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Data harmonization and individual patient data meta-analysis: exploring new avenues for evidence summaries and intervention development

Jovana Stojanovic, PhD, MSc, MPharm

Postdoctoral fellow

Department of Health, Kinesiology, and Applied Physiology;

Concordia University, Montreal, Canada

May 28, 2020

Contact : jovana.stojanovic@mail.concordia.ca

CONTENT

• Introduction

• Overview of the Action study (DecreAsing sedentary behaviour and inCreasing

physical acTIvity fOr healthy ageing)

• Data harmonization: process & practical aspects

• Individual Patient data (IPD) meta-analysis: process & practical aspects

• Conclusions

INTRODUCTION - POPULATION AGEING

most advanced in Europe and in Northern America global population aged 60 an over:

→ 1980 (382 million) → 2017 (962 million )→ 2050 (2.1 billion )

United Nations: World Population Ageing 2017; Chang et al. Lancet Public Health 2019; 4: e159–67

79 % of older individuals will be living in the developing regions

GBD (195 countries 1990 – 2017): age-related disease burden accounts for 51.3% of all burden

• % of older adults meeting current guidelines: 2.4-83%

Guthold et al. Lancet Glob Health 2018; Sun et al. BMC Public Health 2013; Hallal et al. Lancet 2012; Harvey et al. Journal of Aging and Physical Activity, 2015; Palmer et al. Gerontologist, 2019; Copeland JL et al. Br J Sports Med 2017; Hansen et al. Med Sci Sports Exerc. 2012 Feb; Cunningham et al. Scand J Med Sci Sports; 2020.

• ≈ 9.4 hr∕day (Objective)

• ≈ 5.3 hr∕day sitting ≈ 3.3 hr∕day TV watching (Subjective)

↓ overall mortality↓ CVD risk ↓ breast and prostate cancer↓fractures, and recurrent falls↑ functional and cognitive capacity↓ depression↓ dementia and Alzheimer

↓ overall mortality↓ CVD risk ↓ functional and cognitive capacity ↓ autonomy↑ functional and cognitive decline ? depressionLack of longitudinal evidence The importance of context → cognitively engaging SB

PHYSICAL ACTIVITY (PA) SEDENTARY BEHAVIOUR (SB)

HEALTHY AGEING

healthy ageing = active ageing = successful ageing = productive ageing

Rowe and Kahn 1987

Usual ageing vs Successful aging 1997:

low probability of disease and disease-related disability and related risk factors high cognitive and physical functional capacity sustained engagement in social and productive activities

2015: societal-level principles (opportunities for employment, voluntary work, social

activity…)

Adapted from: Lu et al. Gerontologist, 2019;

HEALTHY AGEING

Domains of Healthy Aging: A review of 55 articles published within March 2017.Number of papers measuring each domain of HA

Adapted from: Lu et al. Gerontologist, 2019;

ACTION STUDY OVERVIEW

Through the use of existing international longitudinal studies, we aim to understand whether engaging in PA and SB after the age of 65 might contribute to healthy ageing in later life.

Screening for potentially eligible cohorts• PUBMED (171 retrieves)

• Inter-university Consortium for Political and Social Research (77 studies)

Studies Continent Number of studies

Number of individuals

Americans' Changing Lives (ACL); Canadian longitudinal study on Ageing (CLSA); Cardiovascular Health Study (CHS); Charleston Heart Study; Cornell Study of Occupational Retirement; Costa Rican Longevity and Healthy Aging Study (CRELES); Framingham cohort study; Framingham Gen III; Framingham Offspring; Hispanic Established Population for the Epidemiological Study of the Elderly (HEPESE); Honolulu Heart Program (HHP); Long Beach Longitudinal Study; Midlife in the United States (MIDUS) Series; Multi-Ethnic Study of Atherosclerosis (MESA); National Health and Aging Trends Study (NHATS); National Social Life, Health, and Aging Project (NSHAP); Sacramento Area Latino Study on Aging (SALSA Study); Study of Women's Health Across the Nation (SWAN) Series; The Health and Retirement Study (HRS); Women's Health Initiative Study (WHI)

