Text Report

1. INTRODUCTION

Each year in the United States, more than 228,000 people are diagnosed with lung cancer

and nearly 160,000 die of the disease. Lung cancer takes more lives than breast, prostate

and colon cancers combined which accounts for about 27% of all cancer deaths. The rate

of lung cancer death is higher among veterans than non-veterans. Smoking is a significant

problem in the U.S. military. The smoking rates of military personnel, particularly

deployed personnel, are 50% greater than in the civilian population, according to an

Institute of Medicine (IoM) report in 2009.Beyond smoking, other inhaled exposures are

elevated among military personnel including radon, asbestos, depleted uranium used in

weapons and armor shielding, beryllium, fuel exhaust, and other battlefield emissions.

Lung cancer is a disease in which the cells of the lung tissues grow uncontrollably

and form tumors. Lung cancer is classified into two major groups: non-small cell lung

cancer (“NSCLC”), and small cell lung cancer (“SCLC”). According to the American

Cancer Society, approximately 85-90 percent of lung cancer cases are NSCLC and it is

further divided into three tumor histologic subtypes: adenocarcinoma (40%), squamous

cell carcinoma (25%-30%) and large cell carcinoma (10%-15%) whilst SCLC consist of

only one subtype; small cell (10%-15%).

Different types of treatments are available for lung cancer patients. Some

treatments are standard (the currently used treatment), and some are being tested in

clinical trials. A clinical trial is a research study meant to help improve current treatments

or obtain information on new treatments for patients with cancer.

A patient’s performance status also dictates the type of treatment regime

employed. Performance status can be assessed using a variety of assessment including

Karnofsky Performance Status (“KPS”) scale. The KPS scores range from 0 to 100. A

higher score means the patient is better able to carry out daily activities.

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This report was mainly concerned with a study and analysis an estimation of the

survivorship time. The trial includes 137 advanced lung cancer patients collected by

Veterans Administration Lung Cancer Study Group (VALCSG). It focus on the

population of male veterans, in which patients aged 34 and above who have diagnosed

with advanced or inoperable lung cancer were treated with chemotherapy. Chemotherapy

is a treatment of cancer-killing drugs used to kill lung cancer cells. Patients were

randomized to one of the two chemotherapeutic agents; standard and test.The censored

time-to-event outcome is the lung cancer survival time ranging from 1 to 999 days. The

data set also contains the values of six other variables namely treatment type, tumor cell

type, performance status, months from diagnosis, age and prior therapy which were

recorded for each patient.

The objectives of this report are to identify important prognostic factors that

influence the survival of male patients with inoperable lung cancer among currently

living veterans. The primary purpose of the study was to compare patient survival for the

two treatment group i.e. patients who received standard treatment (the currently used

treatment) and test treatment (clinical trial). Besides that we want to study the main

covariates that influence the survival of lung cancer patients. The analysis is to determine

whether or not there exists a difference in types of tumor cells (adeno, squamous, large,

or smallcell), performance status (KPS score), patient’s age, and prior therapy attempted

have different survival rates. Furthermore we also want to look for the best model of any

variables that most influence the survival time of patients with inoperable lung cancer.

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2. LITERATURE REVIEW

This assignment presents a report of an investigation of lung cancer for two treatments of

standard and test with other factors affect. The study was about role of chemotherapy in

improving survival of patients with inoperable small lung cancer. Shajeem et al. (2003)

had taken the data of 82 patients with operable lung cancer which were treated under

Institute of Medical Education & Research in India for a period of January to June 2001.

There were divided into two groups based on treatment received which were

chemotherapy group and best supportive care group.

The survival was the main outcome of the study. Survival time was calculated

from the date of diagnosis. There were predictors of age, sex, histology, stage and

performance status (Karnofsky scale) were included to compare between two groups.

Method of Log-rank test was used for the differences between two groups and then

presented in a table. Kaplan Meier was used to represent in survival curve for all patients,

chemotherapy group and supportive care group from the trend of survival of patients

which is more patients were survived in chemotherapy group. The median survival for

chemotherapy group was higher as compared to supportive care group. There was

showed a significant improvement in survival in patients with inoperable lung cancer

with chemotherapy group as compared with best supportive care group.

