WHI Longitudinal Data

Aaron K. Aragaki

September 9th, 2016

Survey of:

How to use WHI’s longitudinal data

• Measurements of the same individual are taken

repeatedly through time

• f(when, what, whom) = how

• f(when, what, whom) = statistical models

Other considerations

• Data cleaning

• Missing data

Mary Pettinger covered ‘data collected’:

What Questionnaires

Clinical exams

Outcomes

Biospecimens

Whom

All

Subsamples

Subsets

When Screening

WHI (thru 2005)

Extension Studies

I (2005-2010)

II (2010-2015)

III (2015-present)

Motivation for Data Collection: ‘what’ in the OS

to examine the associations of known or putative risk factors<exposure> (and protective factors<exposure>) to disease <response>

to find new risk factors<exposure> and/or biomarkers<exposure>using the stored biologic samples and data as a resource

to examine the association of change in individual characteristics<exposure> on disease<response> and mortality<response>

Motivation for Data Collection: ‘what’ in the CT

Designated primary and secondary clinical

outcomes in each CT<response>

Interest in intermediate outcomes (i.e. lipid levels in HT) <response>

Trial monitoring, safety, adherence<response; adherence analysis>

Potential effect modifiers <subgroup>

• Baseline characteristic • Statistically valid • Age at randomization (50-59, 60-69, 70-79 years)

• Follow-up characteristic: improper subgroup • Not valid • Weight 1-year after randomization

Yusuf, Salim, et al. "Analysis and interpretation of treatment effects in subgroups of patients in randomized clinical trials." Jama 266.1 (1991): 93-98.

Statistical modeling of ‘what’ (response)

Continuous distribution somewhat normal • Repeated measures(profile means) model

• Linear mixed effects (LME) model

• Frequency of collection helpful when choosing from above

Other distributions • Generalized estimating equations (GEE)

model

• Generalized linear mixed effects (GLME) model

Statistical modeling of ‘what’ (exposure)

Time-dependent exposure/covariate

• OK in survival analysis (time-to-first event)

• Often not OK for longitudinal response • Because this key assumption does not hold:

• Current and past values of response, given current and past values of covariate, do not predict future values of covariate

• Example where assumption is violated: • Logitudinal systBP ~ HT + antihyp med(time-dependent)

• Alternate methods are used to estimate underlying (untreated) BP : sensible constant, Tobit model, non-ignorable missing data (NMAR) methods

Statistical modeling of ‘what’ (response & exposure)

Full cohort Sensible constant Adherence to HT

Subgroup by MX Use

The effect of CEE+MPA randomization arm on systolic blood pressure (Shimbo, 2014)

Screening

CT and OS CT OS

Form

# Form Name SV

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nual

3

Yr

6

Yr

9

Yr

2 Eligibility Screen X

4 HRT Washout H

10 HT Manage/Safety Interview H H H H H H H H H H H H H H H H H H H H

17 CaD Manage/Safety Interview C C C C C C C C C C C C C C C C C C

20 Personal Information X X X

25 Participant Treatment

Assignment – HT1

28 Participant Treatment

Assignment – CaD

X

30 Medical History X

31 Reproductive History X

32 Family History X

33 Medical History Update X X X X X X X X X X X X X X X X X X X X

34 Personal Habits X

35 Personal Habits Update X X X X

37 Thoughts & Feelings X X

38 Daily Life X % % % X

39 Cognitive Assessment %H %H %H %H %H

40 Addendum to Medical History1

41 Addendum to Personal Info1

42 OS Questionnaire1 O

43 Hormone Use Interview X

44 Current Medications X X X X X X

45 Current Supplements X X X X X X

48 OS Follow-Up Questionnaire1

49 E+P Survey1

55 E-Alone Survey1

60 Food Questionnaire X D %D %D %D %D %D %D %D %D X

80 Physical Measures X X X X X X X X X X X BD

80 Waist/Hip Measures X X % % % X

81 Pelvic H H H H H H H H H H

82 Endometrial Aspiration H %H %H %H

83 Transvaginal Uterine Ultrasound1

84 Clinical Breast Exam HD H H H H H H H H H

85 Mammogram HD H X H X H X H X H H

86 ECG HD X X X

87 Bone Density BD BD BD BD BD BD BD BD BD

Statistical modeling of ‘when’

