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. 2026 Feb 5;44:100422. doi: 10.1016/j.scog.2026.100422

Keep your participants close, but your informants closer? The added value of high and low contact informants to supplement self-report for the cognitive impairment of schizophrenia

Jayda L Melnitsky a, Joanne Yoon a, Megan R Mayer a,b, Anne P Lewandowski a, Madison C Teets a, James Gangwisch a,b, Corey Reuteman-Fowler c, Wenbo Tang c, Philip D Harvey d, Richard SE Keefe e, Joshua T Kantrowitz a,b,f,⁎
PMCID: PMC12906021  PMID: 41695668

Abstract

Background

The input of an “informant,” a person in the participant's life who can evaluate levels of functioning, can supplement potentially unreliable self-reports in schizophrenia. However, this adds to the complexity of clinical trial design. To evaluate the relative utility of information from informants and participants in a clinical trial context, we compared the participant (self-report), informant, and interviewer ratings for the Schizophrenia Cognition Rating Scale (SCoRS).

Methods

We compared the participant (self-report), informant, and interviewer ratings using paired t-tests, Pearson correlations and Fisher z-transformation and inter-rater reliability (IRR). To assess to importance of informant “closeness,” we conducted a categorical analysis split by informant contact (above/below 30 h/week) or relationship to the participant (family or friend).

Results

The informant ratings for the SCoRS Total Score had an excellent IRR, (ICC = 0.91), similar to interviewer IRR (0.91). The highest level of impairment was rated by the interviewers, followed by the informant and participant self-reports. The correlation for the SCoRS total between the interviewer and informant (r = 0.92) was significantly larger than between the interviewer and participant self-reports or informant and participant self-reports (both p < 0.01). There were no significant correlations between contact hours and total ratings, and between-group correlations remained highly significant within the categorical analysis subgroups (r > 0.9).

Conclusions

Our results suggest that interviewers relied on informant reports significantly more than the participant self-report, even with an informant who spends as little as 2 h a week with a participant. Future research should assess the relationship of informant ratings with cognition or symptom scales.

Keywords: Schizophrenia, Function, Clinical trial design, Informant

Highlights

  • •

    An “informant” can supplement potentially unreliable self-reports in clinical trials for schizophrenia.

  • •

    It is unknown whether lower contact or “acquaintance” informants would be valued by trained interviewers.

  • •

    Correlations for the SCoRS total between the interviewer and informant (r=0.92) was significantly larger (both p<0.01) than between the interviewer and participant self-reports or informant and participant self-reports.

  • •

    There were no significant correlations between contact hours and total ratings.

  • •

    Results suggest an informant who spends as little as two hours a week with a participant was valued by the interviewer, simplifying clinical trial design.

1. Introduction

Along with positive symptoms, such as delusions and hallucinations, schizophrenia is associated with negative symptoms (Correll and Schooler, 2020; Galderisi et al., 2021; Govil and Kantrowitz, 2025; Howes et al., 2023; Strauss et al., 2021) and cognitive impairment associated with schizophrenia (CIAS) (Donde et al., 2023; Kraeplin, 1907; McCutcheon et al., 2023; Sehatpour and Kantrowitz, 2025). There are no regulatory agency approved treatments for negative symptoms or CIAS (Kantrowitz et al., 2023).

Trials for CIAS require a co-primary functional assessment, either a performance based assessment or an interview based scale with skilled raters such as the Schizophrenia Cognition Rating Scale (SCoRS; (Keefe et al., 2006)) "or" the Cognitive Assessment Interview (CAI; (Ventura et al., 2010)). This increases the burden on patients and site-staff, exacerbating difficulties in site management and quality control (Kantrowitz et al., 2025; Yoon et al., 2025) – factors which may be contributing to increased placebo effect and failed studies (Horan et al., 2025; Kantrowitz et al., 2025).

As opposed to inpatient acute monotherapy trials, CIAS trials are typically conducted with outpatients. While this approach is more relevant for assessing real-world behavior, outpatients are more difficult to directly observe, and outcomes can rely on participant self-reports to assess function. While self-report offers valuable insight into participants' internal processes and perception of their abilities, self-report of objective functioning can differ greatly from other objective evidence (Durand et al., 2021; Gould et al., 2013; Harvey, 2013; Harvey et al., 2011, Harvey et al., 2013, Harvey et al., 2007; Silberstein et al., 2018). Many participants are poor self-reporters, usually overestimating their general functioning (Durand et al., 2021), as well as social cognitive abilities and functioning (Gould et al., 2013). Moreover, these studies suggest that participants with lower functional capacities – usually those who may benefit the most from CIAS improvement – are more likely to provide inaccurate information regarding their cognition and everyday functioning.

To supplement participant self-reports, the input of an “informant” or study partner, a person in the participant's life who can attest to their daily experience, behavior, and functional level, is an essential tool for participant assessment. The added value of an informant has been previously studied (Kaneko and Okamura, 2019; Keefe et al., 2015; Silberstein and Harvey, 2019), and it has been suggested that informants may be particularly useful for items that are directly observable, like doing chores, as opposed to internal representations, such as the ability to understand how people feel.

