UNDERSTANDING CLINICAL TRIALS

STATISTICSDR.MAGDY KHAMES ALY

CRITICAL CARE MEDICINE

ZMH ALBATAYEH

OVERVIEW

Clinicians examine and intervene with individual patients, must take clinical decision on a sold base according to guidelines that evidence-based.

Statistical analysis is one of the foundations of evidence-based clinical practice, a key in conducting new clinical research and in evaluating and applying prior research.

Reading and getting through clinical trials and researches is most important part in our daily practice.

Understanding basic statistical concepts will allow you to become a more critical consumer of the medical literature, and ultimately be able to produce better research and make better clinical decisions.

OBJECTIVES

Describe how to interpret the results of a trial, including what statistical significance means.

Appreciate that results of trials have a direction, size, and statistical significance

Understand the information provided by P values and confidence intervals

Know how to interpret statistical significance

Appreciate when there is an important difference between the treatment and control arms of a trial.

CLINICAL TRIALS

Classification of Clinical trails:

Experimental(Intervention)

Randomized Controlled Trials (RCT)

Non-randomized Trials

Epidemiologic

Observational

Cohort studies

Case-control studies

Cross-sectional studies

Non-epidemiologic: Case-study （Case-series）

Opinion from a specialist based on biological and clinical principles

Importanceas

an evidence

CLINICAL TRIALS

Classification of Study Design

CROSS SECTIONAL

PROSPECTIVE COHORT

RETROSPECTIVE CASE-CONTROL

RETROSPECTIVE COHORT

PROSPECTIVE CASE-CONTROL

PAST CURRENT FUTURE

CLINICAL TRIALS

CONCEPTS AND TERMINOLOGY:

Cohort:

A group of people who share a common characteristics, sample subjects without knowing their outcome status.

Case-Control Study:

Need to know who has an outcome when deciding which subjects to include in a study.

Follow to observe their outcome

Compare their exposure status

CLINICAL TRIALS

COHORT STUDY CASE-CONTROL STUDY

Smoker Non-smoker With Lung Cancer Without Lung Cancer

N= 4000 N=4000 N=200 N=200

Lung CA - Lung CA + Lung CA - Lung CA + Non-smoker Smoker Non-smoker Smoker

N= 3800 N=200 N=3950 N=50 N=40 N=160 N=180 N=20

CLINICAL TRIALS

CLINICAL TRIALS

BIAS

Bias is the intentional or unintentional adjustment in the design and/or conduct of a Clinical trial, and analysis and evaluation of the data that may affect the results.

Bias may affect the results of a clinical trial and cause them to be unreliable.

Bias can occur at any phase of research, e.g. during trial design, data collection, data analysis and publication.

CLINICAL TRIALS

MAJOR TYPES OF BIAS:

Selection bias

Occurs when the selection of subjects into a sample or their allocation to a treatment group produces a sample that is not representative of the population, or treatment groups that are systematically different

prevented by random selection and random allocation

Detection bias

Occurs when observations in one group are not sought as diligently as in the other

prevented by observer blinding

Observer bias

Occurs when the observer is able to be subjective about the outcome

prevented by observer blinding and outcome measure design

CLINICAL TRIALS

MAJOR TYPES OF BIAS:

Recall bias

Occurs when patients know which group they have been allocated to,

Which influences the way they report past history and symptoms

ie. if patient knows the are in the placebo group they may exaggerate their ‘untreated’ symptoms

Prevented by patient blinding

Response bias

Occurs when patients who enroll in a trial may not represent those of the population as a whole

ie. the obese patients who enroll in a weight loss medication trial may be more motivated than those in the general population

Prevention -> random sampling from population

CLINICAL TRIALS

MAJOR TYPES OF BIAS:

Publication bias

Occurs because negative studies less likely to be submitted and/or published than positive ones

prevented by clinical trials registries and ensuring all well conducted studies are submitted and published (should be mandatory)

in meta-analysis, the possibility of absent negative studies should be sought for by funnel plot analysis

Regression to the mean

Occurs when random effects may cause a rare, extreme variation on a measurement if the measurement is repeated, the likelihood is that the measurement will be less extreme thus, if a treatment had been given after the first measurement, it would erroneously appear, on the basis of the second measurement, that it had had an effect

prevented by having a control group

Hawthorne effect

Occurs when the process of studying and following up patients itself influences the outcome

ie. chronic headache may improve in patients who are being studied and regularly followed up

prevented by having a control group and masking the intention of study from patients and observers

