McNemar's test

From Wikipedia, the free encyclopedia

In statistics, McNemar's test is a normal approximation used on nominal data. It is applied to 2 × 2 contingency tables with a dichotomous trait, with matched pairs of subjects, to determine whether the row and column marginal frequencies are equal ("marginal homogeneity"). It is named after Quinn McNemar, who introduced it in 1947.[1] An application of the test in genetics is the transmission disequilibrium test for detecting linkage disequilibrium.[2]

Definition

The test is applied to a 2 × 2 contingency table, which tabulates the outcomes of two tests on a sample of n subjects, as follows.

Test 2 positive Test 2 negative Row total
Test 1 positive a b a + b
Test 1 negative c d c + d
Column total a + c b + d n

The null hypothesis of marginal homogeneity states that the two marginal probabilities for each outcome are the same, i.e. pa + pb = pa + pc and pc + pd = pb + pd.

Thus the null and alternative hypotheses are[1]

\begin{align} H_0 & :~p_b=p_c \\ H_1 & :~p_b \ne p_c \end{align}

Here pa, etc., denote the theoretical probability of occurrences in cells with the corresponding label.

The McNemar test statistic is:

\chi^2 = {(b-c)^2 \over b+c}.

The statistic with Yates's correction for continuity[3] is given by:[citation needed]

\chi^2 = {(|b-c|-0.5)^2 \over b+c}.

An alternative correction of 1 instead of 0.5 is attributed to Edwards [4] by Fleiss,[5] resulting in a similar equation:

\chi^2 = {(|b-c|-1)^2 \over b+c}.

Under the null hypothesis, with a sufficiently large number of discordants (cells b and c), \chi^2 has a chi-squared distribution with 1 degree of freedom. If either b or c is small (b + c < 25) then \chi^2 is not well-approximated by the chi-squared distribution. [citation needed] The binomial distribution can be used to obtain the exact distribution for an equivalent to the uncorrected form of McNemar's test statistic.[6] In this formulation, b is compared to a binomial distribution with size parameter equal to b + c and "probability of success" = ½, which is essentially the same as the binomial sign test. For b + c < 25, the binomial calculation should be performed, and indeed, most software packages simply perform the binomial calculation in all cases, since the result then is an exact test in all cases. When comparing the resulting \chi^2 statistic to the right tail of the chi-squared distribution, the p-value that is found is two-sided, whereas to achieve a two-sided p-value in the case of the exact binomial test, the p-value of the extreme tail should be multiplied by 2.

If the \chi^2 result is significant, this provides sufficient evidence to reject the null hypothesis, in favour of the alternative hypothesis that pb ≠ pc, which would mean that the marginal proportions are significantly different from each other.

Example

In the following example, a researcher attempts to determine if a drug has an effect on a particular disease. Counts of individuals are given in the table, with the diagnosis (disease: present or absent) before treatment given in the rows, and the diagnosis after treatment in the columns. The test requires the same subjects to be included in the before-and-after measurements (matched pairs).

After: present After: absent Row total
Before: present 101 121 222
Before: absent 59 33 92
Column total 160 154 314

In this example, the null hypothesis of "marginal homogeneity" would mean there was no effect of the treatment. From the above data, the McNemar test statistic with Yates's continuity correction is

\chi^2 = {(|121 - 59| - 0.5)^2 \over {121 + 59}}

has the value 21.01, which is extremely unlikely to from the distribution implied by the null hypothesis. Thus the test provides strong evidence to reject the null hypothesis of no treatment effect.

Discussion

An interesting observation when interpreting McNemar's test is that the elements of the main diagonal do not contribute to the decision about whether (in the above example) pre- or post-treatment condition is more favourable.

An extension of McNemar's test exists in situations where independence does not necessarily hold between the pairs; instead, there are clusters of paired data where the pairs in a cluster may not be independent, but independence holds between different clusters.[citation needed] An example is analyzing the effectiveness of a dental procedure; in this case, a pair corresponds to the treatment of an individual tooth in patients who might have multiple teeth treated; the effectiveness of treatment of two teeth in the same patient is not likely to be independent, but the treatment of two teeth in different patients is more likely to be independent.[7]

Information in the pairings

John Rice wrote:[8]

85 Hodgkin's patients [...] had a sibling of the same sex who was free of the disease and whose age was within 5 years of the patient's. These investigators presented the following table:

\begin{array}{c|c|c} \hline & \text{Tonsillectomy} & \text{No tonsillectomy} \\ \hline\text{Hodgkins} & 41 & 44 \\ \hline\text{Control} & 33 & 52 \end{array}

