This document provides an overview of clinical trials and statistics. It discusses key concepts like randomized controlled trials, bias, standard deviation, p-values, confidence intervals, risk, odds ratios, and numbers needed to treat. The objectives are to help understand how to interpret clinical trial results and appreciate statistically significant versus clinically meaningful differences. Understanding basic statistics is important for critically evaluating the medical literature and making evidence-based clinical decisions.
CROSSOVER STUDY DESIGN, DESIGN OF PHARMACOKINETIC STUDIES, FACTORS INFLUENCING BIOAVAILABILITY STUDIES, STUDY DESIGN, PARALLEL DESIGN, CROSS-OVER STUDIES, LATIN SQUARE DESIN, TWO-PERIOD CROSSOVER STUDY DESIGN, BALANCED INCOMPLETE BLOCK DESIGN (BIBD), REPLICATE CROSSOVER STUDY DESIGN , DIFFERENCE BETWEEN PARALLEL AND CROSSOVER STUDY DESIGN.
Webinar Series on Demystifying Phases in Clinical Trials & COVID-19 Updates organized by Institute for Clinical Research (ICR), NIH
Speaker: Dato Dr Chang Kian Meng, Haematologist from Sunway Medical Centre
More information, please visit: https://clinupcovid.mailerpage.com/resources/p9f2i7-introduction-to-phase-2-3-trial-s
CROSSOVER STUDY DESIGN, DESIGN OF PHARMACOKINETIC STUDIES, FACTORS INFLUENCING BIOAVAILABILITY STUDIES, STUDY DESIGN, PARALLEL DESIGN, CROSS-OVER STUDIES, LATIN SQUARE DESIN, TWO-PERIOD CROSSOVER STUDY DESIGN, BALANCED INCOMPLETE BLOCK DESIGN (BIBD), REPLICATE CROSSOVER STUDY DESIGN , DIFFERENCE BETWEEN PARALLEL AND CROSSOVER STUDY DESIGN.
Webinar Series on Demystifying Phases in Clinical Trials & COVID-19 Updates organized by Institute for Clinical Research (ICR), NIH
Speaker: Dato Dr Chang Kian Meng, Haematologist from Sunway Medical Centre
More information, please visit: https://clinupcovid.mailerpage.com/resources/p9f2i7-introduction-to-phase-2-3-trial-s
Bias, confounding and causality in p'coepidemiological researchsamthamby79
A brief description of three issues (Bias, Confounding and Causality) commonly encountered while performing pharmacoepidemiological research. A big THANK YOU to Mr. Strom and Mr. Kimmel.
An overview of the ICH E9 guidance. Easy to follow, and I can provide a live presentation of this to your team! Great for those who are not familiar with statistics.
In clinical trials and other scientific studies, an interim analysis is an analysis of data that is conducted before data collection has been completed. If a treatment is particularly beneficial or harmful compared to the concurrent placebo group while the study is on-going, the investigators are ethically obliged to assess that difference using the data at hand and to make a deliberate consideration of terminating the study earlier than planned.
In interim analysis, whenever a new drug shows adverse effect on human being while testing the effectiveness of several drugs, we immediately stop the trial by taking into account the fact that maximum number of patients receive most effective treatment at the earliest stage. Interim analysis is also used to possibly reduce the expected number of patients and to shorten the follow-up time needed to make a conclusion. One wouldn't have to spend extra money if he/she already have enough evidence about the outcome. In this presentation, the total sample size is divided into four equal parts to perform the analysis and decision is made based on each individual step.
Cross over design, Placebo and blinding techniques Dinesh Gangoda
A crossover design is a modified randomized block design in which each block receives more than one treatment at different dosing periods.
A block can be a patient or a group of patients.
Patients in each block receive different sequences of treatments.
A crossover design is called a complete crossover design if each sequence contains all treatments under investigation.
A placebo is a dummy medicine containing no active substance.
This substance has no therapeutic effect, used as a control in testing new drugs.
Latin- ‘ I shall please’
Biostatistics in clinical research involves the application of statistical methods to analyze and interpret data from clinical trials. It plays a crucial role in study design, sample size determination, data analysis, and result interpretation. Biostatisticians ensure that clinical research findings are valid, reliable, and meaningful, contributing to evidence-based medicine. Their expertise helps researchers make informed decisions, assess treatment efficacy, and draw accurate conclusions about the safety and effectiveness of interventions.
