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. 2018 May 5;18:2029–2046. doi: 10.1016/j.dib.2018.04.135

Statistical data analysis of cancer incidences in insurgency affected states in Nigeria

Patience I Adamu 1, Pelumi E Oguntunde 1,⁎, Hilary I Okagbue 1, Olasunmbo O Agboola 1
PMCID: PMC5998707  PMID: 29904711

Abstract

This article provides details about the various cancer types recorded in Northeastern states of Nigeria currently being affected by insurgency in Nigeria. The dataset was described and chi-square test was used to determine the dependency of the variables under consideration on each other. Also, linear, logarithmic, inverse, quadratic, cubic, power, growth, exponential and logistic regression models were fitted to the dataset to show the relationship between them.

Keywords: Cancer, Chi-square test of independence, Insurgency, Nigeria, Regression model, Statistics

Specifications Table

Subject area Medicine
More specific subject area Oncology, Public health, Biostatistics
Type of data Table and text file
How data was acquired Secondary data from University of Maiduguri Teaching Hospital.
Data format Raw and partially analyzed (Descriptive and Inferential)
Experimental factors Analysis of cancer incidences
Experimental features Observations on the age, gender and the topographical location of cancer on the body of affected patients
Data source location University of Maiduguri Teaching hospital, Maiduguri, Borno state, Northeast Nigeria.
Data accessibility All the data are available this article
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Value of the data

  • •

    The data is useful in the study of epidemiology of cancer in the affected areas.

  • •

    The data is an indication of the public health crisis in insurgency affected region in Nigeria.

  • •

    The data can be useful in cancer awareness, management and treatment.

  • •

    The data could be used in oncologic studies.

  • •

    The data can be used to test the performance of statistical models.

1. Data

The data set represents the age, gender and topological (Top) location of cancer on the body of cancer patients in the University of Maiduguri Teaching hospital located in Maiduguri, the capital of Borno state, Nigeria.

The teaching hospital is the only tertiary health care facility in the state and often serves the other northeast states like Yobe, Taraba, Adamawa, Bauchi and Gombe.

A total of 1671 patients were considered between the period of study and SPSS version 20 was used to perform the analysis. The dataset is available as Supplementary data while a brief summary of the data is presented in Table 1.

Table 1.

Brief summary of the data.

Statistics
Gender Age Top
N Valid 1670 1671 1671
Missing 1 0 0
Mean 1.53 50.06 37.59
Mode 2 60 5
Variance 0.249 281.086 816.431
Skewness −0.115 −0.258 0.241
Std. Error of Skewness 0.060 0.060 0.060
Kurtosis −1.989 −0.220 −1.149
Std. Error of Kurtosis 0.120 0.120 0.120
Minimum 1 3 1
Maximum 2 95 117
Sum 2553 83,658 62,806
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It was observed from Table 1 that information about the gender of a patient was not available, hence the missing data of 1.

The frequency distribution of the gender of the patients is presented in Table 2.

Table 2.

Frequency distribution of the patients’ gender.

Gender Frequency Percent Cumulative Percent
Valid Male 787 47.1 47.1
Female 883 52.8 100.0
Total 1670 99.9
Missing System 1 .1
Total 1671 100.0
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Remark:Table 2 indicates that there are more female patients with cancer diseases than males. This is represented in a pictorial form in Fig. 1.

The frequency distribution of the patients’ age is presented in Table 3.

Table 3.

Frequency distribution of the patient's age.

