Check out this brief paper if you want to know more about P value calculations. There is a misconception that a very small p value means the difference between groups is highly relevant. Looking at the p value alone deviates our attention from the effect size. Consider an experiment in which 10 subjects receive a placebo, and another 10 receive an experimental diuretic. After 8 h, the average urine output in the placebo group is 769 mL, versus 814 mL in the diuretic group—a difference of 45 mL. How do we know if that difference means the drug works and is not just a result of chance? Read on and let me know if you have any questions...
When estimating sample sizes for clinical trials there are several different views that might be taken as to what definition and meaning should be given to the sought-for treatment effect. However, if the concept of a ‘minimally important difference’ (MID) does have relevance to interpreting clinical trials (which can be disputed) then its value cannot be the same as the ‘clinically relevant difference’ (CRD) that would be used for planning them.
A doubly pernicious use of the MID is as a means of classifying patients as responders and non-responders. Not only does such an analysis lead to an increase in the necessary sample size but it misleads trialists into making causal distinctions that the data cannot support and has been responsible for exaggerating the scope for personalised medicine.
In this talk these statistical points will be explained using a minimum of technical detail.
When estimating sample sizes for clinical trials there are several different views that might be taken as to what definition and meaning should be given to the sought-for treatment effect. However, if the concept of a ‘minimally important difference’ (MID) does have relevance to interpreting clinical trials (which can be disputed) then its value cannot be the same as the ‘clinically relevant difference’ (CRD) that would be used for planning them.
A doubly pernicious use of the MID is as a means of classifying patients as responders and non-responders. Not only does such an analysis lead to an increase in the necessary sample size but it misleads trialists into making causal distinctions that the data cannot support and has been responsible for exaggerating the scope for personalised medicine.
In this talk these statistical points will be explained using a minimum of technical detail.
Part 4 of 5 of experimental design presentations. This one is focused on randomisation, assigning groups, independence and ways to stratify your research cohort. Decent opportunities to present about ethics.
Text form available via www.lantsandlaminin.com
We wrote this brief paper to help you to better understand the calculation of sample size. In clinical research, our goal is to make an inference regarding something about a population by studying a sample of that population. This sample has to be representative of the target population, and the number of participants must be appropriate. It should be large enough that the probability of finding differences between groups by mere chance is low and that of detecting true, clinically significant differences is high. Let me know if you have any questions.
Clinical trials: quo vadis in the age of covid?Stephen Senn
A discussion of the role of clinical trials in the age of COVID. My contribution to the phastar 2020 life sciences summit https://phastar.com/phastar-life-science-summit
There are many questions one might ask of a clinical trial, ranging from what was the effect in the patients studied to what might the effect be in future patients via what was the effect in individual patients? The extent to which the answer to these questions is similar depends on various assumptions made and in some cases the design used may not permit any meaningful answer to be given at all.
A related issue is confusion between randomisation, random sampling, linear model and true multivariate based modelling. These distinctions don’t matter much for some purposes and under some circumstances but for others they do.
Personalised medicine a sceptical viewStephen Senn
Some grounds for believing that the current enthusiasm about personalised medicine is exaggerated, founded on poor statistics and represents a disappointing loss of ambition.
Part 1 of a 5 part lecture series on experimental design.
This section deals with hypothesis generation, correlative vs manipulative experiments and choosing an appropriate model system.
Text version of this content available at www.lantsandlaminins.com
Unfortunately, some have interpreted Numbers Needed to Treat as indicating the proportion of patients on whom the treatment has had a causal effect. This interpretation is very rarely, if ever, necessarily correct. It is certainly inappropriate if based on a responder dichotomy. I shall illustrate the problem using simple causal models.
One also sometimes encounters the claim that the extent to which two distributions of outcomes overlap from a clinical trial indicates how many patients benefit. This is also false and can be traced to a similar causal confusion.
Experimental design part 2 measurements Kevin Hamill
Part 2 of a 5 part lecture series on experimental design. This section deals with direct and indirect measurements, experimental units, independent and dependent variables, pilot studies.
