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5 Design Considerations: Adherence F or a new biological HIV prevention product to be effective, users must adhere to the prescribed regimen. During a clinical trial, imper- fect adherence reduces the product’s effectiveness, and also makes it difficult for investigators to assess efficacy. If a trial shows that a product provides an overall benefit, relating the level of protection to the level of adherence is valuable for interpreting the results. Understanding who was protected during the trial and under what circumstances also has important implications for predicting how effective the product will be in various real-world settings. To properly interpret the results of a clinical trial that failed to show a protective effect, investigators need to distinguish the extent to which the product was not biologically efficacious, participants did not use it as directed, or they engaged in more risky behavior because they thought the product was protecting them. Regardless of whether a trial demonstrates an effect, understanding when and why participants did not adhere to the product regimen can provide valuable insights into the design and delivery of future HIV prevention interventions. While interventions given once or a very few times, such as vaccines or circumcision, usually do not entail adherence challenges, other exist- ing and new biomedical HIV interventions such as condoms, PrEP, and microbicides require longer-term administration. Adherence is more than “simply remembering medications, but rather, a complex issue involving social, cultural, economic, and personal factors” (Chesney, 2006). In the antiretroviral treatment (ART) field, there has been insufficient progress in understanding the correlates of adherence and strategies to increase 

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0 METHODOLOGICAL CHALLENGES IN HIV PREVENTION TRIALS adherence (Sankar et al., 2006). Adherence to biomedical HIV prevention interventions is even less well understood. This underscores the importance of understanding the context and factors that affect individuals’ adherence and risk behavior. Researchers should not assume that definitions or models of adherence (or risk reduction) developed in one sociocultural context are equally as valid in another (Ware et al., 2006). They should be evaluated and adapted to new settings as appropriate (see Ware et al., 2006, for one such adaptation model). Studies that disregard the sociocultural context of adherence, or that rely on models of adherence that are not appropriate to the cultural context, are more likely to find their efforts to understand and improve adherence ineffective or irrelevant (Sankar et al., 2006; Ware et al., 2006). Although underutilized in the HIV field, qualitative research methods can be particularly useful to understanding the multiple factors that influence adherence and risk behavior patterns and developing suitable adherence measurements and improvement strategies (Friedland, 2006; Sankar et al., 2006). This chapter examines four important aspects of adherence: defining it, measuring it, improving it, and analyzing data on it. The committee makes recommendations for future practice and research in each area. The chapter also highlights the need for multidisciplinary teams to col- laborate in addressing challenges to adherence in trials of HIV prevention interventions. DEFINING ADHERENCE Although researchers agree on the importance of product adherence in both research and real-world settings, there is less agreement on how to define it. Despite the complexity of adherence, clinical trials often report it as a simple number, such as the percentage of coital acts in which par- ticipants use a gel, or the percentage of pills they take over a given time period (Chesney, 2006). The use of a single number to define adherence may mask crucial insights into adherence problems, product acceptability, and potential areas for intervention (Kerr et al., 2005; Berg and Arnsten, 2006). This number can also reflect variability stemming from adherence behaviors that the measure is not intended to address (“construct-irrelevant variance”) (Kerr et al., 2005). Consider a trial to investigate whether suppression of HSV-2, the her- pes simplex virus, prevents HIV infection. The perfectly adherent patient would take one dose in the morning and one in the evening at the same times each day (Vrijens et al., 2006). Suppose the study identifies four participants with imperfect adherence who take 79 percent of prescribed doses during an observation interval. That simple percentage can mask highly disparate adherence patterns (see Figure 5-1): The first patient was

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V VVVVVVVVVVVVVV VVVVVVVVVV V V VV 03:00 03:00 24:00 24:00 18:00 18:00 12:00 12:00 Dosing Time Dosing Time 06:00 06:00 03:00 03:00 Comp Comp Start V1 V2 V3 End Start V1 V2 V3 End fdur fdur Non Pers. Pers. Daily Intake 0 20 40 60 80 100 120 0 20 40 60 80 100 120 Dosing Date Dosing Date 2 doses 1 dose V VVVV VVVVV V 03:00 03:00 24:00 24:00 0 doses 18:00 18:00 12:00 12:00 Dosing Time Dosing Time 06:00 06:00 03:00 03:00 Comp Comp Start V1 V2 V3 End Start V1 V2 V3 End fdur fdur Pers. Pers. 0 20 40 60 80 100 120 0 20 40 60 80 100 120 Dosing Date Dosing Date alternate fig 5-1--grabbed from Methods for Compliance Measurement, B. Vjirens and J. Urquhart, 2007 FIGURE 5-1 Differing patterns of product use among four patients—all of whom are 79 percent adherent. SOURCE: Vrijens, 2007. 