Biostatistics

Tools used to

Analyze

Present

InterpretMake clinical decisions

Collect

Organize

Biostatistics

TYPES OF STATISTICS:

To Organize Use information from

Display descriptive statistics to

Describe data using tables, make decisions or predictions

graphs about a population

Descriptive Inferential

Biostatistics

STANDARD DEVIATION

HOW TO USE SD

PROBABILITY: (P value)

CONFIDENCE INTERVAL

ODDS AND ODDS RATIO

RISK AND RISK RATIO

CALCULATE NNT

HOW TO INTERPRET A CLINICAL TRIAL

Biostatistics

STANDARD DEVIATION(SD):

Standard deviation (SD) is used for data which are “normally distributed” , to provide information on how much the data vary around their mean.

SD indicates how much a set of values is spread around the average.

A range of one SD above and below the mean

(abbreviated to ± 1 SD) includes 68.2% of the values.

± 2 SD includes 95.4% of the data.

± 3 SD includes 99.7%.

Bell shaped curve when normally distributed

Biostatistics

STANDARD DEVIATION(SD):

Biostatistics

STANDARD DEVIATION(SD):

SD should only be used when the data have a normal distribution. However, means and SDs are often wrongly used for data which are not normally distributed.

A simple check for a normal distribution is to see if 2SDs away from the mean are still within the possible range for the variable.

For example, if we have some length of hospital stay data with a mean stay of 10 days and a SD of 8 days then:

mean – (2 × SD) = 10 – (2 × 8) = 10 – 16 = –6 days.

This is clearly an impossible value for length of stay ( out of range), so the data cannot be normally distributed. The mean and SDs are therefore not appropriate measures to use.

Biostatistics

MEDIAN AND INTERQUIRTILE RANGE(IQR):

Median used in case of SKEWED result (not normally distributed)

IQR: it’s a range of result

Presenting 3 percentages:

25% of results

50% which is the Median

75% of the results

Biostatistics

Biostatistics

Biostatistics

PROBABILITY (P value):

The P (probability) value is used when we wish to see how likely a hypothesis is true. The hypothesis is usually that there is no difference between two treatments, known as the “null hypothesis”.

Null hypothesis:

The Original rule of any comparison or testing something is that no difference or no effect, the role of biostatistics and P value is to disprove that. Nullhypothesis is impossible to prove it only can be disproved.

You can say that the Null hypothesis not true and there is difference between two treatment.

The question the P value try to answer is this difference is significant and I can depend on to make clinical decision or not.

Biostatistics

The P value gives the probability of any observed difference have happened by chance (the probability of play of chance)

P = 0.5 means that the probability of a difference this large or larger have happened by chance is 0.5 in 1, or 50:50.

P = 0.05 means that the probability of a difference this large or larger have happened by chance is 0.05 in 1, i.e. 1 in 20.

The lower the P value, the less likely it is that the difference happened by chance and so the higher the significance of the finding.

P = 0.01 is often considered to be “highly significant”. It means that a difference of this size or larger will only have happened by chance 1 in 100 times. This is unlikely, but still possible.

P = 0.001 means that a difference of this size or larger will have happened by chance 1 in 1000 times, even less likely, but still just possible. It is usually considered to be “very highly significant”.

Biostatistics

Pit falls of P-value:

P-value becomes larger with a smaller difference.

P-value becomes larger with a smaller sample size.

Thus, we cannot really tell why statistical significance is absent, due to small effect, or small sample size?

Not to confuse statistical significance with clinical relevance. If a study is too small, the results are unlikely to be statistically significant even if the intervention actually works.

Conversely a large study may find a statistically significant difference that is too small to have any clinical relevance.

Biostatistics

In this table we can say that the admission APACHII score and Antibiotic delay have HIGH STATISTICALLY SIGNIFCANT impact on hospital mortality

Biostatistics

Confidence intervals CI:

Confidence intervals (CI) are typically used when, instead of simply wanting the mean value of a sample, we want a range that is likely to contain the true population value. This “true value” is the mean value that we would get if we had data for the whole population.

Statisticians can calculate a range (interval) in which we can be fairly sure (confident) that the “true value” lies.

Biostatistics

Relationship between p-value and 95% Confidence Interval, CI:

95% CI including the null value P>0.05 No difference detected

95% CI excluding the null value P<=0.05 A difference detected

Null value = 1 when a ratio between two means (or proportions) is evaluated.