Bias, confounding and causality in p'coepidemiological researchsamthamby79
A brief description of three issues (Bias, Confounding and Causality) commonly encountered while performing pharmacoepidemiological research. A big THANK YOU to Mr. Strom and Mr. Kimmel.
An overview of the ICH E9 guidance. Easy to follow, and I can provide a live presentation of this to your team! Great for those who are not familiar with statistics.
In clinical trials and other scientific studies, an interim analysis is an analysis of data that is conducted before data collection has been completed. If a treatment is particularly beneficial or harmful compared to the concurrent placebo group while the study is on-going, the investigators are ethically obliged to assess that difference using the data at hand and to make a deliberate consideration of terminating the study earlier than planned.
In interim analysis, whenever a new drug shows adverse effect on human being while testing the effectiveness of several drugs, we immediately stop the trial by taking into account the fact that maximum number of patients receive most effective treatment at the earliest stage. Interim analysis is also used to possibly reduce the expected number of patients and to shorten the follow-up time needed to make a conclusion. One wouldn't have to spend extra money if he/she already have enough evidence about the outcome. In this presentation, the total sample size is divided into four equal parts to perform the analysis and decision is made based on each individual step.
Cross over design, Placebo and blinding techniques Dinesh Gangoda
A crossover design is a modified randomized block design in which each block receives more than one treatment at different dosing periods.
A block can be a patient or a group of patients.
Patients in each block receive different sequences of treatments.
A crossover design is called a complete crossover design if each sequence contains all treatments under investigation.
A placebo is a dummy medicine containing no active substance.
This substance has no therapeutic effect, used as a control in testing new drugs.
Latin- ‘ I shall please’
Biostatistics in clinical research involves the application of statistical methods to analyze and interpret data from clinical trials. It plays a crucial role in study design, sample size determination, data analysis, and result interpretation. Biostatisticians ensure that clinical research findings are valid, reliable, and meaningful, contributing to evidence-based medicine. Their expertise helps researchers make informed decisions, assess treatment efficacy, and draw accurate conclusions about the safety and effectiveness of interventions.
Study of the distribution and determinants of
health-related states or events in specified populations and the application of this study to control health problems.
John M. Last, Dictionary of Epidemiology
Observational research designs are those in which the researcher/investigator merely observes and does not carry out any interventions/actions.
to change the result. The three most common types of observational studies are cross-sectional studies, case-control studies, and cohort (or longitudinal) studies.
In cross-sectional studies, exposure/risk factors and outcomes are determined at a single point in time. You can bid
information on disease prevalence and an overview of likely relationships that can be used to form a hypothesis. Control cases In
studies, participants are selected based on the presence/absence of an outcome and risk factors are identified during the study.
after enrollment of study participants.The relationship between exposure and outcome is reported as an odds ratio. This research; However,
carries a high risk of bias, which should be taken into account when designing the study. Cohort studies are prospective and include participants
were selected based on presence/absence of exposure and results were obtained at the end of the study. This research can deliver The incidence/impact of the disease and the relationship between exposure and outcome are presented as relative risks. They are useful
establish causality.A problem that arises in these studies could be the high fluctuation and dropout of study participants.
Descriptive studies generally describe the magnitude of a problem and characteristics of the population/individuals.
The various types of such studies include
case reports
case series or surveys.
A case report generally describes a patient presenting with an unusual disease, or simultaneous occurrence of more than one condition, or uncommon clinical features in a known disease.
A case series is a collection of similar cases. Such studies, other than providing some advancement to knowledge of a disease, are of limited value. Another method often used in epidemiological health care research is conducting surveys.
Surveys are done during a defined time-period and information on several variables of interest is collected from the target population. They provide estimates of prevalence of the various variables of interest, and their distribution. Such studies could also provide insight into individual opinions and practices. Advantages include ease of conduct and cost efficiency. The disadvantages include low response rates and a variety of biases.
An analytical study tests a hypothesis to determine an association between two or more variables, like causation, risk, or effect. Such studies have two or more study groups for comparison.
The primary focus of this article will be the three most common types of analytical observational studies –
cross-sectional,
case control (also known as retrospective) and
cohort (or longitudinal, also known as prospective) studies.
It may be pertinent to note that the primary objective of most clinical studies is to determine one of the following - burden of disease (prevalence
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Antibiotics are prescribed in daily base to ICU critically ill patients
it needs understanding to PK, PD of these group of drugs to achieve a desirable outcome
IN CASE OF SHOCK STATE IN GENERAL AND IN SEPTIC SHOCK PARTICULARLY, YOUR PRIORITY IS TO REACH GOOD RESUSCITATION TO THE PATIENT HOW TO GUIDE YOUR RESUSCITATION?