A study of conducted by Schiller et al. (2002) to compare four chemotherapy

regimens for advanced non-small cell lung cancer with total of 1207 patients. The

patients were randomly assigned to a reference regimen of cisplatin and paclitaxel or

another three experimental regimes which were cisplatin and gemcitabine, cisplatin and

docetaxel or carboplatin and paclitaxel.

Survival was calculated from date of enrollment to date of death or date of patient

still alive. The variables that included in study were performance status, disease stage,

survival time and response of disease. Two-sided long rank test is used for comparison

between each survival distributions for three experimental regimens with control-

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reference regimen. All survival distributions were estimated by Kaplan-Meier method.

P-values are two-sided and were adjusted according to O’Brien-Fleming method. As a

conclusion, there was no significant difference in survival among four chemotherapy

regimens of advanced non-small cell lung cancer.

Leah et al.(2013) examined the association between race and receipt of timely

non-small cell lung cancer care and survival among Veteran Affairs health care system

patients. Patients with late stage disease which are stage 3 and 4 have five-year survival

rates of 2%-15%. Receipt of timely was stage of appropriate care for non-small cell lung

cancer patients can increase length of survival. There were total of 2200 patients which

were including African American and Caucasian patients. The variables were included

demographic characteristics (age at diagnosis, marital status, and geographic region) and

clinical factors (stage at diagnosis and performance status) associated with timeliness of

care.

The Kaplan-Meier method was used to estimate time-to-event curves for racial

distribution of key variables for stage at diagnosis, performance stage and age. Then, the

comparison between African American and Caucasian patients was measured by log-rank

and Wilcoxon tests. Multivariate Cox proportional hazard models were used to test the

association between race and time to event after controlling the variables. Veteran Affairs

health care system provides racially equitable care but future work need for longer

periods of follow-up in other veteran groups.

Lee (2012) had compared the survival rate in patients with non-small cell lung

cancer among elderly patients that treated with radiofrequency ablation, surgery, or

chemotherapy according to staging of lung cancer. There are 77 patients meeting criteria

which were between year 2000 and 2004 that approved by Institutional Ethics

Committee. The comparison for long-term survival of patients was compared between

radiofrequency ablation and other treatments such as surgery and chemotherapy.

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The demographic factors were age, sex, WHO performance status, clinical stage,

median of follow up interval and histology of cell. Chi-square test was used to compare

radiofrequency ablation group with comparison group for sex and age, WHO

performance status, clinical stage, follow-up interval, cell type and tumour size according

to staging of lung cancer. Deaths would be events of lung cancer. Kaplan Meier method

and log-rank test were used to calculate survival curves in patients treated with different

treatment and show differences between survival curves. Gehan-Breslow-Wilcoxon test

was used to make comparisons for different therapeutic modality (radiofrequency

ablation, chemotherapy and surgery). It could be concluded that radiofrequency ablation

can be used as an alternative treatment to surgery for elderly patients with stage I to II

inoperable cancer and therapy with chemotherapy was suitable for patients with stage III

to IV lung cancer.

The review of journal showed that Kaplan Meier method was used to show the

survival distributions with survival curve and median value for patients of lung cancer.

Log-rank test was then used for comparison between two different groups for different

study for lung cancer. Then, proportional hazard model was approach modeling for

survival data for different study to cure lung cancer. From the review of journal, the

survival time for the death as outcome of study which can be shown through Kaplan

Meier method by survival curve for different groups of treatment. Then, proportional

hazard model is built for other factors included.

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3. MATERIAL AND METHOD

3.1 Clinical Trial

This dataset is originated obtained from the book of The Statistical Analysis Of

Failure Time Data by J.D. Kalbfleisch and R.L. Prentice (1980) which construct a clinical

trial of inoperable lung cancer patients that taking almost 3 years. Usually for inoperable

or advanced lung cancer patient, there are two main treatment that can be used which is

chemotherapy and radiotherapy because these treatment can either reduce or stop the

cancer so that the symptoms of lung cancer will be reduced and prolong the patients’

lifespan. For this study, Kalbfleisch and Prentice (1980) focused on treatment based on

chemotherapy. The primary endpoint for therapy comparison was time to death.