Statistical modeling of ‘when’ (mean; cont’d)

The effect of a low fat dietary intervention (I minus C) on blood pressure (Allison, 2015)

Statistical modeling of ‘when’ (mean; cont’d)

Repeated measures model

• Effect (I vs. C; I minus C) by year (categorical) • Many parameters many tests

• Sometimes overall ‘average’ is good; depends • Parsimonious • Single omnibus test • Not useful if effect varies temporally

LME model • Parsimonious • Effect at Y1 (intercept) • Change of effect after year 1 (slope)

Parsimonious primary analysis leads to parsimonious secondary analyses (e.g., subgroup, sensitivity)

Avoid multiple cross-sectional models

Statistical modeling of ‘when’ (cont’d)

Repeated measures vs LME model

• Either accounts for within-participant correlation • Ideally temporal variance-covariance matrix

Repeated measures (profile means) • Specify variance-covariance structure • Unstructured covariance matrix (ideal)

• Data determines correlation between each visit; unrestricted • May become intractable with many visits • Need to estimate v(v+1)/2 parameters; v is # of visits

• 4 visits 10 parameters; 9 visits 45 parameters

• Compound symmetry (often poor choice)

LME model • Include random effect(s) • Random intercept and random slope (ideal)

• Implicitly models variance & correlation as functions of time • Computationally tractable regardless of visit number • Random intercept only (often poor choice)

Unstructured • Correlation decreases

as time between visits increases

• Variance decreases with time

• 10 parameters; tractable

Statistical modeling of ‘when’ (covariance; cont’d)

Visit Baseline Year 1 Year 3 Year 6

Baseline 1(35.6) 79% 67% 51%

Year 1 R01 1(35.3) 73% 55%

Year 3 R03 R13 1(34.2) 59%

Year 6 R06 R16 R36 1(33.8)

Visit Baseline Year 1 Year 3 Year 6

Baseline 1(34.9) 65% 65% 65%

Year 1 R 1(34.9) 65% 65%

Year 3 R R 1(34.9) 65%

Year 6 R R R 1(34.9)

LDL-C:Variance-covariance matrix

Compound symmetry • 2 parameters;

unreasonable • Unfavorable influence

on efficiency of estimates

• Unfavorable influence on test validity

Statistical modeling of ‘when’ (covariance; cont’d)

In most situations: corr(baseline, Year 1) > corr(baseline, Year 3) corr(Year 1, Year 3) > corr (Year 1, Year 6)

• No good reason to assume compound symmetry

• No good reason to use only random intercept

Consequences of incorrectly modeling variance-covariance structure • Throwing away information

