Biostatistics. Advantages of a randomized controlled trial over a Essay

Biostatistics. Advantages of a randomized controlled trial over a trial with systematic allocation - Essay Example Random controlled trials are experimental set ups with treatment and control groups, and elements of a study’s sample space have an equal probability of selection and allocation to either the treatment of control group. Systematic sampling however follows a defined approach with unequal probability for each sample space elements. One of the advantages of randomized control trials over systematic allocation is its ability to eliminate bias. Researchers and participants in randomized controlled trials lack influence of sample selection and allocation to either of the experimental groups and this eliminates chances of sampling bias. Eliminated bias in the randomized trials has significant effects on research processes such as enhanced reliability and validity which systematic allocation into biased sample may compromise (Miller, Strang and Miller 2010, p. 38). Randomized controlled trials also offer higher probability of homogeneity of background information on research participants within control and treatment groups as compared to systematic allocation that may have a set of background characteristics in one group and generate another group with different background characteristics. Such variations may result from bias, challenge comparability of observations from a study’s group, and identifies systematic allocations with validity and reliability challenges. Randomized controlled trials also have the advantage of probability sampling basis, which offers validity to data analysis, over the non-probability based systematic sampling (Friedman, Furberg and DeMets 2010, p. 71-72). Lack of a standard approach to systematic allocation also identifies benefit of randomized trials because variation in sampling criteria may be formulated to achieve bias and periodic sampling is an example (Miller, Strang and Miller 2010, p. 38). Variable data type and justification Number of adverse events Number of adverse events is a quantitative variable because it assumes numeric values. It can further be classified as discrete quantitative variable because it can only assume positive whole numbers (Weiers 2010, p. 8). Air blast sensitivity Air blast sensitivity, based on applied scale in the study, is a quantitative variable because of its numeric value on an ordinal scale that further classifies it as a discrete quantitative variable (Weiers 2010, p. 8; Neuhaus et al. 2013, p. 352). Tactile hypersensitivity Tactile hypersensitivity, based on the research, is a qualitative variable because it lacks numeric values. Its values are either yes or no and are further subjective, sub ject to study participants’ judgment (Weiers 2010, p. 8; Neuhaus et al. 2013, p. 352). Age Age is a quantitative variable because it assumes a numeric values and can further be categorized as a continuous quantitative variable because of its scope that can assume unlimited range of non-negative real numbers (Weiers 2010, p. 8). Gender Gender is a qualitative variable because it lacks numerical value (Weiers 2010, p. 8). Question 2 Graphical summary of the mean tactile sensitivity and variability The table bellow summarizes the mean and standard, for variability, for the tactile sensitivity values across the three groups at the end of 28 days. Table 1: Mean and standard deviation after 28 days Treatment Mean Standard deviation test A 21.48 11.86 Test B 20.58 11.32 Control 11.77 5.95 This data can be presented in graphical form as follows. Graph 1: Graph of mean and standard deviation after 28 days Interpretation of the sample mean and standard deviation of each group Mean of t est group A indicates that each of the group subjects is expected to have an average score of 21.48. The group’s standard deviation, 11.48, defines the mean deviation of each of the group’s scores from the group’s mean. Mean for the test group B shows that members reported an average score of 20.58 and the standard deviation