Clinical Research Designs
Define the following:
Predictor variable
Outcome variable
Dichotomous variable
Continuous variable
PART 2: (50 points)
Sample size calculations:
The research question is whether there is a difference in the efficacy of Study drug A and Study drug B (which
is the standard of care) for the treatment of type 2 diabetes. The investigator has planned a quadruple blind
randomized clinical trial to study whether the study drug A is effective in reducing glycosylated hemoglobin
(HbA1c) as compared to study drug B (standard of care ) in participants with Type 2 Diabetes after 12 weeks.
A previous study has reported the mean HbA1C level in subjects treated for diabetes is 8 % (% here is a unit of
measure) with a standard deviation of 2% (% here is a unit of measure). The investigator would like to be able
to detect a difference of 20% or more in the mean HbA1C levels between the two groups
Please note: HbA1c is measured and expressed in percentage as units and not as a percentage of
proportions.
Use a two-sided ⍺ = 0.05
And power 0.80.
Calculate the sample size (Answer points 1 – 6)
Hint: Use appropriate table (6A, 6B or 6C depending on the test you select) from the textbook.
Now, considering a 10% dropout rate what would be the increased sample size. (Answer point 7)
State in details:
The null hypothesis (5 points)
The alternative hypothesis (5 points)
Type of test to be used and justification for its use (10 points)
The information provided (5 points)
Steps for calculating the sample size (10 points)
Final answer (5 points)
Calculations for sample size considering the dropout rate (10 points)
PART 3: (30 points)
Using the Clinical Trial Workbook, 2nd edition – review the SWP Pharmaceutical protocol.
Is(are) the statistical test(s) used correct. Yes or no and justify.
Assume that the sample size provided in the protocol is a fixed sample size. Work backward and calculate the
detectable effect size. If you need power for your calculations use the power of 80%.
Sample Solution
Clinical Research Designs
Predictor variable is the name given to an independent variable used in regression analyses. It provides information on an associated dependent variable regarding a particular outcome. At the most fundamental level, predictor variables are variables that are linked with particular outcomes. An outcome variable is an event or metric that can be observed and measured in a valid fashion. Within applied statistics and research, outcomes variables can be categorical (non-parametric statistics), ordinal (non-parametric statistics), or continuous (parametric statistics). A dichotomous variable is one that takes on one of only two possible values when observed or measured. The value is most often a representation for a measured variable or an attribute. A continuous variable is one which can take on an uncountable set of values. For example, a variable over a non-empty range of the real numbers is continuous, if it can take on any value in that range.