As previously reviewed (Kantrowitz, 2017), the finding of a suitable informant for a clinical trial participant can be an obstacle. Patients with schizophrenia, especially those with pronounced negative symptoms or high levels of “Asociality” symptoms, may not have a person in their lives who knows them well (Bellack et al., 2007; Patterson et al., 1996). For these patients, socialization can often be limited to their clinicians or case managers, many of whom are reluctant or not permitted by their employer to participate as an informant. Additionally, informants can vary greatly in characteristics that could prove relevant to the measurement of treatment effects. An analysis by Keefe et al. in 2015 consisting of data from two SCoRS studies with sites located in the United States and several Eastern European countries, found that the U.S. fell behind Europe in terms of participant-informant frequency of contact. In the U.S., informant frequency of contact ranged from family members who spent a median of 40 h a week with the participant, to non-family members who spent a median of only 6 h a week with the participant. From this same analysis, the European participants, who also demonstrated higher SCoRS test-retest reliability and a stronger treatment effect, had informants that spent a median of 70 h per week with them. Along with contact frequency, the type of relationship between informant and participant may also create variability within a study, as informants can be relatives, friends, acquaintances, or even clinicians or case managers. As opposed to nonprofessional informants, clinicians have been found to be more consistently correlated with participant performance on cognitive measures (Sabbag et al., 2011). Nevertheless, a requirement for professionally trained or caregiver informants that spend multiple hours per day with the participant can complicate recruitment, and it is unknown whether lower contact or “acquaintance” informants would be equivalently valued by trained interviewers in a clinical trial setting.

The primary purpose of this secondary analysis is to assess and quantify the added value of the informant in assessing function in CIAS clinical trials. In particular, we assess whether informant “closeness,” as defined by number of contact hours or relationship type is essential. To address this question, we utilized the live interview data from a recently published non-interventional, quantitative study of the 20-item SCoRS (Tulliez et al., 2025), which was primarily designed to assess inter-rater reliability (IRR). In this study, a trained SCoRS interviewer rated the participant based on both their self-report and an informant who spent at least 1 h a week with the participant. As published, this study demonstrated a strong IRR, with an interclass correlation (ICC) of 0.91 [and a lower 95% CI boundary of 0.88] for interviewer ratings for the SCoRS total. The sample was rated by interviewers specializing in clinical trials populations, with the SCoRS total rated as moderate-to-moderately-severely impaired, and enrollment criteria was consistent with a typical clinical trial CIAS population. Thus, our results are highly pertinent for assessing the added value of informants in a clinical trial setting.

In this secondary analysis, we assessed informant inter-rater reliability (IRR), and differences and correlations between informant, interviewer and patient self-report ratings for the SCoRS total and individual items. To assess to importance of informant “closeness,” we conducted a categorical analysis divided by high and low informant contact (above & below 30 h/week) or relationship to the participant (e.g. family or friend).

2. Methods

2.1. Study design

Design/methodology was previously published (Tulliez et al., 2025). Briefly, this was a secondary analysis of a non-interventional, multi-center, quantitative, standalone study to assess the IRR of the 20-item SCoRS (i.e. how consistently different interviewers rate the same participant). Structured, one-to-one, 10–15-min separate interviews with participants with schizophrenia and their informant were conducted by trained SCoRS interviewers. Each live interview was video recorded. These recordings were then assessed independently by two additional SCoRS raters.

As previously published (Tulliez et al., 2025), SCoRS raters were required to be medical professionals or experienced raters with at least 1 year of experience working with patients with schizophrenia and they needed to be fully qualified and trained in conducting SCoRS interviews. The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008.

2.2. Study population

See Tulliez et al. (2025) for full inclusion and exclusion criteria. Briefly, participants met the following criteria: DSM-V diagnosis of schizophrenia; on a stable regimen of up to 2 antipsychotics for at least 12 weeks, with a stable dose for ≥35 days; functional impairment in day-to-day activities (e.g., conversational, focus, or memory difficulties); had not taken part in a SCoRS interview within the past 3 months; were deemed reliable and physiologically capable of participating in the SCoRS interview in the opinion of the study investigator; and had a suitable informant (study partner; e.g., a family member, friend, study nurse, or social worker) who knew the participant well and interacted with them regularly (for at least 1 h/week, ideally at least twice/week).

2.3. Statistical methodology

As previously, for each of the 20 SCoRS items, one of the following responses could be recorded: ‘Not at all’ (1), ‘Mild’ (2), ‘Moderate’ (3), ‘Severe’ (4), or ‘Not applicable’. These responses were summed to yield a total score of 20–80.

Demographics, baseline characteristics and SCoRS ratings were summarized using means and standard deviations for continuous variables and proportions and frequencies for categorical variables. The interrater reliability (IRR) was calculated for the informant ratings using ICC using the initial live interview and video raters as described previously (Tulliez et al., 2025).

