Session 2 Extending Principal Stratification for Characterising Trajectories of Change Richard Emsley Evaluation of Potential Mediators in Randomized Trials

Session 2

Extending Principal Stratification for Characterising Trajectories of Change

Richard Emsley

Evaluation of Potential Mediators in Randomized Trials of Complex Intervention

(Psychotherapies)

Methodology Research Group

Research funded by:MRC Methodology Grants G0600555

G0900678, G0800606, G0802418MHRN Methodology Research Group

http://www.mrc.ac.uk/

Outline for session 2

• More examples of Principal Stratification

• Introduction to growth curve models

• Mediation analysis using growth mixture models

• Latent class trajectory models

3

Principal Stratification – SoCRATES example

Frangakis C & Rubin D, Biometrics (2002); Jo B, Psych. Methods (2008).

• Considering therapeutic alliance as our mediator/process variable.

• We can postulate the existence of two principal strata:

– High alliance participants – those observed to have a high alliance in the therapy group together with those in the control group who would have had a high alliance had they been allocated to receive therapy.

– Low alliance participants – those observed to have a low alliance in the therapy group together with those in the control group who would have had a low alliance had they been allocated to receive therapy.

4

Principal Strata – SoCRATES example

αβ

γ

ITT effectHigh Alliance

Low Alliance

Randomisation

TherapeuticAlliance

PANSS

Randomisation PANSS

ITT effectRandomisation PANSS

5

SoCRATES - results

Estimated ITT effects on 18 month PANSS

Missing data ignorable (MAR)Low alliance High alliance

+7.50 (8.18) -15.46 (4.60)

Missing data latently ignorable (LI)Low alliance High alliance

+6.49 (7.26) -16.97 (5.95)

6

Principal Strata – SoCRATES example

High Alliance

Low Alliance

Randomisation

SessionsAttended

PsychoticSymptoms

dX

dY

U

α β

Randomisation

SessionsAttended

PsychoticSymptoms

dX

dY

U

α β

7

SoCRATES – effect of Sessions

Standard Structural Equation Model Missing data assumption: MAR(uncorrelated errors – no hidden confounding)

Low alliance High allianceα +14.96 (0.96) +16.91 (0.45)β +0.59 (0.38) -0.75 (0.23)

IV Structural Equation Model (with correlated errors – hidden confounding)

Low alliance High allianceα +14.90 (0.97) +16.95 (0.46)β +0.37 (0.47) -0.80 (0.29)

α - effect of randomisation on sessions

β - effect of sessions on 18-month PANSS

8

Mechanisms Evaluation

Compliance with allocated treatment Does the participant turn up for any therapy?

How many sessions does she attend?

Fidelity of therapyHow close is the therapy to that described in the treatment manual? Is it a cognitive-behavioural intervention, for example, or merely emotional support?

Quality of the therapeutic relationshipWhat is the strength of the therapeutic alliance? Is it associated with the effect of treatment?

9

Mechanisms Evaluation

What is the concomitant medication?Does psychotherapy improve compliance with medication which, in turn, leads to better outcome? What is the direct effect of psychotherapy? Is there any?

What is the concomitant substance abuse?Does psychotherapy reduce cannabis use, which in turn leads to improvements in psychotic symptoms?

What are the participant’s beliefs?

Does psychotherapy change attributions (beliefs), which, in turn, lead to better outcome? How much of the treatment effect is explained by changes in attributions?

10

Simple example – Two principal strata:Potential Compliers vs Non-compliers

Random allocation to treatment or no treatment (control).Those allocated to no treatment cannot get access to therapy.

Principal stratum 1: CompliersTreated if allocated to the treatment arm, not treated otherwise.

Principal stratum 2: Non-compliersNever receive treatment, regardless of allocation.

Possible to identify these two classes in those allocated to treatment; but they remain hidden in the control group.

11

Simple example – Two principal strata:Potential Low alliance vs Potential High alliance

Random allocation to treatment or no treatment.Those allocated to no treatment cannot get access to therapy.

Principal stratum 1: Low allianceTreated with low alliance if allocated to the treatment arm, not treated otherwise.

Principal stratum 2: High allianceTreated with high alliance if allocated to the treatment arm, not treated otherwise.

