www.elsevier.com/locate/schres

Schizophrenia Research 71 (2004) 113–125

Antisaccade performance in biological relatives of

schizophrenia patients: a meta-analysis

Deborah L. Levya,*, Gillian O’Driscollb, Steven Matthyssea, Samantha R. Cookc,Philip S. Holzmana, Nancy R. Mendelld

aPsychology Research Laboratory, McLean Hospital, 115 Mill Street, Belmont, MA 02478, USAbDepartment of Psychology and Douglas Hospital Research Center, McGill University, Montreal, Quebec, Canada

cDepartment of Statistics, Harvard University, Cambridge, MA 02138, USAdDepartment of Applied Mathematics and Statistics, State University of New York at Stony Brook, Stony Brook, NY 11794, USA

Received 4 September 2003; accepted 12 November 2003

Available online 22 January 2004

Abstract

Poor performance on the antisaccade (AS) task has been interpreted as a potential indicator of genetic liability that may

enhance the power of linkage studies of a multidimensional phenotype for schizophrenia. Every study has replicated the finding

of significantly worse performance in schizophrenia patients regardless of which specific antisaccade paradigm was employed.

In some studies involving a standard version of the antisaccade task, relatives of schizophrenia patients made an increased

number of errors, but in other studies that used this same paradigm, relatives of schizophrenia patients did not differ from

controls. In this paper, we report the results of a meta-analysis on studies that used the standard antisaccade paradigm. The

meta-analysis shows that those studies that reported large effect sizes and statistically significant differences between relatives

of schizophrenia patients and controls used inclusion/exclusion criteria that were not symmetrical between the two groups,

whereas those studies that reported small and nonsignificant differences between relatives of schizophrenia patients and controls

used symmetrical inclusion/exclusion criteria. Specifically, studies that applied stricter psychopathology exclusion criteria to

controls than to relatives of schizophrenia patients had larger effect sizes than studies that applied comparable exclusion criteria

to both groups, suggesting that antisaccade performance is compromised by psychopathology in general rather than by

schizophrenia per se. Since symmetrical inclusion/exclusion criteria between relatives of schizophrenia patients and controls are

essential for a genetic analysis, and those studies that did apply symmetrical criteria had small effect sizes, the available data

suggest that poor antisaccade performance is unlikely to be useful in identifying clinically unaffected carriers of genes for

schizophrenia.

D 2003 Elsevier B.V. All rights reserved.

Keywords: Schizophrenia; Antisaccade performance; Meta-analysis

0920-9964/$ - see front matter D 2003 Elsevier B.V. All rights reserved.

doi:10.1016/j.schres.2003.11.006

* Corresponding author. Tel.: +1-617-855-2854; fax: +1-617-

855-2778.

E-mail address: levy@wjh.harvard.edu (D.L. Levy).

1. Introduction

The risk for schizophrenia in first-degree biological

relatives of schizophrenics (RSP) is only about 6.5%

(Kendler et al., 1993), possibly too low to have

D.L. Levy et al. / Schizophrenia Research 71 (2004) 113–125114

adequate power to detect linkage even when it is

present. Traits that are substantially more penetrant

than schizophrenia in RSP could increase the power of

linkage studies (Matthysse and Parnas, 1992; see

Greenberg et al., 2000, for the usefulness of such traits

in relation to genetic studies of nonpsychiatric disor-

ders). The finding of increased errors on the antisac-

cade (AS) task not only in schizophrenia patients, but

also in some studies of RSP, has led to the speculation

that poor AS performance may tap processes related to

genetic vulnerability (Clementz et al., 1994; Katsanis

et al., 1997; McDowell and Clementz, 1997; Crawford

et al., 1998; McDowell et al., 1999; Thaker et al.,

2000; Karoumi et al., 2001; Curtis et al., 2001a; Ross

et al., 1998).

The AS task requires the subject, who is fixating a

central target, to inhibit a saccade to an abrupt-onset

peripheral stimulus and to generate a voluntary sac-

cade to the mirror location in the opposite periphery,

where there is no visible target. Correct saccades

away from the target are called ‘antisaccades’. Sac-

cades toward the peripheral target are considered

errors. Three AS paradigms have been used in studies

of RSP. In the most widely used version of the AS

task (which we refer to as the ‘‘standard’’ paradigm),

offset of the central fixation point and onset of the

peripheral target occur simultaneously. Other ver-

sions, which have been used in fewer studies, are

the ‘‘overlap’’ and the ‘‘gap’’ paradigms. In the

‘‘overlap’’ paradigm, the central fixation point stays

on for a short time after the peripheral target has been

illuminated. In the ‘‘gap’’ paradigm, the offset of the

central fixation point precedes the appearance of the

peripheral target by a short time. In any of these

paradigms, peripheral targets may appear at single

(e.g., F 5j) or multiple (e.g., F 5j, 10j, 15j)eccentricities and timing parameters can be fixed or

variable.

In this paper, we review the results of studies on

RSP from each of the various AS paradigms. Since

paradigm variations affect error rate and other

measures (e.g., latency) (Fischer and Weber, 1992,

1997), the results of studies of RSP on the AS task

are discussed for each of the three paradigms

separately. We report here the results of a meta-

analysis of studies of RSP that used the standard AS

paradigm, including a test for heterogeneity in effect

size and planned contrasts to examine the effects of

possible moderator variables on variability in effect

size. We also test for heterogeneity in effect size in

studies of RSP that used the overlap AS paradigm.

In addition, we examine the extent to which the

data are consistent with a genetic model of co-

familial transmission.