America 20 240K

Chinese Longitudinal Healthy Longevity Survey (CLHLS); Survey of Midlife in Japan (MIDJA); Social Environment and Biomarkers of Aging Study (SEBAS) in Taiwan

Asia 3 99K

The Irish Longitudinal Study on Ageing (TILDA); Swedish Adoption/Twin Study on Aging (SATSA); English Longitudinal study on Ageing (ELSA)

Europe 3 20K

The Survey of Health, Ageing and Retirement in Europe (SHARE); Study on Global AGEing and Adult Health (SAGE) International 2 180K

Australian [Adelaide] Longitudinal Study of Aging Australia 1 2K

Total 29 540-545K

ACTION STUDY OVERVIEW

Data harmonization

Harmonized Dataset

Dataset C

Dataset A

Dataset B

Process of retrospective data harmonization involves poolingheterogeneous data from disparate data sets and transforming them into1 cohesive data set

Rolland et al.. 2015;

Assemble information and

select studies

Process data Estimate quality of

the harmonized dataset

Define variables and evaluate

harmonization potential

Step 2

Step 4Step 3

Step 1

Step O Step 5

Pre-harmonization Core process Post-harmonizationProject Proposal Dissemination

Data harmonization

Fortier et al. International Journal of Epidemiology, 2017;

Data harmonization – simple example

Harmonized variable: Ever alcohol drinking

0=No1=Yes

Did you drink alcohol in the past?

0=No, never

1=Yes, more than 3 servings per

week

2=Yes, less than 3 servings per

week

Do you currently drink alcohol?

0=No

1=Yes, less than 3 servings

per week

2=Yes, more than 3 servings

per week

Do you consume alcohol in the

present time?

0=No, ex-drinker

1=Yes

Did you ever drink alcohol?

0=No

1=Yes

If noIf yes

Study 2

Study 1

Data harmonization exampleHRS SHARE TILDA CRELES

w1 - w13 w1-w2, w4-w7 w1-w2 w1-w3 Waves

aerobics, running,

swimming, bicycling

sports, heavy housework,

job involves physical labor

heavy lifting, digging, aerobics,

fast bicycling

sports, jogging,

dancing, or heavy

work

Activity

every day,

more than 1

time/week,

1 time/week,

1-3 times/month,

never

> than 1 time/week,

1 time/week,

1-3 times/month,

never

Number of days/week

&

Number of hours/day

3 times/week Unit

cathegorical cathegorical continious binary Type

w7-w13 w1-w2, w4-w7 w1-w2 - Waves

gardening, cleaning

the car, walking

gardening, cleaning the

car, walking

carrying light loads, bicycling at a

regular pace, or doubles tennis- Activity

every day,

more than 1,

time/week,

1 time/week,

1-3 times/month,

never

> than 1 time/week,

1 time/week,

1-3 times/month,

never

Number of days/week

&

Number of hours/day

- Unit

cathegorical cathegorical continious - Type

w7-w13 - w1 w1-w3 Waves

laundry, home

repairswalking walking Activity

every day,

more than 1,

time/week,

1 time/week,

1-3 times/month,

never

-

Number of days/week

&

Number of hours/day

number of hours Unit

cathegorical - continious continious Type

Mild

Vigorous

ModeratePhysical Exercise

Measure

HRS SHARE TILDA CRELES

w1 - w13 w1-w2, w4-w7 w1-w2 w1-w3 Waves

aerobics, running,

swimming, bicycling

sports, heavy housework,

job involves physical labor

heavy lifting, digging, aerobics,

fast bicycling

sports, jogging,

dancing, or heavy

work

Activity

every day,

more than 1

time/week,

1 time/week,

1-3 times/month,

never

> than 1 time/week,

1 time/week,

1-3 times/month,

never

Number of days/week

&

Number of hours/day

3 times/week Unit

cathegorical cathegorical continious binary Type

Measure

Physical Exercise Vigorous

Harmonized Vigorous physical exercise

≤ than 1 time/weekNever ≥ than 1 time/week Everyday

WHAT IS IPD meta-analysis

• Instead of extracting aggregate data from the published literature, IPD meta-analysis allows the original participant level data to be requested and re-analysed