Null value = 0 when a difference between two means is evaluated.

Biostatistics

Biostatistics

SD and CI:

Standard deviation tells us about the variability (spread) in a sample. The CI tells us the range in which the true value (the mean if the sample were infinitely large) is likely to be. Use SD to describe sampled data, and use 95% CI to make statistical inference.

Meta-analysis CI 95% graph:

A technique for bringing together results from a number of similar

studies to give one overall estimate of effect.

Biostatistics

Risk and Risk ratio(RR):

Risk:

Is the probability that an event will happen.

It is calculated by dividing the number of events by the number of people at risk.

E.g: if 2 from 10 patients taking aspirin will bleed so the Risk of bleeding is 2/10=0.2

Risk Ratio(RR):

Risk in treatment group divided by Risk in controlled group (to compare the 2 Risks)

If RR > 1 that means the Risk increased in treatment group

If RR < 1 that means the Risk increased in control group

If RR = 1 means no difference.

Biostatistics

Odds and Odds ratio:

Odds:

Used by epidemiologists in studies looking for factors which do harm, it is a way of comparing patients who already have a certain condition (cases) with patients who do not (controls) – a “case–control study”.

calculated by dividing the number of times an event happens by the number of

times it does not happen.

E.g: 2 of 10 patients taking aspirin have bleeding Odds is 2/8=0.25

Odds ratio:

Calculated by dividing the odds of having been exposed to a risk factor by the odds in the control group.

Biostatistics

Odds ratio:

An odds ratio of 1 indicates no difference in risk between the groups, i.e. the odds in each group are the same.

If the odds ratio of an event is >1, the rate of that event is increased in patients who have been exposed to the risk factor.

If <1, the rate of that event is reduced.

Biostatistics

Risk reduction and numbers needed to treat(NNT):

Risk reduction:

It refer to Relative or Absolute Risk Reduction(RRR,ARR) and it answer and quantify how often the treatment or intervention works?

ARR is the difference between the event rate in the intervention group and that in the control group. It is usually given as a percentage.

RRR is the proportion by which the intervention reduces the event rate. It is calculated by dividing the ARR by the control event rate.

NNT is the number of patients who need to be treated for one to get benefit.

It is 100 divided by the ARR, i.e. NNT = 100/ARR

Biostatistics

Risk reduction and numbers needed to treat(NNT):

In a study testing the effect of a new antihypertensive patients 100 patient have been given the new treatment and another 100 have been given placebo and followed up for 1 month the results shown:

ARR : (improved drug group – improved placebo group): 80%-60%=20% so ARR=20%

NNT: (100/ARR) 100/20=5 so we need at least 5 patient taking the drug to see the effect in 1 of them.

RRR: (ARR/event or not improved in Control group) 20/40=0.5 so RRR is 50% by this drug

Antihypertensive group Placebo group

Improved Not improved Improved Not improved

80 20 60 40

SUMMARY

Mean and Standard deviation are useful when data are normally distributed, otherwise, you may consider using Median and Inter-quartile range because they are more robust for data distribution.

P-value indicates the probability of falsely detecting a difference when there is no difference. Larger effect and larger sample size leads to more significant result (smaller p-value). So even clinically meaningless difference could reach statistical significance, so be careful.

Using confidence interval can refer statistical significance. When 95% CI does not include null value, which links with P < 0.05.

Use SD to describe sampled data, and use 95% CI to make statistical inference.

Biostatistics are important topic to learn if you keen to be up-to-date with medical researches and literatures

References

Harris M, Taylor G, Dunitz M. Medical Statistics Made Easy. London and New York: 3rd

edition; 2014.

Altman DG, Bland JM. Absence of evidence is not evidence of absence. BMJ 1995;311(7003):485.

Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ 2003;327(7414):557-60.

Barratt A, Wyer PC, Hatala R, et al. Tips for learners of evidence-based medicine: 1. Relative risk reduction, absolute risk reduction and number needed to treat. CMAJ 2004;171(4):353-8.

Cates C. P values and confidence intervals (Update Article 2005). [Full text (http://www.nntonline.net/pvalues-and-confidence-intervals/)]

Guyatt G, Jaeschke R, Heddle N, Cook D, Shannon H, Walter S. Basic statistics for clinicians: 1. Hypothesis testing. CMAJ 1995;152(1):27-32.