Using the initial live interviews, we compared the three ratings (participant self-report, informant, and interviewer). For the SCoRS total followed by paired t-tests between the three raters, Pearson correlations and Fisher z-transformations as indicated. A similar analysis was conducted within the 20 individual items. Finally, we conducted an analysis for participants' and informants' ratings, categorically divided by the mean number of weekly contact hours in our sample (30h). ≥30 contact hours were defined as high contact informants and < 30 contact hours as low contact informants and self-reported relationship to the participant (family or friend/roommate).

3. Results

3.1. Subjects

Demographics and sample characteristics are presented in (Table 1). Consistent with the enrolment criteria (Tulliez et al., 2025), demographics and characteristics were representative of a typical schizophrenia clinical trial population. The present report includes an additional participant/informant pair that was excluded from the previous report (Tulliez et al., 2025) for clozapine use, for 45 pairs total. Most informants were either family or a friend, with a mean of 30.8 ± 34.0 contact hours/week, with a minimum of 2 h a week.

Table 1.

Demographics and sample characteristics:

All participants (N = 45) All informants (N = 45)
Age, years mean ± SD 39 (33.5–44.5) 49.5 ± 15.6
Male, n (%) 29 (64.4) 25 (55.5)
Highest education level, n (%)
 High school (or less) 24 (53.3) 22 (48.9)
 Some College 13 (28.9) 12 (26.7)
 Bachelor's degree 6 (13.3) 8 (17.8)
 Advanced degree 1 (2.2) 2 (4.4)
 Other 1 (2.2) 1 (2.2)
Race, n (%)
 White 15 (33.3)
 Non-white 30 (66.7)
Living status, n (%)
 Alone 12 (26.7)
 With partner 4 (8.9)
 With parents 8 (17.8)
 With other family members 6 (13.3)
 With roommates 6 (13.3)
 In residential care 2 (4.4)
 In another supported care environment 3 (6.7)
 Not listed 3 (6.7)
Work status, n (%)
 Full-time employment 3 (6.7)
 Part-time employment 5 (11.1)
 Freelance/contractor 2 (4.4)
 Student 2 (4.4)
 Unable to work due to condition 23 (51.1)
 Unemployed (unrelated to condition) 9 (20.0)
 Not listed 1 (2.4)
Total contact with participant, hours/week, n (%)
 1 0
 2–4 9 (20.0)
 5–10 10 (22.2)
 11–19 4 (8.9)
 20–29 4 (8.9)
 >30 18 (40)
Informant category, n (%)
 Family 21 (46.7)
 Friend/roommate 22 (48.9)
 Staff 2 (4.4)
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3.2. SCoRS total

The informant ratings for the SCoRS Total Score had an excellent IRR, with an ICC of 0.91 and a lower 95% CI boundary of 0.85, similar to interviewer IRR (0.91). The highest level of impairment was rated by the interviewers, followed by the informant and participant self-reports (Table 2). On follow-up pairwise t-tests (Table 2), highly significant mean differences were seen between the interviewer and both informant ratings (t44 = 5.2, p < 0.001) and participant self-reports (t44 = 7.4, p < 0.001). By contrast, no significant differences were seen between the informant and the participant self-reports (t44 = 1.1, p = 0.27).

Table 2.

SCoRs Total for participant, informant and the interviewer.