2. Results

2.1. Standard AS task

2.1.1. Magnitude of effect

Nine studies compared the performance of RSP

and nonpsychiatric controls on a standard version of

the AS task. In five of these studies, RSP showed a

significantly higher error rate than controls did

(Clementz et al., 1994; Katsanis et al., 1997; McDo-

well and Clementz, 1997; Curtis et al., 2001a;

Karoumi et al., 2001), and in four studies RSP did

not differ from controls in mean error rate (Thaker et

al., 1996, 2000; Crawford et al., 1998; Brownstein et

al., 2003). Table 1 presents descriptive statistics as

well as effect sizes for the differences between RSP

and controls, calculated in three ways. Regardless of

the method of calculation, the effect sizes vary

considerably across studies. Using Cohen’s method

[d=(meanRSP�meanCONTROLS)/pooled standard de-

viation], d ranges from � 0.05 to 0.84, consistent

with effects ranging from small (V 0.2) to large (0.8)

(Cohen, 1977). The mean d is 0.43 (S.D.: 0.32) and

the median is 0.51 [95% confidence intervals (CIs):

0.19–0.68]. The correlations between group mem-

bership and performance, r, range from a low of

� 0.025 to a high of 0.39, a positive correlation

indicating a higher error rate in RSP than in controls.

The mean value of r is 0.20 (S.D.: 0.15) and the

median is 0.25 (95% CIs: 0.09–0.32), again consis-

tent with small to medium effects (Rosenthal and

Rosnow, 1984).

The effect size d assumes equal variances in the

groups being compared (Cohen, 1977), but in some of

the studies of AS performance in RSP, the variances

were not equal. The value of d is under- or over-

estimated in such cases, because the denominator in

the calculation of d is the pooled standard deviation of

the two groups. In order to take into account unequal

variances, we also calculated Glass’s delta, in which

Table 1

Antisaccade studies (standard paradigm) of first-degree relatives of schizophrenia patients ordered by effect size (Cohen’s d)

Study First-degree relatives of

schizophrenia patients

Nonpsychiatric controls Effect size

N % Error Scorea N % Error Scorea Cohen’s d r Glass’ delta

Brownstein et al.

(2003)

98 23.2 (18.5) 24 24.2 (19.1) � 0.05 � 0.025 � 0.05

Thaker et al.

(1996)b26 26.7 (10.8) 68 24.3 (19.2) 0.14 0.07 0.13

Thaker et al.

(2000)b55 28.96 (23.2) 62 24.98 (18.7) 0.19 0.095 0.21

Crawford et al.

(1998)

50 33.0 (29.0) 38 27.0 (23.0) 0.23 0.114 0.26

Clementz et al.

(1994)

32 32.1 (23.2) 33 22.0 (15.9) 0.51 0.25 0.63

McDowell and

Clementz

(1997)

60 17.0 (21.0) 32 7.0 (16.0) 0.51 0.25 0.625

Curtis et al.

(2001a)

116 38.2 (22.3) 109 24.6 (17.0) 0.68 0.32 0.80

Karoumi et al.

(2001)

21 36.6 (24.1) 21 20.4 (13.6) 0.83 0.38 1.20

Katsanis et al.

(1997)

55 45.0c (26.2d) 38 25.0c (19.5d) 0.84 0.39 1.00

Mean (standard deviation).a Error rate was age-adjusted in some studies but not in others.b Regardless of the method of calculation, all of the effect sizes in the Thaker et al. (1996, 2000) studies become smaller when the controls

are restricted to community subjects without schizophrenia spectrum personality (SSP) traits. Thus, including all community subjects in the

control group in the effect sizes shown above did not mask a larger difference between RSP and controls.c Median; for estimating effect size, the median was considered equivalent to the mean.d Estimated based on interquartile range as follows: (3/4)(Q3�Q1), where Q3 and Q1 were the upper and lower ends of the interquartile

range, respectively.

D.L. Levy et al. / Schizophrenia Research 71 (2004) 113–125 115

the denominator is the standard deviation of the

control group (Glass, 1976; see Rosenthal, 1984).

As Table 1 shows, Glass’s delta tends to be larger

than d, especially in those studies in which RSP and

controls differed in variance. The mean Glass’ delta is

0.53 (S.D.: 0.42) and the median is 0.625 (95% CIs:

0.21–0.86).

One-sample t-tests on the values of Cohen’s d, r,

and Glass’ delta for the various studies indicated that

that each of the means differed significantly from 0

(Ps < 0.005). Although this result would seem to rule

out the possibility of no difference in AS performance

between RSP and controls, such an interpretation is

clouded not only because there is significant hetero-

geneity among the studies, but also because the

heterogeneity is traceable to a specific methodological

feature of the studies (see next two sections below).

Therefore, the mean effect sizes are not interpretable

as a population indicator, because the values differ

significantly among the studies depending on the

methods used and the overall mean of the group of

studies will depend on the number of studies using

each method.

2.1.2. Heterogeneity analysis

We evaluated whether the effect sizes could be

considered consistent with each other, or were con-

tradictory, by performing an analysis for heterogeneity

in effect size (Rosenthal and Rosnow, 1984). The

results showed evidence of significant heterogeneity

(X2 = 19.68, = 8, 0.01 <P < 0.02), indicating that the

effect sizes among the various studies are not consis-

tent with a single mean and thus were drawn from

more than one distribution. Having found significant

1 Although the overlap condition increased effect size relative

to the standard condition in one study (McDowell and Clementz,

1997) and reduced it in another (Curtis et al., 2001b), the results of

the two studies may, nevertheless, be consistent with each other. The

single eccentricity (F 10j) target used by Curtis et al. (2001b) is

comparable to the ‘‘near’’ target (F 8j) used by McDowell et al.