Possible to identify these two classes in those allocated to treatment; but they remain hidden in the control group.

12

Simple example – Three principal strata:Non-compliers vs Low alliance vs High alliance

Random allocation to treatment or no treatment.Those allocated to no treatment cannot get access to therapy.

Principal stratum 1: Non-compliersNever receive treatment, regardless of allocation.

Pricipal stratum 2: Low alliance (Partial compliance)Treated with low alliance if allocated to the treatment arm, not treated otherwise.

Principal stratum 3: High alliance (Full compliance)Treated with high alliance if allocated to the treatment arm, not treated otherwise.

Possible to identify these three classes in those allocated to treatment; but they remain hidden in the control group.

13

Three principal strata: Compliers vs Always admitted vs Never admitted

Random allocation to Hospital admission or Community care.Some of those allocated to Hospital admission never get admitted because of bed shortages. Some allocated to Community care have a crisis and have to be admitted.

Principal stratum 1: CompliersHospital admission if allocated to hospital, Community care, otherwise.

Principal stratum 2: Always admittedHospital admission, regardless of allocation.

Principal stratum 3: Never admittedCommunity care, regardless of allocation.

If allocated to Hospital admission and admitted then either Complier or Always admitted. If allocated to Hospital and receive Community care, then Never admitted. If allocated to Community care and receive Community care then either Complier or Never admitted. If allocated to Community care and admitted then always admitted.

14

Four principal strata based on a potential mediator.

Random allocation to CBT or no CBT (control).Those allocated to no CBT cannot get access to therapy. Intermediate outcome – taking antidepressant medication.

PS1: take medication irrespective of allocation.PS2: never take medication irrespective of allocation.PS3: take medication only if allocated to CBT.PS4: take medication only if allocated to control.

ITT effects in PS1 and PS2 tell us about direct effects of CBT.ITT effects in PS3 and PS3 tell us about the joint effects of CBT and medication.

15

Principal strata based on remission

Participants recruited to the trial during a psychotic episode. Random allocation to CBT or no CBT (control).Those allocated to no CBT cannot get access to therapy. Intermediate outcome –remission of psychotic symptoms.PS1: remission, irrespective of allocation.PS2: no remission, irrespective of allocation PS3: remission only if allocated to CBT.PS4: remission only if allocated to control (PS4 ruled out a priori? – the monotonicity assumption)

What if our final outcome is relapse? Only makes sense to look at relapse rates in PS1. No-one to relapse in PS2. No controls for those in PS3.

We’ll leave this one for another day!

16

Notation

Zi – treatment group: the outcome of randomisation (Zi=1 for treatment, 0 for controls).

Xi′ = X1i, X2i … Xpi – baseline covariates.

Yi – observed outcome.

Mi – intermediate outcome that is a putative mediator of the effects of treatment on outcome (either a quantitative measure or binary).

Ri – response: missing value indicator (Ri=0 if Yi is missing, 1 if observed).

We also define the following potential (counterfactual) outcomes:

Mi(0) – mediator if randomised to the control condition.Mi(1) – mediator if randomised to treatment.

17

Principal Strata with a binary mediators

• There are now four distinct possibilities (classes) for the joint combination of Mi(1) and Mi(0).

• It is sometimes assumed that only one of classes 3 and 4 is present (known as the monotonicity assumption).

• Individuals’ class membership is not known: for example, an individual with Zi=1 and Mi=1 is only known to belong to class 2 or class 3.

Class C (stratum)

Mi(1) Mi(0) (Mi(1), Mi(0)) Proportion Treatment Effect

1 0 0 (0,0) P1 ITT1

2 1 1 (1,1) P2 ITT2

3 1 0 (1,0) P3 ITT3

4 0 1 (0,1) p4 ITT4

18

Principal Strata with a binary mediators

• If the mediator is only in the treatment condition (e.g. therapeutic alliance when no intervention offered in the control group), then Mi(0)=0 for everyone.