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 METHODOLOGICAL CHALLENGES IN HIV PREVENTION TRIALS adherent but discontinued the prescribed regimen early. The second patient took the morning doses consistently but missed several evening doses. The third patient took the dosages erratically and stopped altogether for a period of time. The fourth patient decided to take only the morning dose and stopped taking the evening dose. Even though each patient was “79 percent adherent,” the clinical consequences of those four patterns could be very different, and the actions needed to improve adherence would differ for the four participants. As Figure 5-1 shows, product adherence is a complex concept. It involves three major components: acceptance of the prescribed regimen, execution of the regimen, and discontinuation of the regimen (Vrijens and Urquhart, 2005; Friedland, 2006) While acceptance and discontinuation tend to happen at a single point in time, quality of execution varies over time. Two aspects of adherence are particularly important in analyzing the results of a clinical trial: persistence, and quality of execution—commonly called compliance. Persistence is the amount of time between when a patient first uses a product and when she or he discontinues it. Quality of execu- tion is the correspondence between the prescribed regimen and the patient’s actual application history during the period of persistence (Vrijens and Urquhart, 2005). That is, quality of execution measures whether a patient has complied with instructions for using a product. Figure 5-2 illustrates these concepts in 20,000 patients who were pre- scribed regular doses of drugs for various diseases, aggregated across multi- ple clinical trials (Vrijens, 2007). The horizontal line at the top of the graph represents perfect adherence. The persistence curve reflects the proportion of patients who continued product use during the first year. While persis- tence varied across disease and trials, these data indicate that 40 percent of participants discontinued the prescribed intervention in the first 12 months. The adherence/compliance curve reflects on a daily basis the proportion of trial participants who executed product use according to the ideal regimen. About half of the participants did so. Describing and analyzing persistence separately from quality of execu- tion provides insights into whether a product is acceptable at both the individual and population levels, and suggests different modes of action. A trial population consisting of 50 percent nonpersisters who are otherwise perfect executers would have very different implications than if all subjects skip every other application. In the first instance, the product would work very well for a select subgroup. The second situation would require more complex analysis and intervention. Recommendation 5-1: Because simple measures of adherence can mask substantially different underlying adherence problems, investigators

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 DESIGN CONSIDERATIONS: ADHERENCE Non acceptance Perfect adherence 100 Treatment 90 discontinuation 80 Percent Persis tence 70 Adher n io ence / 60 c ut omplia ec nce ex 50 n No 40 0 100 200 300 Days PKC: N > 20,000 patients FIGURE 5-2 Different aspects of adherence among 20,000 patients in multiple clinical trials. SOURCE: Vrijens, 2007. 5-2 should develop and use adherence measures that can capture different adherence patterns over time. MEASURING ADHERENCE, SEXUAL BEHAVIOR, AND CONDOM USE Investigators can gather information on product adherence and risk behavior through a variety of indirect and direct measures. Adherence and risk behavior are commonly measured by self-report through self- administered questionnaires, audio computer-assisted self-interviewing (ACASI), face-to-face interviews with participants, and participant diaries. Other indirect measures include pill counts, electronic product monitoring, pharmacy refills, and biomarkers of product exposure and risk behavior, such as applicator staining for vaginal insertion, or presence of semen

