Preliminary analysis method

(HMRN)

-74-

3.5 Preliminary analysis method

In order to have a better understanding of the type of information contained in the HMRN database, a number of preliminary analyses were conducted. They were descriptive statistics, test of homogeneity, missing data analysis, and treatment pathway. The overview of preliminary analyses is illustrated in Figure 3.6, while the detailed analysis methods are discussed in the following sections.

3.5.1 Descriptive statistics

All statistical analyses were done by means of SAS/Stats program (SAS software program package, version 8.02, SAS Institute, Cary, NC)

a. Characteristics of study population

According to the information available in the HMRN database, the following demographic variables of the study population were analyzed: gender, age, diagnosis result, disease transformation, index of deprivation, mortality, and place of death.

Information such as gender, mortality, place of death, disease transformation, diagnosis and index of deprivation is categorical data. Therefore, the analyses were descriptive in percentages for these variables.

In the case of the ‘age’ variable, it was decided that the ratio scale should be applied, despite the fact that originally it was continuous data. The reason for this was that the age group was an important determinative factor for clinicians for deciding which was the optimal treatment to patients (Normally, old patients would have poorer response than

Figure 3.6 Overview of preliminary analyses of HMRN database

3.5 Preliminary analysis method (HMRN)

-75-

younger adult patients [154]). Therefore, the results presented by continuous scale (‘average age’) would not be helpful enough for portraying the study population.

Therefore, the analysis results were presented in age category format. The age data were categorized into three different age groups (based on the AML / APML treatment guideline [155, 156]), namely: less than 59, between 60 and 74, and over 75 year-old. The age of APML patients was divided into two categories: between 18 and 59, and over 60 year-old.

b. Types of treatment

Different patients undertook different treatments, and also most of patients received more than one treatment for AML / APML. To portray the type of treatments that AML / APML patients received, three analyses were conducted.

•Numbers of treatment

•Types of treatment by diagnosis (AML/APML)

Treatment type was an important piece of information for understanding what kind of treatment AML/APML a patient normally received. To reveal this information, all the treatment episodes that used for treating AML/APML were analyzed and presented in percentage. Since the diagnosis (AML / APML) also plays an important role in treatment decision making [155, 156], the analysis was further broken down into two parts by diagnosis.

•Types of primary induction treatment by diagnosis and age groups

Type of primary induction treatment has a strong connection with patient’s condition on their first diagnosis. Also, the types of primary induction treatment is considered to be related to the prognosis (such as remission rate) and mortality according to the results of many clinical reports [97, 155, 157]. To reveal this connection, the primary induction treatment type was further analyzed with patient’s character, including age and diagnosis.

c. The treatment duration

In the HMRN database, there were three different types of treatment duration that were crucial for treatment cost estimation: 1) ‘treatment time’ which is the duration of the delivery of medication. 2) ‘hospital stay’ which is the number of bed days a patient stays in the hospital for treatment, including treatment time and other bed days involving

3.5 Preliminary analysis method (HMRN)

-76-

relevant supportive care or complication treatments. 3) ‘Antibiotic days’: days of receiving antibiotics, which could represent actual expenses of pricey complication treatment. These three different types of treatment duration were presented by types of regimen / arms of clinical trial.

3.5.2 Missing data analysis (patterns of missing values)

Missing data are commonly observed in patient-oriented research and studies [158, 159].The HMRN database is not an exception, although the data were manually extracted by well trained research nurses.

Missing data could produce substantial biases in analysis and reduce the precision of the statistic results, if it is not handled carefully [160, 161]. On this ground, it is very important to test and make sure that these occurrences of missing data are random and not systematical before starting analyzing the data. This was especially important in the context of the current study, as the interest variables (treatment start and end dates) contained significant amount of missing data, and also because these variables were the crucial parameters for treatment cost estimation at later stages. In current study, a two-stage simple missing data analysis was conducted in order to check the pattern of missing data and whether the missing values occurred systematically.

a. Descriptive statistics

In order to describe the distribution of the missing and non-missing values in the interest variables (treatment start-date and end-date), descriptive statistic analysis was conducted and missing and non-missing values were presented in numbers.

b. Missing data analysis

To check whether the missing values occurred randomly or systematically and whether the missing data were influenced by any other variables, a simple logistic regression method was conducted [162, 163].

3.5 Preliminary analysis method (HMRN)

-77-

3.5.3 Treatment pathway

The tree diagram of the treatment pathway was an attempt to present the longitudinal history of main and relevant treatments, which took place not only within one hospital, but also within all the other relevant hospitals that were involved in the HMRN network.

It provided a holistic view of the entire patient’s treatment pathway. However, the treatment pathway could not be portrayed as straightforward as in other leukemia cases because of rapid progression of the disease that caused complicated treatment processes.

To make the tree diagram simple and easy to read, the treatment pathway was sorted and presented according to treatment start date. In order to do so a compromise had to be made, as information regarding treatment overlapping could be lost.

a. Purpose

•Reveal the treatments which AML patients were received in real world

Treatment pathway for two patients with the same illness could be entirely different depending on patients’ situation or physicians’ decisions. Therefore, the tree diagram allows the treatment pathway to be highly personalized. This made it a preferable way to calculate the ‘actual’ treatment cost, compared to the use of the clinical guidelines.

•Transcend the hospital boundaries

As the data were collected through the network, there were no hospital boundaries in this study. This allowed the collection of information from treatments of individuals in several different hospitals, rather than just form one hospital.

•Display a graphic representation of the pathway in which costs can be later linked to.

This is also known as ‘clinical process cost analysis’.

b. Method

The ‘treatment pathway’ was presented as a route that an AML/APML patient took from diagnosis, through treatments, to the completion of the treatments, follow-up or death. In this section, all the clinical information extracted from the database was plotted into a timeline. The events such as chemotherapy, clinical trial, palliative care, and transplantation were mapped to this timeline according to their treatment start date.

However, events such as supportive care and observation/follow-up were not included because they were not given with curative intent. To plot the treatment pathway tree, four steps were followed:

3.5 Preliminary analysis method (HMRN)

-78-

Step 1: Identifying patients as AML /APML and dividing them into groups by their age:

To plot the AML and APML treatment pathway, the patients were divided into 3 main groups by age, as different age groups of AML / APML patients are suited for different treatment options due to different treatment response rate and treatment tolerance. According to the same definitions of the age groups used to present the descriptive statistics (referred to 3.1.2), AML patients were divided into 3 groups: ≤59, ≥60 and≤74, ≥75, and APML patients were divided into 2 groups: ≥18 and≤59, ≥60

Step 2: Excluding the treatments that were unsuitable to be presented: After the regrouping of treatment types, two treatment types were removed, namely observation/follow-up and supportive care. This was because these two inerventions could make tree diagram plotting problematic as supportive cares were usually given along chemotherapy or other treatments, and observation, and observation were given far too frequently and regularly then any other major treatments.

Step 3: Presenting the main treatment information: Since it was impossible to show all the details in one tree diagram, only the main treatment types were shown in order to portray the whole range of the treatment activities given to a patient without compromising clarity. These treatment types were: chemotherapy, clinical trial, transplantation, palliative care, radiotherapy, and immunosuppressive care.

Step 4: Plot tree diagrams: After identifying and grouping the patients and treatments, all the treatment data were summarized graphically by diagnosis, patient age, and treatment type. Each patient was traced from the diagnosed date onward, to the last follow-up date or death. Patients who died were given a square mark in the end of the branch in the tree diagram. All the sequences of treatments were visualized as a linear timeline according to the chronology of the HMRN records.

However, overlapping treatments were not possible to be shown in this pathway tree diagram.