This document discusses key aspects of study design, data collection, statistical analysis, and reasoning in biomedical research. It covers observational studies, experiments, data registration and validation, effect estimation and bias evaluation. Statistical analysis includes data description, interpretation of outcomes in light of study limitations, and multiplicity issues. Recent developments in different research areas include longitudinal and multilevel analysis, causality models, and registration guidelines.
Root cause Analysis (RCA) & Corrective and Preventive action (CAPA) in MRCT d...Bhaswat Chakraborty
This presentation describes Identification & differentiation of Protocol deviation & violation; Different methods of RCA & best suitable method for Multiregional Clinical Trial; CAPA management and CAPA application to other trial sites/CRO/SMO/ Country that is involved in same trial (Strategic Management and application of CAPA in MRCT)
Strategies for Considerations Requirement Sample Size in Different Clinical T...IJMREMJournal
-------------------------------------------------------ABSTRACT ---------------------------------------------------
Usually the main problem face any investigation it how to determent a sample size, however, some
considerations required in sample size to conduct the efficacy and make realistic well-researched before began
study. This study aimed to determine the maximum possible sample size at different phases of clinical trials and
attempt to achieve the best accuracy of the results. To achieve that the maximum sample size in different phases
we found that the maximum sample size of phase I was (75) relies on largest response rate 20% and the minimal
clinically important difference (MCID) 15%, and because the participants are healthy often that means 15%
enough to show positive results of the transition to the second phase. for the phase II clinical trials; the
maximum sample size was (388) depend on the error 5% and largest response rate 50% when the response rate
should not be less than 20% according to the design used in this phase. Depend on the endpoint and hazard
ratio in phase III clinical trials when the probability of survival of the treatment group equal to median of the
probability of survival 50% we found that the maximum sample size (4796). For the phase IV the maximum
sample size in different phases of clinical trials does not affect whatever the large of the population size and
remains constant as large as possible size.
Root cause Analysis (RCA) & Corrective and Preventive action (CAPA) in MRCT d...Bhaswat Chakraborty
This presentation describes Identification & differentiation of Protocol deviation & violation; Different methods of RCA & best suitable method for Multiregional Clinical Trial; CAPA management and CAPA application to other trial sites/CRO/SMO/ Country that is involved in same trial (Strategic Management and application of CAPA in MRCT)
Strategies for Considerations Requirement Sample Size in Different Clinical T...IJMREMJournal
-------------------------------------------------------ABSTRACT ---------------------------------------------------
Usually the main problem face any investigation it how to determent a sample size, however, some
considerations required in sample size to conduct the efficacy and make realistic well-researched before began
study. This study aimed to determine the maximum possible sample size at different phases of clinical trials and
attempt to achieve the best accuracy of the results. To achieve that the maximum sample size in different phases
we found that the maximum sample size of phase I was (75) relies on largest response rate 20% and the minimal
clinically important difference (MCID) 15%, and because the participants are healthy often that means 15%
enough to show positive results of the transition to the second phase. for the phase II clinical trials; the
maximum sample size was (388) depend on the error 5% and largest response rate 50% when the response rate
should not be less than 20% according to the design used in this phase. Depend on the endpoint and hazard
ratio in phase III clinical trials when the probability of survival of the treatment group equal to median of the
probability of survival 50% we found that the maximum sample size (4796). For the phase IV the maximum
sample size in different phases of clinical trials does not affect whatever the large of the population size and
remains constant as large as possible size.
Medical research relies heavily on statistical inference for generalization of findings, for assessing the uncertainty in applying these findings on new patients. SPSS and similar packages has made complex statistical calculations possible with no or very little understanding of statistical inference. As a consequence, research findings are misunderstood, the presentation of them confusing, and their reliability massively overestimated.
Advancing Learning: Our Adventure in the Twitterversegrodrigo
This a slide set (with annotations added) used at the "Our Adventure in the Twitterverse: Insights & Discoveries" session at the ETC Advancing Learning 2012 conference
Clinical Research Statistics for Non-StatisticiansBrook White, PMP
Through real-world examples, this presentation teaches strategies for choosing appropriate outcome measures, methods for analysis and randomization, and sample sizes as well as tips for collecting the right data to answer your scientific questions.
The Indian Dental Academy is the Leader in continuing dental education , training dentists in all aspects of dentistry and
offering a wide range of dental certified courses in different formats.for more details please visit
www.indiandentalacademy.com
Medical research relies heavily on statistical inference for generalization of findings, for assessing the uncertainty in applying these findings on new patients. SPSS and similar packages has made complex statistical calculations possible with no or very little understanding of statistical inference. As a consequence, research findings are misunderstood, the presentation of them confusing, and their reliability massively overestimated.