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 METHODOLOGICAL CHALLENGES IN HIV PREVENTION TRIALS indicating unprotected sex. Direct measures of product adherence include pharmacokinetic studies (which measure drug levels or metabolites in sub- jects’ blood or bodily fluids), and directly observed therapy. These measures can vary substantially in expense, the effort required of participants and their partners, their perceived invasiveness, and their accuracy and reliabil- ity (Berg and Arnsten, 2006). Complicating matters is the lack of a “gold standard” for measuring adherence. Most research on measuring adherence in HIV studies has examined HIV-infected individuals’ adherence to ART. Measuring individuals’ risk behavior is also critical to knowing whether a product worked. Given the limited published empirical evidence on adherence measures for nonvaccine biomedical HIV prevention interventions, the committee draws heavily on the fields of ART adherence and sexual risk behavior assessment in making its assessment and recommendations. Indirect Measurement Methods Self-Reports of Product Adherence and Risk Behavior Self-report of product adherence and risk behavior is widely used in research and clinical settings because it is relatively inexpensive, easy to administer, allows for probing about nonadherence, and has low participant burden (Berg and Arnsten, 2006). However, the accuracy of self-reports is controversial and has been the focus of substantial research. Reliability and validity are the two most important psychometric aspects of self-report measurements. Reliability refers to whether the instru- ment is free of random error and validity refers to whether the instrument is measuring what it intends to measure (Pequegnat et al., 2000). Self-reports can be incorrect because a person fails to respond truthfully or does not accurately recall their behavior (Pequegnat et al., 2000). Participants may respond untruthfully to questions about their adherence and risk behavior because they want the interviewer, study staff, or other participants to view them more favorably—a phenomenon known as “social desirability” bias (Pequegnat et al., 2000; Schroder et al., 2003b; Simoni et al., 2006b). Even with truthful responses, the accuracy of self-reports can be affected by the length of the recall period (for retrospective reports), the question format, appropriateness of the assessment mode, and individual factors (e.g., such as the frequency of behaviors, educational level, age, or use of alcohol and drugs) (Pequegnat et al., 2000; Schroder et al., 2003a,b, 2007).1 1 See Pequegnat et al., 2000; Simoni et al., 2006b; and Schroder et al., 2003a,b, 2007, for a detailed discussion of psychometric factors related to adherence and risk behavior assessment and associated references.

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 DESIGN CONSIDERATIONS: ADHERENCE Studies have found that self-reports of adherence tend to be positively skewed, producing higher estimates of adherence compared to more objec- tive measures (e.g., electronic drug monitoring) (Berg and Arnsten, 2006; Simoni et al., 2006b). In contrast, studies comparing self-reports of sexual behavior with biomarkers of semen exposure indicate that individuals may underreport sensitive or stigmatized risk behavior (see, for example, Gallo et al., 2007). Researchers have identified several methods to help mitigate these problems. First, investigators can enhance the validity and reliability of self- reports by stressing the importance of truthful responses, ensuring anonym- ity or confidentiality of responses, and allowing for privacy in answering questions (Pequegnat et al., 2000). Guidelines for minimizing social desir- ability bias in ART adherence assessment include using self-administered measures with either open-ended or forced choices, acknowledging the difficulty of perfect adherence to the participant (“normalizing” imperfect adherence), querying reasons for nonadherence, focusing on recent behav- ior, clearly specifying a time frame, using recall aids and anchoring reports to salient events, and conducting reliability checks (Simoni et al., 2006b). Investigators can also use methods of collecting self-reported infor- mation that are more likely to promote reliable and valid self-reports. Face-to-face interviews can be prone to overreporting of adherence and underreporting of sensitive risk behaviors (Jadack et al., 2001; Rogers et al., 2005). Use of interviewers who are independent of the study staff can help create a neutral climate (UNAIDS, 2007). Self-administered question- naires may also decrease the likelihood of social desirability bias. While written self-administered questionnaires are inappropriate in areas with low literacy rates, the use of ACASI can address some of the problems with face-to-face interviews. A number of studies have suggested that participants are more likely to report sensitive behaviors, such as sexual behavior or drug use, if investiga- tors rely on ACASI rather than in face-to-face interviews. ACASI can also help researchers check the consistency of participants’ answers and reduce the number of missing data fields, or “don’t know” responses. Several stud- ies have shown that this technique is feasible and acceptable in a variety of international settings among different at-risk populations with varying literacy rates and computer skills (Van De Wijgert et al., 2001; Simoes et al., 2006a,b; NIMH Collaborative HIV/STD Prevention Trial Group, 2007). Contraception trials have used prospective methods, such as coital diaries, extensively to collect self-reported information, and to validate ret- rospective self-reports of sexual behavior (Schroder et al., 2003a). The first phase 3 trial of a microbicide (Nonoxynol-9, or N9) initially used diaries in the form of pictorial log charts (Van Damme et al., 2002). However, after finding that women sometimes filled in their diaries while waiting at the

