Prof. Dr. Hamit ACEMOĞLU

The aim By the and of this lecture, the studests will be aware of basic statistical significance tests and applications used in the health studies.

The goals Be able to explain the distinction between parametric and nonparametric tests. Be able to specify basic hypothesis testing and their usage with 80% accuracy; One sample t-test, Sign test Dependent samples t-test, Wilcoxon test Independent samples t-test, Mann-Whitney U test One way ANOVA, Kruskal Wallis Repeated measures ANOVA, Friedman analysis Z test Chi square test, McNemar test, Chi-square trend test, Fisher's exact test Cochrane Q test Pearson correlation analysis Spearman correlation analysis

To analyze data Describe data; using descriptive statistics e.g.: Frequencies, Mean, Minimum, Maximum, Standard deviation. Examine relationships; between variables e.g.: Correlation analysis, Regression analysis, Factor analysis. Compare groups; to determine if there are significant differances between groups: hypothesis tests e.g.: T-test, ANOVA, Chi-squared test.

The Flow Chart In statistical analysis the mostly use descriptive statistics (mean, frequency showing with graphics, etc.) and hypothesis tests (t-test, chi-square, etc.) It is very important for researchers to know which hypothesis testing where to be used. The flowcharts should be well examined and it should be beside the researchers up to practice is gained.

The Flow Chart Veri Çeşitleri Numerik (Kan şekeri, Çocuk sayısı) 1 Grup (Örnek 1) - Tek örneklem de t testi - İşaret testi 2 Grup Bağımlı (Örnek 2) - Bağımlı örnekleml erde t testi - Wilcoxon Bağımsız (Örnek 3) - Student t testi - Mann Whitney U testi > 2 Grup Bağımlı (Örnek 4) - Tekrarlay an örneklerd e ANOVA - Friedman Bağımsız Grup değişkeni nominal (Örnek 5) Tek Yönlü ANOVA - Kruskal Wallis Grup değişkeni sıralı (ordinal) (Örnek 6) Tek Yönlü ANOVA ve linearite testi Birden fazla değişken (Örnek 7) Çok yönlü ANOVA Kategorik (Sigara içme, Memnuni yet derecesi) 2 Kategori Sigara içme, Tercih durumu 1 Grup (Örnek 8) - Tek orantı testi (Z testi) - Binomiyal test (işaret testi) 2 Grup Bağımlı (Örnek 9) - Mc Nemar Bağımlı (Örnek 10) - Kappa Bağımsız (Örnek 11) - Ki Kare - Fisher exact > 2 Grup Bağımlı (Örnek 12) Cochran Q Bağımlı (Örnek 13) - Kappa Bağımsız (Örnek 14) - Ki Kare - Ki Kare Trend > 2 Kategori Bağımlı (Örnek 15) - Marginal homogen eity Bağımsız (Örnek 16) - Ki Kare 1 Grup - Ki Kare (Örnek 17) İki Numerik Değişken (Örnek 18) Korelasyo n - Pearson - Spearman Diğer Testler Çoklu Çözümle meler Bağımlı Değişken İkili Kategorik Lojistik Regresyo n (Örnek 19) Bağımlı Değişken Numerik Lineer Regresyo n (Örnek 22) Duyarlılık ve Özgüllük (Örnek 20) ROC Analizi Geçerlilik ve Güvenilirli k (Örnek 21) Sağkalım Analizleri Kalpan Meier Cox Regresyo n

The Flow Chart Numerical Categorical 2 numerical variables Other tests

Tests Parametrik Tests Non-parametrik Tests

Criteria for Parametric Tests Normal distribution n>30 (Central Limit Theorem) Randomized sampling that results in interval or ratio data. (The group varianses should be equal)

Rule 68% of the data 95% of the data 99% of the data

When to use non-parametric tests? Non-parametrics are distribution free; The data may not be put into metric form appropriately, i.e.. unordered qualitative variables (Nominal data); Data may be rank ordered (Ordinal data); Data may be from small samples; There may be non-normal distribution of the variables (Skewed data); Outliers may be present

Non-parametric tests V parametric tests Usually only perform one analysis of a data set choosing between parametric and non-parametric methods. It is usual to use a parametric method, unless there is a clear indication that it is not valid. It is important to realise that if we apply different tests to the same data then we do not expect them to give the same answer, but in general two valid methods will give similar answers. Non-parametric tests are less powerful than the equivalent parametric test (especially in small samples) and will tend to give a less significant (larger) p-value.

