Research Ethics Symposium 2007

• H. F. Gilbert, Ph.D.– Assoc Dean for Academics and Postdoctoral

Research– Graduate School of Biomedical Sciences

Ethics Symposium Research Students and Postdoctorals NIH Topic Instructor 1 Mon

April 30 4:00 Experimental error and honesty--Experimental

measurements and error, replication of experiments, influence of statistics on experimental design, how errors affect your conclusions, statistical significance, correlations, when is it ethical to ignore some

1b Scienctific Misconduct

Gilbert

2 Mon April 30

5:00 Scientific Misconduct- Part 1--Definitions - Falsification, fabrication, plagiarism-policies of the college—allegations, inquiries, investigations

1 Scientific Misconduct

Gilbert

3 Tues May 1

4:00 Scientific Misconduct - Part 2--case studies involving scientific miscondut

1a Scientific Misconduct

Gilbert

4 Tue May 1

5:00 Data Management/Ownership--Keeping a laboratory notebook, maintaining other records/computer files, ownership of scientific materials/data, sharing results and reagents

4 Data Management and Ownership

Slaughter

5 Wed May 2

4:00 Plagiarism/Conflicts of Interest--Plagiarism (definition and examples), attributing credit to others, financial conflicts of interest, conflicts of interest in peer review, plagiarasm and computers, copyright, acceptable use policies of the College

2 & 6

Plagiarism Conflicts of Interest

Slaughter

6 Wed May 2

5:00 Publishing your Work - Authorship/Peer Review--Organizing your paper, preparing manuscripts, who should be an author?, responsibilities of an author, manuscript review systems, responsibilities of a reviewer, dealing with criticism

3 & 5

Authorship Peer Review

Brinkley

7 Thurs May 3

4:00 Research with Human Subjects--definition of research with human subjects, experiments with human material, confidentiality of medical data, experiments involving humans, informed consent, the role of the IRB

8 Human Research

Sharp

8 Thurs May 3

5:00 Ethics of Experiments with Animals--when can animals be used ethically in research, avoiding unnecessary pain/suffering and euthanasia, appropriate selection of numbers/types of animals in research, animal use approval

7 Animal Experiments

Michael

Clinical Ethics Symposium (May 21-24)

Ethics Symposium Research Students and Postdoctorals

NIH Topic Instructor

Monday, May 21

4-5:00 Scientific integrity and research misconduct

1, 1a, 1b Scientific Misconduct

Gilbert

Monday, May 21

5-6:00 Authorship standards and publication practices

2, 3, 4, 5 Authorship, Data Ownership, Plagiarism & Peer Review

Gilbert

Tuesday, May 22

4-5:00 Financial conflicts of interest in biomedical research

6 Conflicts of Interest

Brody

Tuesday, May 22

5-6:00 Institutional policies on the ethical use of animals in research

7 Experiments with Animals

Hopp

Wednesday, May 23

4-5:00 Ethical commitments in human subjects research

8 Human Subject Research

Sharp

Wednesday, May 23

5-6:00 Institutional policies on research with human volunteers

8a Human Subject Research

Wright

Thursday, May 24

4-5:00 Pediatric research and studies of vulnerable populations

8c Human Subject Research

Berg

Thursday, May 24

5-6:00 Clinical trials and ethical issues in pharmaceutical research

8b Human Subject Research

Sharp

Resources List

On Being a Scientisthttp://www.nap.edu/readingroom/books/obas/content.html

Statistics on Linehttp://www.statsoftinc.com/textbook/stathome.html

Ethics Training Sitehttp://www.bcm.edu/gs/ethics/index.html

The Scientific Method & Scientific Integrity

Observations Hypothesisor Model

Predictions

Experiments

falsifiable

controls

Truth

Error in Experiments

Random error - Random error - error that cannot be error that cannot be controlledcontrolled

•pipetting error (2-10%)•temperature variations•biological variability•lot to lot differences in reagents•measurement errors (instrumental errors)

Error in Experiments

Systematic error -Systematic error - errors that occur consistently within the same experiment(s)

•instrument calibration errors•reagent concentrations based on weight or incorrect measurement in stock solutions•systematic losses of material•biological variability - variation between strains

Error in Experiments

Blunders -Blunders - catastrophic errors that occur occasionally

•swapping sample identity•adding the wrong reagent•failing to add something•biological variability - variation between strains

What would you do?