Age (years) Frequency Percent Cumulative Percent
3 6 0.4 0.4
4 5 0.3 0.7
5 1 0.1 0.7
6 5 0.3 1.0
7 5 0.3 1.3
8 2 0.1 1.4
9 1 0.1 1.5
10 2 0.1 1.6
12 4 0.2 1.9
14 4 0.2 2.1
15 8 0.5 2.6
16 6 0.4 2.9
17 4 0.2 3.2
18 9 0.5 3.7
19 6 0.4 4.1
20 15 0.9 5.0
22 9 0.5 5.5
23 12 0.7 6.2
24 11 0.7 6.9
25 17 1.0 7.9
26 11 0.7 8.6
27 15 0.9 9.5
28 19 1.1 10.6
29 7 0.4 11.0
30 51 3.1 14.1
31 6 0.4 14.4
32 22 1.3 15.7
33 7 0.4 16.2
34 10 0.6 16.8
35 74 4.4 21.2
36 16 1.0 22.1
37 15 0.9 23.0
38 27 1.6 24.7
39 13 0.8 25.4
40 94 5.6 31.1
41 13 0.8 31.8
42 18 1.1 32.9
43 15 0.9 33.8
44 11 0.7 34.5
45 74 4.4 38.9
46 18 1.1 40.0
47 13 0.8 40.8
48 32 1.9 42.7
49 11 0.7 43.3
50 134 8.0 51.3
51 12 0.7 52.1
52 23 1.4 53.4
53 19 1.1 54.6
54 23 1.4 56.0
55 94 5.6 61.6
56 26 1.6 63.1
57 18 1.1 64.2
58 19 1.1 65.4
59 7 0.4 65.8
60 161 9.6 75.4
61 9 0.5 75.9
62 13 0.8 76.7
63 9 0.5 77.3
64 8 0.5 77.7
65 82 4.9 82.6
66 6 0.4 83.0
67 10 0.6 83.6
68 16 1.0 84.6
69 2 0.1 84.7
70 128 7.7 92.3
71 5 0.3 92.6
72 8 0.5 93.1
73 4 0.2 93.4
74 3 0.2 93.5
75 26 1.6 95.1
76 5 0.3 95.4
77 5 0.3 95.7
78 6 0.4 96.1
79 2 0.1 96.2
80 36 2.2 98.3
81 1 0.1 98.4
82 1 0.1 98.4
83 1 0.1 98.5
84 2 0.1 98.6
85 13 0.8 99.4
86 2 0.1 99.5
90 6 0.4 99.9
93 1 0.1 99.9
95 1 0.1 100.0
Total 1671 100.0
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Remarks: From Table 3, the lowest age captured is 3 years old while the oldest patient is 95 years old. The cancer diseases affected both young and old but particularly, the age of the patients with highest number of cancer incidence is 60 years old. This information is represented in Fig. 2.

The various parts of the body affected by cancer incidences and the number of people affected (frequencies) are indicated in Table 4.

Table 4.

Parts of the body affected by the various types of cancer.