The Flow Chart Veri Çeşitleri Numerik (Kan şekeri, Çocuk sayısı) 1 Grup (Örnek 1) - Tek örneklem de t testi - İşaret testi 2 Grup Bağımlı (Örnek 2) - Bağımlı örnekleml erde t testi - Wilcoxon Bağımsız (Örnek 3) - Student t testi - Mann Whitney U testi > 2 Grup Bağımlı (Örnek 4) - Tekrarlay an örneklerd e ANOVA - Friedman Bağımsız Grup değişkeni nominal (Örnek 5) Tek Yönlü ANOVA - Kruskal Wallis Grup değişkeni sıralı (ordinal) (Örnek 6) Tek Yönlü ANOVA ve linearite testi Birden fazla değişken (Örnek 7) Çok yönlü ANOVA Kategorik (Sigara içme, Memnuni yet derecesi) 2 Kategori Sigara içme, Tercih durumu 1 Grup (Örnek 8) - Tek orantı testi (Z testi) - Binomiyal test (işaret testi) 2 Grup Bağımlı (Örnek 9) - Mc Nemar Bağımlı (Örnek 10) - Kappa Bağımsız (Örnek 11) - Ki Kare - Fisher exact > 2 Grup Bağımlı (Örnek 12) Cochran Q Bağımlı (Örnek 13) - Kappa Bağımsız (Örnek 14) - Ki Kare - Ki Kare Trend > 2 Kategori Bağımlı (Örnek 15) - Marginal homogen eity Bağımsız (Örnek 16) - Ki Kare 1 Grup - Ki Kare (Örnek 17) İki Numerik Değişken (Örnek 18) Korelasyo n - Pearson - Spearman Diğer Testler Çoklu Çözümle meler Bağımlı Değişken İkili Kategorik Lojistik Regresyo n (Örnek 19) Bağımlı Değişken Numerik Lineer Regresyo n (Örnek 22) Duyarlılık ve Özgüllük (Örnek 20) ROC Analizi Geçerlilik ve Güvenilirli k (Örnek 21) Sağkalım Analizleri Kalpan Meier Cox Regresyo n

The Flow Chart Veri Çeşitleri Numerik (Kan şekeri, Çocuk sayısı) 1 Grup (Örnek 1) - Tek örneklem de t testi - İşaret testi 2 Grup Bağımlı (Örnek 2) - Bağımlı örnekleml erde t testi - Wilcoxon Bağımsız (Örnek 3) - Student t testi - Mann Whitney U testi > 2 Grup Bağımlı (Örnek 4) - Tekrarlay an örneklerd e ANOVA - Friedman Bağımsız Grup değişkeni nominal (Örnek 5) Tek Yönlü ANOVA - Kruskal Wallis Grup değişkeni sıralı (ordinal) (Örnek 6) Tek Yönlü ANOVA ve linearite testi Birden fazla değişken (Örnek 7) Çok yönlü ANOVA Kategorik (Sigara içme, Memnuni yet derecesi) 2 Kategori Sigara içme, Tercih durumu 1 Grup (Örnek 8) - Tek orantı testi (Z testi) - Binomiyal test (işaret testi) 2 Grup Bağımlı (Örnek 9) - Mc Nemar Bağımlı (Örnek 10) - Kappa Bağımsız (Örnek 11) - Ki Kare - Fisher exact > 2 Grup Bağımlı (Örnek 12) Cochran Q Bağımlı (Örnek 13) - Kappa Bağımsız (Örnek 14) - Ki Kare - Ki Kare Trend > 2 Kategori Bağımlı (Örnek 15) - Marginal homogen eity Bağımsız (Örnek 16) - Ki Kare 1 Grup - Ki Kare (Örnek 17) İki Numerik Değişken (Örnek 18) Korelasyo n - Pearson - Spearman Diğer Testler Çoklu Çözümle meler Bağımlı Değişken İkili Kategorik Lojistik Regresyo n (Örnek 19) Bağımlı Değişken Numerik Lineer Regresyo n (Örnek 22) Duyarlılık ve Özgüllük (Örnek 20) ROC Analizi Geçerlilik ve Güvenilirli k (Örnek 21) Sağkalım Analizleri Kalpan Meier Cox Regresyo n

(Numerical data, one group) (ex-1) One-sample t-test We measure the heigth of the individuals in our neighborhood. neighborhood = single group height = numerical data We want to compare our results with the province in general. In the previous studies of individuals in our province, we know that the average height of cm.

(Numerical data, two dependent groups) (ex-2) Dependent samples t test Suppose we apply a nutrition and exercise program for our diabetic patients. We want to investigate whether or not the participants lose weight at the end of the program. weight: variable, numerical data before and after examined the same group: 2 groups

(Numerical data, two independent groups) (ex-3) Independent samples t test (student t test) We want to investigate if any differences between diabetic men and women in regard of body mass index (BMI) -body mass index (BMI): variable, numerical data -men and women : 2 undependent groups

(Numerical data, more than two dependent groups) (ex-4) The repeated measures ANOVA Senaryo-1, single factor (intra-group comparison) Suppose we apply the treatment program consisting of pharmaceutical, nutritional and exercise to our diabetic patients. During the program we want to investigate the participants hemoglobin A1c level. (H0: There is no differance between the hemoglobin A1c level of the individuals before and after the program.) Hemoglobin A1c measurements were done before applying the treatment program, ​​ at 3 and 6 months. Variable we want to measure is numerical ; hemoglobin A1c level The three measurements obtained before and after aplication are dependent. (3 groups)

(Numerical data, more than two dependent groups) (ex-4) The repeated measures ANOVA Senaryo-2, two factors (intra and inter group comparison) Suppose we apply the treatment program consisting of pharmaceutical, nutritional and exercise to our male and female diabetic patients. During the program, we want to investigate the hemoglobin A1c level, according to gender and time. (H0: There is no difference between male and female groups and hemoglobin A1c levels before and after treatment programs and there is no gender*time interaction.)