(1999). The results of both the McDowell et al. (1999) and Curtis et

al. (2001b) studies indicate that ‘‘near’’ overlap targets do not seem

to optimize differences between RSP and controls. Thus, the overlap

condition may not have produced a larger effect than the standard

condition in the Curtis et al. (2001b) study because a ‘‘far’’

peripheral target may be required to detect it, and that study used

only a ‘‘near’’ peripheral target. Averaging across near and far

targets in the McDowell and Clementz (1997) study may have

obscured the difference in effect size between the near and far

overlap targets that became apparent when the eccentricities were

evaluated separately in their 1999 study. This interpretation is

consistent with the finding that an overlap paradigm that used only a

‘‘near’’ target normalized errors in schizophrenia patients (Levy et

al., 1998).

D.L. Levy et al. / Schizophrenia R116

heterogeneity, we tried to identify possible sources of

this heterogeneity.

2.1.3. Sources of heterogeneity

Based on the published literature, we identified

several possible sources of heterogeneity: (1) wheth-

er symmetrical or asymmetrical diagnostic exclusion

criteria were applied to controls and RSP; (2)

whether relatively few or many AS trials were

administered (V 24 vs. >25, respectively); and (3)

whether the peripheral target appeared at one or

multiple eccentricities (1 vs. >1 eccentricity). Stud-

ies were classified as applying symmetrical diagnos-

tic exclusion criteria if the same exclusion criteria

were applied to both groups, and as applying

asymmetrical exclusion criteria if the exclusion

criteria differed for the two groups. The specific

details of the classifications are described in footnote

b of Table 2, but an example of the kinds of

asymmetry that we observed in the studies being

reviewed here would be excluding individuals with

a history of a nonpsychotic mood disorder from the

control group but not excluding them from the RSP

group.

To determine whether any of these variables was

related to variability in the magnitude of the observed

effect sizes, we performed three planned linear con-

trasts. In all of the contrasts, the sum of the weights

for each contrast was 0 and the effect size used was d

(Rosenthal and Rubin, 1982). For each contrast the

nine studies could be divided into two groups of

approximately equal size (i.e., 4 vs. 5). Table 2

presents the weights assigned to each study for each

planned contrast. Each contrast yielded a standard

score, Z, which corresponds to a p-value indicating

whether a particular methodological variable accounts

for significant variability in the magnitude of the

observed effect sizes.

Studies that applied more restrictive diagnostic

exclusion criteria to controls than to RSP had a

significantly larger mean effect size than studies that

applied comparable exclusion criteria to both groups

(Z = 3.60; P= 0.00034). Specific features of the AS

procedure did not account for variability in effect size

across studies: neither the contrast for number of trials

(Z =� 0.16; P= 0.88) nor the contrast for number of

eccentricities (Z =� 0.76, P= 0.44) was statistically

significant.

2.2. Overlap AS task

Two groups of investigators compared RSP and

controls on the overlap AS task (McDowell and Clem-

entz, 1997;McDowell et al., 1999; Curtis et al., 2001b).

Because data were also available from subsets of some

of these samples on the standard AS paradigm, the

effect sizes for the two paradigms can be compared as

well. Table 3 presents descriptive statistics and effect

sizes for those studies that used the overlap paradigm.

In the McDowell and Clementz (1997) study, RSP

made significantly more errors than controls in both

AS paradigms. The effect size, Glass’ delta, was larger

for the overlap paradigm than for the standard para-

digm (0.73 vs. 0.625, respectively), although Cohen’s d

(0.39 vs. 0.51, respectively) and r (0.19 vs. 0.25,

respectively) were actually smaller. In the Curtis et al.

(2001b) study, RSP made significantly more errors

than controls on the standard AS task, but not on the

overlap AS task. All effect sizes for the overlap

paradigm were smaller than those for the standard

paradigm (Glass’ delta: 0.29 vs. 0.50, respectively; d:

0.23 vs. 0.52, respectively; r: 0.11 vs. 0.25, respective-

ly). In the McDowell et al. (1999) study, only the

overlap paradigm was used, so the relative sensitivity

of the overlap vs. standard paradigms cannot be

addressed. That study evaluated the effect of target

eccentricity and found that the effect sizes for ‘‘far’’

overlap targets were much larger than those for ‘‘near’’

overlap targets in each of three samples of RSP.1

esearch 71 (2004) 113–125

Table 2

Relevant statistical values and contrast weights for planned contrast calculations: standard antisaccade paradigm ordered as in Table 1a

Study df t Contrast weights

Symmetric vs. asymmetric

diagnostic exclusion criteriabNumber of

eccentricitiescNumber

of trialsd

Brownstein et al. (2003) 120 � 0.27 � 1.0 � 1.25 � 1.0

Thaker et al. (1996) 92 0.67 � 1.0 1.0 1.25

Thaker et al. (2000) 115 1.02 � 1.0 1.0 1.25

Crawford et al. (1998) 86 1.07 � 1.0 1.0 � 1.0

Clementz et al. (1994) 63 2.02 0.80 � 1.25 � 1.0

McDowell and Clementz (1997) 90 2.42 0.80 1.0 1.25

Curtis et al. (2001a) 223 5.08 0.80 � 1.25 � 1.0

Karoumi et al. (2001) 40 2.62 0.80 1.0 1.25

Katsanis et al. (1997) 91 4.01 0.80 � 1.25 � 1.0

a Based on the information in published articles, supplemented by additional information from the authors when available.b Symmetric diagnostic exclusion criteria applied to RSP and controls (� 1.0) vs. asymmetric inclusion criteria applied to RSP and controls

