In of the treatment. In this test, the initial

In this trial, there are various statistical techniques used to analyse
data in order to determine if result of these test were statistically
significant. Consequently, this allows us to identify if we can be confident
enough to apply the results to patient groups suffering from CLL and SLL
outside of this study.

 

Group
sequential testing provides us with a stopping criterion to evaluate whether we
accept or reject the null hypothesis at each interim stage with conclusive
evidence of the efficacy of the treatment. In this test, the initial sample
size is not fixed, which is useful as there are new cohorts of patients
entering the trial at different points. As there are multiple analysis to be
made, the p value needs to be altered for each analysis to avoid inflating Type
1 error. This allows trials to either be stopped early or allows patients transfer
into the control group. These decisions are made during interim analysis (Chen et al., 2017).

We Will Write a Custom Essay Specifically
For You For Only $13.90/page!


order now

 

The purpose of interim
analysis is to analyse data before the completion of the data collection occurs
in the study. This is useful to researchers as it is often the case that clinical trials
enroll patients in a staggered manner as it is continuous. As a result, they
are able to use the data that has been collected so far to assess the
difference between the treatment group and the placebo group. This in turn,
allows an early termination or changing of the study based on whether there are
beneficial or harmful factors identified post analysis (Chakraborty and Kumar, 2016).

 

The hazard ratio measures the
effect of an intervention on an outcome over a period of time. In this case the
hazard ratio was used to measure the effect of Ibrutinib group compared to the
treatment group and the outcome was progression free survival. The ratio can be
defined as Hazard in the Ibrutinib group/hazard in the placebo group. In the interim
analysis, the hazard ratio was 0.203 and this means that 20% of patients were
more likely to experience PFS compared to the placebo group (Sedgwick and Joekes, 2015).

 

Kaplan Meir method is used
when there are incomplete observations in cases where a patient discontinues or
if they joined the study later. Therefore, having shorter observation time and
so may or may not experience the event within the follow-up time. We cannot
exclude these subjects otherwise our sample size will be too small. (Kishore et al., 2010). Therefore,
Kaplan Meir method is used to estimate survival over time in spite of these
issues. At each interval, a survival probability was calculated. After the
first interval, 91% of the treatment group did not experience PFS. The two
curves can be compared to see if the results are statistically significant and
this is done by long Rank test. In the log rank test, we generate a chi-squared
value. The p-value was 0.0001 > 0.05, therefore there is a significant
difference between the survival times of both groups (Rich et al., 2010).

 

The exposure
adjusted incidence rate (EAIR) can be calculated by dividing the total number
of subjects experiencing a specific event by the time under which the patient
was exposed to risk (Liu et al., 2006) (Siddiqui, 2009). In relation to the
Helios trial, the EAIR can be applied to adverse events for example, major
haemorrhage can be used to see whether the treatment was effective or not over
different durations. The researchers found that 11 patients in the ibrutinib
group suffered from a haemorrhage following a median duration of 4.21 months
(EAIR= 2.61). In the placebo group, there were 5 patients who experienced the
same event in a median duration of 2.3 months (EAIR=2.17); thereby,
highlighting the higher incidence rate of a haemorrhage following ibrutinib
treatment.

 

In efficacy
analysis when there are issues with the compliance of treatment, the results
obtained from those who have followed protocols are analysed in order to
determine whether the experimental therapy is better suited than current/alternative
therapies. The trial also utilised intention-to-treat analysis as a basis for
the efficacy analysis. This is based upon the theory that all patients should
be put into randomly allocated groups regardless of which experimental group
they were initially assigned to or whether they the complete the treatment
(Sphweb.bumc.bu.edu, 2016). Due to compliance issues to the protocols the
efficacy analysis for ibrutinib was required. The sample for the analysis
excludes those who did not follow the regime, patients that were lost via
sample attrition and patients from the crossover group. However, an issue
arises due to the smaller sample size attained for the efficacy analysis. As a
result, the comparison drawn between the ibrutinib treatment and current therapy
is less representative when generalised to the population.

 

The Inverse
probability of censoring weighting (IPCW) is used to assess the clinical
benefit of the experimental and control groups. It attempts to reduce bias
caused by treatment change. It was used to ensure that the outcome of the study
was not affected by crossing over. For example, if a patient in the control
group was experiencing symptoms of relapse, then the patient was crossed over
to the treatment group (90 patients crossed over). This allowed for the
patient’s interests to be put above the studies interests. The patients that were
crossed over were weighted zero and those who remained in the placebo group
were given a higher weighting. It was assumed that there were no measures of
confounding variables and that randomisation was not preserved (Ascopubs.org,
2017)

 

The
Fisher Exact Test was used to analyse negative response to Minimal Residual
Disease (MRD). This allowed us to see whether there was an association between
experiencing negative MRD and receiving Ibrutinib. It uses contingency table to display the probability of different outcomes. The
rows were the outcome and the column is the exposure. The data in the contingency table was entered into the
SAS programmed and a p-value was generated (Biostathandbook.com,
2017).