• Classes 2 & 4 do not exist, so we’re only considering the existence of (0,0) and (1,0) classes.– Similar to only thinking about compliers & non-

compliers when no active control

Class C (stratum)

Mi(1) Mi(0) (Mi(1), Mi(0)) Proportion Treatment Effect

1 0 0 (0,0) P1 ITT1

2 1 1 (1,1) P2 ITT2

3 1 0 (1,0) P3 ITT3

4 0 1 (0,1) p4 ITT4

19

Estimation of stratum-specific treatment (ITT) effects

Let’s say there are two principal strata, with proportions p1

and p2 (with p1 + p2=1).

Let ITTall be the overall ITT effect (which can be estimated

directly in the conventional way)

Similarly let ITT1 and ITT2 be the stratum-specific ITT

effects.

Then

ITTall = p1ITT1 + p2ITT2

20

The identification problem

If

ITTall = p1ITT1 + p2ITT2

and we are not prepared to make any further assumptions, then we cannnot get unique estimates of ITT1 and ITT2. If we increase ITT1 then ITT2 will

decrease to compensate (giving the same value for ITTall).

What can we do?

21

Exclusion restrictions

What if stratum 1 corresponds to the Non-compliers? These are participants who never receive treatment whatever the treatment allocation. Let’s assume that allocation also has no effect on outcome in the Non-compliers (an exclusion restriction).

Example:If you don’t take the tablets it doesn’t matter whether you have been assigned to the placebo or the supposedly active drug.

22

With the exclusion restriction we have an identifiable (estimable) stratum-specific treatment effect Now

ITTall = p1.0 + p2ITT2

ITTall = p2ITT2

And therefore

ITT2 = ITTall/p2

This is the instrumental variable estimator as seen earlier.

CACE = Overall ITT effect/Proportion of Compliers

23

Two exclusion restrictions for the Hospital admission/Community care trial

ITTall = p1ITT1 + p2ITT2 + p3ITT3

(p1+p2+p3=1)

ITTall = p1ITT1 + p2.0 + p3.0

And therefore

ITT1 = ITTall/p1

This is again the instrumental variable estimator(p1 is fairly straightforward to estimate).

CACE = Overall ITT effect/Proportion of Compliers

24

Principal strata based on therapeutic alliance are a problem

An a priori exclusion restriction for the Low alliance stratum extremely difficult to justify. In the three-stratum setting there is also a problem unless we can introduce two exclusion restrictions.

What is the solution?

Answer: Find baseline variables that help predict stratum membership (i.e. help us to discriminate Low and High principal strata).

Although they are not necessary for identification, baseline variables that help predict stratum membership are also useful in the presence of exclusion restrictions – they increase the precision of the estimates.

25

Principal Stratification in Practice

Problems:

• Imprecise estimates – trials not large enough.

• Missing data for intermediate variables (sometimes lots!) – source of imprecision and bias.

• Difficult to find baseline variables that are good predictors of stratum membership.

• Difficult-to-verify assumptions.

26

Principal Stratification in Practice

• Imprecise estimates – trials not large enough.Combine data from several trials? Meta-regression. Need common outcomes.

• Missing data for intermediate variables (sometimes lots!) – source of imprecision and bias.

If you think it’s important then collect the data.

• Difficult to find baseline variables that are good predictors of stratum membership.

Novel designs: Incorporate multiple randomisations to specifically target the intermediate variables.

• Difficult-to-verify assumptions.Sensitivity analyses.

27






28

Situation considered so far…

Mediator

Outcomes

dX

dY

U

Randomisation

29

Extending to repeated measures on the outcome variable

Treatment

Mediator

Outcomes

Time 1

Outcomes

Time 2

Outcomes

Time 3

30

31

Simple linear regression

1 2

2| ~ (0, )

i i i

i i

y x

x N

where εi is the residual or error term for the ith participant, assumed to be independent of all the residuals for other participants.

β1+β2xi is the fixed part of the model

εi is the random part of the model

We also assume that the variance of Var(yi|xi)= Var(εi|xi)=σ2

32

Variance Components

1ij ij

ij j ij

y

y

We split the residual from the first model into two components:

ζj is specific to participant j and constant across occasions

εij is specific to each participant j at each occasion i

ζj is a random effect or random intercept: mean=0, variance=ψ

εij is the random deviation of yij from participant j’s mean: mean=0, variance=θ.

33

Mixed Models

• Mixed models are characterized as containing both fixed effects and random effects.

• The fixed effects are analogous to standard regression coefficients and are estimated directly.

