Application of Big Data analysis in gastrointestinal research

Table 1 Advantages and shortcomings of Big Data analysis (with proposed solutions)

Advantages
Clinical data readily available with minimal resources required
Can study rare exposures
Can study rare events
Can study long-term effects
Real-world data
Large sample size
Subgroup analysis
Sensitivity analysis
Interaction of different variables
Adjustment of outcome to a multitude of risk factors
Precise estimation of effect size
Reliable capture of small variations in incidence or disease flare
No selection bias if n = all
Shortcomings specific of Big Data analysis	Solution
Data validity	Cross reference with medical records in a subset of the sample
Missing data	Statistical methods to deal with missing data, e.g. multiple imputation
	Text mining or natural language processing of unstructured data
Incomplete capture of variables or unavailability of certain diagnosis codes	Surrogate markers (e.g., COPD for smoking, alcohol-related diseases for alcoholism)
	Inclusion of a large set of measured variables
	Text mining or natural language processing of unstructured data
Privacy	De-identification of individuals
	Review of study plan by local ethics committee
Hypothesis-free predictive models	Validation in prospective studies or randomized control trials
Shortcomings of all observational study including Big Data analysis	Solution
Residual and/or unmeasured confounding	Inclusion of a large set of measured variables
	Inclusion of RCT datasets with extensive collection of data and outcomes for trial participants or linkage with other data sources
	Fulfilment of Bradford Hill criteria
Reverse causality/protopathic bias (outcome of interest leads to exposure of interest)	Cohort study design instead of case-control study design
	Excluding prescriptions of drugs of interest (e.g., PPIs) within a certain period (e.g., 6 mo) before development of the outcome of interest (e.g., gastric cancer)
Example: Early symptoms of undiagnosed GC leads to PPI use, rather than PPIs cause GC
Selection bias	Encompassing entire study population (n = all)
Indication bias (or confounding by indication/disease severity)	Balance of patient characteristics, in particular comorbidities that are indications for a certain treatment (e.g., PS matching of a large set of measured variables)
	Negative control exposure
Confounding by functional status and cognitive impairment	Balance of patient characteristics, in particular comorbidities that can affect functional and cognitive status (e.g., PS matching)
Healthy user bias / adherer bias (individuals who are more health conscious tend to have better health outcomes)	Adjustment for other lifestyle factors – text mining or natural language processing of unstructured data
Immortal time bias (arises when the study outcome cannot occur during a period of follow-up due to study design)	Landmark analysis
	Analysis using time varying covariates
Ascertainment bias / surveillance bias / detection bias (differential degree of surveillance or screening for the outcome among exposed and unexposed individuals) Example: PPI users may undergo upper endoscopy more frequently than non-PPI users, and hence more GC detected in PPI users	Selection of an unexposed group with a similar likelihood of screening/testing
	Selection of an outcome that are likely to be diagnosed equally in exposed and control groups
	Adjustment for the surveillance rate
Access to healthcare	Stratified analysis according to patients’ residential regions (e.g., rural vs urban), socioeconomic status, immigration status, race/ethnicity, institutional factors (e.g., restrictive formularies)
Selective prescription and treatment in frail and very sick patients	PS methodology (trimming of areas of non-overlap, PS matching, PS by treatment interaction)

COPD: Chronic pulmonary obstructive disease; RCT: Randomized controlled trial; GC: Gastric cancer; PPI: Proton pump inhibitor; PS: Propensity score.

Table 2 Advantages of propensity score methodology

Advantages	Remarks
Addressing “curse of dimensionality” when EPV < 10	Traditional multivariable regression models yield similar results if EPV ≥ 10
Recognition of subjects with absolute indications (or contraindications) of an intervention	Exclusion of areas of non-overlap of the PS distribution between exposed and unexposed groups to ensure comparability
Identification of PS interaction with treatment	Variation of effectiveness of an intervention according to indications (PS) may only be identified via stratified analysis by PS

EPV: Events per variable; PS: Propensity score.