Item (Mean ± SD)
 Paired differences (mean ± SD)a
 ra
Participant Informant Interviewer Participant-informant Participant-interviewer Informant-interviewer Participant-informant correlations
1. Names of people 2.1 ± 0.7 1.7 ± 0.7 2.1 ± 0.6 0.4 ± 1 −0.1 ± 0.6 −0.4 ± 0.6* 0.09
2. Get to places 1.6 ± 0.9 1.7 ± 0.9 1.8 ± 0.9 −0.1 ± 0.8 −0.2 ± 0.7 −0.1 ± 0.4 0.56
3. Following a TV show 1.9 ± 1 2 ± 1.1 2.2 ± 1 −0.1 ± 1.2 −0.4 ± 0.8 −0.2 ± 0.7 0.31
4. Where you put things 2.2 ± 1.1 2.1 ± 0.9 2.5 ± 0.9 0.2 ± 1.3 −0.3 ± 0.8 −0.4 ± 0.8* 0.21
5. Chores 1.9 ± 0.9 2.1 ± 1.1 2.3 ± 0.9 −0.1 ± 1.2 −0.4 ± 0.9 −0.3 ± 0.5* 0.36
6. Gadgets 1.7 ± 0.9 1.6 ± 0.8 1.9 ± 0.9 0.1 ± 0.8 −0.2 ± 0.5 −0.3 ± 0.6* 0.52
7. Info/instructions 2.2 ± 1 2.1 ± 1.1 2.4 ± 1 0.1 ± 1 −0.2 ± 0.6 −0.4 ± 0.6* 0.54
8. Going to say 2.2 ± 1 2 ± 1.1 2.5 ± 1 0.3 ± 1.3 −0.2 ± 0.9 −0.5 ± 0.7* 0.28
9. Track of your money 1.7 ± 1 1.8 ± 0.9 2.1 ± 1.1 −0.1 ± 1.2 −0.4 ± 0.8 −0.3 ± 0.7 0.26
10. Jumbled words 1.7 ± 0.8 1.7 ± 0.9 2 ± 0.8 −0.1 ± 1.1 −0.3 ± 0.8 −0.2 ± 0.5 0.17
11. Book 2 ± 0.9 2 ± 0.9 2.3 ± 0.9 0 ± 1 −0.2 ± 0.6 −0.3 ± 0.7 0.42
12. Familiar tasks 1.1 ± 0.4 1.4 ± 0.8 1.4 ± 0.8 −0.3 ± 0.7 −0.3 ± 0.7 0 ± 0.2 0.46
13. Staying focused 2 ± 1 2.1 ± 1 2.3 ± 0.9 −0.2 ± 1.1 −0.3 ± 0.8 −0.1 ± 0.6 0.32
14. Learning new things 1.5 ± 0.7 1.8 ± 0.9 2 ± 0.9 −0.2 ± 0.9 −0.4 ± 0.7* −0.2 ± 0.4 0.43
15. Speaking fast 1.5 ± 0.8 1.7 ± 1 1.8 ± 0.9 −0.2 ± 1.1 −0.2 ± 1.1 −0.1 ± 0.6 0.27
16. Doing things quickly 1.5 ± 0.8 2.1 ± 1 2.2 ± 1 −0.6 ± 1.2* −0.6 ± 1* 0 ± 0.6 0.13
17. Changes in routine 1.6 ± 0.8 1.8 ± 0.9 2 ± 0.9 −0.2 ± 1 −0.4 ± 0.7* −0.2 ± 0.6 0.19
18. What people mean 1.6 ± 0.9 2 ± 0.8 2.1 ± 0.9 −0.4 ± 1 −0.5 ± 0.8* −0.1 ± 0.4 0.34
19. How other people feel 1.8 ± 1 2.1 ± 1 2.3 ± 1 −0.3 ± 1.4 −0.5 ± 1* −0.2 ± 0.7 0.13
20. Group conversation 1.6 ± 0.9 1.8 ± 1 2 ± 1 −0.2 ± 1.3 −0.5 ± 0.9* −0.2 ± 0.6 0.08
Total 35.4 ± 9.6 37.3 ± 11.1 42.2 ± 10.3 −1.9 ± 11.3 −6.8 ± 8.7* −4.9 ± 4.4* 0.42
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a

Values are difference between raters as noted in column heading and statistics for paired t-test; p < 0.05 in italics; p < 0.01 in bold; p ≤ 0.001 in bold*.

The correlation coefficients for the SCoRS total were all statistically significant between the three pairs of raters. The correlation between the interviewer and informant (r = 0.92, p < 0.001, Fig. 1A) was significantly larger than between the interviewer and participant self-reports (r = 0.62, p < 0.001, Fisher z-transformation (Z) = 3.96, p = 0.0001, Fig. 1B) and between informant and participant self-reports (r = 0.42, p = 0.004, Z = 5.2, p < 0.000001, Fig. 1C).

Fig. 1.

Fig. 1

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Scatter plot of SCoRS total for informant by interviewer (A), participant by interviewer (B), and participant by informant (C).

3.3. Individual items

Across the 20 individual items, significant differences were seen between informant and participant self-reports (Table 2) on items 1 (Names of people), 16 (Doing things quickly) and 18 (What people mean). Paired t-tests examining differences between the interviewer and the other two sources were generally larger than they were between the informant and participant (Table 2). Interviewer/informant differences were largest (p < 0.001) for items 1 and 4–8, while Interviewer/participant differences were largest (p < 0.001) for items 14 and 16–20.

3.4. Informant characteristics

Finally, to assess the role of informant characteristics, we conducted several subset analyses exploring the relationship between-group and differences between participant self-report and informant ratings. We conducted a categorical analysis for high and low contact above and below 30 h/week and by informant relationship, restricted to family or friend/roommate, as there were only a limited number of staff informants (n = 2).

Total contact with participant (hours/week) was not significantly correlated with paired differences on the SCoRS total, and the between-group differences between the informant and participant self-report ratings within high and low contact subgroups was numerically large but not statistically significant (high = 3.89, low = 0.52, p = 0.33). For the individual items, informant ratings were significantly higher than participant self-reports for item 16 (Paired Difference = 1.1; p = 0.002) within the high contact group on a paired t-test. Informant ratings were significantly higher for Item 18 (Paired Difference = 0.44; p = 0.016) for the low contact group on a paired t-test, but a numerically similar, paired-difference was seen for the high contact group (Paired difference = 0.39). No between-group comparisons were statistically significant on an independent sample t-test between high and low contact informant groups, but a trend level difference was seen for item 16 (Fig. 2A, p = 0.053).