• The random effects are not directly estimated but are summarized according to their estimated variances and covariances.

34

Stata commands

xtmixed - Multilevel mixed-effects linear regressionxtmelogit - Multilevel mixed-effects logistic regression

Can also use: xtreg - Fixed-, between-, and random-effects, and

population-averaged linear modelsgllamm – Generalised Linear Latent and Mixed Models

35

Growth Curves for repeated outcome measures

• Diagram assumes balanced occasions.

• This is the random part of a random coefficient model, which is the same as a traditional linear growth curve model.

• Occasion i, subject j

Outcome 1 Outcome 2 Outcome 3

I S

1 2 1 2

fixed part random part

ij ij j j ij ijy x x

1 1 112

36

Are missing data a problem?

• Are missing data a problem? Why can’t we simply analyse the observed data using an appropriate analysis model?

• There are a number of reasons – Invalidity of the estimation method– Loss of precision– Violation of the intention-to-treat principle– Lack of generalisability

• The main issue is selection bias.

37

Missingness mechanisms

• Subjects in the sample provide two types of information– The pattern of missing values– The observations actually made

• The missing value mechanism is the probability that a set of values are missing given the values taken by the (later) observed and missing observations.

• For a number of analysis approaches based on the observed data it will then be possible to specify the MV mechanism or assumptions under which they provide valid inferences.

38

Rubin’s classification

• Rubin’s classification of MV generating mechanisms has proved useful for this purpose

– Missing Complete at Random (MCAR)

– Missing at Random (MAR)

– Not Missing at Random (NMAR)

39

How to decide between them?

• It is not possible to determine empirically which of the three classes we are in– We can never rule out NMAR empirically

• Although the data can tell us some things– Can choose between MCAR and MAR

• The final decision has to be made on empirical [subject-matter?] as well as theoretical grounds

40

Some Good News!

• Mixed models or random effects models estimated using maximum likelihood estimation is also VALID under the missing at random assumption.

• So we can model ALL the data – including the intermediate outcomes.

• Ensures participants with some missing values are NOT dropped from the subsequent analysis (as would be performed in SPSS – akin to complete case analysis).

41

Example: SoCRATES Trial again

• We examine the primary outcome, The Positive and Negative Syndromes Schedule (PANSS), an interview-based scale for rating 30 psychotic and non-psychotic symptoms administered blind to condition allocation.

• The PANSS was administered– at baseline, – once a week over the first 6 weeks (we just use 6 weeks

here)– and then at 3 months, 9 months and 18 months.

• In analyses we log transformed the timescale, and considered linear and quadratic terms.

42

Observed trajectories for 30 patients

43

Model fitted quadratic curves for 30 same patients

44






45

Extending to repeated measures on the outcome variable

Treatment

Mediator

Outcomes

Time 1

Outcomes

Time 2

Outcomes

Time 3

46

Principal Strata – with growth curves

High Alliance

Low Alliance


I S


I S

47

Mixture Modelling

• Mixture modeling refers to modeling with categorical latent variables that represent subpopulations where population membership is not known but is inferred from the data – such as principal strata.

• The simplest longitudinal mixture model is latent class growth analysis (LCGA). In LCGA, the mixture corresponds to different latent trajectory classes. No variation across individuals is allowed within classes.

• Another longitudinal mixture model is the growth mixture model (GMM). In GMM, within class variation of individuals is allowed for the latent trajectory classes. The within-class variation is represented by random effects, that is, continuous latent variables, as in regular growth modeling.

48

Growth Mixture Model

• Since c is a categorical latent variable, the interpretation of this GMM is not the same as for a continuous latent variable.

• The arrows from c to the growth factors indicate that the intercepts in the regressions of the growth factors on Z vary across the classes of c.

Y1 Y2 Y3

I

Y4 Y5

S

C

Z

X

49

SoCRATES Analysis in MPlus

• We simultaneously fit the following models using maximum likelihood:– Principal strata membership on covariates (log of

duration of untreated psychosis, centre, years of education)

– Quadratic growth curve model within each class, allowing the variances of the intercept

– Effect of randomisation on the slope within each class

• Bootstrap the procedure to obtain valid standard error estimates.