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 METHODOLOGICAL CHALLENGES IN HIV PREVENTION TRIALS clinic, the trial abandoned the diaries in favor of interviews (Van Damme et al., 2002). The Microbicide Development Program’s phase 3 trial of the PRO2000 microbicide is now using coital diaries in a subset of women to capture information on sexual behaviors and product use. In a feasibility study prior to the start of the phase 3 trial, investigators found that women tended to report sexual behaviors more frequently in diaries than in face- to-face interviews (Allen et al., 2007). Electronic diaries such as personal digital assistants (Bartley et al., 2004), mobile phones (Hays et al., 2001), pagers (Shrier et al., 2005), and Web applications (Baer et al., 2002) have also been tested, and could offer important advantages over paper diaries. Electronic diaries can allow internal checks on the validity of the data, and could record time and date information (Raymond and Ross, 2000). Electronic diaries also eliminate the need to transcribe and code data, allowing more detailed and timely analysis (Bartley et al., 2004). Studies have shown that subjects are willing to use electronic diaries, and that they may actually do so more often than paper diaries (Hufford, 2002). Despite extensive work, several psychometric aspects of self-report mea- surements remain unresolved. A key problem is the lack of standardization in adherence and risk behavior measurement instruments across research and clinical settings (Schroder et al., 2003b; Berg and Arnsten, 2006). These measures vary in terms of length of recall period, the type of measure (quali- tative versus quantitative measures), the question format, among other factors. There is poor agreement between various self-report measures and the variation makes it difficult to compare results across studies (Berg and Arnsten, 2006). Recent review papers suggest that cognitive interviewing, which examines how target audiences interpret questions, process infor- mation, and form responses to survey questions, and additional empirical evaluations of self-reported adherence questions can further improve the validity and reliability of self-reports (Berg and Arnsten, 2006). Pill and Applicator Counts Many clinical trials rely on less specific but possibly more objective measures of product adherence, such as pill counts. Asking participants to return unused pills and product applicators during routine visits is a rela- tively inexpensive measure. However, such measures can be time consuming and subject to bias if participants “dump” unused products prior to a visit (Berg and Arnsten, 2006). To address these concerns, some studies have found that unannounced pill counts at participants’ homes can provide a reliable measure of ART adherence (Bangsberg et al., 2000, 2001). This approach reduces the problems with asking participants to return unused product at scheduled visits, reduces their opportunity to empty pill contain-

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 DESIGN CONSIDERATIONS: ADHERENCE ers, and does not require them to bring all their medications to the clinic (Bangsberg et al., 2001). Yet unannounced pill counts at participants’ homes can be expensive and logistically difficult, and some participants may prefer not to have home visits (Kalichman et al., 2007). In a recent study of 77 HIV infected individuals in Atlanta, Kalichman and colleagues (2007) found that unan- nounced telephone-based pill counts were a logistically feasible and eco- nomical method for monitoring adherence to ART medication. Pharmacy Refills Maintaining pharmacy refill records is another method for indirectly measuring product adherence. If patients do not receive timely refills from the pharmacy, the investigator can assume that the patient is either miss- ing doses or not taking the medication at all. “Medication gaps” or the period that the patient’s supply of product is assumed to be exhausted is determined by a comparison of the actual refill dates with the expected refill dates (Berg and Arnsten, 2006). This method of measuring adherence relies on two major assumptions. Participants who do not receive timely refills are not obtaining product from other sources, and participants who do receive timely refills use the product as prescribed. In the clinical setting, a patient may have multiple opportunities to access medication, such as through family members and friends or other pharmacies. However, in the trial setting, access to the product usually is limited to the study pharmacy and study population, though product shar- ing among trial participants has been raised as a concern in some studies. Given that distribution of the study product is more tightly controlled, the use of pharmacy refill records may have greater applicability for measuring adherence in the trial setting than in the clinical setting. Similar to pill counts, pharmacy refill records are not immune to bias, as participants may “dump” or share unused products. Timely refills do not guarantee that the participant took the product as instructed. Despite this limitation, several HIV treatment studies have shown a significant correla- tion between pharmacy refill adherence and HIV viral load (Maher et al., 1999; Low-Beer et al., 2002; Grossberg et al., 2004; Fairley et al., 2005) all studies can be found in Berg and Arnsten (2006). Electronic Medication Monitoring A commonly used approach to measuring adherence to ART is the “medication event monitoring” system (MEMs). MEMs uses microcircuitry in pharmaceutical packages to detect, time-stamp, analyze, and store infor-