Fig. 2.

Fig. 2

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Bar graph of categorical (A) high and low informant contact hours (above and below 30 contact hours) and (B) relationship to participant (family or friend). * indicates p < 0.05.

Similar to the categorical contact hour split, no significant differences on paired t-tests were seen for the SCoRS total when the groups were split by informant relationship. Family informants rated significantly lower than participant self-report on item 1 (Paired Difference = 0.27; p = 0.03) and significantly higher on item 16 (Paired Difference = 1.0; p < 0.001). Friend/Roommate informants rated significantly lower than participant self-report on item 1 (Paired Difference = 0.62; p = 0.024) and item 4 (Where you put things, Paired Difference = 0.57; p = 0.042) and significantly higher on item 18 (Paired Difference = 0.57; p = 0.019). On an independent sample t-test between informant relationship subgroups, significant differences were seen for items 4, 6 and 16 (Fig. 2B, all p < 0.05), while comparison for the SCoRS total was numerically large but not statistically significant (Family = 3.77, Friend/roommate = 0.86, p = 0.36).

Finally, the correlation between the interviewer and informant for the SCoRS total remained similar when split by contact hours (low contact: r = 0.90, p < 0.001; high contact: r = 0.93, p < 0.001) or relationship (Family: r = 0.93, p < 0.001; or Friend/Roommate: r = 0.90, p < 0.001).

4. Discussion

The concept that having an informant increases the reliability of ratings is empirically supported (Keefe et al., 2015), but has multiple challenges in implementation. Adding a requirement to have an informant further adds to the burden and cost of conducting clinical trials in schizophrenia, which are already complicated and expensive endeavors. Information from different informants has already been shown to be poorly correlated (Sabbag et al., 2011), highlighting the need for careful selection.

The present report compares interviewer, informant, and participant self-reports for the SCoRS. Similar to the interviewer ratings, informant ratings exhibited strong ICC. Despite non-significant between-group differences for informant and participant self-report ratings, there were significantly larger correlations between the interviewer and informant ratings than between the interviewer and participant self-reports. Thus, results suggest that the interviewers “trusted” the informants more and relied on their reports significantly more than the participant self-report for the basis of their ratings, and demonstrate the added value of an informant who spends as little as two hours a week with a participant.

Total contact with participant (hours/week) was not significantly correlated with any of the three paired differences, and the correlations between the interviewer and informant for the SCoRS total were nearly identical when spilt by contact hours or relationship type. This suggests that even lower contact informants provide valued input.

Reviewing the individual items with significant differences between the participant self-reports and the informants, the informants rated significantly higher on items 12 (Familiar tasks), 16 (Doing things quickly) and 18 (What people mean) and significantly lower on item 1 (Names of people). For item 1, these differences were also within both subgroups of informant types. We note that item 1 overlapped with the largest differences between the interviewer and the informant, and that this was the individual item with the lowest inter-rater reliability in our previous report (Tulliez et al., 2025). We previously speculated that informants may have primarily considered the difficulty participants had in remembering their names or the names of other people with whom the participant interacts regularly and not considered the names of people they just met. Informants may need further instructions in answering this item. Conversely, items 16 and 18 overlapped with the largest differences between the interviewer and the participant self-reports, suggesting that informant input was particularly valued for these items.

While our primary focus was on contrasting the informant and the participant self-reports, we note that the largest between-group differences were found with comparisons to the interviewers. The interviewer differed from the informant on items 1 and 4–8, while participant/interviewer differences were largest (p < 0.001) for items 14 and 16–20. While prior factor analyses of the SCoRS have not identified any subfactors, this pattern is somewhat inconsistent with the expected split of interviewers of primarily relying on the participant's self-report for items that are not directly observable, particularly items 8 (Going to say), 18 (What people mean), 19 (How other people feel) and 20 (Group conversation).

Item 16 (Doing things quickly) showed significant differences in paired t-test analyses between both informants and interviewers with participants and significant/trend level differences for categorical analyses of informant subgroups. Informants in general, and higher contact informants specifically rated this item significantly worse than participant self-reports. This item covers the participant's ability to do things quickly, such as doing chores or taking messages, and may be the functional equivalent of the speed of processing domain in formal cognitive testing. Speed of Processing may be specifically related to function (Kern et al., 2011).

This secondary analysis has several limitations. First, and most crucially, we did not incorporate an external measure of cognition or symptoms and thus cannot definitively answer whether the inclusion of the informant improved accuracy, in terms of convergence with objective cognitive performance. This question was partially addressed in a previous study (Keefe et al., 2015) in which interviewer ratings that incorporated an informant strongly correlated with the Measurement and Treatment Research to Improve Cognition in Schizophrenia Consensus Cognitive Battery (MCCB) composite, with 13 individual items and the SCoRS total correlating at p < 0.01 level, while for the participant self-reports, only 4 individual items and the SCoRS total correlated similarly. In this previous study, item 16 had the amongst the strongest correlations with both the MCCB total and the speed of processing domain, further supporting the item 16 being a functional equivalent of the speed of processing domain. However, this prior study did not specifically report on correlations with informant ratings.

