Jonathon Hopkins Course Module 3

Question 1

Gold standard for analysis of clinical trials?

Answer 1

including all the participants in the analysis regardless of their actual treatment.

Question 2

synonimus for outcome?

Answer 2

endpoint but we encourage people to use term outcome couse patients can be followed after the endpoint.

Question 3

definition of outcome

Answer 3

Usually, we try to use the definition of the outcome to make what may be a qualitative thing a quantitative thing

Question 4

what outcome reflects?

Answer 4

objectives of trial:

• efficacy
• safety
• process
• costs

Question 5

primary outcomes?

Answer 5

reflects objectives and primary hypothesis
design variable
related to stage/type of research

So, what I mean by that is in, in preliminary phase one studies, the outcome may be a safety Related at any adverse events. In later stages of research the outcome may be a blood level, such as cholesterol, and then in another stage it may actually be a clinical outcome.

Question 6

secondary outcomes?

Answer 6

other important potential treatments effects:

• defined safety outcomes
• mechanism of effect

Question 7

other outcomes?

Answer 7

patient compliance

exploratory

Question 8

criteria for primary outcome?

Answer 8

1. It should be set before looking at trial data
2. relevant and likely to be influenced by a tretment
3. reliable measurment (proven outcome)
4. power considerations (variability, frequency,..)

Question 9

possible outcomes for asthma treatment

Answer 9

• exhaled NO
• spirometry
• asthma symptoms
• composite measures (exacebaration, symptom index)

Question 10

outcomes in perioperative procedure

Answer 10

• time window
• specific events to be considered as an outcome
• procedures to establish outcomes (So are you going to be interviewing patients?)

Question 11

Metrics for events as a outcomes

Answer 11

Dichotomous:
clinical state or cut-off value (hemoglobin value)
at a specific time point

Time to event:
adds dimension of time to dichotomous
allows for censoring
more powerful than dichotomous

Question 12

Metrics for events as a outcomes part 2

Answer 12

Rates:

• 1/0 but allows for repeats
• need follow up time
• Analize count or rate (events within a person is usually not independent)

Composite measures:

• two or more events related to disease process
• couldbe considered dichotomous or time-to event or a rate

Question 13

Metrics for events as a outcomespart 3

Answer 13

Continuous variables (cholesterol example):

• outcome is a value or chane from a baseline
• standard units, lab values, score
• need to define a important diference
• repeated measurments possible
• typically more powerful than discrete outcomes
• dichotomos require more patients

Ordinal scale (subcategory from continuous)

• adverse event grading for example
• ranked categories 1-5 A-D
• difference between categories is usually qualitative

Question 14

Objective vs Subjective outcome

Answer 14

Objective:

• Rigorous interpetation to limit interpretation
• may include test results for confirmation

Subjective:
karnofsky score(1-10), histological evaluations, need for medications
-masking is more important

Question 15

How to enhance accuracy and objectivity

Answer 15

defined criteria for evaluation

training evaluators

Question 16

Hawthorne effect? and how to quantify subjective outcomes

Answer 16

The Hawthorne effect is a type of reactivity in which individuals modify an aspect of their behavior in response to their awareness of being observed.

To quantify we use scales

Question 17

Difference between efficacy and effectiveness?

Answer 17

efficacy trials are usually those that are looking to be able to evaluate the condition under the best of all circumstances. Does this intervention change outcomes given sort of every benefit, versus effectiveness, which is looking how the intervention will work in a more real world setting.

Question 18

efficacy and effectiveness- 2 example?

Answer 18

vaccine trial:
effectiveness-clinical case of influenza
efficacy-clinical case with laboratory conformation

asthma:
effectiveness-hospitralizations/steroid courses
efficacy- FEV1

Question 19

factors that could determine choice of an outcome?

Answer 19

prior research
key factor of design (eligibility criteria, sample size, duration and frequency of follow up, need for masking)
personell and resources required
QA procedures
Costs
Generezibility of results

Question 20

Three Bs to determine outcome

Answer 20

Biology , Budzet and Biostatistics

Question 21

Example for outcomes on antiretroviral rherapy

Answer 21

1 survival
2 immunological respose
3 virologic response
4-QoL
5 toxicity
6-side effects
...
If we want to look fro a Phase 4 we would look at points 1,4,5,6

Question 22

How to protect a outcome?

Answer 22

defining it before the trial
masking
randomization
standard methods for measurments
standard follow up scheadules

Question 23

Analysis issues to be covered?

Answer 23

A: Analysis by asigned treatment (intention to treat)
B: Subgroup analysis

Question 24

Randomized to treatment but not take that, example?

Answer 24

NETT, surgical and medication group, but some participants refused

Question 25

What should we do when we have those unplaned crossovers?

Answer 25

analize date base purely on randomization and ignore: ineligebility, complete nonadherence, partial adherence, tretment termination, treatment switches.

Question 26

Violation of ITT (intention to treat)

Answer 26

Any exclusio nafter randomization or adjustment for anything occuring after baseline has potential to introduce bias and make observational instead of experimental trial

Nonadherence in not random

Question 27

ITT properties

Answer 27

ITT is unbiased
ITT measures effect in global sense
ITT are not post hoc (adherence is something that’s hard to quantify before you’ve seen the data. So to include adherence in our primary analysis plan would be difficult to specify ahead of time.)
ITT data are collected regardles of adherence

Question 28

Why adjusting for adherence is not goood idea?

Answer 28

since randomization has already given us comparable groups, by adjusting to adherence, we may then introduce non-comparability.

Question 29

what is subgroup analysis?

Answer 29

In the context of clinical trials subgroup analyses are analyses where we’re looking to see if there are varying treatment effects in different subsets of patients.

Question 30

why do we want subgruop analysis?

Answer 30

we’d like to check the consistency of the treatment effect across subgroups. For instance, we’d like to know if the effect is the same in women and men or in children and adults. Or we might think that perhaps the treatment effect would vary for people with less severe disease versus those with more severe disease.

Question 31

2 most common methods of doing subgroup analysis

Answer 31

stratified analysis and test for interaction

Question 32

stratification subgroups?

Answer 32

To do a subgroup analysis by stratification, you estimate the treatment effect separately in each of the subgroups. So you estimate an effect in men and you estimate an effect in women. And using this method, you can test whether there is a significant effect in men, and you can test if there’s a significant effect in women.

But what you cannot do is test whether the effect in men differs from the effect in women.

Question 33

test for interaction - subgroups

Answer 33

So, to do this test we build a statistical model that includes main effects for our treatment group and for our subgroups and we use interaction terms to test for interactions between treatments in subgroup. And in this way, we can see if the treatment effects vary by subgroup. We can also use that model to estimate the treatment effects in the various subgroups like we could do with the stratification method.

Question 34

significance test in subgroup analysis?

Answer 34

the more subgroup analyses that we do, the more likely it is by chance alone that we will find a statistically significant difference.

Question 35

guidlines for subgroup analysis?

Answer 35

prespecification of subgroupo analysis (inflate a sample size to plan for subgroup analysis)
reporting a number of subgroup analysis peformed
adjustment for multiple comparisons
reporting confidence intervasl instead of or in addition to p-values