Sharon, a graduate student, was putting her thesis together, and constructing a table of doubling times for a series of experiments to determine the effects of glucose vs galactose as a carbon source on a mutant strain of yeast. She was looking for mutations in the galactose transporter that would decrease the import of galactose and make it harder for the cells to grow on galactose but not glucose. In a growth screen, she found one cell line that grew more slowly on galactose than glucose. She has been working the past year on identifying the site of the mutation and has recently found a point mutation in the galactose transporter in this strain.

What would you do?

When writing up the paper for publication her PI wanted to include the actual doubling time of the strain on glucose and galactose. When she looked back in her notebook, she found that she had measured the growth rate for this strain only once. For wt yeast, growth rates on glucose and galactose are usually similar. Since the growth on glucose and galactose medium was so different, Sharon was sure that her conclusion that the mutation caused the slow growth was right. However, she and her PI wanted to be sure of her result so she decided to repeat the measurement.

+gal +glucose +gal +glucose4.3 2.4 4.3 2.4

3.1 4.5

Avg 4.3 2.4 3.7 3.5Stdev #DIV/0! #DIV/0! 0.8 1.5

n 1 1 2 2

Doubling Time (hr) Doubling Time (hr)

She repeated the measurements the next day andobtained the following results. Now what?

Original Data

2nd expt

All Data

What if the experiments had come out this way?Would she have been finished?How many times should you repeat an experiment to be

certain of the results?

+gal +glucose +gal +glucose4.3 2.4 4.3 2.4

4.1 2.6

Avg 4.3 2.4 4.2 2.5Stdev #DIV/0! #DIV/0! 0.1 0.1

n 1 1 2 2

Doubling Time (hr) Doubling Time (hr)

Original Data All Data

The t-test

Lets you statistically test to see iftwo means differ significantly

Mean1 - Mean2

12 (n1-1)+2

2(n2-1)

(n1+n2 -2)( n1 n2) 1 1

+t =

+gal +glucose4.3 2.43.1 4.5

3.7 3.50.8 1.52 2

df 2.0t Stat 0.2P(T<=t) 0.4t Critical 4.3

Doubling Time (hr)

Raw Data

mean

stdevn

How many experiments to you have to do to make up for your mistake? Can you ever?

+gal +glucose +gal +glucose +gal +glucose4.3 2.4 4.3 2.4 4.3 2.43.1 4.5 3.1 4.5 3.1 4.53.9 2.7 3.9 2.7 3.9 2.7

4.1 2.6 4.1 2.64.6 2.3

Avg 3.8 3.5 Avg 3.9 3.1 Avg 4.0 2.9Stdev 0.6 1.1 Stdev 0.5 1.0 Stdev 0.6 0.9

n 3 3 n 4 4 n 5 5

Doubling Time Doubling TimeDoubling Time

df 4.00 df 6 df 8t Stat 0.76 t Stat 1.440 t Stat 2.30P(T<=t) 0.25 P(T<=t) 0.210 P(T<=t) 0.05t Critical 3.18 t Critical 2.500 t Critical 2.30

Outliers

(Suspect - Nearest Point)

(High Point - Low Point)Q > =

Q 0.66 0.86 0.66 0.86 0.53 0.81

n Q3 0.944 0.765 0.646 0.567 0.518 0.479 0.44

10 0.4111 0.3912 0.37

2.6-5.0 1.3-5.7 2.9-4.9 1.1-5.1 2.8- 5.2 1.1-4.7

2

+gal +glucose +gal +glucose +gal +glucose4.3 2.4 4.3 2.4 4.3 2.43.1 4.5 3.1 4.5 3.1 4.53.9 2.7 3.9 2.7 3.9 2.7

4.1 2.6 4.1 2.64.6 2.3

Avg 3.8 3.5 3.9 3.1 4.0 2.9Stdev 0.6 1.1 0.5 1.0 0.6 0.9

n 3 3 n 4 4 n

Doubling Time Doubling TimeDoubling Time

Throw out when

Throw out when greater than 2 away from average

Graphical Data and Correlations

Outliers can affect data drastically. Failing to exclude a true outlier can bias your data. Excluding a false outlier can also bias your data.