Table 3 Examples of studies on gastric cancer research by utilization of large healthcare datasets

Gastric cancer
Country/Region	Database	Area of research	Sample size	Design, statistical methods and 3V	Application
Taiwan, China	Taiwan National Health Insurance Database (NHID)	GC	80255	Nationwide retrospective cohort study	Early vs late H. pylori eradication on GC risk
		Wu et al[46], 2009
				Comparison with general population to derive SIR
				Volume, Velocity and Variety
		GC	52161	Nationwide retrospective cohort study	Association between NSAIDs and GC
		Wu et al[48], 2010
				Comparison with general population to derive SIR
				Volume, Velocity and Variety
Hong Kong, China	Clinical Data Analysis and Reporting System (CDARS)	GC	63397	Territory-wide retrospective cohort study	Association between PPIs and GC
		Cheung et al[51], 2018
				PS regression adjustment
				Volume, Velocity and Variety
		GC	63605	Territory-wide retrospective cohort study	Association between aspirin and GC
		Cheung et al[49], 2018
				PS regression adjustment
				Volume, Velocity and Variety
		GC	63397	Territory-wide retrospective cohort study	Effect of H. pylori eradication among different age groups
		Leung et al[47], 2018
				Comparison with general population to derive SIR
				Volume, Velocity and Variety
		GC	7266	Territory-wide retrospective cohort study	Association between metformin and GC
		Cheung et al[50], 2018
				PS regression adjustment
				Sensitivity analysis: PS weighting by IPTW and PS matching
				Volume, Velocity and Variety
Sweden	Swedish Cancer Registry	GC	797067	Nationwide retrospective cohort study	Association between PPIs and GC
		Brusselaers et al[53], 2017
	Swedish Prescribed Drug Registry			Comparison with general population to derive SIR
				Volume, Velocity and Variety
		GC	95176	Nationwide retrospective cohort study	Effect of H. pylori eradication on GC risk
		Doorakkers et al[45], 2018
				Comparison with general population to derive SIR
				Volume, Velocity and Variety
United States	Kaiser Permanente (KP)	GC	61684	Retrospective cohort study	Association between different PPIs and GC
		Schneider et al[55], 2016
				Volume, Velocity and Variety

This list is not exhaustive, but serves to provide a few distinct examples of how Big Data analysis can generate high-quality research outputs in the field of gastroenterology and hepatology. 3V: Volume/velocity/variety; GC: Gastric cancer; SIR: Standardized incidence ratio; H. pylori: Helicobacter pylori; NSAIDs: Non-steroidal anti-inflammatory drugs; PS: Propensity score; PPIs: Proton pump inhibitors; IPTW: Inverse probability of treatment weighting.

Table 4 Examples of studies on gastrointestinal bleeding and/or proton pump inhibitor research by utilization of large healthcare datasets

Gastrointestinal bleeding and/or proton pump inhibitors
Country/Region	Database	Area of research	Sample size	Design, statistical methods and 3V	Application
Taiwan, China	Taiwan National Health Insurance Database (NHID)	PUD	403567	Nationwide retrospective cohort study	Effect of H. pylori therapy and PPIs on PUD
		Wu et al[58], 2009
				Volume, Velocity and Variety
		PUD	32235	Nationwide retrospective cohort study	Risk of rebleeding from PUD in ESRD patients
		Wu et al[95], 2011
				Volume, Velocity and Variety
		PPIs	6552	Nationwide retrospective cohort study	Effect of clopidogrel and PPIs on ACS
				Volume, Velocity and Variety
		Wu et al[59], 2010
South Korea	Korean Health Insurance Review and Assessment Service (HIRA)	PPIs	59233	Nationwide retrospective cohort study	Effect of PPIs on thrombotic risk
		Kim et al[96], 2019
				Volume, Velocity and Variety
Hong Kong, China	Clinical Data Analysis and Reporting System (CDARS)	Dabigatran	5041	Territory-wide retrospective cohort study	Risk factors for dabigatran-associated gastrointestinal bleeding
		Chan et al[62], 2015
				Volume, Velocity and Variety

This list is not exhaustive, but serves to provide a few distinct examples of how Big Data analysis can generate high-quality research outputs in the field of gastroenterology and hepatology. 3V: Volume/velocity/variety; PUD: Peptic ulcer disease; H. pylori: Helicobacter pylori; PPIs: Proton pump inhibitors; ESRD: End-stage renal disease; ACS: Acute coronary syndrome.