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 METHODOLOGICAL CHALLENGES IN HIV PREVENTION TRIALS mation on when users remove a dose, and communicates that information to investigators (Urquhart, 1997). MEMs provides an “objective” and time- specific measure of adherence, and thus is often considered more valid than other types of measures, such as self-reports (Berg and Arnsten, 2006). Yet MEMs has several important limitations. It requires participants to store all medications in the MEMs container, to remove only the correct number of pills at each dosing, to open the container only during dosing, and to close the monitor after dosing (Bangsberg et al., 2001). Several stud- ies have documented “pocket dosing,” in which participants remove mul- tiple doses from the container at one time, which can lead to underestimates of adherence (Bova et al., 2005). Other studies have identified “curiosity checks,” in which participants open the containers to test them, or to see how many tablets they have left, which may lead to overestimates of adher- ence (Bangsberg et al., 2001; Bova et al., 2005; Berg and Arnsten, 2006). MEMs may be valuable in assessing adherence to medication-based approaches such as PrEP or HSV-2 suppression therapy. Researchers have also tested modifications of MEMs-type monitors for topical medications and found them feasible (Tusa et al., 2006). Investigators may find it useful to adapt MEMs to microbicide applicators. Other approaches being devel- oped is the “smart” vaginal microbicide applicator, called Xigo, that stores information about product use (Rosenberg, 2007) and the “sexometer”—an intravaginal ring called Paragon that attempts to capture the time and date of sexual intercourse based on motion indicators (Rosenberg, 2007). Biomarkers of Product Use and Sexual Activity Researchers have developed biomarkers of product use and sexual activity that have potential for validating self-reported information in trial settings. To assess microbicide product use, researchers developed an assay that assesses lactobacillus growth on returned used applicators to determine exposure to the vagina (Wallace et al., 2004; Hogarty et al., 2007). This method was used in the Carraguard microbicide trial to distinguish appli- cators that had been inserted vaginally versus those that were not inserted (Skoler, 2007). The assay cannot reveal when the gel was used, which is important information for coitally dependent microbicides, or whether it was used by the participant or someone else. Nor can it determine whether the product was used each time it should have been. It can, however, reveal the absolute level of exposure to a product. A subsequent study of this approach in a 14-day microbicide trial of 0.5% PRO 2000 and placebo gel returned applicators found that there was high concordance between self- report and applicator staining (Hogarty et al., 2007). Although incidence of sexually transmitted infections (STIs) has been suggested as a potential biomarker for unprotected sexual behavior, trans-

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 DESIGN CONSIDERATIONS: ADHERENCE mission of STIs also depends on individual susceptibility, sexual partners, and the characteristics of the infection (Pequegnat et al., 2000). Researchers have recently examined biomarkers of semen exposure as potential tools for validating self-reports of condom use and recent sexual activity. These candidate biomarkers can be classified into two categories: those that detect seminal plasma, and those that detect spermatozoa and other cells present in semen (see Mauck and Doncel, 2007 for a review of methods). The seminal plasma biomarker with the most extensive testing in clinical trials is the prostate specific antigen (PSA) (Lawson et al., 1998; Macaluso et al., 1999). Because PSA begins to clear from the vagina immediately after it is exposed to semen, PSA detection likely underestimates exposure (Gallo et al., 2007). Another biomarker for semen exposure is the Y-chromosome (Yc) DNA. Researchers developed a Yc polymerase chain reaction (PCR) assay (Jadack et al., 2001; Zenilman et al., 2005). Yc-DNA may offer advantages over PSA because it can be detected several weeks post-coitus (Mauck and Doncel, 2007). Such biomarkers have been used primarily to evaluate the effective- ness of contraceptives, but they may have a role in validating self-reports of sexual activity in HIV/STI prevention trials (Mauck and Doncel, 2007). For example, a recent study used PSA to assess the validity of self-reported condom use among female sex workers in Kenya, and found that 11 per- cent of samples from women who reported no unprotected sex in the prior 48 hours tested positive for PSA (Gallo et al., 2007). A follow-up study to the phase 3 MIRA diaphragm trial also used PSA to assess the validity of self-reported sexual behavior (Mauck and Doncel, 2007). Upon completion of the trial, investigators randomized a subset of women to an additional session using either ACASI or face-to-face interview. They then conducted a PSA analysis on women who reported no intercourse in the previous 48 hours, to assess whether misreporting differed by interviewing technique. The investigators have not yet reported the results of this analysis. These or future assays may help validate self-reports of sexual behavior, and possibly help investigators assess the safety and efficacy of microbicides (Mauck and Doncel, 2007). However, the use of vaginal products, vaginal washing, menses, and infection may affect the sensitivity of the assays, so the ultimate value of the approach requires further research (Zenilman et al., 2005; Mauck and Doncel, 2007). Direct Measurement Methods Drug Monitoring Biological assays of active drug, metabolite, or other markers in blood, urine, or other specimens can provide information on individuals’ expo-