There are 137 male patients that suffered from inoperable lung cancer were

taking into this clinical trial. Out of 137 male patients, there only 9 of them were

censored. They were assigned into two main treatment group which is one of them is

standard treatment (treatment 1). This treatment referred to patient that received

chemotherapy of any routine for lung cancer disease and actually was currently used

treatment. According to Curtis L. Meinert (2012), standard treatment is a treatment that

widely practiced and routinely applied for a specified disease. Meanwhile the other one

treatment is test treatment (treatment 2) which basically used for clinical trial which is

best known as new treatment. According to Pigeot et al.(2003) in Modern Clinical Trial

Analysis by Wan Tang and Xin Tu (2012), test treatment is actually treatment that used to

compare with standard treatment which is its efficacy is clinically relevant. The dataset

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shows that about 69 male patients within age (34-81 years old) were assigned in treatment

1 and treatment 2 consists of 68 male patients with age between (35 -81 years old).

There are six variables in this dataset that can be linked to the survival times of a

patient which is month from diagnosis to randomization., age in years ,

treatment that consists of standard and test treatment which had been explained above,

cell type that consists of 4 type (squamous, small, adeno and large), patients’ performance

score (Karnofsky Score) that had been randomized (10-30 completely hospitalized, 40-60

partial confinement, 70-90 able to care for self) and prior therapy (0 = no, 10 = yes).

Refer to Table 1 for brief explanation about Karnofsky Score. Adjustments were made for

several demographic and clinical factors, including age in years, Karnofsky Score and

time in months from diagnosis to randomization.

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Table 1 : Karnofsky Performance Status Scale Definitions Rating (%) Criteria.

(Source : Oxford Textbook of Palliative Medicine, Oxford University Press. 1993;109)

For the cell types, there are 2 major types of lung cancer which is small cell lung

cancer (SCLC) that consists of only one subtype of SCLC which is small cell

undifferentiated carcinoma and the other one is non-small cell lung cancer (NSCLC) that

consists of 3 subtypes of NSCLC which is adenocarcinoma, squamous cell carcinoma

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Score (%) Definition

0 Dead

10 Moribund; fatal processes progressing rapidly.

20 Very sick; hospital admission necessary; Active supportive treatment

necessary.

30 Severely disabled; hospital admission is indicated although death not

imminent.

40 Disabled; requires special care and assistance.

50 Requires considerable assistance and frequent medical care.

60 Requires occasional assistance, but is able to care for most of his personal

needs.

70 Cares for self; unable to carry on normal activity or to do active work

80 Normal activity with efforts; some signs or symptoms of disease.

90 Able to carry on normal activity; Minor signs or symptoms of disease

100 Normal no complaints; no evidence of disease

and large cell carcinoma. For this study, these two major types are included. The dataset

shows that 31 male patients from both treatment suffered from squamous cell (celltype 1),

45 of them suffered from small cell (celltype 2), 26 of them suffered from adeno cell

(celltype 3) and the balance of 26 patients suffered from large cell (celltype 4). The more

brief explanation will be shown in next chapter.

Most important variables in this clinical data trial is absolutely survival time of all

patients in days. The survival times started to record from the moment the patients

entered this study and end whenever the patients died or lost to follow up in the 3 years

this data was studied. As was mentioned above, there were 9 out of 137 male patients

were diagnosed as censored data which means either the patients were survived until the

end of study or lost to follow up.

3.2 Statistical Analysis

For the analysis data, survival time was measured since the beginning of diagnosis

until the date of death or follow up of a patient was occured. Since the censored data was

present when this study was conducted, the best method that will be used is

nonparametric method that consists of Kaplan-Meier method and Actuarial method. As

for this study, survival analysis was performed using the well-known Kaplan-Meier

method. The main reason was using this method is because it is applicable either to small,

moderate and large samples. Kaplan-Meier method also is based on survival times of an

individual which means the time of a patient starting from certain point until the

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occurence of the given event. Kaplan and Meier (1958) develop a product-limit (PL)

method of estimating the survivorship function.

Where is duration of study at point i, is the number of deaths up to the point i and

is number of individuals at risk just prior to . According to Kaplan and Meier (1958), if

a subject is last to followed up at time and then leaves the study for any reason, is

counted as their censoring time. Based on the assumptions, censored data can not be

tested to avoid bias in the data that as well reduces S.