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Daily practice in medicine in general need awareness of critical signs and symptoms that can be the presentation of life threatening and fatal conditions
New Directions in Targeted Therapeutic Approaches for Older Adults With Mantl...i3 Health
i3 Health is pleased to make the speaker slides from this activity available for use as a non-accredited self-study or teaching resource.
This slide deck presented by Dr. Kami Maddocks, Professor-Clinical in the Division of Hematology and
Associate Division Director for Ambulatory Operations
The Ohio State University Comprehensive Cancer Center, will provide insight into new directions in targeted therapeutic approaches for older adults with mantle cell lymphoma.
STATEMENT OF NEED
Mantle cell lymphoma (MCL) is a rare, aggressive B-cell non-Hodgkin lymphoma (NHL) accounting for 5% to 7% of all lymphomas. Its prognosis ranges from indolent disease that does not require treatment for years to very aggressive disease, which is associated with poor survival (Silkenstedt et al, 2021). Typically, MCL is diagnosed at advanced stage and in older patients who cannot tolerate intensive therapy (NCCN, 2022). Although recent advances have slightly increased remission rates, recurrence and relapse remain very common, leading to a median overall survival between 3 and 6 years (LLS, 2021). Though there are several effective options, progress is still needed towards establishing an accepted frontline approach for MCL (Castellino et al, 2022). Treatment selection and management of MCL are complicated by the heterogeneity of prognosis, advanced age and comorbidities of patients, and lack of an established standard approach for treatment, making it vital that clinicians be familiar with the latest research and advances in this area. In this activity chaired by Michael Wang, MD, Professor in the Department of Lymphoma & Myeloma at MD Anderson Cancer Center, expert faculty will discuss prognostic factors informing treatment, the promising results of recent trials in new therapeutic approaches, and the implications of treatment resistance in therapeutic selection for MCL.
Target Audience
Hematology/oncology fellows, attending faculty, and other health care professionals involved in the treatment of patients with mantle cell lymphoma (MCL).
Learning Objectives
1.) Identify clinical and biological prognostic factors that can guide treatment decision making for older adults with MCL
2.) Evaluate emerging data on targeted therapeutic approaches for treatment-naive and relapsed/refractory MCL and their applicability to older adults
3.) Assess mechanisms of resistance to targeted therapies for MCL and their implications for treatment selection
Knee anatomy and clinical tests 2024.pdfvimalpl1234
This includes all relevant anatomy and clinical tests compiled from standard textbooks, Campbell,netter etc..It is comprehensive and best suited for orthopaedicians and orthopaedic residents.
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Basavarajeeyam is an important text for ayurvedic physician belonging to andhra pradehs. It is a popular compendium in various parts of our country as well as in andhra pradesh. The content of the text was presented in sanskrit and telugu language (Bilingual). One of the most famous book in ayurvedic pharmaceutics and therapeutics. This book contains 25 chapters called as prakaranas. Many rasaoushadis were explained, pioneer of dhatu druti, nadi pareeksha, mutra pareeksha etc. Belongs to the period of 15-16 century. New diseases like upadamsha, phiranga rogas are explained.
Tom Selleck Health: A Comprehensive Look at the Iconic Actor’s Wellness Journeygreendigital
Tom Selleck, an enduring figure in Hollywood. has captivated audiences for decades with his rugged charm, iconic moustache. and memorable roles in television and film. From his breakout role as Thomas Magnum in Magnum P.I. to his current portrayal of Frank Reagan in Blue Bloods. Selleck's career has spanned over 50 years. But beyond his professional achievements. fans have often been curious about Tom Selleck Health. especially as he has aged in the public eye.
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Introduction
Many have been interested in Tom Selleck health. not only because of his enduring presence on screen but also because of the challenges. and lifestyle choices he has faced and made over the years. This article delves into the various aspects of Tom Selleck health. exploring his fitness regimen, diet, mental health. and the challenges he has encountered as he ages. We'll look at how he maintains his well-being. the health issues he has faced, and his approach to ageing .
Early Life and Career
Childhood and Athletic Beginnings
Tom Selleck was born on January 29, 1945, in Detroit, Michigan, and grew up in Sherman Oaks, California. From an early age, he was involved in sports, particularly basketball. which played a significant role in his physical development. His athletic pursuits continued into college. where he attended the University of Southern California (USC) on a basketball scholarship. This early involvement in sports laid a strong foundation for his physical health and disciplined lifestyle.