(Numerical data, more than two independent groups) (ex-5) One-way ANOVA Suppose we want to investigate our diabeic patients whether they differ in heigth according to neighborhoods they come. (H0: There is no difference between the heigths of diabetic patients from different neighborhoods). The variable (height) which we want to measure is a continuous numeric variable. We have five groups; south, north, east, west and central.

(Numerical data, more than two independent groups)(ex-6) Two-way ANOVA (MANOVA) We want to investigate our diabeic patients whether they differ in fasting blood glucose level according to gender and helath care satisfaction situation. (H0: There is no difference between fasting blood glucose levels of the patient having different gender and health care satisfaction situations.) The variable (fasting blood glucose level) which we want to measure is a continuous numeric variables. Our first undependent categorical variable is gender which consist of men and women undependent variables. Our second undependent categorical variable which is satisfaction situation; consisting of four undependent categories; not satisfied, fairly satisfied, satisfied and very satisfied.

(Categorical data, one group)(ex-7) Z test a-Suppose that, we investigate the smoking status in patients with diabetes, we want to look at smoking status in our sample whether it is different from the community. -smoking status: smokers, nonsmokers: 2 categories -collecting data only once: one group b-Suppose that, we give two different types of biguanide drugs for our patients with diabetes and try to thing about which drug is preffered in terms of taste, ease of use, side effects ets. -2 types of biquinids: 2 cetegories -collecting data only once: one group

(Categorical data, two dependent groups) (ex-8) McNemar test Let's think that, we provide training on the importance of blood glucose control in diabetic patients and training on the use of insulin if needed. We want to examine that wheter the drug of choices has been changed after the training of individuals in our sample. -drug of choices changed or not changed: 2 categories -collecting data 2 times of same group: 2 groups (dependent)

(Categorical data, two independent groups)(ex-9) Chi-square test, Fisher's exact test We want to investigate whether any differences of drug preferences in terms of gender in diabetic patients of our clinic. The studied variable (drug of choice ; 1 - insulin, 2- oral antidiabetic) is a categorical variable. There are two gorups which are men and women.

(Categorical data, more than two dependent groups)(ex-10) Cochran Q test We provided training on the importance of blood glucose control in diabetic patients and training on the use of insulin if needed. We want to examine that wheter the drug of choices has been changed after the training of individuals in our sample. The variable which we examined (drug of choice, 1 - insulin 2- oral antidiabetic) is a dichotomous categorical variable. We collected data from the same individuals before consulting, after consulting 1. month after consulting 6. month. There are three dependent groups present.

(Categorical data, more than two independent groups)(ex-11) Chi-square test, chi-square trend test a-We want to examine whether there are differences between marital status of our diabetes patients from different neighborhoods. marital status: categories (single, married, divorced) neighborhoods: groups (undependent) b-We want to examine whether there is a trend between smoking status and education level in diabetics.We assume that as education status increases, the percentage of smoking decreases. Smoking satatus: 2 categories (smokers, nonsmopers) education level: >2 groups

( Categorical data, more than two dependent categories)(ex-12) Marginal homogeneity test Suppose that there is an implementation of quality improvement programs on a health care provider serving about our diabetic patients. Patient satisfaction before and after program implementation as; I'm not satisfied I'm less satisfied I'm satisfied I'm very satisfied ordinal categorical data will be obtained (>2 categories) It is asked to investigate whether there is a significant change in patient satisfaction after the program. (collecting data 2 times of same group: 2 groups (dependent)

(Categorical data, more than two individual category)(ex-13) Chi-square test We want to examine whether any assosiation with marital status (married, single, divorced = more than two categories) and antidiabetic drug of choice (oral antidiabetics, insulin = two independent groups) in our sample of individuals.

( Categorical data, more than two categories, one group)(ex-14) Chi-square test (Jeneric) We want to examine whether there is a significant difference among the treatments used by individuals in our sample. (One group) The treatment is a categorical variable with 6 categories.

(Two numerical variables)(ex-15) Correlation analysis We suspect that as diabetes duration (x) gets longer HbA1c levels (y) are incresed also, in our diabetic patients. We want to look at whether a linear relationship between the duration of diabetes and Hba1c level.

The End