• Inefficient estimates of the mean

• Less powerful statistical tests

Screening

CT and OS CT OS

Form

# Form Name SV

0

SV

1

SV

2

SV

3

4-6

wk

6

m

1

Yr

4

wk

6

m

2

Yr

6

m

3

Yr

6

m

4

Yr

6

m

5

Yr

6

m

6

Yr

6

m

7

Yr

6

M

8

Yr

6

m

9

Yr

Close

Out

An-

nual

3

Yr

6

Yr

9

Yr

2 Eligibility Screen X

4 HRT Washout H

10 HT Manage/Safety Interview H H H H H H H H H H H H H H H H H H H H

17 CaD Manage/Safety Interview C C C C C C C C C C C C C C C C C C

20 Personal Information X X X

25 Participant Treatment

Assignment – HT1

28 Participant Treatment

Assignment – CaD

X

30 Medical History X

31 Reproductive History X

32 Family History X

33 Medical History Update X X X X X X X X X X X X X X X X X X X X

34 Personal Habits X

35 Personal Habits Update X X X X

37 Thoughts & Feelings X X

38 Daily Life X % % % X

39 Cognitive Assessment %H %H %H %H %H

40 Addendum to Medical History1

41 Addendum to Personal Info1

42 OS Questionnaire1 O

43 Hormone Use Interview X

44 Current Medications X X X X X X

45 Current Supplements X X X X X X

48 OS Follow-Up Questionnaire1

49 E+P Survey1

55 E-Alone Survey1

60 Food Questionnaire X D %D %D %D %D %D %D %D %D X

80 Physical Measures X X X X X X X X X X X BD

80 Waist/Hip Measures X X % % % X

81 Pelvic H H H H H H H H H H

82 Endometrial Aspiration H %H %H %H

83 Transvaginal Uterine Ultrasound1

84 Clinical Breast Exam HD H H H H H H H H H

85 Mammogram HD H X H X H X H X H H

86 ECG HD X X X

87 Bone Density BD BD BD BD BD BD BD BD BD

Statistical modeling of ‘whom’

Statistical modeling of ‘whom’ (cont’d)

Whom: full cohort or subsample

• Could depend on visit (e.g., quality of life)

Decision: weighted or unweighted model

• Target population

• Target will never be a case-ctrl sample

• Disease population is over represented; always weight!

• No free lunch with weighting

• Loss of precision

• Increase in complexity (analysis & publication)

• Often OK to use unweighted model

Statistical modeling of ‘whom’ (cont’d)

Unweighted estimates are often used

Mean percent energy from fat in the Dietary trial (WHI WG, 2004)

Effect at Year 1 if often primary interest

• Closest visit to randomization

• Less influenced by compliance

• Data available on full cohort

• Context

Statistical modeling of ‘whom’ (cont’d)

Results for Other HRQoL at Year 1 in the WHI Hormone Therapy Trials (Manson, 2013)

Refine cohort to address temporal trends

Average weight change trajectories vary by age group

Advantageous to differentiate between intended and unintended weight loss • What’s the public health

message?

Advantageous to restrict cohort to select age groups

Mean weight change in the WHI CT (Caan, 2007)

Refine cohort to address temporal trends

Identify those that lost weight intentionally

Identify those that lost weight unintentionally

Good strategy for OS (Crandall, 2015); Form 143

Data cleaning

Longitudinal data allows for easier cleaning

• Leverage multiple measurements to clean data via EDA

• Association between exposure and outcome should not be part of EDA Biased, overly optimistic results

Data is collected

Data is processed

Useable data

Exploratory Data Analysis (EDA)

Statistical modeling of hypothesis

Publication

WHI Study:

Data cleaning (height; response; cont’d)

Examination of baseline data is a good idea

Data cleaning (height; response; cont’d)

Contrast baseline with Year 1

• Obvious bad data; < 4’ at randomization

• Not corroborated at Y1

• Other shades of potential errors; likely wrong conversion factor (e.g., 1.5, 2, 3 cm per inch)

• Set to missing most obvious (0.10% of all height measurements; Crandall 2016)

Data cleaning (cont’d; exposure; obesity)

Examination of baseline data is a good idea

• Exposure is baseline BMI and interest in obese and very obese participants

• Leverage longitudinal measures to validate the ‘extreme’ observations of interest

• Top 0.5% of all CT participants

Data cleaning (cont’d; exposure; obesity)

Contrast top 0.5% with Year 1 and other baseline variables • Of these, BMI at Year 1 is less than half that at baseline; 20%

Data cleaning (cont’d; exposure; obesity)

Further corroborated with weight (Neuhouser 2015)

• Of these 33% have suspect weight (kg)

Data cleaning(cont’d; gross anomalies)

Data cleaning(cont’d; gross anomalies)