Transition to Acting
Selleck's transition from an athlete to an actor came with its physical demands. His first significant role in "Magnum P.I." required him to perform various stunts and maintain a fit appearance. This role, which he played from 1980 to 1988. necessitated a rigorous fitness routine to meet the show's demands. setting the stage for his long-term commitment to health and wellness.
Fitness Regimen
Workout Routine
Tom Selleck health and fitness regimen has evolved. adapting to his changing roles and age. During his "Magnum, P.I." days. Selleck's workouts were intense and focused on building and maintaining muscle mass. His routine included weightlifting, cardiovascular exercises. and specific training for the stunts he performed on the show.
Selleck adjusted his fitness routine as he aged to suit his body's needs. Today, his workouts focus on maintaining flexibility, strength, and cardiovascular health. He incorporates low-impact exercises such as swimming, walking, and light weightlifting. This balanced approach helps him stay fit without putting undue strain on his joints and muscles.
Importance of Flexibility and Mobility
In recent years, Selleck has emphasized the importance of flexibility and mobility in his fitness regimen. Understanding the natural decline in muscle mass and joint flexibility with age. he includes stretching and yoga in his routine. These practices help prevent injuries, improve posture, and maintain mobilit
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MANAGEMENT OF ATRIOVENTRICULAR CONDUCTION BLOCK.pdfJim Jacob Roy
Cardiac conduction defects can occur due to various causes.
Atrioventricular conduction blocks ( AV blocks ) are classified into 3 types.
This document describes the acute management of AV block.
Flu Vaccine Alert in Bangalore Karnatakaaddon Scans
As flu season approaches, health officials in Bangalore, Karnataka, are urging residents to get their flu vaccinations. The seasonal flu, while common, can lead to severe health complications, particularly for vulnerable populations such as young children, the elderly, and those with underlying health conditions.
Dr. Vidisha Kumari, a leading epidemiologist in Bangalore, emphasizes the importance of getting vaccinated. "The flu vaccine is our best defense against the influenza virus. It not only protects individuals but also helps prevent the spread of the virus in our communities," he says.
This year, the flu season is expected to coincide with a potential increase in other respiratory illnesses. The Karnataka Health Department has launched an awareness campaign highlighting the significance of flu vaccinations. They have set up multiple vaccination centers across Bangalore, making it convenient for residents to receive their shots.
To encourage widespread vaccination, the government is also collaborating with local schools, workplaces, and community centers to facilitate vaccination drives. Special attention is being given to ensuring that the vaccine is accessible to all, including marginalized communities who may have limited access to healthcare.
Residents are reminded that the flu vaccine is safe and effective. Common side effects are mild and may include soreness at the injection site, mild fever, or muscle aches. These side effects are generally short-lived and far less severe than the flu itself.
Healthcare providers are also stressing the importance of continuing COVID-19 precautions. Wearing masks, practicing good hand hygiene, and maintaining social distancing are still crucial, especially in crowded places.
Protect yourself and your loved ones by getting vaccinated. Together, we can help keep Bangalore healthy and safe this flu season. For more information on vaccination centers and schedules, residents can visit the Karnataka Health Department’s official website or follow their social media pages.
Stay informed, stay safe, and get your flu shot today!
2. 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.
3. 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.
4. 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
Importance
as
an evidence
5. CLINICAL TRIALS
Classification of Study Design
CROSS SECTIONAL
PROSPECTIVE COHORT
RETROSPECTIVE CASE-CONTROL
RETROSPECTIVE COHORT
PROSPECTIVE CASE-CONTROL
PAST CURRENT FUTURE
6. 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
7. 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
9. 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.
10. 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
11. 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
12. 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
14. 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
15. 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
16. 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
18. 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.
19. 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
22. 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. Null
hypothesis 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.
23. 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”.
24. 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.
25. Biostatistics
In this table we can say that the admission APACHII score and Antibiotic delay have
HIGH STATISTICALLY SIGNIFCANT impact on hospital mortality
26. 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.
27. 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.
29. 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.
30. 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.
31. 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.
32. 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.
33. 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
34. 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
35. 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
36. References
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Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-
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Barratt A, Wyer PC, Hatala R, et al. Tips for learners of evidence-based medicine: 1.
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Cates C. P values and confidence intervals (Update Article 2005). [Full text
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Guyatt G, Jaeschke R, Heddle N, Cook D, Shannon H, Walter S. Basic statistics for
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