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 DESIGN CONSIDERATIONS: ADHERENCE TABLE 5-1 What Adherence Analyses Can Reveal Analysis Possible Results Interpretation Analyze level of product The measured levels Efficacy trial: This shows to adherence in each study indicate how much what extent the measured arm. participants deviate from effect should indeed be seen as the prescribed dose timing, a measure of efficacy. and possibly the method of using the product. Effectiveness trial: This may reveal problems with the feasibility and acceptability of the intervention in the study population within the trial setting. Analyze differences The measured levels of This might indicate that in product exposure, product adherence and the active product versus adherence, and risk sexual behavior differ placebo produce different side behavior between study between study arms. effects, prompting subjects to arms.a comply differently with the assignment.b Analyze differences People within study arms Some subpopulations might be in product exposure, differ greatly in how better suited to another type adherence, and risk they use the product, and of protection than the one behavior between subjects possibly also in their risk- under study. within randomized arms. taking behavior. Analyze changes over People within randomized If rates of adherence among time in product exposure, arms differ greatly in how individual subjects drop adherence, and risk they use the product over dramatically over time, behavior among subjects time. this might indicate that the within randomized arms. intervention is not sustainable. That, in turn, may indicate that side effects emerge after cumulative use, or the need for supportive measures to improve adherence. If long- term use of the intervention is envisaged, suggestions that sustainability is limited would require investigators to further examine the appropriateness of the intervention. If adherence rates increase over time, subjects may be getting better at adhering to the intervention. continued

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 METHODOLOGICAL CHALLENGES IN HIV PREVENTION TRIALS TABLE 5-1 Continued Analysis Possible Results Interpretation Analyze how levels of Subjects appear to be This could reflect risky behavior differ from highly sexually active, or disinhibition, or that subjects those that can be expected they engage in more risky want to become pregnant outside the trial. behavior than expected within the healthy study outside the trial context. setting. Such behavior could lead to sexual acts against which the product is not designed to protect, and may put subjects at higher risk of HIV infection. Analyze how levels of risky Subjects differ between This could distort the behavior differ between arms in the amount of intention-to-treat effect. That, study arms. risky behavior they pursue in turn, could give a distorted against which the product view of the direct impact of is not designed to protect the product under real-world (such as anal sex). conditions. aAny interpretation of observed differences in adherence between arms will need to account for differential dropout rates over time. Such differences in dropout rates could occur, for in- stance, if a product is protective, and HIV incidence differs between arms. The least-protected people will tend to drop out first owing to HIV infection. bThis does not mean that blinding has failed. A small increase in a mild side effect in the study arm could lead to lower product adherence without participant awareness. These dif- ferences may emerge early in the trial or become apparent with accumulated product use over time. other hand, if a product is protective and HIV incidence differs between arms, the least-protected participants would tend to drop out sooner. Effect of Measurement Error Investigators would have no reason to expect measurement error to differ between study arms in a blinded randomized trial. They can there- fore expect significant differences in adherence between arms to reflect true differences. The interpretation of reported adherence differences between arms in an unblinded trial is usually less clear. For example, in the MIRA diaphragm/ Replens trial, observed rates of condom use were much higher in the “con- dom-only” control arm than in the intervention arm (Padian et al., 2007). One explanation is that subjects in the condom-only arm adhered to guide- lines on condom use more than did subjects in the condom-plus-diaphragm arm, because they felt that this was their only form of protection against