50


FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASSESBASED ON THE ESTIMATED MODEL

Latent Classes 1 69.89513 0.34774 2 131.10487 0.65226

FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASS PATTERNS BASED ON ESTIMATED POSTERIOR PROBABILITIES

Latent Classes 1 69.89513 0.34774 2 131.10487 0.65226

CLASSIFICATION QUALITY Entropy 0.641

51


CLASSIFICATION OF INDIVIDUALS BASED ON THEIR MOST LIKELY LATENT CLASS MEMBERSHIP

Class Counts and Proportions

Latent Classes 1 63 0.31343 2 138 0.68657

Average Latent Class Probabilities for Most Likely Latent Class Membership (Row) by Latent Class (Column)

1 2 1 0.859 0.141 2 0.114 0.886

52


Fixed Part of the Model

MODEL RESULTS Bootstrap Two-

Tailed Estimate S.E. Est./S.E. P-Value

INTER |

PANTOT 1.000 0.000 999.000 999.000

PAN1 1.000 0.000 999.000 999.000

PAN3 1.000 0.000 999.000 999.000

PAN9 1.000 0.000 999.000 999.000

PANT18 1.000 0.000 999.000 999.000

SLOPE |

PANTOT 0.000 0.000 999.000 999.000

PAN1 1.946 0.000 999.000 999.000

PAN3 2.565 0.000 999.000 999.000

PAN9 3.611 0.000 999.000 999.000

PANT18 4.369 0.000 999.000 999.000

53


Bootstrap Two-Tailed Estimate S.E. Est./S.E. P-Value

Quadratic |

PANTOT 0.000 0.000 999.000 999.000

PAN1 3.787 0.000 999.000 999.000

PAN3 6.579 0.000 999.000 999.000

PAN9 13.039 0.000 999.000 999.000

PANT18 19.092 0.000 999.000 999.000

54


Latent Class 1 – Low Alliance Group

SLOPE ON

GROUP 1.808 1.644 1.100 0.271

Q WITH

INTER 0.543 1.628 0.334 0.739

Means

INTER 90.444 3.441 26.281 0.000

Q 2.269 0.460 4.928 0.000

Intercepts

SLOPE -17.118 2.697 -6.346 0.000

Variances

INTER 101.743 53.936 1.886 0.059

Q -0.149 0.419 -0.356 0.722

55


Latent Class 1 – Low Alliance Group

Residual Variances

PANTOT 155.448 25.475 6.102 0.000

PAN1 225.422 33.614 6.706 0.000

PAN3 134.520 29.049 4.631 0.000

PAN9 96.053 33.022 2.909 0.004

PANT18 37.002 24.554 1.507 0.132

SLOPE 9.209 8.681 1.061 0.289

56

Estimated means for class 1 and observed trajectories

57


Latent Class 2 – High Alliance Group

SLOPE ON

GROUP -2.843 1.136 -2.502 0.012

Q WITH

INTER 0.543 1.628 0.334 0.739

Means

INTER 87.844 1.954 44.955 0.000

Q 1.784 0.309 5.769 0.000

Intercepts

SLOPE -11.558 1.763 -6.556 0.000

Variances

INTER 163.426 33.921 4.818 0.000

Q 0.625 0.298 2.095 0.036

58


Latent Class 2 – High Alliance Group

Residual Variances

PANTOT 155.448 25.475 6.102 0.000

PAN1 225.422 33.614 6.706 0.000

PAN3 134.520 29.049 4.631 0.000

PAN9 96.053 33.022 2.909 0.004

PANT18 37.002 24.554 1.507 0.132

SLOPE -4.728 5.382 -0.878 0.380

59

Estimated means for class 2 and observed trajectories

60

Sample and estimated means by class

61






62

Growth Curves for repeated outcome measures

• Diagram assumes balanced occasions.

• This is the random part of a random coefficient model, which is the same as a traditional linear growth curve model.

• Occasion i, subject j


I S

1 2 1 2


ij ij j j ij ijy x x

1 1 112

63

Latent Trajectory Models

• Here the ς1j are represented by discrete trajectory classes c with probability πc:

(ςit | c) = e1c + e2ct

where

• e1c is the trajectory origin or intercept for class c

• e2c is the trajectory slope for class c

• Prevalence of trajectory class is πc

Pickles and Croudace (2010). SMinMR.

