Because the participants are selected on outcome, the case-control study reveals the prevalence of exposure among cases and controls. In case-control studies we calculate odds ratios because it is often a good estimate of the relative risk.
Odds are the probability of an event occurring divided by the probability of the event not occurring. Using algebra to re-arrange the formula, the OR can be calculated as Table 9 :. In an analysis from the Framingham Heart Study of risk factors for syncope, investigators identified patients with a positive history of syncope They then matched two controls without a history of syncope on age, gender, and follow-up time to every one case.
Using a case: control ratio of is a strategy to increase statistical power of the analysis when the number of cases is limited. Among other variables, they compared odds of high blood pressure BP between the syncope and non-syncope patients. Regarding high blood pressure prevalence in the two groups, Table 10 shows the findings. The OR was calculated to be indicating that having hypertension makes study participants 1. The relative risk or risk ratio RR is calculated from a cohort study where exposed and non-exposed participants are followed over time and the incidence of disease is observed.
Because the hallmark of a cohort study is following a population over time to identify incident cases of disease, the cohort is screened to assure that no participant enrolled in the study has already experienced the outcome or disease event. Then, the cohort is followed for a specific period of time, and the incidence of events for the exposed and unexposed groups is measured.
The relative risk can also be used to analyze clinical trial data. In another study from the Framingham Heart Study, investigators followed a cohort of women who were classified according to four categories of BMI Over 24 years of follow-up, women developed coronary heart disease CHD ; of those women died from CHD 21 and 63 from the 1st and 4th quartiles of BMI, respectively.
The , which means that persons in the 4th quartile BMI group have 3. The method is similar to the one used for the binomial probability distribution. A test of independence, as a category of methods, tests the hypothesis that the proportion of an outcome is independent of the grouping category. Chi-square tests are used to determine the degree of belief that an observed frequency table could have occurred randomly by comparing it to an expected frequency table.
The expected frequency table is derived based on the assumption that the row and column totals are true as observed and fixed. The most commonly used chi-square test is the Pearson's chi-square test. This is used to analyze a frequency table with two rows and two columns.
When the table is not symmetrical or is of dimensions other than 2-by-2, the method is still valid, and when used is called the Cochran's chi-square test. At the very least, the largest observed difference is significant if the table is significant.
If the overall table is significant, this global significance can allow stratified sub-analyses of the individual comparisons of interest. It can also be helpful to look at the contribution to the chi-square test statistic by each cell and conclude that the largest of these cells are where the observed frequencies most deviated from the expected frequencies.
If the chi-square tests on the tables for OR and RR result in p-values less than 0. This being revealed, we are ready to illustrate how the chi-square test statistic is used to calculate the CI 2.
The upper CI uses the same formula except by adding, rather than subtracting, the distance to the ratio:. The use of chi-square tables is covered in elementary texts on statistics, and the method for calculating the test is beyond the scope of this article. Thus, for the OR example, 1. The CI range does not include 1 so the OR is statistically significant and validated the deductive inference that hypertension increases the odds of experiencing syncope.
Similarly for the RR example, 3. Most often, the exposure is under study because it is considered harmful, so ratios greater than 1 and significant by not including 1 in the range of the CI are the more familiar result.
However, ratios less than 1 and significant indicate that exposure is protective. An analysis from this viewpoint is helpful when the exposure is some behavior or event that is hypothesized to be therapeutic or helpful in building immunity.
Tests of proportional disagreement are for paired data, either repeated measures in the same participants or participants matched on demographic factors then given different exposures and followed to compare outcomes.
The best known of the tests of proportional disagreement is the McNemar's chi-square test. A relatively new application of tests for paired data is the Combined Quality Improvement Ratio CQuIR , which uses the McNemar's chi-square test as the basis, but combines participants with repeated measures and case-control matched pairs into one large database of analyzable pairs. This process maximizes the statistical power available from the population 25 , Included in the tests of disproportion is the Kappa statistic of agreement.
Thus far, we have used examples for analyses from observational studies. Experimental studies or clinical trials are analyzed in much the same manner. In clinical trials, patients are followed until some outcome is observed in the planned study period; these are incidence studies.
As incidence studies, the RR will be the measure of association tested for statistical significance. Additionally, many clinical trials lend themselves to straightforward analyses with chi-square tests, ANOVA, or other methods that result only in a p-value. Table 13 summarizes common methods used to analyze healthcare data. For one example, we review the results of a trial of the beta-blocker, bucindolol, used in patients with advanced chronic heart failure CHF While it is accepted that beta-blockers reduce morbidity and mortality in patients with mild to moderate CHF, these investigators enrolled patients designated as New York Heart Association NYHA class III or IV to test the efficacy of the beta-blocker in reducing morbidity and mortality in patients with high baseline severity.
Once enrolled, patients were randomly assigned to receive either placebo or the beta-blocker, and neither the patient nor the physician knew to which treatment the patient was assigned. This study was stopped after the seventh interim analysis due to the accruing evidence of the usefulness of beta-blockers for CHF patients from other studies. After the follow-up data was completed, adjustments for varying follow-up time could be made.
