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Statistical Aspects of Grant

Proposals

Dr Laura Gray

With thanks to Sarah Lewis and Victoria Owen for some

slides and ideas!

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Study design

Meta A

RCT

Quasi Experimental

Observational StudiesCohort>Case-control>Cross-sectional

Expert opinion / Case studies

More subjectto bias

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Study design

The design fits the question

Methodologically sound

Feasible Practicalities should be discussed

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Observational or Experimental?

In an observational study the researcher

has no control over who is exposed to what

e.g. the researcher cannot decide who smokes

cigarettes and who does not, they can only observe

In an experiment, the researcher is in controlof who is exposed to what participants are

allocated to treatment

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Prospective or Retrospective?

Prospective

data collected about subsequent events

temporal associations clearer, but more expensive

Retrospective

data collected about events in the past

cheaper, but may rely on memory

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Longitudinal or Cross-sectional?

Longitudinal

data are collected at more than one time-point

powerful, but time-consuming

Cross-sectional

data are collected at one time-point

cheaper, but can only infer association (not

causation)

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Things to consider CC study

How to select cases

How to select a comparable control group

Selection bias

Matching Analysis needs to take matching into account

Recall bias

Assessment bias

Confounding

Age

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Confounding

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Cohort Study

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Things to consider Cohort

How to get comparable groups apart from exposure of

interest

Length of follow up

Losses How to deal with changes in situation

Not suitable for rare events

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RCT

2 arm parallel trial

Population Sample

Test

Control

Randomise

Measure

outcome

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Things to consider - RCT

Control group standard care / placebo

Blinding

Randomisation

Patient level

Clusters if contamination

Length of follow up

Inclusion and exclusion criteria

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Sample size

Sample size is a fundamental part of any researchdesign

Too small may fail to detect clinically important

differences

Too big more patients, more costly

However, too many pts is better than too few

Allow for attrition (lost data)

Should take into account response rates

Sample size calculations may not be needed for pilot

studies

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17/23Copyright 2009 BMJ Publishing Group Ltd. Dennis, C-L et al. BMJ 2009;338:a3064

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Sample

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Sample

Where do they come from?

Have they explained why these are an appropriate

group?

How will subjects be selected? Inclusion/exclusion criteria

Is your sample size achievable using this population?

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Outcome / Explanatory variables

What are you measuring? How?

When?

What type of data?

Is this a valid measurement?

Is it measuring what you think it is?

Is this a reliable measurement?

If you repeated the measure on average would you get the same

results?

Can you assume that taking measurements over the telephone are

comparable to those collected by post?

Is inter-rater reliability an issue?

How many variables of interest?

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Use Statisticians!!

From the RfPB guidance notes

Support and advice from trials methodologists is

crucial

Statistical support is another essential. A last minutephone call to a statistician for a power calculation is

unlikely to be enough

.how far the team has thought through exactly

how they will go about data analysis. Detail on all ofthese is crucial.

Research Design Service, are a first port of call