1 2 1


ij ij j ijy x

64


• Hypothetical example with 2 distinct classes

TimeTime

Class 1 Class 2

Outcome

65


• Three type of models:1. Unconditional trajectory classes and unconditional

class probabilities2. Unconditional trajectory classes and conditional

class probabilities– Allow the probability to depend on covariates

3. Conditional trajectory classes and unconditional class probabilities– Covariate effects included in fixed part of the model– Classes now represent groups having accounted for

covariate differences

– Similarity with Cluster Analysis.

66

Latent Class Growth Model (LCGM)

I

S1111 b1

1

b2

y1 y2 y3 y4

CLatent classes

67

Posterior Class Membership

• For example, we can fit a growth model in which we postulate just two underlying patterns of change.

• The model identifies those two patterns of change and their relative size so as to best fit the data.

• It is then possible to calculate how likely each participant is, given their data, to belong to one or other class.

• We can then group participants according to their most likely class and display the data from those subjects and examine how membership of those groups are associated with other variables.

68

Deciding the number of classes-1

• Tests for k-1 versus k classes:– Standard LR test rejects far too often over-

estimating the number of classes as LR statistic is not chi-square distributed.

– Lo-Mendell-Rubin LR test derives an approximate distribution as a mixture of LR comparisons. Performs much better.

– Parametric Bootstrap LR test (standard LR test compared to LR-test distribution of k-1 versus k comparison of bootstrap samples simulated under k-1 model). Performs still better than LMR test for both Type-1 error rate and power. Performance declines in sample sizes <200.

• See Nylund, Asparouhov and Muthen, SEM, 2007 for details.

69

• Information Criteria (p=parameters, n=sample size)

AIC = -2logL+2p : poorCAIC = -2logL+p(log(n)+1) : better but not goodBIC = -2logL+plog(n) :performs well, less so in

small samples

Sample-size adjusted BIC = as above with n*=(n+2)/24 : generally performed worse than BIC

Overall BLRT slightly better than BIC but computationally intensive and assumes k-1 model correctly specified.

Deciding the number of classes-2

70

Example: SoCRATES Trial again

• We examine the primary outcome, The Positive and Negative Syndromes Schedule (PANSS), an interview-based scale for rating 30 psychotic and non-psychotic symptoms administered blind to condition allocation.

• The PANSS was administered– at baseline, – once a week over the first 6 weeks (we just use 6 weeks

here)– and then at 3 months, 9 months and 18 months.

• In analyses used log transformed the timescale, and considered linear and quadratic terms (GLLAMM).

• See Pickles, A. & Croudace, T. (2010) Latent Mixture Models for Multivariate and Longitudinal Outcomes. Statistical Methods in Medical Research. 19(3), pp 271-290.

71

SoCRATES example: Increasing classes and fit criteria

72

Trajectories for the 4-class model for the SoCRATES study

10

15

20

25

30

Po

sitiv

e s

ymp

tom

s

0 1 2 3 4logweek

Class 1 (42%) Class 2 (6%)Class 3 (37%) Class 4 (15%)

73

Assigned classAverage probabilityClass 1

Average probabilityClass 2



Class 1 0.84 0.02 0.14 0.00

Class 2 0.00 0.87 0.09 0.04

Class 3 0.18 0.03 0.75 0.04

Class 4 0.00 0.03 0.11 0.86

Class assignment certainty

74

Prevalence of treatment by class

Class Control Treatment Total

1 39 (30%) 90 (70%) 129

2 8 (62%) 5 (32%) 13

3 37 (31%) 83 (69%) 120

4 19 (40%) 28 (60%) 47

Total 103 (33%) 206 (67%) 309

75

Latent Class Growth Model (LCGM) with predictors of class membership

I

S1111 b1

1

b2

y1 y2 y3 y4

CLatent classes treatment

76

Extend model to include predictors of trajectory class membership

Predicting class from randomised group – gllamm output.

log odds parameters (SEs) class 1 rgrp: .37211754 (.44317846) _cons: .89785223 (.40652414) class 2 rgrp: -1.8011231 (.87633211) _cons: .03280307 (.51026448) class 3 rgrp: .45207293 (.49493849) _cons: .70044474 (.43137194)

77

Latent trajectory models for mediators

• We can also apply this methodology to repeated measures on the mediator.