They calculated a chi-squared statistic of 1.53, which is not significant.[...] [they] had made an error in their analysis by ignoring the pairings.[...] [their] samples were not independent, because the siblings were paired [...] we set up a table that exhibits the pairings:

\begin{array}{cc} & \text{Sibling} \\ \text{Patient} & \begin{array}{c|c|c} \hline & \text{No tonsillectomy} & \text{Tonsillectomy} \\ \hline\text{No tonsillectomy} & 37 & 7 \\ \hline\text{Tonsillectomy} & 15 & 26 \end{array} \end{array}

It is to the second table that McNemar's test can be applied. Notice that the sum of the numbers in the second table is 85—the number of pairs of siblings—whereas the sum of the numbers in the first table is twice as big, 170—the number of individuals. The second table gives more information than the first. The numbers in the first table can be found by using the numbers in the second table, but not vice versa. The numbers in the first table give only the marginal totals of the numbers in the second table.

Related tests

  • The binomial sign test gives an exact test for the McNemar's test.
  • The Cochran's Q test for two "treatments" is equivalent to the McNemar's test.
  • The Liddell's exact test is an exact alternative to McNemar's test.[9][10]
  • The Stuart–Maxwell test is different generalization of the McNemar test, used for testing marginal homogeneity in a square table with more than two rows/columns.[11][12][13]
  • The Bhapkar's test (1966) is a more powerful alternative to the Stuart–Maxwell test.[14][15]

See also

References

  1. ↑ 1.0 1.1 McNemar, Quinn (June 18, 1947). "Note on the sampling error of the difference between correlated proportions or percentages". Psychometrika 12 (2): 153–157. doi:10.1007/BF02295996. PMID 20254758. 
  2. ↑ Spielman RS; McGinnis RE; Ewens WJ (Mar 1993). "Transmission test for linkage disequilibrium: the insulin gene region and insulin-dependent diabetes mellitus (IDDM)". Am J Hum Genet. 52 (3): 506–16. PMC 1682161. PMID 8447318. 
  3. ↑ Yates, F (1934). Contingency table involving small numbers and the χ2 test. Supplement to the Journal of the Royal Statistical Society 1(2), 217–235.JSTOR Archive for the journal
  4. ↑ Edwards, A (1948). "Note on the "correction for continuity" in testing the significance of the difference between correlated proportions". Psychometrika 13: 185–187. 
  5. ↑ Fleiss, J. L. (1981). Statistical methods for rates and proportions (2nd ed.). New York: John Wiley & Sons. p. 114. ISBN 0-471-06428-9. 
  6. ↑ Sheskin (2004)
  7. ↑ Durkalski, V.L.; Palesch, Y.Y.; Lipsitz, S.R.; Rust, P.F. (2003). "Analysis of clustered matched-pair data". Statistics in medicine 22 (15): 2417–28. doi:10.1002/sim.1438. PMID 12872299. Retrieved April 1, 2009. 
  8. ↑ Rice, John (1995). Mathematical Statistics and Data Analysis (Second ed.). Belmont, California: Duxbury Press. pp. 492–494. ISBN 0-534-20934-3. 
  9. ↑ Liddell, D. (1976). "Practical Tests of 2 × 2 Contingency Tables". Journal of the Royal Statistical Society 25 (4): 295–304. JSTOR 2988087. 
  10. ↑ "Maxwell's test, McNemar's test, Kappa test". Rimarcik.com. Retrieved 2012-11-22. 
  11. ↑ Sun, Xuezheng; Yang, Zhao (2008). "Generalized McNemar's Test for Homogeneity of the Marginal Distributions". SAS Global Forum. 
  12. ↑ Stuart, Alan (1955). "A Test for Homogeneity of the Marginal Distributions in a Two-Way Classification". Biometrika Trust. 
  13. ↑ Maxwell, A.E. (1970). "Comparing the Classification of Subjects by Two Independent Judges". The British Journal of Psychiatry. 
  14. ↑ "McNemar Tests of Marginal Homogeneity". John-uebersax.com. 2006-08-30. Retrieved 2012-11-22. 
  15. ↑ Bhapkar, V.P. (1966). "A Note on the Equivalence of Two Test Criteria for Hypotheses in Categorical Data". American Statistical Association. 

External links

 
Categorical data
Multivariate statistics
Time series analysis
General
Specific tests
Time domain
Frequency domain
Survival analysis
This article is issued from Wikipedia. The text is available under the Creative Commons Attribution/Share Alike; additional terms may apply for the media files.