Study design and benefits
The following questions refers to the data from the paper by Liu S, Manson EJ, Stampfer MJ, Hu FB et al. A prospective study of whole-grain intake and risk of type II diabetes Mellitus in US Women. American Journal of Public Health, 2000; 90:9. The paper is located in the assignment 2 folder in vUWS
(a) What was the study design and what are the advantages of using this design? [2 mark]
This was a prospective study design because it aimed at evaluating some outcomes within a specified period of time which started in the year 1976 to 1976 with questionnaire-based evaluations among the study participants (nurses) being done in between the years at various intervals (Liu et al., 2018). The findings in prospective study design makes relations to other factors like the suspected risks (Burón et al., 2017)
An advantage of this design is that it has few possibilities of bias and confounders (Du, 2016).
(b) Compute the crude incidence rates of diabetes among women [4 marks]
Crude incidence= new case/ at risk population multiplied by 100,000
1879/ 75,521 multiplied by 100,000
= 2488.05
(c) Table 2 presents the adjusted relative risk of type II diabetes according to quintiles of Total Grain consumption (first 5 lines) relative to the 1st quintile (the lowest consumption as a reference). Calculate the unadjusted (crude, quintile –specific) incidence rate and the crude relative risk (RR) of each quintile in comparison to the first quintile. Comment in your own words whether any pattern of association can be observed from the unadjusted crude RR [4 marks]
Q1 392/144698 * 100,000 = 270.90
Q2 356/144403 *100,000 = 246.53
Q3 368/144438*100000 = 254.78
Q4 358/144471* 100000 = 247.80
Q 5 405/144409 *100000 = 280.45
(d) In Table 2 model 2 (multivariable adjustment) of Whole Grain showed the RR of each quintile (increased consumption) relative to the lowest 1st.
What is the pattern of the association? [2 marks]
The pattern of the RR was on a decreasing trend from the first to the fifth quantile. This means that as the consumption of whole grain increased, the risk for the associated disease among the study participants decreased.
(e) Why the authors adjusted their analysis for physical activity, smoking alcohol intake and family history. You need to back up your argument with evidence from the data presented in Table 1. [4 marks]
The data for various parameters were adjusted in order to prevent the effects of confounding variables. These variables have effects to the health condition being investigated in that they are predisposing factors (Ericsson et al., 2017). Therefore, if the values for these variables were too wide apart, then data analysis would not be good enough for this study. This is the reason as to why the data collected for smoking habit as indicated in Table 1 was in the range of 35 to 16.
Crude incidence rates of diabetes among women
(f) What is the main possible bias in this study? Provide arguments for your answer and how it will affect the estimates. [4 marks]
The main source of bias in this study could be sampling bias (Frund et al., 2016). This is because, while the article indicates the sample sizes and proportions of the gender during sampling, there is no clear evidence concerning how the exact sample size was finally arrived at (Rocha et al., 2017). There is also a possibility of loss to follow-up bias bearing in mind that this research was carried out for so long. The publication bias is also possible because no negative incidence possibly encountered in this study have been mentioned.
Question 2: (20 points in total)
A case control study was conducted to investigate the relationship between history of sexual abuse during childhood and mental illness during adolescent. Sixty-three male adolescents who were recently diagnosed with mental illness and 158 controls were enrolled to the study. A history of sexual abuse was identified in 9 controls. The prevalence of sexual abuse among cases was 3.9 –fold when compared to control.
- a) Build a table to summarise the data above [hint; you need to calculate prevalence of child abuse in the control (as representing the health population) before correctly inserting the numbers in each cell)[4 points]
|
Mental illness |
|||
|
Sexual abuse |
Yes |
No |
|
|
Yes |
9 |
149 |
158 |
|
No |
36 |
27 |
63 |
- b) Calculate the odds ratio of exposure to child abuse and risk of mental illness and explain in words the meaning of what you found [4 points].
Odds ratio = 9/36 divided by 149/27
= 0.25/ 5.51
= 045
This means that there are less odds associated with exposure to sexual risks and development of mental illnesses.
- c) What can possibly bias this estimate, explain how it will affect our measure of association- try to think of more than one possibility [4 points]
This estimate could be affected by the possibility of some controls having not correctly reported their history of sexual abuse. Since this is a matter which is quite confidential, some participants might fail to disclose their sexual life hence cause a sampling bias (Boria et al., 2014). Additionally, communicating with the subjects with mental illnesses might not be clear enough and hence some data may be lost from sampling bias. Additionally, there is a possibility of loss to follow up bias, affecting the overall research findings.