(0.80). Specifics are as follows: symmetric criteria: Thaker et al. (1996): both groups: no personal Axis I disorder other than one episode of past

major depression (none within the preceding 2 years), for which no hospitalization, tricyclic antidepressant or electroshock treatment was

received; no substance abuse within 2 years; both controls and RSP included two subgroups: subjects who met subthreshold criteria (less one

criterion) for schizotypal, schizoid, or paranoid personality disorder, and subjects who did not meet subthreshold criteria for these disorders;

normal controls (NC): no family history of major psychosis. Crawford et al. (1998): both groups: no substance abuse or heavy alcohol use

within 1 year; RSP: recurrent major depressive disorder (N = 5), bulimia (N = 1), schizotypal personality disorder (SPD) (N = 3); NC: no

personal or family history of psychotic illness. Thaker et al. (2000): both groups: no personal Axis I disorder; both controls and RSP included

subjects who met subthreshold criteria for schizophrenia-related personality disorders as in Thaker et al. (1996); NC: no family history of

psychosis. Brownstein et al. (2003): both groups: no psychotic disorder, no schizotypal, schizoid or paranoid personality disorder, no current or

past substance abuse or dependence within 1 year; RSP: nonpsychotic affective disorders (N = 26), anxiety disorders (N = 6), substance use

disorders (N = 18), adjustment disorder (N = 1); NC: no family history of psychosis; nonpsychotic affective disorders (N= 9), anxiety disorders

(N = 3), substance use disorders (N = 4). Asymmetric criteria: Clementz et al. (1994): RSP: schizophrenia (N= 1), SPD (N= 3), past major

depressive disorder (N= 7); NC: no major affective disorder, psychotic disorder, or current psychoactive substance use disorder; no

schizophrenia-related personality disorder; no T score >70 on MMPI-2 scales L, F, 2, 6, 7, 8; no family history of psychotic disorder, suicide or

psychiatric hospitalization. McDowell and Clementz (1997): RSP: schizophrenia (N = 1); morbid risk rate for schizophrenia: 2.3%; ‘‘Relatives

were given SCID diagnoses’’, but no other diagnostic information about specific disorders in RSP was included; NC: no Axis I disorder, no T

score >70 on MMPI-2 scales L, F, 2, 6, 7, 8; no family history of psychotic disorder, suicide or psychiatric hospitalization. Katsanis et al. (1997):

RSP: schizophrenia (N = 4), past or current major depressive disorder without psychotic features (N = 5), past bipolar disorder without psychotic

features (N = 1), past bipolar disorder with psychotic features (N = 1), psychotic disorder not otherwise specified (N = 1); past substance abuse or

dependence (N = 5), current substance dependence and past major depressive disorder without psychotic features (N = 1); NC: no personal or

family history of major affective, psychotic or substance use disorder. Curtis et al. (2001a): RSP: past but not current psychotic disorder

(schizophrenia, bipolar disorder, delusional disorder) (N = 8); nonpsychotic Axis I disorders (depression, substance dependence) (N = 36); NC:

no mood disorder, psychotic symptoms, lifetime substance dependence, or current substance abuse; no personal or first-degree family history of

treatment for any psychiatric disorder. Karoumi et al. (2001): both groups: no Axis I disorder; no SPD; NC: no first-degree family history of

Axis I disorder.c Eccentricity: one (� 1.25) vs. >1 (1.0). Each study had the following number of eccentricities: 1 (Clementz et al., 1994; Katsanis et al.,

1997; Curtis et al., 2001a; Brownstein et al., 2003); 2 (McDowell and Clementz, 1997; Crawford et al., 1998); 3 (Thaker et al., 1996, 2000;

Karoumi et al., 2001).d Number of trials: V 24 (� 1.0) vs. >25 (1.25). Each study had the following number of trials: 14 (Brownstein et al., 2003); 20 (Clementz et

al., 1994; Katsanis et al., 1997; Curtis et al., 2001a); 24 (Crawford et al., 1998); 40 (McDowell and Clementz, 1997); 60 (Thaker et al., 1996;

2000; Karoumi et al., 2001).

D.L. Levy et al. / Schizophrenia Research 71 (2004) 113–125 117

As Table 3 indicates, there is substantial variabil-

ity in the effect sizes found in studies that used the

overlap AS paradigm. The effect sizes for the Salt

Lake City and Palau samples are much larger than

the effect sizes for the San Diego sample (McDowell

et al., 1999). The effect sizes for the single peripheral

target (F 10j) used by Curtis et al. (2001b) are

substantially smaller than the effect sizes for the

‘‘near’’ (F 8j) peripheral target used by McDowell

et al. (1999). We evaluated whether there was

Table 3

Antisaccade studies (overlap paradigm) of relatives of schizophrenia patients ordered chronologically

Study First-degree relatives of

schizophrenia patients

Nonpsychiatric controls Effect size

N % Error score

(near/far)

N % Error score

(near/far)

Cohen’s d

(near/far)

r

(near/far)

Glass’ delta

(near/far)

McDowell and

Clementz (1997)a60 10.0 (24.0)b 32 2.0 (11.0)b 0.39 0.19 0.73

McDowell et al.

(1999)a

San Diego 60 31.0 (25.0)/19.0 (23.0) 94 22.0 (18.0)/8.0 (8.0) 0.43/0.70 0.21/0.33 0.50/1.375

Salt Lake City 29 48.0 (29.0)/38.0 (28.0) 94 22.0 (18.0)/8.0 (8.0) 1.23/1.97 0.52/0.70 1.44/3.75

Palau 41 47.0 (22.0)/36.0 (24.0) 94 22.0 (18.0)/8.0 (8.0) 1.30/1.90 0.54/0.70 1.39/3.5

Curtis et al.

(2001b)c42 12.3 (11.6) 38 9.9 (8.4) 0.23/NA 0.11/NA 0.29/NA

Mean (standard deviation).a Peripheral targets at F 8j (near), F 16j (far); San Diego RSP is the same group as in the 1997 study; error rate is not age-adjusted.b Age-adjusted error rates averaged across F 8j (near) and F 16j (far) targets.c Peripheral target at F 10j; error rate is not age-adjusted.

