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CUSTOMER_CODE SMUDE DIVISION_CODE SMUDE EVENT_CODE JAN2016 ASSESSMENT_CODE MCR202_JAN2016 QUESTION_TYPE DESCRIPTIVE_QUESTION QUESTION_ID 46221 QUESTION_TEXT Name and explain the different terms associated to sample theory? SCHEME OF EVALUATION Population: The collection of all persons, things or measurement values that are of interest to the researcher is called population Sample: it is a subset of study population Sampling: it is the method of selecting a sample Sampling units: The elementary units to be considered for sampling are called sampling units Sampling frame: The complete list of all sampling units in the study of populationis called sampliong frame Sampling fraction: The proportion of sampling units to be selected from a specified sampling fgramefor inclusion in the sample is called sampling fraction. Parameter: A descriptive index whose values refer to the population at large is called a parameter. Statistic: A descriptive index whose values refer to a sample is called a statistic Sampling error The difference that occurs purely due to chance between the value of a sample statistic and the corresponding population parameter. QUESTION_TYPE DESCRIPTIVE_QUESTION QUESTION_ID 46222 QUESTION_TEXT List down the different sources of multiplicity? SCHEME OF EVALUATION 1. A trial may have multiple treatment groups, hence multiple comparisons between each groups result in multiplicity 2. A trial may have multiple time points of observation and perform tests of significance at each time point. 3. A trial may have multiple looks at the data during sequential interim analysis. Sometimes it is desirable to analyze the data periodically, primarily for ethical reasons. Also in early phase trials interim analysis may be desired to make critical decisions timely. 4. Analyzing data in various predefined subgroups of interest QUESTION_TYPE DESCRIPTIVE_QUESTION QUESTION_ID 125818 QUESTION_TEXT List out any ten properties of normal distribution. The curve is bell shaped and symmetrical about the line x= μ Mean , median and mode of the distribution coincide Since mean and median coincide, the normal distribution is symmetric about the mean. 4. Since mean and mode coincide, the maximum peak of the curve observed at x= μ 5. As x increases numerically, f(x) decreases rapidly, the maximum probability occurring at that point x= μ 6. Since f(x) being the probability, can never be negative, no portion of the curve lies below the x-axis. 7. Linear combination of independent normal variables is also a normal variable. 8. Approximately 68% of distribution is within one standard deviation of the mean. 9. Approximately 95.5% of distribution is within two standard deviation of the mean. 10. Approximately 99.7% of distribution is within three standard deviation of the mean, 11. the mean μ and the standard deviation σ are the only two parameters for a normal distribution. 1. 2. 3. SCHEME OF EVALUATION QUESTION_TYPE DESCRIPTIVE_QUESTION QUESTION_ID 125820 a. QUESTION_TEXT What is survival analysis? b. What do you mean by meta analysis? What are the purposes of Meta analysis in clinical research? a. Survival analysis deals with analysis of data emanating from reallife problems where the response variable is the length of time taken to reach certain end point. SCHEME OF EVALUATION b. Meta analysis is the process of formally combining the quantitative results of separate studies in order to increase the statistical precision of estimated effects. Purposes: 1. To provide a more precise estimate of the overall treatment effects. 2. To evaluate whether overall positive results are also seen in prespecified subgroups of patients. 3. To evaluate an additional efficacy outcome that requires more power than the individual trials can provide. 4. To evaluate safety in a subgroup of patients , or a rare adverse event in all patients. 5. To improve the estimations of the dose-response relationship. 6. To evaluate apparent conflicting study results. QUESTION_TYPE DESCRIPTIVE_QUESTION QUESTION_ID 125821 QUESTION_TEXT a. Write down the advantages and disadvantages of non-parametric tests over parametric tests. b. What are the assumptions for simple linear regression? Advantages: 1. Non parametric tests allow for the testing of hypothesis that are not statements about population parameters unlike parametric tests. 2. Non-parametric tests can be used when the form of the sampled population is unknown but parametric tests depend on the distribution of the study population. SCHEME OF EVALUATION 3. Non-parametric tests can be applied when the data being analysed consist merely of ranking or variables measured in nominal scale. Disadvantages: 1. The use of nonparametric procedures with the data that can be handled with a parametric procedure results in the waste of data. 2. Non-parametric tests are of less power compared to parametric tests. Assumptions: 1. The sample must be representative of the population to which the inference will be made. 2. The dependent variable y must have a nominal distribution, that is the distribution of scores must have approximate the normal curve. 3. For every value of x, the distribution of y scores must have approximately equal variability. 4. The relationship between x and y must be linear. 5. The random error term ε is assumed to have mean zero and constant variance. QUESTION_TYPE DESCRIPTIVE_QUESTION QUESTION_ID 125823 QUESTION_TEXT What do you mean by blinding? Briefly explain different types of blinding? Treatment blinding or masking is an effective way to increase the objectivity of the persons observing experimental outcomes. SCHEME OF EVALUATION a. Open label b. Single blind c. Double blind d. Triple blinding