Furthermore, according to Mantel, 1996; Peto & Peto, 1972, the most commonly

used for treatment differences with censored survival data is the log-rank test. The log-

rank test actually used to compare two or more group of survival times and also test the

null hypothesis of group that come from the same population. It is also known that if the

survival time t conditional on treatment assignment x follows the proportional hazards

model that was proposed by Cox (1972);

,

or equivalently to;

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Where denotes the conditional hazard rate of failing at time t given treatment

X=x for x = 0,1 respectively, the log-rank test is optimal for testing for treatment

difference, for example to test null hypothesis . For this clinical trial,

differences between two treatments were assessed by using log-rank test.

Even though this log-rank test was used to test the differences between two

treatment but does not mean that the other explanatory variables could be taken into

account also. So, for the other explanatory variables to be tested, cox proportional

hazards models were used. According to John Fox (2002), this model is semi parametric

because while the baseline hazard can take any form, the covariates enter the model

linearly. Besides that, cox proportional hazards model also used to identify association

between explanatory variables and survival times. Statistical significance that had been

considered to use in this study is at . All the statistical analysis were performed

by using MINITAB 16.0.

4. RESULT

A total of 137 patients who suffered from inoperable lung cancer were screened and 9

patients is lost to follow up. Among the rest, patients was divided into two group of

treatment ; standard teratment and test treatment and were follow up for a period of 3

years or until death. Out of these, for each treatment, 64 patiens received a standard

treatment and test treatment. Baseline characteristics of the two groups are shown in

Table 1.

Treatment

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Standard

Treatment 1 (n=64)

Test

Treatment 2 (n=64)

Celltype

1- Squamous 13 (41.9%) 18 (58.1%)

2- Smallcell 28 (62.1%) 17 (37.9%)

3- Adeno 9 (34.6%) 17 (65.4%)

4- Large 14 (53.9%) 12 (46.3%)

Age in Years ;mean (±SD) 57 ± 11 (54-60) 60 ± 10 (57-62)

Month from Diagnosis 8.9 ± 9.0 (6.9-11.1) 8.9 ± 12.5 (5.7-12.0)

Prior Therapy

Yes = 10 20 (54.1%) 17 (45.9%)

No = 0 44 (48.4%) 47 (51.6%)

Karnofsky Score 58.20 ± 18.68 56.88 ± 21.17

Table 2 : Characteristic of Patients

From Table 2, the patients with Squamous celltype was 13 patients (41.9%) and 18

patients (58.1%) for standard and test treatment respectively. For the Smallcell celltpe,

there was about 28 patients (62.1%) for standard treatment and 17 patients (37.9%) for

test treatment. Meanwhile, for patients with Adeno celltype, there was 9 and 17 patients

for standard and test treatment respectively. From the Large celltype, there was 14

patients and 12 patients for standard and test treatment respectively.

The mean (±SD) age for standard treatment is 57 years (range, 55-60), whereas for test

treatment is 60 years (range, 57-62). The standard and test treatment was conducted from

an average 8.9 months from the diagnosis with (range, 6.9-11.1). and (range, 5.7-12.0),

respectively. Among the patients, 20 patients for standard treatment and 17 patients for

test treatment has a prior therapy history.

Figure 1 below, shows the Kaplan-Meier survival curve of all patients. The overall

median survival when both groups were taken together was 80 days.

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10008006004002000

100

80

60

40

20

0

Survival in Days

Perc

ent

Mean 132.777Median 80IQR 137

Table of Statistics

Survival Plot for Survival in Days

Censoring Column in StatusKaplan-Meier Method

Figure 1 : Kaplan-Meier for both treatments

Figure 2 below, shows the median survival in the standard treatment group was 103 days

and in test treatment group, it was 52 days. The difference was not statistically significant

(p=0.928>0.05, log rank test). Both group were comparable for each celltype, age,

duration of diagnosis and previous treatment of the disease.

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10008006004002000

100

80

60

40

20

0

Survival in Days

Perc

ent

123.928 103 135142.061 52 116

Mean Median IQRTable of Statistics

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Treatment

Survival Plot for Survival in Days


Figure 2 : Kaplan Meier survival analysis by treatment groups

10008006004002000

100

80

60

40

20

0

Survival in Days (CI)

Perc

ent

136.790 110 102293.362 201 359


12

Treatment

Survival Plot for Survival in Days (CI)

Censoring Column in Status (C1)Kaplan-Meier Method

Figure 3 : Kaplan Meier survival analysis for Squamous (C1) vs treatment group (p=0.117)

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Figure 3 shows that the median survival for patients with Squamous celltype (C1) for

standard treatment group (T1) was 110 days and for test treatment group (T2) is 201

days. Survival difference between the two groups for Squamous celltype (C1) was

statistically not significant (p=0.117<0.05, log rank test).