2 McDowell et al. (1999) have speculated that the much larger

effect sizes in the Palau and Salt Lake City samples than in the San

Diego sample may reflect differences in genetic loading for

schizophrenia. They reasoned that all of the Palau and Salt Lake

City families had multiple cases of schizophrenia, whereas all but

one of the San Diego families had only one schizophrenic member.

This explanation may account for variability in results based on the

‘‘overlap-far’’ AS paradigm, but it does not convincingly account

for variability in results based on the standard or ‘‘overlap-near’’ AS

paradigms. Using the standard AS paradigm to compare RSP from

multiplex families with controls, Crawford et al. (1998) obtained a

small effect size. Using the ‘‘overlap-near’’ AS paradigm to

compare RSP primarily from simplex families with controls, Curtis

et al. (2001b) obtained a much smaller effect size than McDowell et

al. (1999) obtained for the San Diego families. Since submitting this

manuscript, we have become aware of another study that compared

the performance of RSP and controls on the standard AS task. The

full study is currently unpublished, but the results have been

presented as an abstract (MacCabe et al., 2002). The results showed

that neither ‘‘obligate carrier’’ RSP, other relatives from multiplex

families, nor relatives from families with only one schizophrenic

member made significantly more errors than controls did. The effect

sizes (Glass’ delta) ranged from � 0.41 to 0.14. As in the Crawford

et al. (1998) study, symmetrical exclusion criteria were applied to

RSP and controls.

D.L. Levy et al. / Schizophrenia Research 71 (2004) 113–125118

evidence of significant heterogeneity in these effect

sizes using an extension of the Hedges and Olkin

(1985) Q statistic. The Q statistic was developed to

provide a test for heterogeneity when effect sizes are

correlated because multiple variables or outcomes are

measured on each subject. Cook (submitted for

publication) has extended this method to estimate

the correlation between effect sizes due to the non-

independence of the control group in the McDowell

et al. (1999) study (three groups of RSP were

compared with one control group), and used an

estimate of this correlation in the Q statistic. The

results of the heterogeneity analysis for the three

samples in McDowell et al. (1999) were significant

for both ‘‘near’’ (X2(2) = 22.95, P < 0.0001) and ‘‘far’’

targets (X2(2) = 134.53, P < 0.00000001), indicating

that the RSP-control differences in the three compar-

isons for each target eccentricity were unlikely to be

drawn from the same distribution. The analysis of the

effect sizes for the ‘‘near’’ targets in four samples of

RSP and controls (i.e., three from the McDowell et

al., 1999 study and one from the Curtis et al., 2001b

study) also showed significant heterogeneity

(X2(3) = 27.25, P < 0.00005). We did not try to eval-

uate possible sources of effect size heterogeneity in

studies that used the overlap paradigm, because of

the small number of independent studies and control

groups. Both studies used similarly asymmetric ex-

clusion criteria, indicating that some other factor

must be the source of the heterogeneity in effect

size.2

2.3. Gap AS task

One study (Ross et al., 1998) used the gap version

of the AS task to compare the performance of RSP

D.L. Levy et al. / Schizophrenia Research 71 (2004) 113–125 119

and controls. They divided eight ‘‘parental dyads’’

into two groups, one composed of the parent in each

pair with a family history of schizophrenia (‘‘most

likely gene carriers’’) and the other composed of the

parent with no such family history (‘‘least likely gene

carriers’’). The performance of each group was com-

pared with that of controls. They found that the group

of ‘‘most likely gene carrier’’ parents (but not the

group of ‘‘least likely gene carrier’’ parents) made

significantly more errors than controls did (Glass’

delta: 0.75; d: 0.81; r: 0.37).

3. Discussion

Our results indicate that the effect sizes for com-

parisons of RSP and controls on the standard version

of the AS task were significantly heterogeneous. The

mean effect size is therefore not interpretable as a

population indicator. Variability in effect size was

accounted for by subject selection criteria but not

by aspects of the experimental procedures. Signifi-

cantly larger effect sizes were found in studies that

applied less stringent exclusion criteria to RSP than to

controls than in studies that applied comparable

exclusion criteria to RSP and controls. Significant

heterogeneity in effect size was also found for the

overlap version of the AS task, but the specific

sources of this heterogeneity could not be identified

based on the existing literature. These findings, their

implications for sample composition, and considera-

tions relevant to a genetic model of AS performance

are discussed below.

3.1. Asymmetric diagnostic exclusion criteria in

controls and RSP

Of the five studies that employed asymmetric

diagnostic exclusion criteria (Clementz et al., 1994;

McDowell and Clementz, 1997; Katsanis et al., 1997;

Curtis et al., 2001a; Karoumi et al., 2001), four

allowed psychiatric disorders in RSP but excluded

those same disorders in the controls (Clementz et al.,

1994; McDowell and Clementz, 1997; Katsanis et al.,

1997; Curtis et al., 2001a). The fifth study (Karoumi

et al., 2001) was asymmetric because although diag-

nostic exclusion criteria were symmetric with respect

to personal psychopathology, they were asymmetric

with respect to familial psychopathology (an asym-

metry that was also present in two studies that applied

asymmetric personal psychopathology criteria). The

effect sizes in these five studies were significantly

larger than the effect sizes in the four studies that

applied symmetrical exclusion criteria to both groups

(Thaker et al., 1996, 2000; Crawford et al., 1998;

Brownstein et al., 2003). Below we discuss each of

these features of asymmetry.