• ‘gold-standard’ in evidence summaries

Stewart et al.. JAMA.2015;

Data harmonization and IPD meta-analysis in the era of open data

*Adapted from: Nevitt SJ et al. BMJ 2017; Ioannidis. Milbank Q. 2016;

Number of IPD- MAs published from 1994 to 2014

Number of SRs and MAs published from 1994 to 2014

Study 1 Study 2 Study 3 Study N

Standard fixed or random effects MA

Study specific

estimates

IPD meta-analysis methods: 2-STAGE APPROACH

Study 1 Study 2 Study 3 Study N

Participant level

Studylevel

IPD meta-analysis methods: 1 STAGE APPROACH Multilevel model

Inconsistently defined exposures and outcomes

Complex data types, such as long-term outcomes, and time-dependent data

Subgroup analysis and exploration of interactions

IPD vs. Aggregate data (AD) meta-analysis

• A review of 39 studies that compared IPD and AD meta-analysis of randomized trials

190 comparisons

80% agreement in statistical significance

! 23 comparisons disagreed in direction of effect

20% disagreement in statistical significance

! 15% IPD-MA significant & AD-MA non-significant! 5% IPD-MA non-significant & AD-MA significant

144 comparisons for the main effect

46 comparisons for effect modifiers

Tudur et al. Cochrane Database of Systematic Reviews 2016.

• https://g2aging.org/

• https://www.maelstrom-research.org/

• https://biolincc.nhlbi.nih.gov/home/

• https://www.icpsr.umich.edu/web/pages/ICPSR/index.html

• https://vivli.org/

• https://yoda.yale.edu/

• https://clinicalstudydatarequest.com/Default.aspx

Harmonization:

Individual patient data access:

Data harmonization and IPD meta-analysis in the era of open data

Opportunities

• Include unpublished, and/or poorly reported data• Include studies with lots of missing data• Standardize/harmonize outcomes and exposures• Suitable for complex data types• ↑ data quality• ↑ generalizability of results• ↑ statistical power• Refined subgroup and sensitivity analysis, explore interactions• Ability to focus on rare outcomes• Encouraging more efficient secondary usage of existing data• Collaboration

Challenges

• Time & resource-consuming

• Multi-disciplinary teams (data management and statistical expertise required)

• Heterogeneity of the collected variables

• Understanding the meaning of specific variables

• Ethical, legal, and consent-related restrictions (especially for international research):

• Data sharing agreements

• Confidentiality & protection agreements

• Availability (selection) bias

Performing an IPD meta-analysis: practical considerations

1. Research objectives: - Outcomes &exposures- Subgroups or covariates- Data type and modelling

2. Data availability Adopt a systematic approach: search for all relevant published and

unpublished studies → contact study authors Convenience: include research groups from collaborative initiatives Beware of availability bias: sensitivity with AD and IPD dataPRISMA-IPD checklist

3. Available resources

Stewart et al. JAMA.2015;

ACTION study → consideration for PA/SB intervention development in the ageing population

development of evidence-based behavioral goals → improve health outcomes

include environmental, organizational and societal perspective → WHO definition of an

age-friendly community

effective communication and messaging

World Health Organization. (2007).

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ACKNOWLEDGEMENTS

ACTION study investigators: International Research team lead by Drs. Bacon and Lavoie Simon L. Bacon, PhD

ProfessorConcordia University, Canada

MBMC Co-director

Kim L. Lavoie, PhDProfessorUQAM, Canada

MBMC Co-director

Funding bodies

MBMC Team

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References: Healthy ageing

• Cunningham et al. Consequences of Physical Inactivity in Older Adults: A Systematic Review of Reviews and Meta-Analyses. Scand J Med SciSports 2020 May;30(5):816-827.