- d) What is the attributable risk fraction (%) of exposure to sexual abuse among adolescents with mental illness and explain it in your own words [4points]
5.51-0.25/ 5.51
= 5.26/5.51
= 0.954
- e) Calculated the population attributable risk of child abuse on adolescents’ mental illness and explain its meaning in your own words [4 points]
(63+ 9)/ (63+158)
72/221
= 0.326
Question 3 (12 points)
In a small cohort study investigating the effect of a rare exposure (E), the following results were found:
Table 1 Disease
|
Yes |
No |
|
|
Yes |
60 |
180 |
|
No |
60 |
180 |
Exposure
- Is there an association between exposure and disease? Show the way you reached the conclusion. [4 points]
The association of a disease to a given exposure can be determined by calculating the relative risks. The relative risk gives the strength of the association between exposure and disease (Binder & Schumacher, 2014).
However, the absolute risks ratios need to be determined:
Disease 60/ (60+180)
=60/240=
=0.25
No disease= 60/ (60+180)
=180/ 240
= 0.25
Relative risk = 0.25/ 0.25
Analysis of adjusted relative risk of type II diabetes
= 1
Since the RR is less equals to 1, it means that the exposed and unexposed groups have the same risk to getting the disease. However, this show that there is no association between exposure and disease since even the unexposed can still get the disease.
A stratified analysis by gender shows the following:
|
Men |
Women |
||||
|
Disease |
Disease |
||||
|
Exposure |
Yes |
No |
Exposure |
Yes |
No |
|
Yes |
30 |
90 |
Yes |
40 |
80 |
|
No |
40 |
80 |
No |
30 |
90 |
- What is the relative risk (RR) of exposure causing disease men and what is the RR in women? [ 4 points]
RR in men
Exposed men:
Disease = 30/ (30+90)
= 30/120
= 0.25
Unexposed men:
= 40/ (40+80)
= 40/120
=0.33
RR= disease / no disease
= 0.25/ 0.33
0.75
Thus men exposed have a less risk of getting a disease than the unexposed men.
In women
Exposed women:
40/ (40+80)
= 40/ 120
=0.33
Unexposed women:
30/ (30+90)
= 30/120
= 0.25
RR= 0.33/ 0.25
= 1.32
c- How do you explain this result? (No more than 60 words) [4 points]
since the relative risk of men is 0.75, and thus less than 1, it means that the exposed men have a more less risk of getting a disease than the unexposed men.
In women, since the relative risk is 1.32, and hence more than 1, it means that the exposed women are 1.32 times more likely to get the disease than the unexposed women.
Question 4 (4 points)
What bias would you suspect in a survey of the prevalence of various electrocardiographic abnormalities after heart attack, conducted by examining all the patients treated for this condition in a university hospital in the city.
Selection or sampling bias can arise when examining the electrocardiographs in order to recruit the subjects for such a study. While the electrocardiographs are being obtained from the machines, the interpretations of the electrocardiographs could have errors during study recruitment (Millán et al., 2018). The sample size of this study might be quite small and hence a need to include some other identified variables in people suffering from heart failure. In some instances, a patient could have a normal electrocardiographic results whereas they have heart failure. Moreover, it is not clear whether this study had a control or not, because this would be significant enough in comparing the findings (Ball & Ritchie, 2014). Therefore, this could have probably led to more bias, because it means only the people with a high likelihood of this disease were included.
In heart related studies, spectral analysis presents some level of variations when being used as a marker for diagnosis (Garcia-Gonzalez et al., 2004). The spectral analysis such as the electrocardiographs presents indices which are only estimates of the condition due to the presented limitations (Chahal et al.,2015). There could be bias in the indices such as the LF, VLF, HF as well as LF/HF ratios, during the analysis of the electrocardiographs. Additionally, in the event that this study will have a low sampling frequency, then there could be bas in the associated heart failure indices which could blur the results during electrocardiograph analysis at the point of sample recruitment (Garcia-Gonzalez et al., 2004). Finally, from this recruitment strategy, selection bias is very common and this the spectral indices of the electrocardiographs could be proportional to the inverse sampling frequency, while bias could be proportional to the square of the sampling frequency.
Pattern of association in adjusted risk
Question 5 [4 points]
A study aims to determine the incidence of type 2 diabetes. A cohort of 200 people ages 65 years or older who were initially disease –free participated in the study. One hundred and fifty people were examined at the end of 3 years. Fifty other participants from the initial cohort could not be examined, including 11 people who had died. Does this loss of participants represent a source of bias? Justify your answer. [4 points]
Yes, the loss of participants during a study represents a source of bias. This is because the final findings cannot be generalized for the whole population because it is not possible to determine the results that the lost cohort would present, had the study proceeded to completion.
Loss to follow-up could cause modest bias, but this bias is still significant to the research outcomes. The magnitude depends on the strength of the association of the exposure and outcome variables. In other cases, the significance of the bias also can be related to the type of bias, that is whether the association is direct or has other common causes.