Topological (Top) location of cancer Frequency Percent Cumulative Percent
Valid C77.9 Lymph node, NOS 9 0.5 0.5
C26.9 Gastrointestinal tract, NOS 9 0.5 1.1
C20.9 Rectum, NOS 54 3.2 4.3
C44.9 Skin, NOS 47 2.8 7.1
C61.9 Prostate gland 253 15.1 22.3
C63.9 Male genital organs, NOS 1 0.1 22.3
C49.6 Soft tissues of trunk 5 0.3 22.6
C50.9 Breast, NOS 92 5.5 28.1
C77.3 Lymph nodes of axilla or arm 2 0.1 28.2
C57.9 Female genital tract, NOS 15 0.9 29.1
C53.9 Cervix uteri 76 4.5 33.7
C22.0 Liver 31 1.9 35.5
C77.0 Lymph nodes of head, face and 6 0.4 35.9
C40.9 Bone of limb, NOS 4 0.2 36.1
C53.8 Overl. lesion of cervix uteri 1 0.1 36.2
C49.2 Soft tissues of lower limb an 7 0.4 36.6
C49.9 Other soft tissues 18 1.1 37.7
C67.9 Urinary bladder, NOS 32 1.9 39.6
C56.9 Ovary 60 3.6 43.2
C40.2 Long bones of lower limb 1 0.1 43.3
C44.2 External ear 1 0.1 43.3
C49.0 Soft tissues of head, face, & 9 0.5 43.9
C44.7 Skin of lower limb and hip 6 0.4 44.2
C39.9 Ill-defined sites within resp 15 0.9 45.1
C49.1 Soft tissues of upper limb, s 4 0.2 45.4
C44.6 Skin of upper limb and shoulder 3 0.2 45.5
C19.9 Rectosigmoid junction 4 0.2 45.8
C64.9 Kidney, NOS 20 1.2 47.0
C40.8 Overl. lesion of bones of lim 1 0.1 47.0
C41.0 Bones of skull and face 2 0.1 47.2
C44.4 Skin of scalp and neck 6 0.4 47.5
C16.3 Gastric antrum 6 0.4 47.9
C18.0 Cecum 20 1.2 49.1
C16.9 Stomach, NOS 7 0.4 49.5
C49.5 Soft tissues of pelvis 3 0.2 49.7
C04.9 Floor of mouth, NOS 2 0.1 49.8
C73.9 Thyroid gland 14 0.8 50.6
C77.1 Intrathoracic lymph nodes 1 0.1 50.7
C52.9 Vagina, NOS 8 0.5 51.2
C10.2 Lateral wall of oropharynx 1 0.1 51.2
C44.5 Skin of trunk 2 0.1 51.3
C69.0 Conjunctiva 14 0.8 52.2
C21.8 Overl. lesion rectum, anal ca 9 0.5 52.7
C49.4 Soft tissues of abdomen 4 0.2 53.0
C18.4 Transverse colon 1 0.1 53.0
C41.9 Bone, NOS 1 0.1 53.1
C76.2 Abdomen, NOS 1 0.1 53.1
C76.5 Lower limb, NOS 1 0.1 53.2
C69.6 Orbit, NOS 1 0.1 53.3
C49.3 Soft tissues of thorax 3 0.2 53.4
C55.9 Uterus, NOS 30 1.8 55.2
C44.8 Overl. lesion of skin 1 0.1 55.3
C51.9 Vulva, NOS 1 0.1 55.4
C10.9 Oropharynx, NOS 2 0.1 55.5
C30.1 Middle ear 1 0.1 55.5
C62.9 Testis, NOS 2 0.1 55.7
C15.0 Cervical esophagus 12 0.7 56.4
C18.7 Sigmoid colon 1 0.1 56.4
C80.9 Unknown primary site 200 12.0 68.4
C77.2 Intra-abdominal lymph nodes 1 0.1 68.5
C11.9 Nasopharynx, NOS 3 0.2 68.6
C50.0 Nipple 168 10.1 78.7
C53.0 Endocervix 105 6.3 85.0
C53.1 Exocervix 1 0.1 85.0
C67.4 Posterior wall of urinary bla 8 0.5 85.5
C16.0 Cardia, NOS 33 2.0 87.5
C21.0 Anus, NOS 17 1.0 88.5
C51.0 Labium majus 3 0.2 88.7
C67.0 Trigone of urinary bladder 57 3.4 92.1
C44.0 Skin of lip, NOS 15 0.9 93.0
C11.0 Superior wall of nasopharynx 16 1.0 94.0
C08.0 Submandibular gland 3 0.2 94.1
C14.0 Pharynx, NOS 5 0.3 94.4
C26.0 Intestinal tract, NOS 7 0.4 94.9
C65.9 Renal pelvis 4 0.2 95.1
C10.0 Vallecula 6 0.4 95.5
C25.0 Head of pancreas 5 0.3 95.8
C60.0 Prepuce 4 0.2 96.0
C21.2 Cloacogenic zone 4 0.2 96.2
C18.6 Descending colon 1 0.1 96.3
C66.9 Ureter 1 0.1 96.3
C50.1 Central portion of breast 1 0.1 96.4
C34.0 Main bronchus 1 0.1 96.5
C21.1 Anal canal 3 0.2 96.6
C18.9 Colon, NOS 1 0.1 96.7
C01.9 Base of tongue, NOS 3 0.2 96.9
C62.0 Undescended testis 4 0.2 97.1
C11.2 Lateral wall of nasopharynx 1 0.1 97.2
C50.6 Axillary tail of breast 1 0.1 97.2
C54.1 Endometrium 2 0.1 97.4
C25.9 Pancreas, NOS 1 0.1 97.4
C30.0 Nasal cavity 1 0.1 97.5
C00.9 Lip, NOS 1 0.1 97.5
C54.2 Myometrium 1 0.1 97.6
C48.8 Overl. lesion of retroperiton 1 0.1 97.7
C76.7 Other ill-defined sites 1 0.1 97.7
C03.0 Upper gum 2 0.1 97.8
C15.9 Oesophagus, NOS 1 0.1 97.9
C69.9 Eye, NOS 1 0.1 98.0
C16.4 Pylorus 1 0.1 98.0
C07.9 Parotid gland 2 0.1 98.1
C67.5 Bladder neck 1 0.1 98.2
C57.4 Uterine adnexa 1 0.1 98.3
C16.2 Body of stomach 1 0.1 98.3
C13.0 Postcricoid region 7 0.4 98.7
C37.9 Thymus 1 0.1 98.8
C17.0 Duodenum 1 0.1 98.9
C06.0 Cheek mucosa 1 0.1 98.9
C04.0 Anterior floor of mouth 4 0.2 99.2
C47.0 Per. nerves & A.N.S. of head, 3 0.2 99.3
C09.0 Tonsillar fossa 2 0.1 99.5
C38.4 Pleura, NOS 1 0.1 99.5
C38.0 Heart 4 0.2 99.8
C67.1 Dome of urinary bladder 1 0.1 99.8
C22.1 Intrahepatic bile duct 1 0.1 99.9
C76.0 Head, face or neck, NOS 1 0.1 99.9
C23.9 Gallbladder 1 0.1 100.0
Total 1671 100.0
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Table 4 shows that the part of the body affected mostly is the prostate gland. This is represented graphically in Fig. 3.