4003002001000

100

80

60

40

20

0

Survival in Days C2

Perc

ent

99.2565 80 9539.0397 24 43


12

Treatment

Survival Plot for Survival in Days C2

Censoring Column in Status C2Kaplan-Meier Method

Figure 4 :Kaplan Meier survival analysis for Smallcell (C2) vs treatment group (p=0.006)

Meanwhile, from Figure 4, the median survival time was 80 days (T1) and 24 days(T2)

for Smallcell (C2) celltype. The survival difference between the two treatment groups

was statistically significant (p=0.006<0.05, log rank test).

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200150100500

100

80

60

40

20

0

Survival in Days C3

Perc

ent

72.8889 92 10562.0556 48 60


12

Treatment



Figure 5 : Kaplan Meier survival analysis for Adeno (C3) vs treatment group (p=0.288)

6005004003002001000

100

80

60

40

20

0

Survival in Days C4

Perc

ent

200.522 177 145132.333 53 121


12

Treatment



Figure 6 : Kaplan Meier survival analysis for Large (C4) vs treatment group (p=0.629)

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From Figure 5 and Figure 6, the median survival for the Adeno (C3) celltype, was 92

days and 48 days for standard treatment (T1) and test treatment (T2) respectively.

Finally, for the Large (C4) celltype, the median survival was 177 days (T1) and 53 days

(T2). Survival difference between the two groups was not statistically significant

(p=0.629>0.05, log rank test) and (p=0.288>0.05, log rank test) for Adeno (C3) and

Large (C4) celltype respectively.

6005004003002001000

100

80

60

40

20

0

Survival in Days 1

Perc

ent 127.966 100 182

105.914 72 126


≤60>60

YearsAge in

Survival Plot for Survival in Days T1

Censoring Column in Status 1Kaplan-Meier Method

Figure 7 : Kaplan Meier survival analysis for Age according to standard treatment (T1)

(p=0.610)

Figure 7 shows that, the median survival time for standard treatment (T1) patients with

age less and equal 60 years is 100 days. Meanwhile the median survival time for patients

with age more than to 60 years is 72 days. Survival difference between the age was

statistically not significant (p=0.610>0.05, log rank test).

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10008006004002000

100

80

60

40

20

0

Survival in Days 2

Perc

ent 172.080 51 166

100.308 53 86


≤60>60

YearsAge in



Figure 8 : Kaplan Meier survival analysis for Age according to test treatment (T2)

(p=0.412)

Meawhile for test treatment, Figure 8 shows that, the median survival time for patients

with age less and equal than 60 years is 51 days. Meanwhile the median survival time for

patients with age more than to 60 years is 53 days. Survival difference between the two

groups was statistically not significant (p=0.412>0.05, log rank test).

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6005004003002001000

100

80

60

40

20

0

Survival in Days 1

Perc

ent

32.889 18 10114.379 63 156146.346 117 70


10-3040-6070-90

ScoreKarnofsky



Figure 9 : Kaplan Meier survival analysis for KPS according to standard treatment (T1)

(p=0.001)

Patients were divided into 3 groups based on their Karnofsky performance score (KPS) ;

Group 1 (10-30), Group 2 (40-60), Group 3 (70-90). 9 patients belong to Group 1, 29

patients and 26 patients belong to Group 2 and 3 respectively. Figure 9 shows that,

according to standard treatment (T1), the median survival time for KPS (10-30) was 18

days, 63 days for KPS (40-60) and 117 days for KPS (70-90). Survival difference

between the KPS scores was statistically significant (p=0.001<0.05, log rank test).

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10008006004002000

100

80

60

40

20

0

Survival in days 2

Perc

ent

25.154 21 1873.480 45 61

232.692 133 256


10-3040-6070-90

ScoreKarnofsky

Survival Plot for Survival in days T2


Figure 10 : Kaplan Meier survival analysis for KPS according to test treatment (T2)

(p=0.000)

Meanwhile, for test treatment, the median survival time for KPS (10-30) was 21 days, 45

days for KPS (40-60) and 133 days for KPS (70-90). The Figure 10 also shows that the

survival difference between the KPS scores was statistically significant (p=0.000<0.05,

log rank test).