Advancing Learning: Our Adventure in the Twitterversegrodrigo
This a slide set (with annotations added) used at the "Our Adventure in the Twitterverse: Insights & Discoveries" session at the ETC Advancing Learning 2012 conference
Clinical Research Statistics for Non-StatisticiansBrook White, PMP
Through real-world examples, this presentation teaches strategies for choosing appropriate outcome measures, methods for analysis and randomization, and sample sizes as well as tips for collecting the right data to answer your scientific questions.
The Indian Dental Academy is the Leader in continuing dental education , training dentists in all aspects of dentistry and
offering a wide range of dental certified courses in different formats.for more details please visit
www.indiandentalacademy.com
Design of experiments is the most common Research design will wide reliability. It is mostly applicable in scientific lab type of research. This method is not applicable for descriptive research.
It involves both qualitative and quantitative data sets. The researchers can manipulate, control, replicate and randomize the experimental variables.
There are several types of experimental design depending on the selection of control, test and standard groups and their experimental setting.
The slides also show the guidelines regarding design of research proposal, Literature survey and important ethics in research. Guiding protocol to prepare a research and review article is also discussed.
sience 2.0 : an illustration of good research practices in a real studywolf vanpaemel
a presentation explaining the what, how and why of some of the features of science 2.0 (replication, registration, high power, bayesian statistics, estimation, co-pilot multi-software approach, distinction between confirmatory and exploratory analyses, and open science) using steegen et al. (2014) as a running example.
Robust biomarker selection from RT-qPCR data using statistical consensus crit...Roger Alexander
These are the slides from a web seminar given by Jack Wiedrick from the Biostatistics and Design Program at Oregon Health & Science University. He discussed a method for finding candidate biomarkers of disease from RNA datasets. The web seminar was presented as part of the Extracellular RNA Communication Consortium (ERCC) seminar series on 07 December 2017.
5 essential steps for sample size determination in clinical trials slidesharenQuery
In this free webinar hosted by nQuery Researcher & Statistician Eimear Keyes, we map out the 5 essential steps for sample size determination in clinical trials. At each step, Eimear will highlight the important function it plays and how to avoid the errors that will negatively impact your sample size determination and therefore your study.
Watch the Video: https://www.statsols.com/webinar/the-5-essential-steps-for-sample-size-determination
3. theory
reasoning study design
outcome hypothesis
statistical analysis data collection
data
4. reasoning study design
statistical analysis data collection
5. To discuss in this presentation
study design
- observation (case report, survey, epidemiological study)
- experiment (phantom, in vitro, in vivo, clinical trial)
data collection
- registration, monitoring, validation, documentation
statistical analysis
- data description, effect estimation, evaluation of bias and uncertainty
reasoning
- interpretation of outcome with respect to the limitations imposed by
study design, data collection and statistical analysis
6. Design features (simplified)
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Design
--------------------------------------------------------
Characteristics Experimental Observational
------------------------------------------------------------------------------------------
Studied effects beneficial harmful
Sample size small large
Follow up short long
Internal validity better worse
Main outcome efficacy effectiveness
External validity worse better
------------------------------------------------------------------------------------------
7. Design features (simplified)
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Design
----------------------------------------------------------------------------------
Characteristics Experimental Observational
----------------------------------------------------------------------------------------------------------------------
Data collection from CRF to database from ? to register
Statistical analysis precision oriented validity oriented
Reasoning multiplicity, missing data, confounding, selection and
compliance, superiority, information bias, measurement
non-inferiority errors
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9. DIRECTIVE 2001/20/EC OF THE EUROPEAN
PARLIAMENT AND OF THE COUNCIL
of 4 April 2001
on the approximation of the laws, regulations and administrative
provisions of the Member States
relating to the implementation of good clinical practice in the
conduct of clinical trials on
medicinal products for human use
4. All clinical trials, including bioavailability and
bioequivalence studies, shall be designed,
conducted and reported in accordance with the
principles of good clinical practice.
10. ICH-GCP
1.24 Good Clinical Practice (GCP)
A standard for the design, conduct, performance,
monitoring, auditing, recording, analyses, and reporting
of clinical trials that provides assurance that the data
and reported results are credible and accurate, and that
the rights integrity and confidentiality of trial subjects are
protected.
11. ICH-GCP
4.9 Records and Reports
The investigator should ensure the accuracy,
completeness, legibility, and timeliness of all the data
reported to the sponsor in the CRFs and I all required
reports.
12. ICH-GCP
5.5 Trial Management, Data Handling, and Record
Keeping
If data are transformed during processing, it should
always be possible to compare the original data and
observations with the processed data.
( Audit trail: Documentation that allows reconstruction
of the course of events)
13. ICH-GCP
1.6 Audit
A systematic and independent examination of trial
related activities and documents to determine whether
th evaluated trial related activities were conducted, and
the data were recorded, analyzed and accurately
reported according to the protocol, sponsor's standard
operating procedures (SOPs), Good Clinical Practice
(GCP), and the applicable regulatory requirement(s).