Likely not an earthshaking discovery

Check if gross anomaly can be attributed to form changes

Compare forms between study periods

Consult data dictionary

Data cleaning

Longitudinal data allows for easier cleaning

• Leverage multiple measurements to clean data via EDA

• Association between exposure and outcome should not be part of EDA Biased, overly optimistic results

Data is collected

Data is processed

Useable data

Exploratory Data Analysis (EDA)

Statistical modeling of hypothesis

Publication

WHI Study:

Missing data

Rule not the exception Missing data mechanism

• Missing completely at random (MCAR) • Observed data is random subset of the ‘complete’ data

• Missing at random (MAR) • Ubiquitous for WHI

• Dependent on enrollment year • Enrollment year associated with age • Younger (older) women have more(less) follow-up due to

earlier(later) enrollment

• Missingness can depend on observed response or covariates

• Profile means and LME models are valid • Cross-sectional & unweighted GEE yield biased results

• Not missing at random (NMAR) • Often arises in QoL studies

Missing data (cont’d)

CT: Mean(median) age at enrollment was 61(61) years in 1994, but 66(65) years in 1998

OS: Mean(median) age at enrollment was 62(62) years in 1994, but 65(65) years in 1998

Youngest women have the most follow-up

Oldest women have the least follow-up

Age is highly predictive of physical measures, disease, and mortality

Missing data (cont’d)

Incorrect assumptions of MCAR have profound influence on mean estimates

Cross-sectional methods yield biased results • Does not summarize mean of the

cohort

Cross-sectional estimates lack precision

Using functional forms (change from baseline; % change from baseline) does not correct bias; only obscures bias

Repeated measures and LME models are statistically valid when missing data is MCAR or MAR

Estimates of average height

Conclusions

WHI is a unique rich resource

• Many participant characteristics are collected throughout follow-up

• Multitudes of uses

Seek parsimony

Be proactive to ensure data is useable

Beware of missing data

• Mitigate bias

• Choose appropriate model

• Consider limiting length of follow-up

Resources/references

WHI website; WHI Forms

Fitzmaurice, Garrett M., Nan M. Laird, and James H. Ware. Applied longitudinal analysis. Vol. 998. John Wiley & Sons, 2012. • Sullivan Pepe, M., & Anderson, G. L. (1994). A cautionary note on inference for

marginal regression models with longitudinal data and general correlated response data. Communications in Statistics-Simulation and Computation, 23(4), 939-951.

• Robins, J. M., Greenland, S., & Hu, F. C. (1999). Estimation of the causal effect of a time-varying exposure on the marginal mean of a repeated binary outcome. Journal of the American Statistical Association, 94(447), 687-700.

Tobin, Martin D., et al. "Adjusting for treatment effects in studies of quantitative traits: antihypertensive therapy and systolic blood pressure." Statistics in medicine 24.19 (2005): 2911-2935.

Shimbo, Daichi, et al. "The effect of hormone therapy on mean blood pressure and visit-to-visit blood pressure variability in postmenopausal women: results from the Women’s Health Initiative randomized controlled trials." Journal of hypertension 32.10 (2014): 2071.

Resources/references

Neuhouser, Marian L., et al. "Overweight, obesity, and postmenopausal invasive breast cancer risk: a secondary analysis of the women’s health initiative randomized clinical trials." JAMA oncology 1.5 (2015): 611-621.

Crandall, Carolyn J., et al. "Calcium plus vitamin D supplementation and height loss: findings from the Women's Health Initiative Calcium and Vitamin D clinical trial." Menopause (2016).

Crandall, Carolyn J., et al. "Postmenopausal weight change and incidence of fracture: post hoc findings from Women’s Health Initiative Observational Study and Clinical Trials." bmj 350 (2015): h25.

Manson, JoAnn E., et al. "Menopausal hormone therapy and health outcomes during the intervention and extended poststopping phases of the Women’s Health Initiative randomized trials." Jama 310.13 (2013): 1353-1368.