With respect to personal psychopathology, two

kinds of asymmetry were present. The first involved

excluding individuals with nonpsychotic Axis I dis-

orders from the control group but not from the RSP

group. In the Clementz et al. (1994) study, 7/32 RSP

met criteria for past major depression, but controls

with a major affective disorder were excluded. In

addition, in the same study, three subjects who met

diagnostic criteria for schizotypal personality disorder

were included in the RSP group, but this condition

was an exclusion criterion for controls. Similarly, in

the Katsanis et al. (1997) and Curtis et al. (2001a)

studies, 12/55 and 36/116 RSP, respectively, met

diagnostic criteria for nonpsychotic mood and sub-

stance use disorders, which were exclusion criteria for

controls. In the McDowell and Clementz (1997) study,

controls with any Axis I disorder were excluded, but

RSP with an Axis I disorder were not (see footnote b

of Table 2). In the Karoumi et al. (2001) study, Axis I

disorders were excluded from both RSP and controls,

but controls were also excluded if there was a family

history of any Axis I disorder, a topic that is discussed

below.

The same four studies that were asymmetric with

respect to nonpsychotic Axis I disorders also includ-

ed individuals with psychotic disorders in the RSP

group (Clementz et al., 1994; McDowell and Clem-

entz, 1997; Katsanis et al., 1997; Curtis et al.,

2001a). Therefore, the magnitude of the contribution

of each type of asymmetry to the effect size cannot

be determined. There are strong a priori reasons,

however, to expect that the presence of psychotic

individuals in the RSP group will inflate the mean

and variance of that group. The magnitude of these

effects will, of course, depend on the proportion of

RSP with psychotic disorders. The empirical litera-

ture clearly shows that the AS performance of

individuals with psychotic conditions would be

expected to increase the mean and variance of error

D.L. Levy et al. / Schizophrenia Research 71 (2004) 113–125120

rate in the RSP group as a whole, because psychotic

conditions, even in remission, are associated with an

increased probability of poor performance. Every AS

study of schizophrenia patients has reported signifi-

cantly worse performance in these patients compared

with controls (Fukushima et al., 1988, 1990a,b,

1994; Thaker et al., 1989; Rosse et al., 1993;

Clementz et al., 1994; Matsue et al., 1994; Crawford

et al., 1995a, 1998; Sereno and Holzman, 1995;

Allen et al., 1996; Tien et al., 1996; Katsanis et

al., 1997; McDowell and Clementz, 1997; Hutton et

al., 1998; Karoumi et al., 1998, 2001; Levy et al.,

1998; Maruff et al., 1998; Ross et al., 1998; McDo-

well et al., 1999; Muller et al., 1999; Brenner et al.,

2001; Curtis et al., 2001a; Gooding and Tallent,

2001; Manoach et al., 2002). Moreover, schizophre-

nia patients in full remission perform as poorly as

acutely psychotic schizophrenia patients (Curtis et

al., 2001a). The mean error rate of the eight RSP

with a history (but no current evidence) of psychosis

was found to be ‘‘more similar’’ to the mean error

rates of both acutely psychotic and remitted schizo-

phrenia patients than to that of RSP with no Axis I

pathology (Curtis et al., 2001a). In the Clementz et

al. (1994) study, the relative with the highest error

rate (more than 2 S.D. above the mean of RSP) was

one of four RSP with a ‘‘schizophrenia-spectrum

disorder’’. In the McDowell and Clementz (1997)

study, the one relative with a diagnosis of schizo-

phrenia had an error rate (on the overlap AS task)

that was outside the range of the controls (perfor-

mance of this individual on the standard AS task was

not described). Similarly, both psychotic (Katsanis et

al., 1997; Curtis et al., 2001a) and remitted bipolar

patients (McDowell and Clementz, 1997; Gooding

and Tallent, 2001) have been reported to have

significantly elevated error rates compared with con-

trols [in other studies bipolar patients did not per-

form more poorly than controls (Fukushima et al.,

1990a; Clementz et al., 1994; Crawford et al.,

1995a)].

With respect to family history of psychopatholo-

gy, stricter exclusion criteria were applied to con-

trols than to RSP in three of the studies with the

largest effect sizes. In addition to the personal

psychopathology screening criteria described above,

controls were also excluded if family members had:

(1) received treatment for a major affective disorder

or for substance abuse (Katsanis et al., 1997), (2)

received any psychiatric treatment (Curtis et al.,

2001a), or (3) any Axis I disorder (Karoumi et al.,

2001). Thus, controls differed from RSP not only

because they were not first-degree relatives of a

schizophrenia patient, but also because they were

not relatives of individuals with many other psychi-

atric disorders. As a result, the control groups

remained more selective with respect to family

history of psychiatric illness even when RSP with-

out Axis I disorders were compared with controls

(Katsanis et al., 1997; Curtis et al., 2001a; Karoumi

et al., 2001).

The four studies with the smallest effect sizes

used the same personal and family history criteria to

exclude both RSP and controls, with the exception

that controls, unlike RSP, also had no family history

of psychosis (Thaker et al., 1996, 2000; Crawford et

al., 1998; Brownstein et al., 2003). In all four

studies, psychotic individuals were excluded from

both groups. In one study, neither group included

individuals with Axis I disorders (Thaker et al.,

2000) and in another both groups were largely free

of Axis I disorders [Thaker et al. (1996) allowed in

both groups a single episode of untreated major

depression if it occurred more than 2 years earlier].

One study allowed nonpsychotic Axis I disorders in

both groups and excluded individuals with schizo-

phrenia-related personality disorders from both

(Brownstein et al., 2003). One study allowed non-

psychotic disorders in both groups (Crawford et al.,

1998).