Fig. 1.

Fig. 1

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Gender of the patients.

Fig. 2.

Fig. 2

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Age of the patients.

Fig. 3.

Fig. 3

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Diagrammatic presentation of the parts of the body affected by cancer.

2. Experimental design, materials and methods

The data set was obtained from the patients’ records at the data center of the University of Maiduguri teaching hospital. The hospital as stated earlier serves a large population from the six Northeastern states of Nigeria and beyond. The Northeastern region in particular and the entire northern region of the country is in variance with their natural endowments such as vast fertile lands, rivers and lakes for irrigation, mineral resources and abundant sunshine for renewable energy. The weak social structure of the region has resulted to excruciating poverty which often manifest as homelessness and destitution, insurgency, violence and crime [1]. The region has high poverty index, low human development index, lack of portable drinking water, electoral violence, dearth of medical personnel, high mortality, low life expectancy, decayed infrastructure and also an epicenter for joblessness, underage and teenage pregnancy, female genital mutilation, epidemics, illiteracy, malnutrition and now terrorism which comes in form of coordinated attacks on military, police formations and remote villages, guerrilla attacks, kidnappings, regicide, suicide bombings, mass killings, abduction of school girls, extra-judicial killings and summary execution, hypnotizing and forced conscriptions, indoctrination and forceful conversion to Islam and so on. The decadence is assumed to be as a result of corruption, tribalism, military intervention in governance, inequality, misappropriation, financial recklessness, bankrupt of ideas and dearth of developmental agendas, reduction of allocation of capital due to shortfalls of Nigeria revenue as a result of decline in crude oil price. Globally, efforts towards improving the healthcare and reducing the incidence of cancer have yielded desired results except in some developing countries. Hence, cancer related deaths remain stubbornly high in those countries. Cancer awareness, screening, prevention, management, treatment strategies are very low in the region/area studied in this article. Regrettably, capital allocations to the health sector are inadequate and the available funds are often allegedly diverted by corrupt government officials.

In addition, maternal death is one area that is currently affected by the Boko haram insurgency in that region as reported by [2]. Moreover, other areas have been seriously affected; for example; food security and dynamics, under five malnutrition, child mortality, escalation of cholera outbreaks, infections, sexually transmitted diseases, unsafe birth practices and abortion, child prostitution, sex for food at the displaced persons camps, increase in polio cases, See [3], [4], [5], [6], [7], [8] for details. Some related article can also be explored [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31].

Next, we analyze the dataset collected using Chi-square test of independence and curve estimation.

2.1. Chi-square test of independence

Chi-square test of independence was used to investigate the relationship between the location of the cancer (top), gender and age of patients.

2.1.1. Test of independency between “Top” and gender of the patients

Hypothesis Testing I:

H0: There is no significant association between the topological location of cancer and the gender of the patients.

Versus.

H1: There is a significant association between the topological location of cancer and the gender of the patients.

The result of the analysis is presented in Table 5.

Table 5.

Result of the chi-square test between gender and “Top”.

