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Related Concept Videos

Trial and Error and Algorithm01:12

Trial and Error and Algorithm

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A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...
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Testing a Claim about Population Proportion01:24

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A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
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Sign Test for Matched Pairs01:17

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The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
To conduct the sign test, we first calculate the differences in...
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Testing a Claim about Standard Deviation01:19

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A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
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Testing a Claim about Mean: Unknown Population SD01:21

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A complete procedure of testing a hypothesis about a population mean when the population standard deviation is unknown is explained here.
Estimating a population mean requires the samples to be approximately normally distributed. The data should be collected from the randomly selected samples having no sampling bias. There is no specific requirement for sample size. But if the sample size is less than 30, and we don't know the population standard deviation, a different approach is used;...
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Testing a Claim about Mean: Known Population SD01:11

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A complete procedure of testing the hypothesis about a population mean is explained here.
Estimating a population mean requires the samples to be distributed normally. The data should be collected from the randomly selected samples having no sampling bias. The sample size needed to be higher than 30, and most importantly, the population standard deviation should be already known.
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The small trial problem.

Jean Raymond1, Tim E Darsaut2, Johanna Eneling3

  • 1Department of Radiology, Service of Neuroradiology, Centre Hospitalier de L'Université de Montréal (CHUM), Montreal, QC, H2X 0C1, Canada. jean.raymond@umontreal.ca.

Trials
|June 22, 2023
PubMed
Summary

Many medical trials are too small to be reliable. Researchers should compare patient outcomes rather than averages to ensure trial results are meaningful and applicable to real-world practice.

Keywords:
DichotomizationNumber of patientsPlacebo-controlled surgical trialsPragmatic trialsProblems with continuous variablesSample sizeSurgeryTrial methodologyTrial size

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Area of Science:

  • Clinical Trials
  • Biostatistics
  • Medical Research Methodology

Background:

  • Many randomized trials are underpowered, questioning the validity of their conclusions.
  • Small sample sizes limit the generalizability of findings for medical and surgical interventions.

Purpose of the Study:

  • To illustrate the problem of small clinical trials using power calculations from vertebroplasty studies.
  • To discuss statistical approaches for determining adequate sample sizes for meaningful trial results.

Main Methods:

  • Analysis of power calculations for five Cochrane-reviewed vertebroplasty versus placebo trials.
  • Examination of statistical methods, including the use of continuous variables versus dichotomous outcomes.

Main Results:

  • Vertebroplasty trials planned for 23-71 patients per group were too small.
  • Four of five studies inappropriately used continuous pain variables (visual analog scale) for sample size calculation.
  • Comparing proportions of patients achieving a threshold outcome requires larger trials but yields more meaningful results.

Conclusions:

  • Most placebo-controlled vertebroplasty trials were underpowered due to comparing means of continuous variables.
  • Randomized trials must be large enough to reflect patient and practice diversity.
  • Trials informing clinical practice need per-patient outcome comparisons and appropriately sized samples.