Second, our study of a single assessment (SCoRS) limits our ability to assess whether our findings are generalizable to other functional assessments that utilize an informant. Third, we used the mean contact hours for the categorical cut off between high and low contact informants, but acknowledge that this cut-off is not empirically based. Fourth, the modest sample limited power to conduct item-level analysis to discern whether informants are more trusted on individual items. Finally, recent studies have also used direct measurement of everyday functioning, including active and passive digital phenotyping (Fulford et al., 2025; Lane et al., 2025). Although these strategies have been widely endorsed as the wave of the future, with previous studies having proven them sensitive to changes in negative symptoms and everyday functioning, the FDA has not agreed to their use in CIAS registration studies. Thus, development of better information on existing tools like the SCoRS is still critical for now.

In conclusion, our findings support the potential utility of even lower contact informants who spend as little as two hours/per week with the participant. Informants, particularly high contact ones that may be in a caregiver role, are hard to find. While ours and previous analyses suggest there may be added value in preferentially having informants who are related to, or live with the patient, particularly those with a caregiver role, this benefit may be outweighed by having to limit the sample to the subset of patients who have informants with this level of contact. Future research should assess the relative correlations of informant ratings with objective cognitive or symptom ratings.

CRediT authorship contribution statement

Jayda L. Melnitsky: Writing – review & editing, Writing – original draft, Visualization, Validation, Methodology, Investigation, Formal analysis, Conceptualization. Joanne Yoon: Writing – review & editing, Validation, Investigation, Data curation. Megan R. Mayer: Writing – review & editing, Validation, Supervision, Project administration, Investigation, Data curation. Anne P. Lewandowski: Writing – review & editing, Validation, Investigation. Madison C. Teets: Writing – review & editing, Validation, Methodology, Data curation. James Gangwisch: Validation, Investigation, Data curation. Corey Reuteman-Fowler: Writing – review & editing, Project administration, Methodology, Funding acquisition, Conceptualization. Wenbo Tang: Writing – review & editing, Software, Data curation. Philip D. Harvey: Writing – review & editing, Visualization, Validation, Methodology, Formal analysis, Conceptualization. Richard S.E. Keefe: Writing – review & editing, Investigation, Formal analysis, Data curation, Conceptualization. Joshua T. Kantrowitz: Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization.

Previous presentation

None.

Funding

Data collection was funded by Boehringer Ingelheim. Data analysis was funded by internal funding (NYSPI) to JTK.

Declaration of competing interest

WT and CRF are employees of Boehringer Ingelheim. JTK has received consulting or speaking fees within the last 24 months from Physicians Postgraduate Press, Alphasights, techspert.io, Minds+Assembly, Health Monitor, KeyQuest Health, Third Bridge, MEDACorp, Marketplus, Tegus, Medscape, Clarivate, ECRI Institute, ExpertConnect, Antheum, Guidepoint, Globaldata, Clearview, Taylor & Francis/Informa, National Association of Managed Care Physicians, Medicys, Oliver Wyman and Cap Vision. He has served on the Leal Advisory Board and the Editorial Board for the International Journal of Neuropsychopharmacology in the past 24 months. He has conducted clinical research supported by the National Institute of Mental Health, Click Therapeutics, Alkermes, Alto Pharma, Neurocrine Biosciences, Inc., Taisho, and Boehringer Ingelheim within the last 24 months. He owns a small number of shares of common stock from GSK. PDH receives consulting income from Alkermes, Boehringer-Ingelheim Bristol Myers Squibb (Karuna Therapeutics), Kynexis, Minerva neurosciences, Neurocrine Biosciences, and WCG. He is chief scientific officer at i-Function, Inc., and has received royalties from the BACS. RK receives consulting income from Kynexis, Merck, WCG, Boehringer-Ingelheim, Neurocrine, Gedeon-Richter, Novartis, Vandria, Damona, Karuna-BMS, and received royalties from the BACS. The other authors reported no biomedical financial interests or potential conflicts of interest.