Text form of some of these points are hosted at www.lantsandlaminins.com
Part 3 of 5 part experimental design lecture series. This presentation deals with controls and the different roles they play in your design. Interpretation, calibration, biological controls, experimental controls, blinding and multiple observers.
Text form of this content available via www.lantsandlaminins.com
Part 4 of 5 of experimental design presentations. This one is focused on randomisation, assigning groups, independence and ways to stratify your research cohort. Decent opportunities to present about ethics.
Text form available via www.lantsandlaminin.com
We wrote this brief paper to help you to better understand the calculation of sample size. In clinical research, our goal is to make an inference regarding something about a population by studying a sample of that population. This sample has to be representative of the target population, and the number of participants must be appropriate. It should be large enough that the probability of finding differences between groups by mere chance is low and that of detecting true, clinically significant differences is high. Let me know if you have any questions.
Clinical trials: quo vadis in the age of covid?Stephen Senn
A discussion of the role of clinical trials in the age of COVID. My contribution to the phastar 2020 life sciences summit https://phastar.com/phastar-life-science-summit
There are many questions one might ask of a clinical trial, ranging from what was the effect in the patients studied to what might the effect be in future patients via what was the effect in individual patients? The extent to which the answer to these questions is similar depends on various assumptions made and in some cases the design used may not permit any meaningful answer to be given at all.
A related issue is confusion between randomisation, random sampling, linear model and true multivariate based modelling. These distinctions don’t matter much for some purposes and under some circumstances but for others they do.
Personalised medicine a sceptical viewStephen Senn
Some grounds for believing that the current enthusiasm about personalised medicine is exaggerated, founded on poor statistics and represents a disappointing loss of ambition.
Part 1 of a 5 part lecture series on experimental design.
This section deals with hypothesis generation, correlative vs manipulative experiments and choosing an appropriate model system.
Text version of this content available at www.lantsandlaminins.com
Unfortunately, some have interpreted Numbers Needed to Treat as indicating the proportion of patients on whom the treatment has had a causal effect. This interpretation is very rarely, if ever, necessarily correct. It is certainly inappropriate if based on a responder dichotomy. I shall illustrate the problem using simple causal models.
One also sometimes encounters the claim that the extent to which two distributions of outcomes overlap from a clinical trial indicates how many patients benefit. This is also false and can be traced to a similar causal confusion.
Experimental design part 2 measurements Kevin Hamill
Part 2 of a 5 part lecture series on experimental design. This section deals with direct and indirect measurements, experimental units, independent and dependent variables, pilot studies.
Text form of some of these points are hosted at www.lantsandlaminins.com
Part 3 of 5 part experimental design lecture series. This presentation deals with controls and the different roles they play in your design. Interpretation, calibration, biological controls, experimental controls, blinding and multiple observers.
Text form of this content available via www.lantsandlaminins.com
Multiple Linear Regression II and ANOVA IJames Neill
Explains advanced use of multiple linear regression, including residuals, interactions and analysis of change, then introduces the principles of ANOVA starting with explanation of t-tests.
Common Statistical Concerns in Clinical TrialsClin Plus
Statistics are a major part of clinical trials. This article breaks down how they are used, and things that people think about when recording statistical data.
P-values the gold measure of statistical validity are not as reliable as many...David Pratap
This is an article that appeared in the NATURE as News Feature dated 12-February-2014. This article was presented in the journal club at Oman Medical College , Bowshar Campus on December, 17, 2015. This article was presented by Pratap David , Biostatistics Lecturer.