Our results indicate that applying more selective

diagnostic exclusion criteria to RSP than to controls

is a major condition for obtaining medium to large

effect sizes in studies of RSP on the standard AS

task. A conservative interpretation of this finding is

that the larger effect sizes were not related to

schizophrenia per se, but to the over-representation

of psychiatric illnesses in RSP and the under-repre-

sentation of the same disorders in the controls and in

the relatives of the controls. This interpretation is

consistent with reports that poor performance on the

AS task is found not only in psychiatric conditions

thought to be related to schizophrenia, such as

psychometric and clinical schizotypy (Holzman et

al., 1995; O’Driscoll et al., 1998; Gooding, 1999;

Larrison et al., 2000; Brenner et al., 2001), but also

D.L. Levy et al. / Schizophrenia Research 71 (2004) 113–125 121

in a range of other psychiatric disorders. Findings

vary for each of these diagnostic groups (for the sake

of completeness we cite both positive and negative

studies), but there is at least some support for

increased error rate in patients with bipolar disorder

(Sereno and Holzman, 1995; Crawford et al.,

1995a,b; Katsanis et al., 1997; McDowell and Clem-

entz, 1997; Curtis et al., 2001a; Gooding and Tallent,

2001), major depressive disorder (Katsanis et al.,

1997; Sweeney et al., 1998; Curtis et al., 2001a),

obsessive-compulsive disorder (Tien et al., 1992;

McDowell and Clementz, 1997; Rosenberg et al.,

1997; Maruff et al., 1999), and attention deficit

hyperactivity disorder (Rothlind et al., 1991; Aman

et al., 1998; Munoz et al., 1999, 2003). The presence

of increased AS errors in a range of psychopatho-

logical conditions suggests that studies of AS perfor-

mance may be less relevant for understanding

schizophrenia per se than for understanding processes

that are common to a broad range of psychiatric

disorders.

3.2. Issues of sample composition

It is clear that subject selection factors affect the

magnitude of the performance difference between

RSP and controls, an outcome that has more general

methodological implications. The optimal inclusion/

exclusion criteria in any study, including the degree

of symmetry and stringency of the exclusion criteria,

depend on the goal of the study. For example, if one

seeks to determine whether a particular process is

associated with schizophrenia, it is inefficient to

compare schizophrenia subjects with a control group

that includes individuals with that disorder. Asym-

metric exclusion criteria are thus appropriate in this

case. If, however, one studies the same process in

RSP in order to determine whether a particular

behavior is useful as an auxiliary trait in linkage

studies (i.e., a pleiotropic gene effect or an endophe-

notype), one must be able to distinguish a diathesis

(the trait either causes the disease or is a pointer to an

underlying causal process) from an epiphenomenon

(a secondary effect caused by the disease) (Mat-

thysse, 1993). Symmetrical exclusion criteria, in

which both RSP and controls are purified of clinical

conditions that could produce the trait as an epiphe-

nomenon (Chapman and Chapman, 1973; Holzman

and Matthysse, 1990; Lenzenweger, 1998), are es-

sential for making this distinction. In this case,

symmetric exclusion criteria are appropriate. Purify-

ing one group of potentially confounding conditions

but not the other makes it likely that the groups will

perform differently, but such a difference would not

provide strong evidence that the trait is heritable,

because it could be a secondary effect of the asym-

metry in sample composition.

3.3. Differences in AS task administration

The aspects of task administration that we exam-

ined did not contribute significantly to effect size

heterogeneity. Specifically, neither number of target

eccentricities nor number of trials was related to the

magnitude of effect size. We had hypothesized that the

number of trials used to assess AS performance might

be related to the magnitude of RSP-control differ-

ences. For example, studies that used a larger number

of trials might be expected to yield more stable

estimates of performance ability than studies that used

fewer trials. However, studies that administered rela-

tively few trials (range: 14–24) (Clementz et al.,

1994; Katsanis et al., 1997; Crawford et al., 1998;

Curtis et al., 2001a; Brownstein et al., 2003) did not

differ in effect size from those that administered a

larger number of trials (range: 40–60) (Thaker et al.,

1996, 2000; McDowell and Clementz, 1997; Karoumi

et al., 2001). Indeed, medium to large effect sizes

were obtained in studies that used as few as 20 trials

(Clementz et al., 1994; Katsanis et al., 1997; Curtis et

al., 2001a), and small effect sizes were obtained in

studies that used as many as 60 trials (Thaker et al.,

1996, 2000). The nonsignificant effect of number of

trials is consistent with the finding of stable perfor-

mance across up to six blocks of 20 trials per block in

schizophrenia, bipolar disorder, and obsessive-com-

pulsive disorder patients as well as in nonpsychiatric

controls (McDowell and Clementz, 1997).

We had also hypothesized that task difficulty might

increase with number of eccentricities, which might

be associated with larger effect sizes. However, num-

ber of eccentricities was not a significant source of

variability in effect size. Medium to large effect sizes

were obtained in studies that used only one eccentric-

ity (Clementz et al., 1994; Katsanis et al., 1997; Curtis

et al., 2001a) and small effect sizes were obtained in

D.L. Levy et al. / Schizophrenia Research 71 (2004) 113–125122

studies that used multiple eccentricities (Thaker et al.,

1996, 2000).

We do not rule out the possibility that variables

relevant to test administration do play a role in effect

size heterogeneity. For example, we were unable to

assess the influence of variables that were not consis-

tently reported but which could have had an effect,

such as inter-trial interval, whether subjects were given

feedback on performance during the experimental

trials, and number of practice trials.