References

  1. Bellack A.S., Green M.F., Cook J.A., Fenton W., Harvey P.D., Heaton R.K., Laughren T., Leon A.C., Mayo D.J., Patrick D.L., Patterson T.L., Rose A., Stover E., Wykes T. Assessment of community functioning in people with schizophrenia and other severe mental illnesses: a white paper based on an NIMH-sponsored workshop. Schizophr. Bull. 2007;33(3):805–822. doi: 10.1093/schbul/sbl035. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Correll C.U., Schooler N.R. Negative symptoms in schizophrenia: a review and clinical guide for recognition, assessment, and treatment. Neuropsychiatr. Dis. Treat. 2020;16:519–534. doi: 10.2147/NDT.S225643. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Donde C., Kantrowitz J.T., Medalia A., Saperstein A.M., Balla A., Sehatpour P., Martinez A., O’Connell M.N., Javitt D.C. Early auditory processing dysfunction in schizophrenia: mechanisms and implications. Neurosci. Biobehav. Rev. 2023;148 doi: 10.1016/j.neubiorev.2023.105098. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Durand D., Strassnig M.T., Moore R.C., Depp C.A., Ackerman R.A., Pinkham A.E., Harvey P.D. Self-reported social functioning and social cognition in schizophrenia and bipolar disorder: using ecological momentary assessment to identify the origin of bias. Schizophr. Res. 2021;230:17–23. doi: 10.1016/j.schres.2021.02.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Fulford D., Correll C.U., Harvey P.D., Cohen A.S. Digital therapeutics for people with schizophrenia spectrum disorders: a systematic literature review of their effect on symptoms and functioning. Schizophr. Bull. 2025 doi: 10.1093/schbul/sbaf134. [DOI] [PubMed] [Google Scholar]
  6. Galderisi S., Mucci A., Dollfus S., Nordentoft M., Falkai P., Kaiser S., Giordano G.M., Vandevelde A., Nielsen M.O., Glenthoj L.B., Sabe M., Pezzella P., Bitter I., Gaebel W. EPA guidance on assessment of negative symptoms in schizophrenia. Eur. Psychiatry. 2021;64(1) doi: 10.1192/j.eurpsy.2021.11. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Gould F., Sabbag S., Durand D., Patterson T.L., Harvey P.D. Self-assessment of functional ability in schizophrenia: milestone achievement and its relationship to accuracy of self-evaluation. Psychiatry Res. 2013;207(1–2):19–24. doi: 10.1016/j.psychres.2013.02.035. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Govil P., Kantrowitz J.T. Negative symptoms in schizophrenia: an update on research assessment and the current and upcoming treatment landscape. CNS Drugs. 2025;39(3):243–262. doi: 10.1007/s40263-024-01151-7. [DOI] [PubMed] [Google Scholar]
  9. Harvey P.D. Assessment of everyday functioning in schizophrenia: implications for treatments aimed at negative symptoms. Schizophr. Res. 2013;150(2–3):353–355. doi: 10.1016/j.schres.2013.04.022. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Harvey P.D., Velligan D.I., Bellack A.S. Performance-based measures of functional skills: usefulness in clinical treatment studies. Schizophr. Bull. 2007;33(5):1138–1148. doi: 10.1093/schbul/sbm040. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Harvey P.D., Ogasa M., Cucchiaro J., Loebel A., Keefe R.S. Performance and interview-based assessments of cognitive change in a randomized, double-blind comparison of lurasidone vs. ziprasidone. Schizophr. Res. 2011;127(1–3):188–194. doi: 10.1016/j.schres.2011.01.004. [DOI] [PubMed] [Google Scholar]
  12. Harvey P.D., Stone L., Lowenstein D., Czaja S.J., Heaton R.K., Twamley E.W., Patterson T.L. The convergence between self-reports and observer ratings of financial skills and direct assessment of financial capabilities in patients with schizophrenia: more detail is not always better. Schizophr. Res. 2013;147(1):86–90. doi: 10.1016/j.schres.2013.02.018. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Horan W.P., Kalali A., Brannan S.K., Drevets W., Leoni M., Mahableshwarkar A., Martin W.J., Rao S., Reuteman-Fowler C., Sauder C., Savitz A., Singh J., Tiller J., Walker G., Wendland J.R., Harvey P.D. Towards enhancing drug development methodology to treat cognitive impairment associated with schizophrenia and other neuropsychiatric conditions: insights from 2 decades of clinical trials. Schizophr. Bull. 2025;51(2):262–273. doi: 10.1093/schbul/sbae151. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Howes O., Fusar-Poli P., Osugo M. Treating negative symptoms of schizophrenia: current approaches and future perspectives. Br. J. Psychiatry. 2023;223(1):332–335. doi: 10.1192/bjp.2023.57. [DOI] [PubMed] [Google Scholar]
  15. Kaneko F., Okamura H. Discrepancies between self- and clinical staff members’ perception of cognitive functioning among patients with schizophrenia undergoing long-term hospitalization. Occup. Ther. Int. 2019;2019 doi: 10.1155/2019/6547096. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Kantrowitz J.T. Managing negative symptoms of schizophrenia: how far have we come? CNS Drugs. 2017;31(5):373–388. doi: 10.1007/s40263-017-0428-x. [DOI] [PubMed] [Google Scholar]
