Summary
Zhang et al. conducted a study to examine if there was a distinction between smoking and developing lung cancer among people with various risks in a cohort, large population. The participants’ risk may be low, high or intermediate. The research also sought to determine how genetics and smoking relate to lung cancer. Based in the U.K., the study started in 2006, recruiting over 500,000 participants over the next four years aged between 40 and 69. The final participants who became a part of the study were 345 794, with 26% being former smokers, 9.5% being current smokers, and 1687 developed lung cancer during follow-up. Per the research they conducted, genetics and smoking had an independent relationship in leading to lung cancer, such as they were not associated with each other. However, an increased smoking risk was more significant than heredity. The study helped provide more insight into evaluating smoking and genetic function in the development of lung cancer.
Type of Research Design
Based on the research presentation, its design is a prospective quantitative study based on several factors, the first being its initiation. At the study’s beginning, the researchers defined the population that would be part of the study and then measured the potential interest. Later, the participants are classified, and the research proceeds. The authors started the survey with over half a million participants, showing their openness and acceptance of all willing to participate. The population the authors recruited were from 22 assessment centers throughout the U.K (Zhang et al., 2022). Further, prospective cohort research has a long follow-up period, as the study indicates. The study started in 2006, and the participants were recruited for four years. The follow-up was between 6.5 and 7.8 years (Zhang et al., 2022). During this period, the researchers followed the subjects in time to study the development of a disease. In the study, years later, the authors identified 1687 incident lung cancer cases from participants who were slightly older than their counterparts, male, had more exposure to smoking, engaged in less physical activity, had higher genetic risk and practised an unhealthy diet.
Additionally, most prospective studies are quantitative, as presented in the study. Quantitative studies have a large sample size, allowing a more precise estimate of the results (Schoch, 2020). Quantitative research relies on building upon determining the mean values of a given dataset. Authors may enrol tens of thousands of participants in health-related research, with multiple years of follow-up. The larger the sample size, the more accurate and statistically valid the average values are. Furthermore, the sample size is cost-effective and manageable. The sample size in the study is 354 794, making it easy to categorize them into manageable characteristics. For example, among the total participants, 90 727 smoked before the research began, 33 994 were smokers throughout the research period, 40 889 had intermediate exposure to smoking, and 19 027 were heavily exposed to smoking. The significance of large sample sizes is that they aid researchers in identifying data outliers and provide small error margins.
Markedly, the questions directed at participants in quantitative research are close-ended, aiming to gather focused, quantitative data. The data is in numbers, dates or one-word answers, making it easy to group, analyze and compare. The participants in the study provided short details for information gathering: blood samples, physical measurements, smoking status and other health-related aspects. More baseline requirements were multiple-choice, prompting the participants to choose one. Their responses can be easily tallied into scores, statistics and percentages that may be tracked over time. The authors created a flow chart on participant enrollment. They tabulated their baseline characteristics and lung cancer incidence risk per genetics and developed a cumulative risk of cancer per smoking and genetic risk (Zhang et al., 2022). The study results were also tabulated into genetic risk and smoking status and genetic risk and smoking pack-years. Hence, when research questions are close-ended, they offer predetermined answers, allowing surveyors to assign them numerical values, tally them into scores, categorize responses quickly and easily identify their trends and correlations. The tables in the article presented are direct, making the study variables easy to categorize.
Furthermore, the study is quantitative because of the ease of measurability and analysis criteria. The data analysis is straightforward because of the minimal variables employed in the study. Using statistical tests and calculations, the study turns the tabulated data into meaningful insights, enabling the researchers and readers to make sense of it by identifying patterns, relationships and trends between variables. The statistical analysis combines all variables, summarizing the study. Further, after tabulating the variables, the authors categorize them into incidence rates, hazard ratios and confidence intervals. The p-value for the trend calculated for each smoking category as continuous variables (Zhang et al., 2022). Observing the data on the tables makes the information easy to understand. The results are consistent with the number of participants and the information they provided, providing an objective result based on facts. The data has mapped out the participants’ journey, giving the reader and authors a better sense of their demographics.
Generally, the study lacks the characteristics of a qualitative study, which represents participants’ feelings and opinions, which cannot be represented as numerical statistics such as averages. Qualitative data is also manually analyzed through transcripts, recordings and sentiments. Moreover, due to the broad range of qualitative data, the analysis is longer and more labour-intensive than quantitative data. Moreover, quantitative research has single-choice and yes or no questions, while qualitative data has more diverse questions, observations, focus groups, content analysis and interviews (Adedoyin, 2020). Undoubtedly, the procedures required for data analysis make the process lengthy because some involve observing participants in various activities, such as shopping or taking walks. Also, qualitative studies require small samples and are conducted at the beginning of a study, compared to quantitative studies with a lengthy recruitment and follow-up process. Instead of examining the relationship between variables, qualitative research focuses on cases and understanding their differences instead of determining response means.
Overall, the article presented a prospective cohort quantitative study, beginning with over half a million participants and choosing the most suitable over time. The study was followed up over nearly eight years, leading to more accurate results. The study’s quantitative characteristics were its large population, close-ended questions, ease of analysis and data measurability. The characteristics differ from a qualitative study, which requires a small sample and has more research categories because of the open-ended nature of the questions. Instead of analyzing data, qualitative research interprets it, and the data is interpreted in text instead of numbers, making it a more complex research process.
References
Adedoyin, O. B. (2020). Qualitative research methods. Principles of Social Psychiatry: pp. 77–87. https://www.researchgate.net/profile/Olasile-Adedoyin/publication/340594471_Qualitative_Research_Methods/links/5e9354724585150839d952b5/Qualitative-Research-Methods
Schoch, K. (2020). Case study research. Research design and methods: An applied guide for the scholar-practitioner, pp. 245–258. https://www.researchgate.net/profile/Subhash-Basu-3/post/How_do_i_determine_the_sample_size_for_a_study_looking_at_the_treatment_outcomes_of_mental_health_patients_in_a_community_house/attachment/5ebbae3eead4db0001551c21/AS%3A890646755811328%401589358142328/download/105275_book_item_105275.pdf
Zhang, P., Chen, P. L., Li, Z. H., Zhang, A., Zhang, X. R., Zhang, Y. J., … & Mao, C. (2022). Association of smoking and polygenic risk with the incidence of lung cancer: a prospective cohort study. British Journal of Cancer, 126(11), 1637–1646. https://www.nature.com/articles/s41416-022-01736-3
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