3.4. Confidence limits

It is interesting to note that if the underlying

sources of heterogeneity had been unknown, an

analysis based on the confidence limits alone would

have revealed that a systematic difference between

RSP and controls, if there is one, must be small, but a

large effect could be ruled out. That result would not

represent the underlying reality accurately, because

the heterogeneity analysis shows that there is not a

consistent small effect.3 Rather, we have a collection

of studies, some of which show large effects and some

of which show small effects, with the magnitude of

3 Using a normal theory approximation, the upper 95%

confidence limit for the difference between the mean error scores

of RSP and relatives of nonpsychiatric controls in the Brownstein et

al. (2003) study is 6.31, indicating that a mean difference between

the groups smaller than 6.31 cannot be ruled out, even though the

observed mean difference was � 1.0. The upper 95% confidence

limits for the three other studies whose effect sizes were small

(Thaker et al., 1996, 2000; Crawford et al., 1998) are 7.64, 10.50,

and 15.22, respectively, showing substantial overlap with the

differences in means reported in the studies with medium– large

effect sizes. These findings indicate that the studies reporting small

effect sizes do not rule out a small effect, or an effect in a small

proportion of RSP, but they do rule out a consistent large group

effect. When applying this procedure in the converse way, the lower

confidence limits of the studies with medium– large effect sizes

were 1.82, 3.48, 5.97, and 9.25, respectively (Clementz et al., 1994;

McDowell and Clementz, 1997; Karoumi et al., 2001; Curtis et al.,

2001a), barely overlapping the upper confidence intervals of the

studies with small effect sizes [the Katsanis et al. (1997) study was

excluded, because it presented medians and interquartile ranges,

rather than means and standard deviations]. Therefore, using this

method, the confidence intervals for the two groups of studies have

sufficient overlap that it is not possible to rule out a consistent small

effect underlying all of the findings, but a consistent large effect can

be ruled out. The meta-analysis, however, does rule out a consistent

small effect.

the effects varying as a function of the subject

selection criteria used.

3.5. Expectations from a genetic model

As indicated earlier, the finding of increased errors

on the AS task in schizophrenia patients and RSP has

led to the speculation that AS performance may tap

processes related to genetic vulnerability. According

to a genetic model that involves co-familial transmis-

sion, RSP would be expected to have both a higher

mean error rate and a larger variance than controls. As

we showed above, however, the magnitude of the

observed mean difference between RSP and controls

varies as a function of subject selection criteria. From

the data presented in published studies, it is possible

to test whether RSP had a significantly larger variance

than controls. The variance ratios for the eight inde-

pendent studies that provided means and standard

deviations of RSP and controls are shown in Table

4. Under the null hypothesis the variance ratio is 1 and

is distributed as F. A test of whether the variances of

RSP were larger than those of controls was not

statistically significant (F values were converted to

Table 4

Variance ratiosa in antisaccade studies (standard paradigm) of first-

degree relatives of schizophrenia patients (ordered from smallest to

largest)

Study Variance ratiob P-value ln(variance

ratio)c

Thaker et al.

(1996)

F = 0.32,

df= 25,67

>0.995 � 1.14

Brownstein et al.

(2003)

F = 0.90,

df= 97,23

>0.50 � 0.10

Karoumi et al.

(2001)

F = 1.40,

df= 20,20

0.10 <P< 0.25 0.33

Thaker et al.

(2000)

F = 1.54,

df= 54,61

0.05 <P< 0.10 0.43

Crawford et al.

(1998)

F = 1.59,

df= 49,37

0.05 <P< 0.10 0.46

McDowell and

Clementz

(1997)

F = 1.72,

df= 59,31

0.05 <P< 0.10 0.54

Curtis et al.

(2001a)

F = 1.72,

df= 115,108

0.01 <P< 0.025 0.54

Clementz et al.

(1994)

F = 2.13,

df= 31,32

0.025 <P< 0.05 0.76

a s2RSP/s2NORMALS (one-sided to the right).

b Mean: 1.4; standard deviation: 0.56; median: 1.6.c Median: 0.445.

D.L. Levy et al. / Schizophrenia Research 71 (2004) 113–125 123

the natural log of F so that studies with F’s above and

below 1.0 would be equally weighted: Wilcoxon

Signed Rank Test; S = 9.0, P= 0.11, one sided).

From the meta-analysis, we conclude that the use of

a purified control group yields large and statistically

significant RSP-control effects, whereas applying ex-

clusion criteria of comparable stringency in the two

groups yields small and statistically nonsignificant

effects. On the basis of the studies in the literature,

therefore, we conclude that performance on the stan-

dard AS task does not appear to be a pleiotropic effect

of a schizophrenia gene. For the purpose of detecting

the presence of a pleiotropic effect of a schizophrenia

gene, applying criteria of comparable stringency in the

RSP and control groups is the proper strategy. Using

asymmetrical inclusion/exclusion criteria is bound to

create an upward bias in the direction of magnifying

group differences (Smith and Iacono, 1986; Tsuang et

al., 1988; Schwartz and Link, 1989; Kendler, 1990),

and may tend falsely to suggest an effect related to

schizophrenia gene. In an ideal genetic study, two

groups are compared that differ only in the probability

that members of the groups have a gene relevant to

schizophrenia and not in the presence of unrelated

psychopathology. Although perfect symmetry of in-

clusion/exclusion criteria typically cannot be achieved

(i.e., in the absence of definitive diagnostic informa-

tion, a family history of psychosis will usually exclude

a control but not an RSP), reasonable symmetry can be

achieved. The significant within-family correlation in

performance, both in RSP (Crawford et al., 1998;

Curtis et al., 2001a; Brownstein et al., 2003) and in

twins who were not ascertained for being RSP

(Malone and Iacono, 2002), suggests that genetic

effects may contribute to variability in AS perfor-

mance, but in order to show convincingly that this

trait is related to a schizophrenia genotype, RSP must

be shown to perform worse than controls in samples

ascertained on the basis of comparable diagnostic

exclusion criteria.

Acknowledgements

This study was supported in part by NIMH grants

MH49487, MH31340, MH01021, MH31154, by a

grant from The Roy Hunt Foundation, by an operating

grant from the Canadian Institute of Health Research,

and by a Harvard University Graduate School of Arts

and Sciences Merit Fellowship. The authors thank

Donald Rubin for his helpful comments. We also

thank Drs. Thierry d’Amato, Monica Calkins, Brett

Clementz, Jennifer McDowell, and Gunvant Thaker

for clarifying methodological questions. The method

developed by Cook to handle correlated effect sizes is

available on request.

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