• MIDAS (Motivational Intervention for Drug and Alcohol use in Schizophrenia) is an RCT of 327 patients with dual diagnosis.

• For example, in the MIDAS trial we are interested in the use of cannabis – the intervention aimed to reduce the amount of substance use over time, with the aim of this reducing the psychotic symptoms.

• Here we look at 160 cannabis users at baseline, and examine their cannabis use over time; measured as average daily weight per using day.

78

Observed trajectories for 20 random patients

79

Sample means over time for latent classes

80

Model based means over time for latent classes

81

Connecting mediators with outcomes

Substance Use 0

Substance Use 6

Substance Use 12

Substance Use 18

Substance Use 24

I S

Symptoms 0 Symptoms 12 Symptoms 24

I S

More work is needed to develop these models to allow for unmeasured confounders between the mediators and outcomes – we’re working on this now!

82

What do others do for causal mediation analysis?

• A group based at HSPH, led by Tyler VanderWeele and Stijn Vansteelandt, have developed methods based on regression models. These allow for interactions between the exposure/treatment and the mediators, but are still based on the no unmeasured confounders assumption.

• A group based in Penn State, led by Tom Ten Have and Marshall Joffe, have promoted the use of rank preserving structural models. We have demonstrated that these are merely an unnecessary complex use of the instrumental variables method we introduced earlier – currently being written up.

83

Summary

• There is a large literature of mixture models for analysing longitudinal data.

• Viewing the mixtures as being principal strata allows us to use these mixture models for mediation analysis without the unmeasured confounding assumption between mediator and outcomes.

• We can use repeated measures on the mediator to form latent classes based on their trajectories over time – the trick is to link this with outcome trajectories and classes.

84

Further Reading – principal stratification and Mplus

Emsley RA, Dunn G & White IR (2010). Mediation and moderation of treatment effects in randomised trials of complex interventions. Statistical Methods in Medical Research 19 (3), pp237-270.

Emsley RA & Dunn G. (2010). Evaluation of potential mediators in randomized trials of complex interventions. In preparation for “Statistical Methods in Causal Inference”, Berzuini, C., Dawid, P. & Bernardinelli, L. (Eds).

Jo B (2008). Causal inference in randomized experiments with mediational processes. Psychological Methods 13, 314-336.

Jo B & Muthén BO (2001). Modeling of intervention effects with noncompliance:a latent variable approach for randomized trials. In: Marcoulides GA, Schumacker RE, eds. New Developments and Techniques in Structural Equation Modeling. Mahwah, New Jersey: Lawrence Erlbaum Associates pp. 57-87.

Jo B & Muthén BO (2002). Longitudinal Studies With Intervention and Noncompliance: Estimation of Causal Effects in Growth Mixture Modeling. In: Duan N, Reise S, eds. Multilevel Modeling: Methodological Advances, Issues, and Applications. Lawrence Erlbaum Associates pp. 112-39.

Dunn, G., Maracy, M. & Tomenson, B. (2005). Estimating treatment effects from randomized clinical trials with non-compliance and loss to follow-up: the role of instrumental variable methods. Statistical Methods in Medical Research 14, 369-395.

Mplus User’s Guide illustrates CACE estimation.

85

Further Reading – growth mixtures

Pickles, A. & Croudace, T. (2010) Latent Mixture Models for Multivariate and Longitudinal Outcomes. Statistical Methods in Medical Research 19(3), pp271-290.

Emsley, R.A, Pickles, A. & Dunn, G. (2010) Mediation analysis with growth mixture modelling. In preparation for Statistics in Medicine.

Muthén B, Brown CH, Masyn K et al. General growth mixture modeling for randomized preventive interventions. Biostatistics 2002; 3: 459–75.

Muthén, B. & Brown, H. (2009). Estimating drug effects in the presence of placebo response: Causal inference using growth mixture modeling. Statistics in Medicine, 28, 3363-3385.

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Documents

Session 2 Extending Principal Stratification for Characterising Trajectories of Change Richard Emsley Evaluation of Potential Mediators in Randomized Trials