Table 5 Examples of studies on inflammatory bowel disease research by utilization of large healthcare datasets

Inflammatory bowel disease
Country/Region	Database	Area of research	Sample size	Design, statistical methods and 3V	Application
South Korea	Korean Health Insurance Review and Assessment Service (HIRA)	UC	11233	Nationwide retrospective cohort study	Incidence and clinical impact of perianal disease in UC
		Song et al[97], 2018
				Comparator: general population
				Volume, Velocity and Variety
Taiwan, China	Taiwan National Health Insurance Database (NHID)	IBD	38039	Nationwide retrospective cohort study to compare IBD patients with general population to derive SIR	Association between IBD and herpes zoster infection
		Chang et al[98], 2018
				Hospital based nested case-control study
				Volume, Velocity and Variety
Sweden	Swedish Patient Registry	UC	63711	Nationwide retrospective cohort study	Association between appendectomy and UC
		Myrelid et al[99], 2017
				Volume, Velocity and Variety
	Swedish Medical Birth Register (child-mother link)	IBD	827,239 children born between 2006 and 2013	Nationwide prospective population-based register study	Association between maternal exposure to antibiotics during pregnancy and very early onset IBD in adulthood
		Ortqvist et al[72], 2019
				Volume, Velocity and Variety
	Swedish Multigeneration Register (child-father link)
	Swedish Prescribed Drug Register National Patient Register
United States	NCBI Gene Expression Omnibus (GEO)	IBD	Not applicable	Signature inversion study	Topiramate as a potential therapeutic agent against IBD
		Dudley et al[70], 2011
				Volume, Velocity and Variety
United States	Not applicable	IBD	1585	Retrospective cohort study Natural language processing	Association between arthralgia and biologics (anti-TNF vs vedolizumab)
		Cai et al[20], 2018
				Volume, Velocity and Variety
Not applicable	International IBD Genetics Consortium's Immunochip project	IBD	53279	Machine learning algorithm	Predictors of IBD
		Wei et al[64], 2013
				Volume, Velocity and Variety
United States	Not applicable	IBD	575 colonoscopy reports	Retrospective cohort study Natural language processing	Differentiation of surveillance from non-surveillance colonoscopy
		Hou et al[100], 2013
				Volume, Velocity and Variety
United States	Not applicable	IBD	1080	Retrospective cohort study	Prediction of IBD remission in thiopurine users
		Waljee et al[66], 2017
				Random Forest machine learning algorithm
United States	Not applicable	IBD	20368	Retrospective cohort study	Prediction of hospitalization and outpatient steroid use
		Waljee et al[65], 2017
				Random Forest machine learning algorithm
Not applicable	Phase 3 clinical trial data	IBD	491	Retrospective cohort study	Prediction of steroid-free endoscopic remission with vedolizumab in UC
		Waljee et al[67], 2018
				Random Forest machine learning algorithm
				Volume, Velocity and Variety

This list is not exhaustive, but serves to provide a few distinct examples of how Big Data analysis can generate high-quality research outputs in the field of gastroenterology and hepatology. 3V: Volume/velocity/variety; UC: Ulcerative colitis; IBD: Inflammatory bowel disease; SIR: Standardized incidence ratio; anti-TNF: anti-tumour necrosis factor.

Table 6 Examples of studies on colorectal cancer research by utilization of large healthcare datasets