IF YOUR CONCLUSION CHANGES WHEN YOU INCLUDE OR EXCLUDE A POINT - YOU NEED MORE POINTS.

Confidence Interval Testing of Data Points

Y = A + B * X

Parameter Value Error------------------------------------------------------------A 8.42857 4.4964B 4.50714 1.74145------------------------------------------------------------

R SD N P------------------------------------------------------------0.69928 6.86837 9 0.03604-------------------------------------------------

0 1 2 3 40

5

10

15

20

25

30

35

40

45 Linear Fit of DATA1_B Upper 95% Confidence Limit Lower 95% Confidence Limit

Abs

orba

nce

Concentration

Y = A + B * X

Parameter Value Error t-Value Prob>|t|---------------------------------------------------------------------------A 5.41667 1.3934 3.88739 0.0081B 6.91667 0.59415 11.64136 <0.0001

0 1 2 3 40

5

10

15

20

25

30

35

40

45 Linear Fit of DATA1_B Upper 95% Confidence Limit Lower 95% Confidence Limit

Abs

orba

nce

Concentration

Outliers

If you are sure of a blunder record it in your notebook and eliminate it from consideration. Being sure meansthat you are positive that you made a mistake and can document it, not simply that the results are not what you expect

Rule of thumb is that you should not remove more than oneoutlying point from a given data set

Each field/PI may have different standards for how data areselected for inclusion. However, if data are excluded it shouldbe stated in the paper, including the criteria that were used.

What would you do?

Tom is investigating how vitamin E protects cells againstoxidative stress. He is examining how a human HeLa cell lineresponds to hydrogen peroxide treatment.

Using an antibody to superoxide dismutase he used a western blot of extracts from HeLa cells to observed the amount of SOD present after peroxide treatment in conjunction with vitamin E, a known antioxidant.

How should Tom present his data?

Original Data Contrast enhanced Cropped

- + + - + - + -

H2O2

Vit E- + + - + - + -

- + + - + - + -

What would you do?

Mai is trying to determine how well a protease activated at the G2-M transition of the cell cycle clips and inactivates a downstream cell cycle repressor.

She treats the purified repressor with a small amount ofher purified protease, waits 10 min and detects the repressor fragments with a polyclonal antibody.

She performs the Western blot and probes with her antibodyand then a second antibody to visualize the protein by ECL.

Which exposure time is the best representation

5s 10s 30s 60s 3min 20 min 1hr

What would you do?

Howard is trying to knock out the gene for thioredoxin in a mouse model. Thioredoxin is one of two cofactors for ribonucleotide reductase, which is responsible for making deoxyribonucleotides for DNA synthesis. Thioredoxin is also thought to have other cellular functions.

After verifying the genotype in his first generation mouse, Howard breeds a male and female heterozygote and examines the genotype of the offspring.

Genotype Number+/+ 4+/- 8-/- 0

Total 12

Howard obtained the following results.

What would you conclude?

How sure are you?

How to be sure?

Genotype Number Expected+/+ 4 3+/- 8 6-/- 0 3

Total 12 Chi-square 0.135335

Can test statistical significance or use “rules of thumb” established by experience

Genotype Number Expected+/+ 12 8+/- 20 16-/- 0 8

Total 32 Chi-square 0.004087

Natasha wanted to make a mutant of a protein kinase that was already known to become fully active when auto-phosphorylated on a Ser102. If this amino acid is mutated to Glu, it mimics the phosphorylated serine residue and keeps the kinase constantly active.

She wanted to express the constitutively active kinase to examine the inactivation mechanisms in mammalian cells.

What should she do to confirm that the kinase is a mutant?

How sure are you that she has the right mutation?

What would you do to be sure that you had the mutation.

How can you be sure that you have no other mutations in the protein?

GlyArgCysTrpAlaSerLysAlaWT ACGTAACCGTAGCCGTACTCTACT ::::::::::::::::*:::::::Mut ACGTAACCGTAGCCGTCCTCTACT

Summary

Selecting which data to believe and use is an integralpart of an experiment

There are ways (statistical and traditional) to maximize theprobability that you will draw the correct conclusions from your experiments.

In your notebooks and any papers/grants derived from them, be sure to state how many times a given experiment was done and give some estimate of how accurate and precise your data are.