However, a sub-analysis of the secondary endpoint of cardiac death did yield a significant hazard ratio HR of 0. This HR being less than the value 1 means that the beta-blocker was protective against cardiac death in the follow-up period. This secondary analysis is consistent with the decision of the study group to stop the trial early. This concludes Part I of the series. In the next issue of The Ochsner Journal , we will present Part II which includes discussion of the significance of the study results, relevance of the results in clinical practice, and study limitations.
National Center for Biotechnology Information , U. Journal List Ochsner J v. Ochsner J. Marie A. Richard B. Author information Copyright and License information Disclaimer. Corresponding author: Marie A.
This article has been cited by other articles in PMC. Introduction This two-part series will present basic statistical principles for the practicing physician to use in his or her review of the literature and to the physician engaged in clinical research. What is the study question? What are the study goals? What is the appropriate study design to answer the study question? What are the appropriate statistical tests to utilize? What Is the Study Question?
What Are the Study Goals? Some questions that may facilitate the process of identifying the study goals follow: Is the goal to determine: — how well a drug or device works under ideal conditions i.
Is the study goal to provide information for a quality management activity? Open in a separate window. Descriptive Studies: Correlational studies, also called ecologic studies, employ measures that represent characteristics of entire populations to describe a given disease in relation to some variable of interest e.
Analytic Studies: Analytic studies can be observational or experimental. Other Classification Schemes: Some other classification schemes in use today are based on the use of epidemiology to evaluate health services.
What Are the Appropriate Statistical Tests? Measures of Central Tendency There are three commonly referred to measures of central location: mean, median, and mode. Measures of Dispersion Measures of dispersion or variability provide information regarding the relative position of other data points in the sample. Table 4: Example of a Standard Deviation Calculation. Comparing Central Tendencies with Respect to Dispersions Error Terms Once central tendency and dispersion are measured, it follows that a comparison between various groups e.
Table 5. Probability: Fundamental Concepts in Evidence-Based Medicine Armed with a basic understanding of algebra and user-friendly statistical software, most clinicians and clinical researchers can follow the cookbook method of statistical inference.
Probability Distributions: The final rate of an event that is measured, as the size of the sample being measured grows to include the entire population, is the probability that any individual in the population will experience the event. For this example, the SE is calculated by:. Evaluating Diagnostic and Screening Tests In order to understand disease etiology and to provide appropriate and effective health care for persons with a given disease, it is essential to distinguish between persons in the population who do and do not have the disease of interest.
Common Measures of Association and Statistical Tests Measures of association are summary statistics that estimate the risk of an outcome or disease for a given exposure between two groups.
Table Table Classification of Random Error. References Gordis L. Philadelphia: W. Fundamentals of Biostatistics. Practical Nonparametric Statistics. Epidemiology: Principles and Methods. Epidemiology in Medicine. Primer of Biostatistics, 5th Edition. Factors associated with cardiac mortality in developed countries with particular reference to the consumption of wine.
Case reports of heart failure after therapy with a tumor necrosis factor antagonist. Ann Intern Med. Relationship between cigarette smoking and novel risk factors for cardiovascular disease in the United States. Effect of blood pressure on early decline in kidnery function among hypertensive men.
The Framingham Study. Assessment of frequency of progression to hypertension in non-hypertensive participants in the Framingham Heart Study: a cohort study. Genetic and environmental contributions to platelet aggregation: the Framingham Heart study. Primary and subsequent coronary risk appraisal: New results from the Framingham study. Am Heart J. Am J Epidemiol. Effects of caregiver specialty on cost and clinical outcomes following hospitalization for heart failure.
Am J Cardiol. Foundations of Clinical Research Applications to Practice. The prognostic impact of immunosuppression and cellular rejection on cardiac allograft vasculopathy: time for a reappraisal. Stat Biosci Download citation. Received : 30 October Revised : 16 April Accepted : 02 June Published : 23 June Anyone you share the following link with will be able to read this content:.
Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative. Skip to main content. Search SpringerLink Search. References 1. Stat Med 13 4 — Article Google Scholar 8. Springer, New York, p Google Scholar Pharm Stat — Article Google Scholar Society for Industrial and Applied Mathematics Acknowledgement This manuscript was sponsored by AbbVie. View author publications. Supplementary Information.
Rights and permissions Reprints and Permissions. About this article. Cite this article Sui, Y. The number of drugs in phases two and three varies significantly, which suggests that the transition from one to the other is particularly difficult.
For new drugs in the United States, the probability of phase two success is less than 50 percent in almost every disease area. Before new treatments can be sold in the U.
Over 50 novel products were approved by CDER in , meaning the benefits to patients outweigh the known risks. This text provides general information. Statista assumes no liability for the information given being complete or correct. Due to varying update cycles, statistics can display more up-to-date data than referenced in the text. Total cost of developing a drug in U. Clinical trial participation Number of registered clinical studies in U.
Success rates Transition from phase two to three in U. Interesting statistics In the following 7 chapters, you will quickly find the 39 most important statistics relating to "Clinical trials". Statistics on the topic. Industry sectors - expenditure on research and development vs. Research and development expenditure: U. Research and development in European pharmaceutical industry by country Total clinical research funding by National Institutes for Health Increase in clinical trials' complexity Total number of registered clinical studies with posted results worldwide
0コメント