Chi-Square Tests Value df Asymp. Sig. (2-sided)
Pearson Chi-Square 928.735 116 0.000
Likelihood Ratio 1214.083 116 0.000
Linear-by-Linear Association 64.659 1 0.000
N of Valid Cases 1670
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Remarks: The null hypothesis (H0) is rejected since the p-value (0.000) is less than the level of significance (0.05). Therefore, it can be concluded that there is a significant association between the topological location of cancer and the gender of the patients.

The information about the correlation coefficient and its corresponding p-value is presented in Table 6.

Table 6.

Correlation coefficient.

Symmetric Measures Value Asymp. Std. Error Approx. T Approx. Sig.
Interval by Interval Pearson's R 0.197 0.024 8.199 0.000
Ordinal by Ordinal Spearman Correlation 0.253 0.024 10.661 0.000
N of Valid Cases 1670
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2.1.2. Test of independency between “Top” and age of the patients

Hypothesis Testing II:

H0: There is no significant association between topological location of cancer is not dependent on the age of the patients.

Versus.

H1: There is a significant association between topological location of cancer is dependent on the age of the patients.

The result of the analysis is presented in Table 7.

Table 7.

Result of the chi-square test between age and “Top”.

Chi-Square Tests Value Df Asymp. Sig. (2-sided)
Pearson Chi-Square 10762.735 9628 0.000
Likelihood Ratio 3148.516 9628 1.000
Linear-by-Linear Association 50.758 1 0.000
N of Valid Cases 1671
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Remarks: Since the p-value is also less than 0.05, we conclude that there is a significant association between the topological location of cancer and the age of the patients.

Information about the correlation coefficient and its corresponding p-value is presented in Table 8.

Table 8.

Correlation coefficient result.

Symmetric Measures Value Asymp. Std. Error Approx. T Approx. Sig.
Interval by Interval Pearson's R −.174 0.024 −7.233 0.000
Ordinal by Ordinal Spearman Correlation −.189 0.025 −7.881 0.000
N of Valid Cases 1671
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2.2. Curve estimation

Linear, logarithmic, inverse, quadratic, cubic, power, growth, exponential and logistic regression models were fitted to the dataset. “Top” is the dependent variable while Age is the independent variable. The summary of the variables used is presented in Table 9.

Table 9.

Summary of the variables.

Variable Processing Summary Variables
Dependent Independent
Top Age
Number of Positive Values 1671 1671
Number of Zeros 0 0
Number of Negative Values 0 0
Number of Missing Values User-Missing 0 0
System-Missing 0 0
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2.2.1. Simple linear regression

The summary of the simple linear regression model is presented in Table 10.

Table 10.

Model summary.

R R Square Adjusted R Square Std. Error of the Estimate
0.174 0.030 0.030 28.144
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The independent variable is Age.

The corresponding analysis of variance (ANOVA) table testing for the fitness of the model is presented in Table 11.

Table 11.

ANOVA table for the linear model.

ANOVA
Sum of Squares df Mean Square F Sig.
Regression 41440.679 1 41440.679 52.318 0.000
Residual 1321998.748 1669 792.090
Total 1363439.427 1670
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The independent variable is Age.

The linear regression model is significant at 0.05 level of significance and with R-square value of 3%.

2.2.2. Logarithmic model

The summary of the logarithmic model is presented in Table 12.

Table 12.

Model summary for the logarithmic model.

R R Square Adjusted R Square Std. Error of the Estimate
0.130 0.017 0.016 28.340
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The independent variable is Age.

Estimating the model parameter gives the result in Table 13.

Table 13.

Parameter estimation for the logarithmic model.

Coefficients Unstandardized Coefficients
 Standardized Coefficients T Sig.
B Std. Error Beta
ln(Age) −8.130 1.520 −0.130 −5.349 0.000
(Constant) 68.755 5.869 11.716 0.000
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The ANOVA table for the logarithmic model is presented in Table 14.

Table 14.

ANOVA table for the logarithmic model.

ANOVA
Sum of Squares df Mean Square F Sig.
Regression 22977.216 1 22977.216 28.609 0.000
Residual 1340462.210 1669 803.153
Total 1363439.427 1670
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The independent variable is Age.

The logarithmic model is significant at 0.05 level of significance and with R-square value of 1.7%.

2.2.3. Inverse model

The summary of the inverse model is presented in Table 15.

Table 15.

Summary of the inverse model.

