision

Statistical Tests: Effect is probably real if signal-to-noise ratio Signal/Noise is large enough.

Importance of reducing “noise”, which incorporates subject variability and N.

Back to Efficiency of Design

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A B A B Δ=B-A

…….… ….

…….… ….

Δ3 3

3

Unpaired

A and B Separate Groups

Paired

A and B in a Paired Set

Noise

Noise

Signal = 3

Study Size

Number of Subjects

The authors say, in the second column on the fourth page:

Intuitively, what should go into selecting the study size?

We will make this intuition rigorous in Session 4.

Attributability of Study Results

Other Effects, Potential Biases

The top of the second column on the fourth page mentions other effects on diet :

The issue here is: Could apparent diet differences (e.g., -0.26 B vs. -0.44 Placebo) be attributable to something else?

Efficacy vs. Effectiveness

Non-Completing or Complying Subjects

What is the most relevant group of studied subjects: all randomized, mostly compliant, fully compliant?

Study Goal:

Scientific effect?

Societal impact?

Exploring vs.

Proving

Multiple and Mid-Study Analyses Many more analyses could have been performed on each of the individual behavior ratings that are described in the first column of the 3rd page.

Wouldn’t it be negligent not to do them, and miss something?

Is there a downside to doing them?

Should effects be monitored as more and more subjects complete?

Multiple Analyses

GHA: Global Hyperactivity Aggregate

Teacher ADHD

Parent ADHD

Class ADHD

Conner

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Many

Separate

Measures

Torture data long enough and it will confess to something

Mid-Study Analyses

Effect

0

Number of Subjects EnrolledTime →

Too many analyses, as on previous slide

Wrong early conclusion

Need to monitor, but also account for many analyses

Bad Science That Seems So Good

1. Re-examining data, or using many outcomes, seeming to be due diligence.

2. Adding subjects to a study that is showing marginal effects; stopping early due to strong results.

3. Emphasizing effects in subgroups. See NEJM 2006 354(16):1667-1669.

Actually bad? Could be negligent NOT to do these, but need to account for doing them.