Colorectal cancer
Country/Region	Database	Area of research	Sample size	Design, statistical methods and 3V	Application
Hong Kong, China	Clinical Data Analysis and Reporting System (CDARS)	CRC	197902	Territory-wide retrospective cohort study	Epidemiology, characteristics, risk factors and prognosis of postcolonoscopy Colorectal cancer in Asians
		Cheung et al[101], 2019
				Volume, Velocity and Variety
		CRC	187897	Territory-wide retrospective cohort study	Association between statins and CRC
		Cheung et al[69], 2019
				PS matching
				Volume, Velocity and Variety
United States	Nurses’ Health Study II (NHSII)	CRC	134763	Prospective cohort study	Association between DM and CRC
		Ma et al[74], 2018
				Volume and Variety
	Health Professionals Follow-up Study (HPFS)
	Nurses’ Health Study (NHS)	CRC	1660	Prospective cohort study	Effect of calcium intake, coffee and fibre on survival after CRC diagnosis
		Yang et al[78], 2018
			1599
				Volume and Variety
	Health Professionals Follow-up Study (HPFS)	Hu et al[77], 2018
			1575
		Song et al[79], 2018
	Nurses’ Health Study (NHS)	CRC	141143	Prospective cohort study	Risk factors of serrated polyps and conventional adenomas
		He et al[76], 2018
	Nurses’ Health Study II (NHSII)
		de Jong et al[80], 2006
				Volume and Variety
	Health Professionals Follow-up Study (HPFS)
	Nurses’ Health Study II (NHSII)	CRC	85256	Prospective cohort study	Association between obesity and CRC
		Liu et al[75], 2018
				Volume and Variety
Netherlands	Dutch Lynch syndrome Registry	Various cancers including	2788	Retrospective cohort study	Decrease in CRC-related mortality in Lynch syndrome families by surveillance
				Volume, Velocity and Variety
		CRC
Netherlands, Germany, Finland	Dutch Lynch syndrome Registry	CRC	2747 patients with 16327 colonoscopies	Retrospective cohort study	Surveillance interval on CRC incidence and stage
		Engel et al[81], 2018
				Volume, Velocity and Variety
	German HNPCC Consortium
	Finland

This list is not exhaustive, but serves to provide a few distinct examples of how Big Data analysis can generate high-quality research outputs in the field of gastroenterology and hepatology. 3V: Volume/velocity/variety; CRC: Colorectal cancer; DM: Diabetes mellitus.

Table 7 Examples of studies on hepatocellular carcinoma research by utilization of large healthcare datasets

Hepatocellular carcinoma
Country/Region	Database	Area of research	Sample size	Design, statistical methods and 3V	Application
Taiwan, China	Publicly available data on HCC-related genes	HCC	Not applicable	Signature inversion study	Anti-cancer effects of chlorpromazine and trifluoperazine on HCC
		Chen et al[17], 2011
				Volume, Velocity and Variety
	Connectivity Map (CMap) -- includes 6100 drug-mediated expression profiles
	Taiwan National Health Insurance Database (NHID)	HCC	4569	Nationwide retrospective cohort study	Association between NA therapy and HCC recurrence among patients with HBV-related HCC after liver resection
		Wu et al[89], 2012
				Volume, Velocity and Variety
	Taiwan National Health Insurance Database (NHID)	HCC	292290	Nationwide case-control study	Association between DM and HCC
		Chen et al[91], 2013
				Volume, Velocity and Variety
	Taiwan National Health Insurance Database (NHID)	HCC	43190	Nationwide retrospective cohort study	Association between NA therapy and HCC among CHB patients
		Wu et al[87], 2014
				PS matching
				Volume, Velocity and Variety
China	The Cancer Genome Atlas (TCGA) database	HCC	Not applicable	Signature inversion study	Anti-cancer effect of prenylamine on HCC
		Wang et al[18], 2016
				Volume, Velocity and Variety
	Connectivity Map (CMap)
South Korea	Korean Health Insurance Review and Assessment Service (HIRA)	HCC	24156	Nationwide retrospective cohort study	Difference between tenofovir and entecavir on reducing HCC risk
		Choi et al[90], 2018
				Volume, Velocity and Variety
Hong Kong, China	Clinical Data Analysis and Reporting System (CDARS)	HCC	Entire Hong Kong population between 1999 and 2012	Territory-wide retrospective cohort study	Association between NA therapy and HCC among CHB patients
		Seto et al[88], 2017
				Volume, Velocity and Variety
Sweden	Swedish Cancer Registry	HCC	9160 CHB patients	Nationwide retrospective cohort study	Association between concomitant HBV/HDV infection and HCC
		Ji et al[102], 2012
	Swedish Patient Registry
				Comparison with general population to derive SIR
				Volume, Velocity and Variety

This list is not exhaustive, but serves to provide a few distinct examples of how Big Data analysis can generate high-quality research outputs in the field of gastroenterology and hepatology. 3V: Volume/velocity/variety; HCC: Hepatocellular carcinoma; NA: Nucleos(t)ide analogue; DM: Diabetes mellitus; PS: Propensity score; CHB: Chronic hepatitis B; SIR: Standardized incidence ratio; HDV: Hepatitis D virus.