R R Square Adjusted R Square Std. Error of the Estimate
0.047 0.002 0.002 28.550
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The independent variable is age.

The result for the estimation of parameters using the inverse model is presented in Table 16.

Table 16.

Parameter estimation using inverse model.

Coefficients Unstandardized Coefficients
 Standardized Coefficients t Sig.
B Std. Error Beta
1/Age 49.544 25.664 0.047 1.930 0.054
(Constant) 36.327 0.956 38.018 0.000
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The corresponding ANOVA table is presented in Table 17.

Table 17.

The ANOVA table for the inverse model.

ANOVA
Sum of Squares df Mean Square F Sig.
Regression 3037.719 1 3037.719 3.727 0.054
Residual 1360401.707 1669 815.100
Total 1363439.427 1670
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The independent variable is age.

The inverse model is not significant as its p-value is greater than the level of significance (0.05).

2.2.4. Quadratic model

The summary for the quadratic model is presented in Table 18.

Table 18.

Summary for the quadratic model.

R R Square Adjusted R Square Std. Error of the Estimate
0.195 0.038 0.037 28.043
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The independent variable is age.

The result for the estimation of parameter using the quadratic model is presented in Table 19.

Table 19.

Parameter estimation for the quadratic model.

Coefficients Unstandardized Coefficients
 Standardized Coefficients t Sig.
B Std. Error Beta
Age 0.348 0.183 0.204 1.897 0.058
Age ** 2 −0.007 0.002 −0.388 −3.607 0.000
(Constant) 38.929 4.329 8.992 0.000
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The corresponding ANOVA table is presented in Table 20.

Table 20.

ANOVA table for the quadratic model.

ANOVA
Sum of Squares df Mean Square F Sig.
Regression 51674.289 2 25837.144 32.854 0.000
Residual 1311765.138 1668 786.430
Total 1363439.427 1670
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The independent variable is age.

The quadratic model is significant at 0.05 level of significance and with R-square value of 3.8%.

2.2.5. Cubic model

The summary for the cubic model is presented in Table 21.

Table 21.

Summary for the cubic model.

R R Square Adjusted R Square Std. Error of the Estimate
0.197 0.039 0.037 28.036
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The independent variable is age.

The result for the estimation of parameter for the cubic model is presented in Table 22.

Table 22.

Parameter estimation for the cubic model.

Coefficients Unstandardized Coefficients
 Standardized Coefficients t Sig.
B Std. Error Beta
Age 0.951 0.477 0.558 1.993 0.046
Age ** 2 −0.021 0.011 −1.230 −1.970 0.049
Age ** 3 0.000 0.000 0.504 1.369 0.171
(Constant) 32.108 6.601 4.864 0.000
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The corresponding ANOVA table is presented in Table 23.

Table 23.

ANOVA table for the cubic model.

ANOVA
Sum of Squares df Mean Square F Sig.
Regression 53146.668 3 17715.556 22.538 0.000
Residual 1310292.759 1667 786.018
Total 1363439.427 1670
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The independent variable is age.

The cubic model is significant and with R-square value of 3.9%.

2.2.6. Power model

The summary for the power model is presented in Table 24.

Table 24.

Summary for the power model.

R R Square Adjusted R Square Std. Error of the Estimate
0.159 0.025 0.025 1.125
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The independent variable is age.

The result for the estimation of parameter for the power model is presented in Table 25.

Table 25.

Parameter estimation for the power model.

Coefficients Unstandardized Coefficients
 Standardized Coefficients t Sig.
B Std. Error Beta
ln(Age) −0.397 0.060 −0.159 −6.583 0.000
(Constant) 105.955 24.692 4.291 0.000
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The dependent variable is ln(Top).

The corresponding ANOVA table is presented in Table 26.

Table 26.

ANOVA table for the power model.

ANOVA
Sum of Squares df Mean Square F Sig.
Regression 54.875 1 54.875 43.330 0.000
Residual 2113.710 1669 1.266
Total 2168.585 1670
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The independent variable is age.

The power model is significant at 0.05 level of significance and with R-square value of 2.5%.

2.2.7. Growth model

The model summary for the growth model is presented in Table 27.

Table 27.

Summary for the growth model.

R R Square Adjusted R Square Std. Error of the Estimate
0.216 0.047 0.046 1.113
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The independent variable is age.