A short introduction to sample size estimation for Research methodology workshop at Dr. BVP RMC, Pravara Institute of Medical Sciences(DU), Loni by Dr. Mandar Baviskar
Most medical research around the world is
empirical and uses data to derive a result. Many
researchers substantially depend on statistical
evidence such as P values to decide that an effect
of a specific factor is present or not. Now, there is a
storm around the world, and the P value, particularly
the resulting statistical significance, has been not
just questioned but also sought to be abolished
altogether. Abandoning statistical significance has
the potential to change research in empirical sciences
such as medicine forever. This article discusses the
arguments in favour and against this contention and
pleads that medical scientists present a balanced
picture in their articles where P values have a role
but not as dominant as is currently seen in most
publications. The following discussion would also
make medical researchers aware of this raging
controversy, help them to understand the involved
nuances and equip them to prepare a better report of
their research
Hypothesis Testing. Inferential Statistics pt. 2John Labrador
A hypothesis test is a statistical test that is used to determine whether there is enough evidence in a sample of data to infer that a certain condition is true for the entire population. A hypothesis test examines two opposing hypotheses about a population: the null hypothesis and the alternative hypothesis.
Page 266LEARNING OBJECTIVES· Explain how researchers use inf.docxkarlhennesey
Page 266
LEARNING OBJECTIVES
· Explain how researchers use inferential statistics to evaluate sample data.
· Distinguish between the null hypothesis and the research hypothesis.
· Discuss probability in statistical inference, including the meaning of statistical significance.
· Describe the t test and explain the difference between one-tailed and two-tailed tests.
· Describe the F test, including systematic variance and error variance.
· Describe what a confidence interval tells you about your data.
· Distinguish between Type I and Type II errors.
· Discuss the factors that influence the probability of a Type II error.
· Discuss the reasons a researcher may obtain nonsignificant results.
· Define power of a statistical test.
· Describe the criteria for selecting an appropriate statistical test.
Page 267IN THE PREVIOUS CHAPTER, WE EXAMINED WAYS OF DESCRIBING THE RESULTS OF A STUDY USING DESCRIPTIVE STATISTICS AND A VARIETY OF GRAPHING TECHNIQUES. In addition to descriptive statistics, researchers use inferential statistics to draw more general conclusions about their data. In short, inferential statistics allow researchers to (a) assess just how confident they are that their results reflect what is true in the larger population and (b) assess the likelihood that their findings would still occur if their study was repeated over and over. In this chapter, we examine methods for doing so.
SAMPLES AND POPULATIONS
Inferential statistics are necessary because the results of a given study are based only on data obtained from a single sample of research participants. Researchers rarely, if ever, study entire populations; their findings are based on sample data. In addition to describing the sample data, we want to make statements about populations. Would the results hold up if the experiment were conducted repeatedly, each time with a new sample?
In the hypothetical experiment described in Chapter 12 (see Table 12.1), mean aggression scores were obtained in model and no-model conditions. These means are different: Children who observe an aggressive model subsequently behave more aggressively than children who do not see the model. Inferential statistics are used to determine whether the results match what would happen if we were to conduct the experiment again and again with multiple samples. In essence, we are asking whether we can infer that the difference in the sample means shown in Table 12.1 reflects a true difference in the population means.
Recall our discussion of this issue in Chapter 7 on the topic of survey data. A sample of people in your state might tell you that 57% prefer the Democratic candidate for an office and that 43% favor the Republican candidate. The report then says that these results are accurate to within 3 percentage points, with a 95% confidence level. This means that the researchers are very (95%) confident that, if they were able to study the entire population rather than a sample, the actual percentage who preferred th ...
Biostatistics are widely used in clinical trials to collect and organize and describe and interpret these result and then give to us proves to take appropriate clinical decisions
Statistical significance vs Clinical significanceVini Mehta
esults are said to be "statistically significant" if the probability that the result is compatible with the null hypothesis is very small. Clinical significance, or clinical importance: Is the difference between new and old therapy found in the study large enough for you to alter your practice?
STATISTICS : Changing the way we do: Hypothesis testing, effect size, power, ...Musfera Nara Vadia
STATISTICS : Changing the way we do: Hypothesis testing, effect size, power, confidence interval, two-tailed and one tailed test, and other misunderstood issues.
This presentation discusses the following topics:
Hypothesis Test
Potential Outcomes in Hypothesis Testing
Significance level
P-value
Sampling Errors
Type I Error
What causes Type I errors?
What causes Type II errors?
4 possible outcomes
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