The loss to follow up thus causes a selection bias and hence a threat to the internal validity of the estimates that could be derived from this cohort study. In this cohort study, there were 200 selected participants, whereby only 150 were available at the end of three years. For the loss to follow-up proportion, while 11 of them died in the course of three years, 39 others dropped from the study. Therefore, the researchers cannot determine whether the 50 participants who were lost during this cohort study had developed diabetes or not. If they had diabetes, then it means the final findings on the incidence rate of diabetes were lower than the actual cases (Schröder et al., 2018). It is not also clear whether the 11 who died had their death due to diabetes or other causes. All these events are a source of selection bias which finally affects the overall results of a study. Therefore, the bias as a result of loss to follow up negatively affects the internal validity of the results obtained from such cohort studies. Additionally, the choice of people aged
Years could be a probable lead to this loss because such an elderly population is vulnerable to so many events that could lead to deaths. Additionally, if people aged
Factors adjusted in analysis and evidence from Table 1
Sixty and above gets diabetes, then they can easily succumb to this condition due to a weakened immune system. The loss to follow up causes biased results especially when the rates of drop out is high and different between the studied groups, and, when the subject who drop out of the study are different from those who remain to complete the study. The effects of loss to follow up is of great importance in cohort studies because the subjects who are lost to follow up have varied prognosis from those who remain to complete the study. While there is no agreed proportion of subjects who when lost to follow up can have no effect, significant bias results (Mueller et al., 2015). For instance, if the remaining patients all had diabetes, then researchers should be worried about the missing data from loss to follow up because they are not sure whether they had diabetes or not, and whether the results would probably change. Additionally, initially the researcher did not indicate the gender proportion of the study participants, thus it’s impossible to determine the loss to follow up causes or proportions on gender basis.
References
Ball, T. S., & Ritchie, S. R. (2014). Sampling biases of the BG-sentinel trap with respect to physiology, age, and body size of adult Aedes aegypti (Diptera: Culicidae). Journal of medical entomology, 47(4), 649-656.
Binder, N., & Schumacher, M. (2014). Missing information caused by death leads to bias in relative risk estimates. Journal of clinical epidemiology, 67(10), 1111-1120.
Boria, R. A., Olson, L. E., Goodman, S. M., & Anderson, R. P. (2014). Spatial filtering to reduce sampling bias can improve the performance of ecological niche models. Ecological Modelling, 275, 73-77.
Burón Pust, A., Alison, R., Blanks, R., Pirie, K., Gaitskell, K., Barnes, I., … & Million Women Study Collaborators. (2017). Heterogeneity of colorectal cancer risk by tumour characteristics: Large prospective study of UK women. International journal of cancer, 140(5), 1082-1090.
Chahal, H., Bluemke, D. A., Wu, C. O., McClelland, R., Liu, K., Shea, S. J., … & Post, W. (2015). Heart failure risk prediction in the Multi-Ethnic Study of Atherosclerosis. Heart, 101(1), 58-64.
Du, X., Pi, Y., Dreyer, R. P., Li, J., Li, X., Downing, N. S., … & Guan, W. (2016). The china patient?centered evaluative assessment of cardiac events (PEACE) prospective study of percutaneous coronary intervention: Study design. Catheterization and Cardiovascular Interventions, 88(7), E212-E221.
Ericsson, N. R., Hendry, D. F., & Hood, S. B. (2017). Milton Friedman and Data Adjustment (No. 2017-05-15). Board of Governors of the Federal Reserve System (US).
Fründ, J., McCann, K. S., & Williams, N. M. (2016). Sampling bias is a challenge for quantifying specialization and network structure: lessons from a quantitative niche model. Oikos, 125(4), 502-513.
Garcia-Gonzalez, M. A., Fernandez-Chimeno, M., & Ramos-Castro, J. (2004). Bias and uncertainty in heart rate variability spectral indices due to the finite ECG sampling frequency. Physiological measurement, 25(2), 489.
Liu, S., Manson, J. E., Stampfer, M. J., Hu, F. B., Giovannucci, E., Colditz, G. A., … & Willett, W. C. (2000). A prospective study of whole-grain intake and risk of type 2 diabetes mellitus in US women. American journal of public health, 90(9), 1409.
Millán, L. F., Livesey, N. J., Santee, M. L., & Clarmann, T. V. (2018). Characterizing sampling and quality screening biases in infrared and microwave limb sounding. Atmospheric Chemistry and Physics, 18(6), 4187-4199.
Mueller, N. T., Whyatt, R., Hoepner, L., Oberfield, S., Dominguez-Bello, M. G., Widen, E. M., … & Rundle, A. (2015). Prenatal exposure to antibiotics, cesarean section and risk of childhood obesity. International journal of obesity, 39(4), 665.
Rocha, L. E., Thorson, A. E., Lambiotte, R., & Liljeros, F. (2017). Respondent?driven sampling bias induced by community structure and response rates in social networks. Journal of the Royal Statistical Society: Series A (Statistics in Society), 180(1), 99-118.
Schröder, M. L., Marlies, P., & Staartjes, V. E. (2018). Are patient-reported outcome measures biased by method of follow-up? evaluating paper-based and digital follow-up after lumbar fusion surgery. The Spine Journal.
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