  17. Kantrowitz J.T., Correll C.U., Jain R., Cutler A.J. New developments in the treatment of schizophrenia: an expert roundtable. Int. J. Neuropsychopharmacol. 2023;26(5):322–330. doi: 10.1093/ijnp/pyad011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Kantrowitz J.T., Mayer M.R., Choo T.H., Liu K., Govani V., Francis S., Javitt D.C. Effect size deflation with increasing sample size in adjunctive trials for negative symptoms in schizophrenia: a meta-analysis and meta-regression across mechanisms of action. Am. J. Psychiatry. 2025 doi: 10.1176/appi.ajp.20250034. [DOI] [PubMed] [Google Scholar]
  19. Keefe R.S., Poe M., Walker T.M., Kang J.W., Harvey P.D. The Schizophrenia Cognition Rating Scale: an interview-based assessment and its relationship to cognition, real-world functioning, and functional capacity. Am. J. Psychiatry. 2006;163(3):426–432. doi: 10.1176/appi.ajp.163.3.426. [DOI] [PubMed] [Google Scholar]
  20. Keefe R.S., Davis V.G., Spagnola N.B., Hilt D., Dgetluck N., Ruse S., Patterson T.D., Narasimhan M., Harvey P.D. Reliability, validity and treatment sensitivity of the Schizophrenia Cognition Rating Scale. Eur. Neuropsychopharmacol. 2015;25(2):176–184. doi: 10.1016/j.euroneuro.2014.06.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Kern R.S., Gold J.M., Dickinson D., Green M.F., Nuechterlein K.H., Baade L.E., Keefe R.S., Mesholam-Gately R.I., Seidman L.J., Lee C., Sugar C.A., Marder S.R. The MCCB impairment profile for schizophrenia outpatients: results from the MATRICS psychometric and standardization study. Schizophr. Res. 2011;126(1–3):124–131. doi: 10.1016/j.schres.2010.11.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Kraeplin E. Macmillan; New York: 1907. Clinical Psychiatry. [Google Scholar]
  23. Lane E., Gray L., Kimhy D., Jeste D., Torous J. Digital phenotyping of social functioning and employment in people with schizophrenia: pilot data from an international sample. Psychiatry Clin. Neurosci. 2025;79(3):125–130. doi: 10.1111/pcn.13786. [DOI] [PubMed] [Google Scholar]
  24. McCutcheon R.A., Keefe R.S.E., McGuire P.K. Cognitive impairment in schizophrenia: aetiology, pathophysiology, and treatment. Mol. Psychiatry. 2023;28(5):1902–1918. doi: 10.1038/s41380-023-01949-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Patterson T.L., Semple S.J., Shaw W.S., Grant I., Jeste D.V. Researching the caregiver: family members who care for older psychotic patients. Psychiatr. Ann. 1996;26(12):772–784. [Google Scholar]
  26. Sabbag S., Twamley E.M., Vella L., Heaton R.K., Patterson T.L., Harvey P.D. Assessing everyday functioning in schizophrenia: not all informants seem equally informative. Schizophr. Res. 2011;131(1–3):250–255. doi: 10.1016/j.schres.2011.05.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Sehatpour P., Kantrowitz J.T. Finding the right dose: NMDA receptor-modulating treatments for cognitive and plasticity deficits in schizophrenia and the role of pharmacodynamic target engagement. Biol. Psychiatry. 2025;97(2):128–138. doi: 10.1016/j.biopsych.2024.08.019. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Silberstein J., Harvey P.D. Cognition, social cognition, and self-assessment in schizophrenia: prediction of different elements of everyday functional outcomes. CNS Spectr. 2019;24(1):88–93. doi: 10.1017/S1092852918001414. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Silberstein J.M., Pinkham A.E., Penn D.L., Harvey P.D. Self-assessment of social cognitive ability in schizophrenia: association with social cognitive test performance, informant assessments of social cognitive ability, and everyday outcomes. Schizophr. Res. 2018;199:75–82. doi: 10.1016/j.schres.2018.04.015. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Strauss G.P., Bartolomeo L.A., Harvey P.D. Avolition as the core negative symptom in schizophrenia: relevance to pharmacological treatment development. NPJ Schizophr. 2021;7(1):16. doi: 10.1038/s41537-021-00145-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Tulliez S., Karantzoulis S., Marcus J.C., Casamayor M., Blanchard C., Goenjian H., Kantrowitz J.T., Shirikjian L., Sonnenberg J., Reuteman-Fowler C., Harvey P.D., Keefe R.S.E. Assessing the inter-rater reliability of the Schizophrenia Cognition Rating Scale: a non-interventional quantitative study. Schizophrenia (Heidelb.) 2025;11(1):71. doi: 10.1038/s41537-025-00619-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Ventura J., Reise S.P., Keefe R.S., Baade L.E., Gold J.M., Green M.F., Kern R.S., Mesholam-Gately R., Nuechterlein K.H., Seidman L.J., Bilder R.M. The Cognitive Assessment Interview (CAI): development and validation of an empirically derived, brief interview-based measure of cognition. Schizophr. Res. 2010;121(1–3):24–31. doi: 10.1016/j.schres.2010.04.016. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Yoon J., Mayer M.R., Berro T., Brazis S., Kantrowitz J.T. Knowing is half the battle: the factors leading to efficient recruitment of representative samples in schizophrenia research. Pharmaceut. Med. 2025;39(1):29–38. doi: 10.1007/s40290-024-00545-8. [DOI] [PubMed] [Google Scholar]

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