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6005004003002001000

100

80

60

40

20

0

Survival in Days

Perc

ent 120.591 95 121

105.600 56 141


NoYes

TherapyPrior



Figure 11 : Kaplan Meier survival analysis for Prior Therapy according to standard

treatment (p=0.700)

For standard treatment, the median survival time for patients with prior therapy is 56 days

(Figure 11). Meanwhile the median survival time for patients with no prior thearpy is 95

days. Survival difference between the two groups was statistically not significant

(p=0.700>0.05, log rank test).

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10008006004002000

100

80

60

40

20

0

Survival in Days 2

Perc

ent 104.277 52 86

194.882 51 182


NoYes

TherapyPrior



Figure 12 : Kaplan Meier survival analysis for Prior Therapy according to test treatment

(p=0.348)

Figure 12 shows that for test treatment, the median survival time for patients with prior

therapy is 51 days. Meanwhile the median survival time for patients with no prior thearpy

is 52 days. Survival difference between the two groups was statistically not significant

(p=0.348>0.05, log rank test).

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Regression with Life Data: S.D versus treat, C.T, K.S, M.D, Age, prior

Response Variable: S.D

Censoring Information CountUncensored value 128Right censored value 9

Censoring value: Status = 0

Estimation Method: Maximum Likelihood

Distribution: Exponential

Regression Table

Standard 95.0% Normal CIPredictor Coef Error Z P Lower UpperIntercept 3.14891 0.710360 4.43 0.000 1.75664 4.54119treat -0.184342 0.182451 -1.01 0.312 -0.541940 0.173257C.T -0.136463 0.0747321 -1.83 0.068 -0.282936 0.0100087K.S 0.0353214 0.0049785 7.09 0.000 0.0255636 0.0450792M.D -0.0029654 0.0089507 -0.33 0.740 -0.0205085 0.0145776Age 0.0010820 0.0090893 0.12 0.905 -0.0167327 0.0188968prior 0.0111529 0.0218412 0.51 0.610 -0.0316551 0.0539610Shape 1

Log-Likelihood = -724.014

Figure 13: Cox Proportional Hazard

Figure 13 above shows the result of testing the prognostic factor that influence the risk of death

in the VA lung cancer using Cox Proportional Hazard Model.

The proportional hazards regression model for the ith individual of male patients are then

Where the subscript i on an explanatory variables represents the value of that variable for ith

individual.

From the result that obtained in MINITAB above, it can be concluded that K.S referred to

Karnofsky Score has p-value (p=0.000) that have big influence on the hazard of death or survival

times of a patients. Based on this output also, we can performed our new model with the known

parameters obtained from the result.

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5. DISCUSSION

This study was planned to evaluate whether there is significant difference between

standard treatment (the currently used treatment) and test chemotherapy treatment

(clinical trial) for inoperable lung cancer and to study the factors involved.

The study first compared the patients who received standard treatment (the

currently used treatment) and test chemotherapy treatment (clinical trial). The main

outcome measure was survival. As to compare both treatments, Kaplan Meier survival

analysis has been done. For standard treatment, the median survival is 103 days while for

test chemotherapy treatment, the median survival is 52 days. Although there is a wide

difference between the median survival, the comparison (p=0.928>0.05, log rank test)

shows that there is no significant difference between them.

As we know, cancer does not have any perfect cure and the ending for the patients

is always death.The introduction of test chemotherapy treatment has given the hope of

prolonging survival in such patients. Since the treatments types show that there is no

significant difference, we assume that the standard treatment and test chemotherapy

treatment is equally likely the same.

However in order to further the study, we then test each of the treatment types and

compare the result within each factor that exist in this study which is cell type, month of

diagnosis, prior therapy and Karnofsky score. As we can see the result for cell types in

treatment 1 and treatment 2, C1 and C2 are significant. By using chemotherapy test

treatment for the celltype Squamous (C1) and Smallcell (C2) the result on prolonging the

survival period is increase. This is maybe because the cancer is still in a lower stage.

However for Adeno (C3) and Large (C4) they are not significant. Meaning that for

serious case as Adeno and Large, the chemotherapy does not affect the patients and result

as same as standard treatment.