14. Obligation to Register Clinical Trials
The ICMJE defines a clinical trial as any research
project that prospectively assigns human subjects to
intervention or concurrent comparison or control groups
to study the cause-and-effect relationship between a
medical intervention and a health outcome. Medical
interventions include drugs, surgical procedures,
devices, behavioral treatments, process-of-care
changes, and the like.
15. Requirements for observational studies
ICMJE - Selection and Description of Participants
Describe your selection of the observational or experimental
participants (patients or laboratory animals, including
controls) clearly, including eligibility and exclusion criteria and
a description of the source population.
16. Requirements for observational studies
The STROBE statement
Describe the setting, locations, and relevant dates, including
periods of recruitment, exposure, follow-up, and data
collection.
For each variable of interest, give sources of data and
details of methods of assessment (measurement).
18. Misunderstandings about statistical calculations
- They reveal otherwise unknown information about the studied
population (sample)
- Their purpose is to find statistically significant differences or
effects
- It is only interesting whether a difference has p<0.05 or “ns”
- If a difference is not significant, it does not exist
- If a difference is significant, it is practically important
- It is important to test all differences, especially for evaluating
the success of randomization (in clinical trials) and matching
(in observational studies)
- Findings can only be published if they are statistically significant
19. Sampling and measurement variability
Experiment A
What do we know about sampling
and measurement variability when
an experiment is performed only
once?
Outcome
20. Sampling and measurement variability
Experiment A Experiment A Experiment A Experiment A Experiment A
First time Second time Third time Fourth time Fifth time
Outcome
Uncertainty
Had we replicated the experiment several times, the variation had
been evident.
21. Sampling and measurement variability
Experiment A
With only one performance of the
experiment, the uncertainty caused
by sampling and measurement
variation can be evaluated using
statistical methodology.
Outcome
Uncertainty
22. Sampling and measurement variability
Hospital A Hospital B Hospital C Hospital D Hospital E
Outcome
Uncertainty
Sampling and measurement variability must be taken into account when comparing
different entities, otherwise the results cannot be meaningfully interpreted. Politicians
and reporters do generally not understand this.
23. Observation vs. inference
For one particular observed sample
Central tendency: Mean, Median (statistic)
Dispersion: SD, Range
For the unobserved population of samples
Central tendency: Mean, Median (parameter)
Uncertainty: SEM, confidence interval
24. Precision and validity of estimates
Lower validity Higher validity
Higher precision
Lower precision
25. Precision and validity
Precision is often presented using a 95% confidence interval.
Like the p-value this is an estimate of the uncertainty related to
sampling and measurement variability.
What about validity?
- Experiments are designed for validity (randomization, blinding, etc.)
- Observational studies are analyzed to reduce bias (validity errors).
29. Methodological development in different research areas
Level of proficiency
Clinical trials
Observational studies
Laboratory experiments
Time
Some time not Today In the not too
too long ago far future
31. - Longitudinal analysis using random effects models
- Multilevel analysis
- Causality models
- The development of publication guidelines
- Debate on registration of epidemiological studies
33. - Random effects models for analysis of FAS
- Closed test procedure strategies for handling multiplicity
issues
- Development of superiority, equivalence, and non-inferiority
trial designs
- The development of ICH guidelines for design and analysis
- The development of publication guidelines
- Registration of trials in a public register
34. P-value and confidence interval
Information in p-values Information in confidence intervals
[2 possibilities] [2 possibilities]
p < 0.05
Statistically significant effect
n.s. Inconclusive
Effect
0
35. P-value and confidence interval
Information in p-values Information in confidence intervals
[2 possibilities] [2 possibilities]
p < 0.05
Statistically significant effect
n.s. Inconclusive
Effect
0 Clinically significant effects
36. P-value and confidence interval
Information in p-values Information in confidence intervals
[2 possibilities] [6 possibilities]
p < 0.05 Statistically but not clinically significant effect
Statistically and clinically significant effect
p < 0.05
p < 0.05 Statistically, but not necessarily clinically, significant effect
n.s.
Inconclusive
n.s. Neither statistically nor clinically significant effect
p < 0.05 Statistically significant reversed effect
Effect
0 Clinically significant effects
37. Superiority, non-inferiority and equivalence
Superiority shown
Superiority shown less strongly
Non-inferiority not shown Superiority not shown
Non-inferiority shown Superiority not shown
Equivalence shown Superiority not shown
Control better New agent better
0
Margin of non-inferiority
or equivalence
38. Statistical analysis of clinical trials
ICH E9 Statistical Principles for Clinical Trials
CPMP Points to consider on multiplicity issues in clinical
trials
CPMP Points to consider on adjustment for baseline
covariates
CPMP Points to consider on missing data
CPMP Point to consider on switching between
superiority and non-inferiority