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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