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 DESIGN CONSIDERATIONS: ADHERENCE HIV infection. However, a different explanation is that they over-reported condom use because they could not report diaphragm use and wished to appear compliant. Comparing Adherence Patterns Within Randomized Arms Large variations in reported adherence between subjects within a study arm may indicate that some subpopulations are better suited for another type of protection. For example, if participants in one arm used a product for 80 percent of sexual acts, it matters whether all subjects failed to use the product for 20 percent of the acts, versus whether 20 percent of subjects did not use the product at all and 80 percent used it every time. In the lat- ter case, investigators would hope to recognize the 20 percent of nontakers early on and offer them a different intervention option. If most people use the product only 80 percent of the time, the intervention may be impracti- cal. Investigators need to address these differences in their analyses, rather than reporting only the percentage of people who comply with the regimen in each arm. Effect of Measurement Error If subjects within a study arm differ significantly in their reported expo- sure to the product, adherence to the product regimen, and sexual behavior, investigators will need to consider whether the differences reflect true dif- ferences, errors in measurement, or a combination of both. Adherence patterns for individuals may change over time. If compli- ance rates for individuals drop dramatically during a trial, investigators should determine whether side effects are emerging. If adherence declines are common across subjects within an arm, investigators should consider providing additional support or other interventions, especially if the prod- uct is intended for long-term use. If measurement errors remain stable, such differences probably reveal actual trends. When Behavior Differs from That Expected Outside the Trial Participants in a clinical trial might pursue higher rates of sexual activ- ity or other risky behavior because of disinhibition, or because they are trying to become pregnant in the healthy environment of the trial. Such behavior would put them at higher risk of HIV transmission, especially if they pursue more risky behavior against which the product is not designed to protect, such as anal sex. In such cases, the results of the trial’s intention- to-treat analysis could give a distorted view of the impact of product use outside the study.

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0 METHODOLOGICAL CHALLENGES IN HIV PREVENTION TRIALS Effect of Measurement Error This aspect of the adherence analysis is quite sensitive to measurement error, although systematic errors are again likely to reflect underreporting among trial participants. Association Analyses: Linking Adherence Patterns to HIV Incidence Investigators can attempt to analyze information on reported sexual activity, adherence, and HIV exposure to assess their association with HIV infection. Such analyses may allow them to interpret and explain the observed intention-to-treat effect, judge whether to explore different ways of administering a product, and estimate the effect the product will have in other populations. The observed association between reported product use and HIV inci- dence is prone to confounding, and does not necessarily reflect a real (causal) effect even if reported use is correct. For microbicides, for instance, higher sexual activity tends to increase the risk of HIV transmission as well as the number of gel applications. Investigators would therefore expect to find a positive association between the product use (such as applications per day) in the experimental arm and HIV incidence, even in the absence of any causal effect of the product. Nevertheless, information on variations in product use can help inves- tigators interpret the effect revealed by an intention-to-treat analysis, and help them estimate the product’s effectiveness in future populations. As in the N-9 trial, higher HIV incidence rates may occur at high levels of prod- uct use in the intervention arm than at corresponding levels of product use in the placebo arm (Van Damme et al., 2002), and this might reflect an increased risk of HIV infection with use of the intervention. However, investigators need to consider and exclude other explanations before regarding such an effect as causal. The main reason is that a mea- sured compliance level—such as use of gel in at least 80 percent of sexual acts—is a post-randomization category that may itself be influenced by the product, and need not be influenced in the same way among study arms. (In technical investigations of causality, this phenomenon is known as a lack of “exchangeability.”) For instance, if a product is associated with more sexually transmitted diseases, some women may start to use it less often, and hence different subpopulations may have similar use levels in both trial arms. However, when the study arms are blinded and investigators observe similar distributions of adherence between them, they can be more confident in the exchangeability of subpopulations with similar compliance levels among study arms. Errors in reported adherence will further complicate the interpreta-