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Based on the study, for the age factor, there is no significant different to show that

age plays an important role in determine the survival time. Cancer survivors basically

survive longer if they have higher spirit to survive. Age factor is not really the best factor

to compare as different patient may have their own inspirations. However, based on the

survival median there is a little difference compare to patients with age less than 60 and

age more than 60.

For standard treatment, patients with age less than 60 survive longer than patients

with age more than 60 while for test chemotherapy treatment, patients with age more than

60 survive longer than patients with age less than 60. Psychology of patients with age

more than 60 for standard treatment is probably down as they feel that they will have no

chance to live as compared to patients with age less than 60, while the psychology for the

patients with age more than 60 for test chemotherapy treatment is high as they believe on

the test chemotherapy treatment.

For Karnofsky performance score, results for treatment 1 and treatment 2 shows

that there is a significant different between the three groups. As we know, Karnofsky

score is used to measure the patients’ quality life. It also used to measure how many dose

of chemotherapy and how the treatment being perform to the patients. The result show

that, the higher the Karnofsky performance score, the higher survival rate. The evidence

assembled in the study indicates that the performance status base on the Karnofsky

performance score. As we can see the definition of the Karnofsky performance score 70

and above the patients able to carry on normal activity and able to work. There also no

need of special care. Doctor should focus on the patients with the lowest performance

score.

For prior therapy, the test shows that it is not statistically significant to conclude

that there is a difference between both prior and no prior therapy. This concludes that

either the patient went or not for the prior therapy the result are the still same. Prior

therapy means patient received therapy prior to the start of the study.

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The Cox Proportional Hazard Model show that, the Karnofsky Score has p-value

(p=0.000), it can be conclude that it have big influence on the hazard of death or survival

times of a patients. Basically, the one that use the Karnofsky score is doctor.

As chances of being survive are very important to patients, newer technology and

formula should be used to increase the performance of test chemotherapy treatment.

Stopping smoking can also reduce the risk of having a lung cancer. Therefore it is

important to take preventive measures against lung cancer such as campaign to avoid

smoking. Other than that, patient also should be advised to exercise most days in a week

and have a diet full of fruits and vegetables.

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6. CONCLUSION

With the Kaplan-Meier survival analysis procedure, we have examined the distribution of

time to effect for two different treatment groups. The comparison tests show that there is

nosignificant difference in survival times (p >0.05, log rank test) between standard and

test treatment.This study cannot prove that test treatment can provide better results that

the standard.

The attention was then focused on the evaluation of the prognostic effect of other

variables. The result for tumor cells types showed that there is a statistically significant

difference in survival times between standard and test treatment for small cell and

squamous cell. Both cell types seem to affect the relationship between treatments groups

with the occurance of lung cancer deaths. While foradeno cell and large cell, the

treatmentdifference (standard versus test) was shown to be statistically not significant.

Therefore both cell types seem to have no effect on the relationship between treatment

groups and the occurance of lung cancer deaths.

In the analysis for the age factor, the patient survival difference between two age

groups (less than or equal to 60 years old versus more than 60 years old) did not reach

statistical significance. Age was not associated with better survival forthe patients.

For Karnofsky Performance Status (“KPS”), survival difference between the

Karnofsky score 10-30, Karnofsky score 40-60 and Karnofsky score 70-90 is statistically

significant. In general, the higher the score performance status, the occurrence of lung

cancer deaths is getting smaller. However, there is no significant difference on the total

score Karnofsky Performance Status in the test group and the standard group.

Further, data on the patients who had received prior therapycompared with patient

without prior therapy does not differ significantly. The study suggests that the survival

time distribution may be the same for individuals with or without prior therapy.

27

The analysis proceeds to identify important prognostic factors and to model the

expected survival in days. By using the Cox Proportional Hazard Model, we are able to

assess the importance of various covariates that influence the risk of death in the Veterans

Administration lung cancer trial. From the Regression Table, we found that only

KarnofskyScore (p = 0.000) has an influence on the hazard of death. The model obtained

can be expressed mathematically as follows:

In conclusion, this analysis would indicate that patient survival does not differ

significantly between treatment groups after taking account of the prognostic effect of

other variables.The poor survival of these veterans is due to performance status (KPS

rating) and cell type, rather than to their treatment received. Karnofsky Performance

Status score has the most influence on the hazard of death or survival times of the

patients.

28

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