The result for the estimation of parameter of the growth model is presented in Table 28.

Table 28.

Parameter estimation for the growth model.

Coefficients Unstandardized Coefficients
 Standardized Coefficients t Sig.
B Std. Error Beta
Age −0.015 0.002 −0.216 −9.038 0.000
(Constant) 3.875 0.086 45.180 0.000
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The dependent variable is ln(Top).

The corresponding ANOVA table is presented in Table 29.

Table 29.

ANOVA table for the growth model.

ANOVA
Sum of Squares df Mean Square F Sig.
Regression 101.181 1 101.181 81.683 0.000
Residual 2067.404 1669 1.239
Total 2168.585 1670
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The independent variable is age.

The growth model is significant at 0.05 level of significance and with R-square value of 4.7%.

2.2.8. Exponential model

The model summary for the exponential model is presented in Table 30.

Table 30.

Summary for the exponential model.

R R Square Adjusted R Square Std. Error of the Estimate
0.216 0.047 0.046 1.113
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The independent variable is age.

The result for the estimation of parameter for the exponential model is presented in Table 31.

Table 31.

Parameter estimation for the exponential model.

Coefficients Unstandardized Coefficients
 Standardized Coefficients t Sig.
B Std. Error Beta
Age −0.015 0.002 −0.216 −9.038 0.000
(Constant) 48.173 4.132 11.660 0.000
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The dependent variable is ln(Top).

The corresponding ANOVA table is presented in Table 32.

Table 32.

ANOVA table for the exponential model.

ANOVA
Sum of Squares df Mean Square F Sig.
Regression 101.181 1 101.181 81.683 0.000
Residual 2067.404 1669 1.239
Total 2168.585 1670
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The independent variable is age.

The exponential model is significant at 0.05 level of significance and with R-square value of 4.7%.

2.2.9. Logistic model

The model summary for the logistic model is presented in Table 33.

Table 33.

Summary for the logistic model.

R R Square Adjusted R Square Std. Error of the Estimate
0.216 0.047 0.046 1.113
Open in a new tab

The independent variable is age.

The estimation of parameters for the logistic model is presented in Table 34.

Table 34.

Parameter estimation for the logistic model.

Coefficients Unstandardized Coefficients
 Standardized Coefficients t Sig.
B Std. Error Beta
Age 1.015 0.002 1.241 615.592 0.000
(Constant) 0.021 0.002 11.660 0.000
Open in a new tab

The dependent variable is ln(1 / Top).

The corresponding ANOVA table is presented in Table 35.

Table 35.

ANOVA table for the logistic model.

ANOVA
Sum of Squares df Mean Square F Sig.
Regression 101.181 1 101.181 81.683 0.000
Residual 2067.404 1669 1.239
Total 2168.585 1670
Open in a new tab

The independent variable is age.

The logistic model is also significant at 0.05 level of significance and with R-square value of 4.7%.

Lastly, all the fitted models are illustrated in Fig. 4.

Fig. 4.

Fig. 4

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The fitted model with respect to the data set.

Important points

  • •

    More females are infected with cancer than men.

  • •

    The age with the highest record (or incidence) of cancer is 60 years old.

  • •

    The part of the body that is mostly affected by cancer is the prostate gland (based on the data set collected).

  • •

    There is a significant association between the topological location of cancer and the gender of the patients.

  • •

    There is a significant association between the topological location of cancer and the age of the patients.

  • •

    All the models fitted to the data produced low R-square values; nevertheless, the models that best fit the data based on their R-square values are growth model, exponential model and logistic model.

Acknowledgement

The authors are grateful to the reviewers and to Covenant University for providing an enabling environment for this research.

Footnotes

Transparency document

Transparency data associated with this article can be found in the online version at doi:10.1016/j.dib.2018.04.135.

Appendix A

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.dib.2018.04.135.

Contributor Information

Patience I. Adamu, Email: peluemman@yahoo.com.

Pelumi E. Oguntunde, Email: pelumi.oguntunde@covenantuniversity.edu.ng.

Transparency document. Supplementary material

Supplementary material

mmc1.pdf (118.9KB, pdf)

Appendix A. Supplementary material

Supplementary material

mmc2.xlsx (128.6KB, xlsx)

.

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