• Chang et al. Measuring population ageing: an analysis of the Global Burden of Disease Study 2017. Lancet Public Health 2019; 4: e159–67• Lu et al. Domains and Measurements of Healthy Aging in Epidemiological Studies: A Review. The Gerontologist, Volume 59, Issue 4, August

2019, Pages e294–e310, • Palmer et al. What Do Older People Do When Sitting and Why? Implications for Decreasing Sedentary Behavior. Gerontologist 2019 Jul

16;59(4):686-697. • Guthold et al. Worldwide Trends in Insufficient Physical Activity From 2001 to 2016: A Pooled Analysis of 358 Population-Based Surveys With

1·9 Million Participants. Lancet Glob Health 2018 Oct;6(10):e1077-e1086. • Copeland et al. Sedentary Time in Older Adults: A Critical Review of Measurement, Associations with Health, and Interventions. Br J Sports

Med 2017 Nov; 51(21):1539. • United Nations, Department of Economic and Social Affairs, Population Division (2017). World Population Ageing 2017 • Harvey et al. How Sedentary Are Older People? A Systematic Review of the Amount of Sedentary Behavior. J Aging Phys Act 2015 Jul;

23(3):471-87. • Sun et al. Physical Activity in Older People: A Systematic Review. BMC Public Health 2013 May 6;13: 449. • Hallal et al. Global Physical Activity Levels: Surveillance Progress, Pitfalls, and Prospects. Lancet 2012 Jul 21; 380(9838):247-57. • Hansen et al. Accelerometer-determined Physical Activity in Adults and Older People. Med Sci Sports Exerc 2012 Feb; 44(2):266-72. • World Health Organization. (2007). Global age-friendly cities: A guide. Geneva, Switzerland.

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References: IPD meta-analysis and data harmonization

• Tierney JF et al. Comparison of aggregate and individual participant data approaches to meta-analysis of randomised trials: An observational study. PLoSMed 17(1): e1003019, 2020.

• Fortier et al. Maelstrom Research guidelines for rigorous retrospective data harmonization. International Journal of Epidemiology, 2017, 103–115 • Nevitt et al. Exploring changes over time and characteristics associated with data retrieval across individual participant data meta-analyses: systematic

review. BMJ 2017; 357: j1390. • Ioannidis. The Mass Production of Redundant, Misleading, and Conflicted Systematic Reviews and Meta-analyses. The Milbank Quarterly, Vol. 94, No. 3,

2016 (pp. 485-514)• Smith et al. Individual participant data meta-analyses compared with meta-analyses based on aggregate data. Cochrane Database of Systematic Reviews

2016, Issue 9. Art. No.: MR000007. • Debray et al. Get real in individual participant data (IPD) meta‐analysis: a review of the methodology. Res Synth Methods. 2015 Dec; 6(4): 293–309. • Rolland et al. Toward Rigorous Data Harmonization in Cancer Epidemiology Research: One Approach. Am J Epidemiol. 2015; 182(12):1033–1038• Stewart et al. Preferred Reporting Items for a Systematic Review and Meta-analysis of Individual Participant Data the PRISMA-IPD Statement. JAMA.2015;

313(16):1-1665.• Huang Y et al. Distribution and Epidemiological Characteristics of Published Individual Patient Data Meta Analyses. PLoS ONE 9(6): e100151, 2014. • Thomas et al. Systematic review of methods for individual patient data meta- analysis with binary outcomes. BMC Medical Research Methodology 2014,

14:79• Ahmed et al. Assessment of publication bias, selection bias, and unavailable data in meta-analyses using individual participant data: a database survey. BMJ

2011; 344:d7762• Riley et al. Meta-analysis of individual participant data: rationale, conduct, and reporting. BMJ 2010; 340:c221• Stewart and Tierney. To IPD or not to IPD? Advantages and Disadvantages of Systematic Reviews Using Individual Patient Data. Evaluation & the health

professions, vol. 25 no. 1, march 2002 76-97.