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 DESIGN CONSIDERATIONS: ADHERENCE tion of data. Such errors may depend on the actual level of adherence, as more bias may be expected when true adherence is low, and on the study endpoint, as participants may be more reluctant to admit to imperfect adherence after high-risk behavior or an HIV diagnosis, for example. If these biases are at play equally in both study arms, an adherence-adjusted comparison of study arms would tend to convey the correct signal, at least qualitatively. However, assessments of safe adherence levels would tend to be overestimates. In unblinded trials, such as the MIRA diaphragm trial, subjects in the unblinded arm may be under higher pressure to comply with condom use, and may therefore overreport adherence to a larger extent. An adherence-adjusted analysis would then tend to compare HIV incidence rates between somewhat different subpopulations. Statisticians have developed sophisticated causal models that attempt to address questions such as: “What would have occurred if exposure had been different?” One such model links potential responses to different levels of treatment and risk behavior, and allows estimation of the model’s parameters based on the randomization used to assign patients to study arms. The corresponding statistical tests are approximately valid under the null hypothesis of no causal effect, even if the assumed form for the causal model is incorrect (Robins and Tsiatis, 1991; Vandebosch et al., 2005). Such models thus provide a valuable level of protection against confounding. Linear structural mean models (Goetghebeur and Vansteelandt, 2005) also remain valid under the null hypothesis in the face of random mea- surement error, and can be adjusted to account for (known or modeled) average systematic error in measuring compliance. However, when there is a causal effect, the validity of these analyses rests on the assumption that the structural causal model is correct, and the latter is typically not testable based on available data. It is important to verify the assumptions made in causal models as much as possible, and to assess the sensitivity of the results to plausible devia- tions from the assumptions. These models allow investigators to predict, under additional assumptions, the expected effects of a given distribution of product exposure within a population, including under full compliance. The models also allow investigators to correct for differential condom use between the study arms, thus allowing estimation of the direct effect of the intervention. In some settings, investigators compare adherence between partici- pants who become HIV infected and matched controls who do not become infected. A comparison of recent behavior and product exposure could help explain the extent to which seroconverters are nonresponders or non- compliers. However, because such a comparison would be nonrandomized, other factors could confound an observed association. Detailed adherence data obtained retrospectively may also suffer from bias, given that a partici-

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 METHODOLOGICAL CHALLENGES IN HIV PREVENTION TRIALS pant’s knowledge that he or she has become HIV infected may change his or her recollection of behavioral details. Investigators could thus complement this retrospective information with simple measures of adherence gathered prospectively, in real time. A substantial difference between prospective (reported by the participant before his or her HIV status is known) and retrospective (reported after the HIV status is known) measures of adher- ence across study arms may indicate differential measurement error. (See Appendix D for more detail on methods for analyzing adherence data.) Recommendation 5-6: Investigators should provide data on product adherence and risk behavior results to the data monitoring committee, as this information may influence the committee’s views of the relative efficacy and safety of the study arms, and the feasibility of the study. Recommendation 5-7: Investigators should analyze adherence and behavior as both outcomes in an HIV prevention trial and modifiers of the effect of the biomedical intervention on HIV infection risk. Recommendation 5-8: Investigators should analyze the potential impact of adherence by doing the following: • Perform a stratified analysis when adherence appears similar between study arms. Such analyses aim to provide unbiased compari- sons of subpopulations across study arms. • Postulate causal models and perform randomization-based analyses. • Perform matched case-control adherence analyses involving sub- jects who become HIV infected. When reporting model-based analyses of adherence, investigators should clearly state the model’s assumptions and discuss their plausibility, as well as the robustness of the analysis to violations of the assumptions. REFERENCES Allen, C. F., S. S. Lees, N. A. Desmond, G. Der, B. Chiduo, I. Hambleton, L. Knight, A. Vallely, D. A. Ross, and R. J. Hayes. 2007. Validity of coital diaries in a feasibility study for the microbicides development programme trial among women at high risk of HIV/AIDS in Mwanza, Tanzania. Sexually Transmitted Infections [Epub ahead of print]. Amico, K. R., J. J. Harman, and B. T. Johnson. 2006. Efficacy of antiretroviral therapy ad- herence interventions: A research synthesis of trials, 1996 to 2004. Journal of Acquired Immune Deficiency Syndromes 41(3):285-297. Baer, A., S. Saroiu, and L. A. Koutsky. 2002. Obtaining sensitive data through the web: An example of design and methods. Epidemiology 13(6):640-645.

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